American healthcare is well known for its extreme cost and worst outcomes among industrialized (such as the 38 OECD member) countries, and beyond that to be remarkably opaque. The high cost of prescription drugs contributes, and little has been done to change that except for the government passing the Affordable Insulin Now Act at the end of 2022, enacted in 2023. But in January 2022 Mark Cuban launched Cost Plus Drugs that has transformed how many Americans can get their prescriptions filled at a fraction of the prevailing prices, bypassing pharmacy benefit managers (PBMs) that control 80% of US prescriptions. That was just the beginning of a path of creative destruction (disruptive innovation, after Schumpeter) of many key components American healthcare that Cuban is leading, with Cost Plus Marketplace, Cost Plus Wellness and much more to come. He certainly qualifies as a master disrupter: “someone who is a leader in innovation and is not afraid to challenge the status quo.”

Below is a video clip from our conversation dealing with insurance companies. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! The audios are also available on Apple and Spotify.

Transcript with External links to Audio

(00:07):

Hello, it's Eric Topol with Ground Truths, and I have our special phenomenal guest today, Mark Cuban, who I think you know him from his tech world contributions and Dallas Mavericks, and the last few years he's been shaking up healthcare with Cost Plus Drugs. So Mark, welcome.

Mark Cuban (00:25):

Thanks for having me, Eric.

Eric Topol (00:27):

Yeah, I mean, what you're doing, you’ve become a hero to millions of Americans getting them their medications at a fraction of the cost they're used to. And you are really challenging the PBM industry, which I've delved into more than ever, just in prep for our conversation. It's just amazing what this group of companies, namely the three big three CVS Caremark, Optum of UnitedHealth and Express Scripts of Cigna with a market of almost $600 billion this year, what they're doing, how can they get away with all this stuff?

Inner Workings of Pharmacy Benefit Managers

Mark Cuban (01:03):

I mean, they're just doing business. I really don't blame them. I blame the people who contract with them. All the companies, particularly the bigger companies, the self-insured companies, where the CEO really doesn't have an understanding of their healthcare or pharmacy benefits. And so, the big PBMs paid them rebates, which they think is great if you're a CEO, when in reality it's really just a loan against the money spent by your sickest employees, and they just don't understand that. So a big part of my time these days is going to CEOs and sitting with them and explaining to them that you're getting ripped off on both your pharmacy and your healthcare side.

Eric Topol (01:47):

Yeah, it's amazing to me the many ways that they get away with this. I mean, they make companies sign NDAs. They're addicted to rebates. They have all sorts of ways a channel of funds to themselves. I mean, all the things you could think of whereby they even have these GPOs. Each of these companies has a group purchasing organization (I summarized in the Table below).

Mark Cuban (02:12):

Yeah, which gives them, it's crazy because with those GPOs. The GPO does the deal with the pharmacy manufacturer. Then the GPO also does the deal with the PBM, and then the PBM goes to the self-insured employer in particular and says, hey, we're going to pass through all the rebates. But what they don't say is they've already skimmed off 5%, 10%, 20% or more off the top through their GPO. But that's not even the worst of it. That's just money, right? I mean, that's important, but I mean, even the biggest companies rarely own their own claims data.

Mark Cuban (02:45):

Now think about what that means. It means you can't get smarter about the wellness of your employees and their families. You want to figure out the best way to do GLP-1s and figure out how to reduce diabetes, whatever it may be. You don't have that claims data. And then they don't allow the companies to control their own formularies. So we've seen Humira biosimilars come out and the big PBMs have done their own version of the biosimilar where we have a product called Yusimry, which is only $594 a month, which is cheaper than the cheapest biosimilar that the big three are selling. And so, you would think in a normal relationship, they would want to bring on this new product to help the employer. No, they won't do it. If the employer asks, can I just add Cost Plus Drugs to my network? They'll say no, every single time.

Mark Cuban (03:45):

Their job is not to save the employer money, particularly after they've given a rebate. Because once they give that loan, that rebate to the employer, they need to get that money back. It's not a gift. It's a loan and they need to have the rebates, and we don't do rebates with them at all. And I can go down the list. They don't control the formula. They don't control, you mentioned the NDAs. They can't talk to manufacturers, so they can't go to Novo or to Lilly and say, let's put together a GLP-1 wellness program. All these different things that just are common sense. It's not happening. And so, the good news is when I walk into these companies that self-insured and talk to the CEO or CFO, I'm not asking them to do something that's not in their best interest or not in the best interest of the lives they cover. I'm saying, we can save you money and you can improve the wellness of your employees and their families. Where's the downside?

Eric Topol (04:40):

Oh, yeah. Yeah. And the reason they can't see the claims is because of the privacy issues?

Mark Cuban (04:46):

No, no. That's just a business decision in the contract that the PBMs have made. You can go and ask. I mean, you have every right to your own claims. You don't need to have it personally identified. You want to find out how many people have GLP-1s or what are the trends, or God forbid there's another Purdue Pharma thing going on, and someone prescribing lots of opioids. You want to be able to see those things, but they won't do it. And that's only on the sponsor side. It's almost as bad if not worse on the manufacturer side.

Eric Topol (05:20):

Oh, yeah. Well, some of the work of PBMs that you've been talking about were well chronicled in the New York Times, a couple of major articles by Reed Abelson and Rebecca Robbins: The Opaque Industry Secretly Inflating Prices for Prescription Drugs and The Powerful Companies Driving Local Drugstores Out of Business. We'll link those because I think some people are not aware of all the things that are going on in the background.

Mark Cuban (05:39):

You see in their study and what they reported on the big PBMs, it's crazy the way it works. And literally if there was transparency, like Cost Plus offers, the cost of medications across the country could come down 20%, 30% or more.

Cost Plus Drugs

Eric Topol (05:55):

Oh, I mean, it is amazing, really. And now let's get into Cost Plus. I know that a radiologist, Alex Oshmyansky contacted you with a cold email a little over three years ago, and you formed Cost Plus Drugs on the basis of that, right?

Mark Cuban (06:12):

Yep, that's exactly what happened.

Eric Topol (06:15):

I give you credit for responding to cold emails and coming up with a brilliant idea with this and getting behind it and putting your name behind it. And what you've done, so you started out with something like 110 generics and now you're up well over 1,200 or 2,500 or something like that?

Mark Cuban (06:30):

And adding brands. And so, started with 111. Now we're around 2,500 and trying to grow it every single day. And not only that, just to give people an overview. When you go to www.costplusdrugs.com and you put in the name of your medication, let's just say it's tadalafil, and if it comes up. In this case, it will. It'll show you our actual cost, and then we just mark it up 15%. It's the same markup for everybody, and if you want it, we'll have a pharmacist check it. And so, that's a $5 fee. And then if you want ship to mail order, it's $5 for shipping. And if you want to use our pharmacy network, then we can connect you there and you can just pick it up at a local pharmacy.

Eric Topol (07:10):

Yeah, no, it's transparency. We don't have a lot of that in healthcare in America, right?

Mark Cuban (07:15):

No. And literally, Eric, the smartest thing that we did, and we didn't expect this, it's always the law of unintended consequences. The smartest thing we did was publish our entire price list because that allowed any company, any sponsor, CMS, researchers to compare our prices to what others were already paying. And we've seen studies come out saying, for this X number of urology drugs, CMS would save $3.6 billion a year. For this number of heart drugs at this amount per year, for chemotherapy drugs or MS drugs this amount. And so, it's really brought attention to the fact that for what PBMs call specialty drugs, whether there's nothing special about them, we can save people a lot of money.

Eric Topol (08:01):

It's phenomenal. As a cardiologist, I looked up a couple of the drugs that I'm most frequently prescribed, just like Rosuvastatin what went down from $134 to $5.67 cents or Valsartan it went down from $69 to $7.40 cents. But of course, there's some that are much more dramatic, like as you mentioned, whether it's drugs for multiple sclerosis, the prostate cancer. I mean, some of these are just thousands and thousands of dollars per month that are saved, brought down to levels that you wouldn't think would even be conceivable. And this has been zero marketing, right?

Mark Cuban (08:42):

Yeah, none. It's all been word of mouth and my big mouth, of course. Going out there and doing interviews like this and going to major media, but it's amazing. We get emails and letters and people coming up to us almost single day saying, you saved my grandma's life. You saved my life. We weren't going to be able to afford our imatinib or our MS medication. And it went from being quoted $2,000 a month to $33 a month. It's just insane things like that that are still happening.

Eric Topol (09:11):

Well, this is certainly one of the biggest shakeups to occur in US healthcare in years. And what you've done in three years is just extraordinary. This healthcare in this country is with its over 4 trillion, pushing $5 trillion a year of expenditure.[New CMS report this week pegs the number at $4.867 trillion for 2023]

Mark Cuban (09:30):

It's interesting. I think it's really fixable. This has been the easiest industry to the disrupt I've ever been involved in. And it's not even close because all it took was transparency and not jacking up margins to market. We choose to use a fixed margin markup. Some choose to price to market, the Martin Shkreli approach, if you will. And just by being transparent, we've had an impact. And the other side of it is, it's the same concept on the healthcare side. Transparency helps, but to go a little field of pharmacy if you want. The insane part, and this applies to care and pharmacy, whatever plan we have, whether it's for health or whether it's for pharmaceuticals, there's typically a deductible, typically a copay, and typically a co-insurance.

Insurance Companies

Mark Cuban (10:20):

The crazy part of all that is that people taking the default risk, the credit risk are the providers. It's you, it's the hospital, it's the clinics that you work for. Which makes no sense whatsoever that the decisions that you or I make for our personal insurance or for the companies we run, or if we work for the government, what we do with Medicare or Medicare Advantage, the decisions we all make impacts the viability of providers starting with the biggest hospital systems. And so, as a result, they become subprime lenders without a car or a house to go after if they can't collect. And so, now you see a bunch of people, particularly those under the ACA with the $9,000, the bronze plans or $18,000 out-of-pocket limits go into debt, significant medical debt. And it's unfortunate. We look at the people who are facing these problems and think, well, it must be the insurance companies.

Mark Cuban (11:23):

It's actually not even the insurance companies. It's the overall design of the system. But underneath that, it's still whoever picks the insurance companies and sets plans that allow those deductibles, that's the core of the problem. And until we get to a system where the providers aren't responsible for the credit for defaults and dealing with all that credit risk, it's almost going to be impossible to change. Because when you see stories like we've all seen in news of a big healthcare, a BUCA healthcare (Blue Cross Blue Shield (BCBS), UnitedHealth, Cigna, and Aetna/CVS) plan with all the pre-authorizations and denials, typically they're not even taking the insurance risk. They're acting as the TPA (third party administrator) as the claims processor effectively for whoever hired them. And it goes back again, just like I talked about before. And as long as CMS hires or allows or accepts these BUCAs with these plans for Medicare for the ACA (Affordable care Act), whatever it may be, it's not going to work. As long as self-insured employers and the 50 million lives they cover hire these BUCAs to act as the TPAs, not as insurance companies and give them leeway on what to approve and what to authorize and what not to authorize. The system's going to be a mess, and that's where we are today.

Academic Health System Partnerships

Eric Topol (12:41):

Yeah. Well, you've been talking of course to employers and enlightening them, and you're also enlightening the public, of course. That's why you have millions of people that are saving their cost of medications, but recently you struck a partnership with Penn Medicine. That's amazing. So is that your first academic health system that you approached?

Cost Plus Marketplace

Mark Cuban (13:00):

I don't know if it was the first we approached, but it was certainly one of the biggest that we signed. We've got Cost Plus Marketplace (CPM) where we make everything from injectables to you name it, anything a hospital might buy. But again, at a finite markup, we make eight and a half percent I think when it's all said and done. And that saves hospital systems millions of dollars a year.

Eric Topol (13:24):

Yeah. So that's a big change in the way you're proceeding because what it was just pills that you were buying from the pharma companies, now you're actually going to make injectables and you're going to have a manufacturing capability. Is that already up and going?

Mark Cuban (13:39):

That's all up and going as of March. We're taking sterile injectables that are on the shortage list, generic and manufacturing them in Dallas using a whole robotics manufacturing plant that really Alex created. He's the rocket scientist behind it. And we’re limited in capacity now, we're limited about 2 million vials, but we'll sell those to Cost Plus Marketplace, and we'll also sell those direct. So Cost Plus Marketplace isn't just the things we manufacture. It's a wide variety of products that hospitals buy that we then have a minimal markup, and then for the stuff we manufacture, we'll sell those to direct to like CHS was our first customer.

Eric Topol (14:20):

Yeah, that's a big expansion from going from the pills to this. Wow.

Mark Cuban (14:24):

It's a big, big expansion, but it goes to the heart of being transparent and not being greedy, selling on a markup. And ourselves as a company, being able to remain lean and mean. The only way we can sell at such a low markup. We have 20 employees on the Cost Plus side and 40 employees involved with the factories, and that's it.

Eric Topol (14:46):

Wow. So with respect to, you had this phenomenal article and interview with WIRED Magazine just this past week. I know Lauren Goode interviewed you, and she said, Mark, is this really altruistic and I love your response. You said, “how much f*****g money do I need? I'm not trying to land on Mars.” And then you said, “at this point in my life, it’s just like more money, or f**k up the healthcare industry.” This was the greatest, Mark. I mean, I got to tell you, it was really something.

Mark Cuban (15:18):

Yeah.

Eric Topol (15:19):

Well, in speaking of that, of course, the allusion to a person we know well, Elon. He posted on X/Twitter in recent days , I think just three or four days ago, shouldn't the American people be getting their money's worth? About this high healthcare administration costs where the US is completely away from any other OECD country. And as you and I know, we have the worst outcomes and the most costs of all the rich countries in the world. There's just nothing new here. Maybe it's new to him, but you had a fabulous response on both X and Bluesky where you went over all these things point by point. And of course, the whole efforts that you've been working on now for three years. You also mentioned something that was really interesting that I didn't know about were these ERISA lawsuits[Employee Retirement Income Security Act (ERISA) of 1974.] Can you tell us about that?

ERISA Lawsuits

Mark Cuban (16:13):

Yeah, that's a great question, Eric. So for self-insured companies in particular, we have a fiduciary responsibility on a wellness and on a financial basis to offer the members, your employees and their families the best outcomes at the best price. Now, you can't guarantee best outcomes, but you have to be able to explain the choices you made. You don't have to pick the cheapest, but again, you have to be able to explain why you made the choices that you did. And because a lot of companies have been doing, just like we discussed earlier, doing deals on the pharmacy side with just these big PBMs, without accounting for best practices, best price, best outcomes, a couple companies got sued. Johnson and Johnson and Wells Fargo were the first to get sued. And I think that's just the beginning. That's just the writing on the wall. I think they'll lose because they just dealt with the big pharmacy PBMs. And I think that's one of the reasons why we're so busy at Cost Plus and why I'm so busy because we're having conversation after conversation with companies and plenty of enough lawyers for that matter who want to see a price list and be able to compare what they're paying to what we sell for to see if they're truly living up to that responsibility.

Eric Topol (17:28):

Yeah, no, that's a really important thing that's going on right now that I think a lot of people don't know about. Now, the government of the US think because it's the only government of any rich country in the world, if not any country that doesn't negotiate prices, i.e., CMS or whatever. And only with the recent work of insulin, which is a single one drug, was there reduction of price. And of course, it's years before we'll see other drugs. How could this country not negotiate drugs all these years where every other place in the world they do negotiate with pharma?

Mark Cuban (18:05):

Because as we alluded to earlier, the first line in every single pharmaceutical and healthcare contract says, you can't talk about this contract. It’s like fight club. The number one rule of fight club is you can't talk about fight club, and it's really difficult to negotiate prices when it's opaque and everything's obfuscated where you can't really get into the details. So it's not that we're not capable of it, but it's just when there's no data there, it's really difficult because look, up until we started publishing our prices, how would anybody know?

Mark Cuban (18:39):

I mean, how was anybody going to compare numbers? And so, when the government or whoever started to negotiate, they tried to protect themselves and they tried to get data, but those big PBMs certainly have not been forthcoming. We’ve come along and publish our price list and all that starts to change. Now in terms of the bigger picture, there is a solution there, as I said earlier, but it really comes down to talking to the people who make the decisions to hire the big insurance companies and the big PBMs and telling them, no, you're not acting in your own best interest. Here's anybody watching out there. Ask your PBM if they can audit. If you can audit rather your PBM contract. What they'll tell you is, yeah, you can, but you have to use our people. It's insane. And that's from top to bottom. And so, I'm a big believer that if we can get starting with self-insured employers to act in their own best interest, and instead of working with a big PBM work with a pass-through PBM. A pass-through PBM will allow you to keep your own claims, own all your own data, allow you to control your own formulary.

Mark Cuban (19:54):

You make changes where necessary, no NDA, so you can't talk to manufacturers. All these different abilities that just seem to make perfect sense are available to all self-insured employers. And if the government, same thing. If the government requires pass-through PBMs, the price of medications will drop like a rock.

Eric Topol (20:16):

Is that possible? You think that could happen?

Mark Cuban (20:19):

Yes. Somebody's got to understand it and do it. I'm out there screaming, but we will see what happens with the new administration. There's nothing hard about it. And it's the same thing with Medicare and Medicare Advantage healthcare plans. There's nothing that says you have to use the biggest companies. Now, the insurance companies have to apply and get approved, but again, there's a path there to work with companies that can reduce costs and improve outcomes. The biggest challenge in my mind, and I'm still trying to work through this to fully understand it. I think where we really get turned upside down as a country is we try to avoid fraud from the provider perspective and the patient perspective. We're terrified that patients are going to use too much healthcare, and like everybody's got Munchausen disease.

Mark Cuban (21:11):

And we're terrified that the providers are going to charge too much or turn into Purdue Pharma and over-prescribe or one of these surgery mills that just is having somebody get surgery just so they can make money. So in an effort to avoid those things, we ask the insurance companies and the PBMs to do pre-authorizations, and that's the catch 22. How do we find a better way to deal with fraud at the patient and provider level? Because once we can do that, and maybe it's AI, maybe it's accepting fraud, maybe it's imposing criminal penalties if somebody does those things. But once we can overcome that, then it becomes very transactional. Because the reality is most insurance companies aren't insurance companies. 50 million lives are covered by self-insured employers that use the BUCAs, the big insurance companies, but not as insurance companies.

Eric Topol (22:07):

Yeah, I was going to ask you about that because if you look at these three big PBMs that control about 80% of the market, not the pass-throughs that you just mentioned, but the big ones, they each are owned by an insurance company. And so, when the employer says, okay, we're going to cover your healthcare stuff here, we're going to cover your prescriptions there.

Mark Cuban (22:28):

Yeah, it's all vertically integrated.

Mark Cuban (22:36):

And it gets even worse than that, Eric. So they also own specialty pharmacies, “specialty pharmacies” that will require you to buy from. And as I alluded to earlier, a lot of these medications like Imatinib, they'll list as being a specialty medication, but it's a pill. There's nothing special about it, but it allows them to charge a premium. And that's a big part of how the PBMs make a lot of their money, the GPO stuff we talked about, but also forcing an employer to go through the specialty mail order company that charges an arm and the leg.

Impact on Hospitals and Procedures

Eric Topol (23:09):

Yeah. Well, and the point you made about transparency, we've seen this of course across US healthcare. So for example, as you know, if you were to look at what does it cost to have an operation like let's say a knee replacement at various hospitals, you can find that it could range fivefold. Of course, you actually get the cost, and it could be the hospital cost, and then there's the professional cost. And the same thing occurs for if you're having a scan, if you're having an MRI here or there. So these are also this lack of transparency and it's hard to get to the numbers, of course. There seems to be so many other parallels to the PBM story. Would you go to these other areas you think in the future?

Mark Cuban (23:53):

Yeah, we're doing it now. I'm doing it. So we have this thing called project dog food, and what it is, it's for my companies and what we've done is say, look, let's understand how the money works in healthcare.

Mark Cuban (24:05):

And when you think about it, when you go to get that knee done, what happens? Well, they go to your insurance company to get a pre-authorization. Your doctor says you need a knee replacement. I got both my hips replaced. Let's use that. Doctor says, Mark, you need your hips replaced. Great, right? Let's set up an appointment. Well, first the insurance company has to authorize it, okay, they do or they don't, but the doctor eats their time up trying to deal with the pre-authorization. And if it's denied, the doctor's time is eaten up and an assistance's time is eaten up. Some other administrator's time is eaten up, the employer's time is eaten up. So that's one significant cost. And then from there, there's a deductible. Now I can afford my deductible, but if there is an individual getting that hip replacement who can't afford the deductible, now all of a sudden you're still going to be required to do that hip replacement, most likely.

Mark Cuban (25:00):

Because in most of these contracts that self-insured employers sign, Medicare Advantage has, Medicare has, it says that between the insurance company and the provider, in this case, the hospital, you have to do the operation even if the deductibles not paid. So now the point of all this is you have the hospital in this case potentially accumulating who knows how much bad debt. And it's not just the lost amount of millions and millions and billions across the entire healthcare spectrum that's there. It's all the incremental administrative costs. The lawyers, the benefits for those people, the real estate, the desk, the office space, all that stuff adds up to $10 billion plus just because the hospitals take on that credit default risk. But wait, there's more. So now the surgery happens, you send the bill to the insurance company. The insurance company says, well, we're not going to pay you. Well, we have a contract. This is what it says, hip replacement's $34,000. Well, we don't care first, we're going to wait. So we get the time value of money, and then we're going to short pay you.

Mark Cuban (26:11):

So the hospital gets short paid. So what do they have to do? They have to sue them or send letters or whatever it is to try to get their money. When we talk to the big hospital systems, they say that's 2%. That's 2% of their revenue. So you have all these associated credit loss dollars, you've got the 2% of, in a lot of cases, billions and billions of dollars. And so, when you add all those things up, what happens? Well, what happens is because the providers are losing all that money and having to spend all those incremental dollars for the administration of all that, they have to jack up prices.

Eric Topol (26:51):

Yeah. Right.

Mark Cuban (26:53):

So what we have done, we've said, look for my companies, we're going to pay you cash. We're going to pay you cash day one. When Mark gets that hip replacement, that checks in the bank before the operation starts, if that's the way you want it. Great, they're not going to have pre-authorizations. We're going to trust you until you give us a reason not to trust you. We're not short paying, obviously, because we're paying cash right there then.

Mark Cuban (27:19):

But in a response for all that, because we're cutting out all those ancillary costs and credit risk, I want Medicare pricing. Now the initial response is, well, Medicare prices, that's awful. We can't do it. Well, when you really think about the cost and operating costs of a hospital, it's not the doctors, it's not the facilities, it's all the administration that cost all the money. It's all the credit risks that cost all the money. And so, if you remove that credit risk and all the administration, all those people, all that real estate, all those benefits and overhead associated with them, now all of a sudden selling at a Medicare price for that hip replacement is really profitable.

Eric Topol (28:03):

Now, is that a new entity Cost Plus healthcare?

Mark Cuban (28:07):

Well, it's called Cost Plus Wellness. It's not an entity. What we're going to do, so the part I didn't mention is all the direct contracts that we do that have all these pieces, as part of them that I just mentioned, we're going to publish them.

Eric Topol (28:22):

Ah, okay.

Mark Cuban (28:23):

And you can see exactly what we've done. And if you think about the real role of the big insurances companies for hospitals, it's a sales funnel.

Getting Rid of Insurance Companies

Eric Topol (28:33):

Yeah, yeah. Well, in fact, I really was intrigued because you did a podcast interview with Andrew Beam and the New England Journal of Medicine AI, and in that they talked about getting rid of the insurers, the insurance industry, just getting rid of it and just make it a means test for people. So it's not universal healthcare, it's a different model that you described. Can you go over that? I thought it was fantastic.

Mark Cuban (29:00):

Two pieces there. Let's talk about universal healthcare first. So for my companies, for our project dog food for the Mark Cuban companies, if for any employee or any of the lives we cover, if they work within network, anybody we have the direct contract with its single-payer. They pay their premiums, but they pay nothing else out of pocket. That's the definition of single-payer.

Eric Topol (29:24):

Yeah.

Mark Cuban (29:25):

So if we can get all this done, then the initial single-payers will be self-insured employers because it'll be more cost effective to them to do this approach. We hope, we still have to play it all through. So that's part one. In terms of everybody else, then you can say, why do we need insurance companies if they're not even truly acting as insurance companies? You're not taking full risk because even if it's Medicare Advantage, they're getting a capitated amount per month. And then that's getting risk adjusted because of the population you have, and then there's also an index depending on the location, so there's more or less money that occurs then. So let's just do what we need to do in this particular case, because the government is effectively eliminating the risk for the insurance company for the most part. And if you look at the margins for Medicare Advantage, I was just reading yesterday, it's like $1,700 a year for the average Medicare Advantage plan. So it's not like they're taking a lot of risk. All they're doing is trying to deny as many claims as they can.

Eric Topol (30:35):

Deny, Deny. Yeah.

Mark Cuban (30:37):

So instead, let's just get somebody who's a TPA, somebody who does the transaction, the claims processing, and whoever's in charge. It could be CMS, can set the terms for what's accepted and what's denied, and you can have a procedure for people that get denied that want to challenge it. And that's great, there's one in place now, but you make it a little simpler. But you take out the economics for the insurance company to just deny, deny, deny. There's no capitation. There's no nothing.

Mark Cuban (31:10):

The government just says, okay, we're hiring this TPA to handle the claims processing. It is your job. We're paying you per transaction.

Mark Cuban (31:18):

You don't get paid more if you deny. You don't get paid less if you deny. There's no bonuses if you keep it under a certain amount, there's no penalties If you go above a certain amount. We want you just to make sure that the patient involved is getting the best care, end of story. And if there's fraud involved as the government, because we have access to all that claims data, we're going to introduce AI that reviews that continuously.

Mark Cuban (31:44):

So that we can see things that are outliers or things that we question, and there's going to mean mistakes, but the bet was, if you will, where we save more and get better outcomes that way versus the current system and I think we will. Now, what ends up happening on top of that, once you have all that claims data and all that information and everybody's interest is aligned, best care at the best price, no denials unless it's necessary, reduce and eliminate fraud. Once everybody's in alignment, then as long as that's transparent. If the city of Dallas decides for all the lives they cover the 300,000 lives they cover between pharmacy and healthcare, we can usually in actuarial tables and some statistical analysis, we can say, you know what, even with a 15% tolerance, it's cheaper for us just to pay upfront and do this single-pay program, all our employees in the lives we cover, because we know what it's going to take.

Mark Cuban (32:45):

If the government decides, well, instead of Medicare Advantage the way it was, we know all the costs. Now we can say for all Medicare patients, we'll do Medicare for all, simply because we have definitive and deterministic pricing. Great. Now, there's still going to be outlier issues like all the therapies that cost a million dollars or whatever. But my attitude there is if CMS goes to Lilly, Novo, whoever for their cure for blindness that's $3.4 million. Well, that's great, but what we'll say is, okay, give us access to your books. We want to know what your breakeven point is. What is that breakeven point annually? We'll write you a check for that.

Eric Topol (33:26):

Yeah.

Mark Cuban (33:27):

If we have fewer patients than need that, okay, you win. If we have more patients than need that, it's like a Netflix subscription with unlimited subscribers, then we will have whatever it is, because then the manufacturer doesn't lose money, so they can't complain about R&D and not being able to make money. And that's for the CMS covered population. You can do a Netflix type subscription for self-insured employers. Hey, it's 25 cents per month per employee or per life covered for the life of the patent, and we'll commit to that. And so, now all of a sudden you get to a point where healthcare starts becoming not only transparent but deterministic.

Eric Topol (34:08):

Yeah. What you outline here in these themes are extraordinary. And one of the other issues that you are really advocating is patient empowerment, but one of the problems we have in the US is that people don't own their data. They don't even have all their data. I expect you'd be a champion of that as well.

Mark Cuban (34:27):

Well, of course. Yeah. I mean, look, I've got into arguments with doctors and public health officials about things like getting your own blood tested. I've been an advocate of getting my own blood tested for 15 years, and it helped me find out that I needed thyroid medication and all of these things. So I'm a big advocate. There's some people that think that too much data gives you a lot of false positives, and people get excited in this day and age to get more care when it should only be done if there are symptoms. I'm not a believer in that at all. I think now, particularly as AI becomes more applicable and available, you'll be able to be smarter about the data you capture. And that was always my final argument. Either you trust doctors, or you don't. Because even if there's an aberrational TSH reading and minus 4.4 and it's a little bit high, well the doctor's going to say, well, let's do another blood test in a month or two. The doctor is still the one that has to write the prescription. There's no downside to trusting your doctor in my mind.

Eric Topol (35:32):

And what you're bringing up is that we're already seeing how AI can pick up things even in the normal range, the trends long before a clinician physician would pick it up. Now, last thing I want to say is you are re-imagining healthcare like no one. I mean, there's what you're doing here. It started with some pills and it's going in a lot of different directions. You are rocking it here. I didn't even know some of the latest things that you're up to. This seems to be the biggest thing you've ever done.

Mark Cuban (36:00):

I hope so.

Mark Cuban (36:01):

I mean, like we said earlier, what could be better than people saying our healthcare system is good. What changed? That Cuban guy.

Eric Topol (36:10):

Well, did you give up Shark Tank so you could put more energy into this?

Mark Cuban (36:16):

Not really. It was more for my kids.

Eric Topol (36:19):

Okay, okay.

Mark Cuban (36:20):

They go hand in hand, obviously. I can do this stuff at home as opposed to sitting on a set wondering if I should invest in Dude Wipes again.

Eric Topol (36:28):

Well, look, we're cheering for you. This is, I've not seen a shakeup in my life in American healthcare like this. You are just rocking. It's fantastic.

Mark Cuban (36:37):

Everybody out there that's watching, check out www.costplusdrugs.com, check out Cost Plus Marketplace, which is business.costplusdrugs.com and just audit everything. What I'm trying to do is say, okay, if it's 1955 and we're starting healthcare all over again, how would we do it? And really just keep it simple. Look to where the risk is and remove the risk where possible. And then it comes down to who do you trust and make sure you trust but verify. Making sure there aren't doctors or systems that are outliers and making sure that there aren't companies that are outliers or patients rather that are outliers. And so, I think there's a path there. It's not nearly as difficult, it's just starting them with corporations, getting those CEOs to get educated and act in their own best interest.

Eric Topol (37:32):

Well, you're showing us the way. No question. So thanks so much for joining, and we'll be following this with really deep interest because you're moving at high velocity, and thank you.

**************************************************

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Footnote

The PBMS (finally) are under fire—2 articles from the past week

A leader for conducting rigorous randomized trials of humans along with animal models for understanding nutrition and metabolism, Dr. Kevin Hall is a Senior Investigator at the National Institutes of Health, and Section Chief of the Integrative Physiology Section, NIDDK. In this podcast, we reviewed his prolific body of research a recent publications. The timing of optimizing our diet and nutrition seems apropos, now that we’re in in the midst of the holiday season!

Below is a video snippet of our conversation on his ultra-processed food randomized trial.

Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! The audios are also available on Apple and Spotify.

Note: I’ll be doing a Ground Truths Live Chat on December 11th at 12 N EST, 9 AM PST, so please mark your calendar and join!

Transcript with links to publications and audio

Eric Topol (00:05):

Well, hello. This is Eric Topol with Ground Truths, and I'm really delighted to have with me today, Dr. Kevin Hall from the NIH. I think everybody knows that nutrition is so important and Kevin is a leader in doing rigorous randomized trials, which is not like what we usually see with large epidemiologic studies of nutrition that rely on food diaries and the memory of participants. So Kevin, it's really terrific to have you here.

Kevin Hall (00:34):

Thanks so much for the invitation.

Ultra-Processed Foods

Eric Topol (00:36):

Yeah. Well, you've been prolific and certainly one of the leaders in nutrition science who I look to. And what I thought we could do is go through some of your seminal papers. There are many, but I picked a few and I thought we'd first go back to the one that you published in Cell Metabolism. This is ultra-processed diets cause excessive caloric intake and weight gain. (Main results in graph below.) So maybe you can take us through the principle findings from that trial.

Kevin Hall (01:10):

Yeah, sure. So that was a really interesting study because it's the first randomized control trial that's investigated the role of ultra-processed foods in potentially causing obesity. So we've got, as you mentioned, lots and lots of epidemiological data that have made these associations between people who consume diets that are very high in ultra-processed foods as having greater risk for obesity. But those trials are not demonstrating causation. I mean, they suggest a strong link. And in fact, the idea of ultra-processed foods is kind of a new idea. It's really sort of appeared on the nutrition science stage probably most prominently in the past 10 years or so. And I first learned about this idea of ultra-processed foods, which is really kind of antithetical to the way most nutrition scientists think about foods. We often think about foods as nutrient delivery vehicles, and we kind of view foods as being the fraction of carbohydrates versus fats in them or how much sodium or fiber is in the foods.

Kevin Hall (02:17):

And along came this group in Brazil who introduced this new way of classifying foods that completely ignores the nutrient composition and says what we should be doing is classifying foods based on the extent and purpose of processing of foods. And so, they categorize these four different categories. And in the fourth category of this so-called NOVA classification scheme (see graphic below) , they identified something called ultra-processed foods. There's a long formal definition and it's evolved a little bit over the years and continues to evolve. But the basic ideas that these are foods that are manufactured by industries that contain a lot of purified ingredients made from relatively cheap agricultural commodity products that basically undergo a variety of processes and include additives and ingredients that are not typically found in home kitchens, but are typically exclusively in manufactured products to create the wide variety of mostly packaged goods that we see in our supermarkets.

Kevin Hall (03:22):

And so, I was really skeptical that there was much more about the effects of these foods. Other than that they typically have high amounts of sugar and saturated fat and salt, and they're pretty low in fiber. And so, the purpose of this study was to say, okay, well if there's something more about the foods themselves that is causing people to overconsume calories and gain weight and eventually get obesity, then we should do a study that's trying to test for two diets that are matched for these various nutrients of concern. So they should be matched for the macronutrients, they should be matched for the sugar content, the fat, the sodium, the fiber, and people should just be allowed to eat whatever they want and they shouldn't be trying to change their weight in any way. And so, the way that we did this was, as you mentioned, we can't just ask people to report what they're eating.

Kevin Hall (04:19):

So what we did was we admitted these folks to the NIH Clinical Center and to our metabolic ward, and it's a very artificial environment, but it's an environment that we can control very carefully. And so, what we basically did is take control over their food environment and we gave them three meals a day and snacks, and basically for a two-week period, they had access to meals that were more than 80% of calories coming from ultra-processed foods. And then in random order, they either received that diet first and give them simple instructions, eat as much as little as you want. We're going to measure lots of stuff. You shouldn't be trying to change your weight or weight that gave them a diet that had no calories from ultra-processed foods. In fact, 80% from minimally processed foods. But again, both of these two sort of food environments were matched for these nutrients that we typically think of as playing a major role in how many calories people choose to eat.

Kevin Hall (05:13):

And so, the basic idea was, okay, well let's measure what these folks eat. We gave them more than double the calories that they would require to maintain their weight, and what they didn't know was that in the basement of the clinical center where the metabolic kitchen is, we had all of our really talented nutrition staff measuring the leftovers to see what it was that they didn't eat. So we knew exactly what we provided to them and all the foods had to be in our nutrition database and when we compute what they actually ate by difference, so we have a very precise estimate about not only what foods they chose to ate, but also how many calories they chose to eat, as well as the nutrient composition.

And the main upshot of all that was that when these folks were exposed to this highly ultra-processed food environment, they spontaneously chose to eat about 500 calories per day more over the two-week period they were in that environment then when the same folks were in the environment that had no ultra-processed foods, but just minimally processed foods. They not surprisingly gained weight during the ultra-processed food environment and lost weight and lost body fat during the minimally processed food environment. And because those diets were overall matched for these different nutrients, it didn't seem to be that those were the things that were driving this big effect. So I think there's a couple of big take homes here. One is that the food environment really does have a profound effect on just the biology of how our food intake is controlled at least over relatively short periods of time, like the two-week periods that we were looking at. And secondly, that there's something about ultra-processed foods that seem to be driving this excess calorie intake that we now know has been linked with increased risk of obesity, and now we're starting to put some of the causal pieces together that really there might be something in this ultra-processed food environment that's driving the increased rates of obesity that we've seen over the past many decades.

Eric Topol (07:18):

Yeah, I mean I think the epidemiologic studies that make the link between ultra-processed foods and higher risk of cancer, cardiovascular disease, type 2 diabetes, neurodegenerative disease. They're pretty darn strong and they're backed up by this very rigorous study. Now you mentioned it short term, do you have any reason to think that adding 500 calories a day by eating these bad foods, which by the way in the American diet is about 60% or more of the average American diet, do you have any inkling that it would change after a few weeks?

Kevin Hall (07:54):

Well, I don't know about after a few weeks, but I think that one of the things that we do know about body weight regulation and how it changes in body weight impact both metabolism, how many calories were burning as well as our appetite. We would expect some degree of moderation of that effect eventually settling in at a new steady state, that's probably going to take months and years to achieve. And so the question is, I certainly don't believe that it would be a 500 calorie a day difference indefinitely. The question is when would that difference converge and how much weight would've been gained or lost when people eventually reached that new plateau? And so, that's I think a really interesting question. Some folks have suggested that maybe if you extrapolated the lines a little bit, you could predict when those two curves might eventually converge. That's an interesting thought experiment, but I think we do need some longer studies to investigate how persistent are these effects. Can that fully explain the rise in average body weight and obesity rates that have occurred over the past several decades? Those are open questions.

Eric Topol (09:03):

Yeah. Well, I mean, I had the chance to interview Chris van Tulleken who wrote the book, Ultra-Processed People and I think you might remember in the book he talked about how he went on an ultra-processed diet and gained some 20, 30 pounds in a short time in a month. And his brother, his identical twin brother gained 50, 60 pounds, and so it doesn't look good. Do you look at all the labels and avoid all this junk and ultra-processed food now or are you still thinking that maybe it's not as bad as it looks?

Kevin Hall (09:38):

Well, I mean I think that I certainly learned a lot from our studies, and we are continuing to follow this up to try to figure out what are the mechanisms by which this happen. But at the same time, I don't think we can throw out everything else we know about nutrition science. So just because we match these various nutrients in this particular study, I think one of the dangers here is that as you mentioned, there's 60% of the food environment in the US and Great Britain and other places consist of these foods, and so they're unavoidable to some extent, right? Unless you're one of these privileged folks who have your backyard garden and your personal chef who can make all of your foods, I'm certainly not one of those people, but for the vast majority of us, we're going to have to incorporate some degree of ultra-processed foods in our day-to-day diet.

Kevin Hall (10:24):

The way I sort of view it is, we really need to understand the mechanisms and before we understand the mechanisms, we have to make good choices based on what we already know about nutrition science, that we should avoid the foods that have a lot of sugar in them. We should avoid foods that have a lot of saturated fat and sodium. We should try to choose products that contain lots of whole grains and legumes and fruits and vegetables and things like that. And there's some of those, even in the ultra-processed food category. I pretty regularly consume a microwavable ready meal for lunch. It tends to be pretty high in whole grains and legumes and low in saturated fat and sugar and things like that. But to engineer a food that can heat up properly in a microwave in four minutes has some ultra-processing technology involved there. I would be pretty skeptical that that's going to cause me to have really poor health consequences as compared to if I had the means to eat homemade French fries every day in tallow. But that's the kind of comparison that we have to think about.

Eric Topol (11:36):

But I think what you're touching on and maybe inadvertently is in that NOVA class four, the bad ultra-processed foods, there's a long, long list of course, and some of those may be worse than others, and we haven't seen an individual ranking of these constituents. So as you're alluding to what's in that microwave lunch probably could be much less concerning than what's in these packaged snacks that are eaten widely. But I would certainly agree that we don't know everything about this, but your study is one of the most quoted studies ever in the ultra-processed food world. Now, let me move on to another trial that was really important. This was published in Nature Medicine and it's about a plant-based diet, which is of course a very interesting diet, low-fat versus an animal-based ketogenic diet. Also looking at energy intake. Can you take us through that trial?

Plant-Based, Low Fat Diet vs Animal-Based, Low Carbohydrate Ketogenic Diet

Kevin Hall (12:33):

Sure. So it's actually interesting to consider that trial in the context of the trial we just talked about because both of these diets that we tested in this trial were relatively low in ultra-processed foods, and so both of them contained more than a kilogram of non-starchy vegetables as a base for designing these, again, two different food environments. Very similar overall study design where people again were exposed to either diets that were vegan plant-based diet that was really high in starches and was designed to kind of cause big insulin increases in the blood after eating the meals. And the other diet had very, very few carbohydrates of less than 10% in total, and we built on that kind of non-starchy vegetable base, a lot of animal-based products to kind of get a pretty high amount of fat and having very low carbohydrates. Both diets in this case, like I mentioned, were pretty low in ultra-processed foods, but what we were really interested in here was testing this idea that has come to prominence recently, that high carbohydrate diets that lead to really large glucose excursions after meals that cause very high insulin levels after meals are particularly obesogenic and should cause you to be hungrier than compared to a diet that doesn't lead to those large swings in glucose and insulin and the prototypical case being one that's very low in carbohydrate and might increase the level of ketones that are floating around in your blood, which are hypothesized to be an appetite suppressant. Same sort of design, these minimally processed diets that one was very high in carbs and causes large swings in insulin and the other that's very low in carbs and causes increases in ketones.

Kevin Hall (14:22):

We ask people, again, while you're in one food environment or the other, don't be trying to gain weight or lose weight, eat as much or as little as you'd like, and we're going to basically measure a lot of things. They again, don't know what the primary outcome of the study is. We're measuring their leftovers afterwards. And so, the surprise in this particular case was that the diet that caused the big swings in glucose and insulin did not lead to more calorie consumption. In fact, it led to about 700 calories per day less than when the same people were exposed to the ketogenic diet. Interestingly, both food environments caused people to lose weight, so it wasn't that we didn't see the effect of people over consuming calories on either diet, so they were reading fewer calories in general than they were when they came in, right. They're probably eating a pretty ultra-processed food diet when they came in. We put them on these two diets that varied very much in terms of the macronutrients that they were eating, but both were pretty minimally processed. They lost weight. They ended up losing more body fat on the very low-fat high carb diet than the ketogenic diet, but actually more weight on the ketogenic diet than the low-fat diet. So there's a little bit of a dissociation between body fat loss and weight loss in this study, which was kind of interesting.

Eric Topol (15:49):

Interesting. Yeah, I thought that was a fascinating trial because plant-based diet, they both have their kind of camps, you know.

Kevin Hall (15:57):

Right. No, exactly.

Immune System Signatures for Vegan vs Ketogenic Diets

Eric Topol (15:58):

There are people who aren't giving up on ketogenic diet. Of course, there's some risks and some benefits and there's a lot of interest of course with the plant-based diet. So it was really interesting and potentially the additive effects of plant-based with avoidance or lowering of ultra-processed food. Now, the more recent trial that you did also was very interesting, and of course I'm only selecting ones that I think are particularly, there are a lot of trials you've done, but this one is more recent in this year where you looked at vegan versus ketogenic diets for the immune signature, immune response, which is really important. It's underplayed as its effect, and so maybe you can take us through that one.

[Link to a recent Nature feature on this topic, citing Dr. Hall’s work]

Kevin Hall (16:43):

Yeah, so just to be clear, it's actually the same study, the one that we just talked about. This is a secondary sort of analysis from a collaboration we had with some folks at NIAID here at the NIH to try to evaluate immune systems signatures in these same folks who wonder what these two changes in their food environment. One is vegan, high carbohydrate low-fat diet and the other, the animal-based ketogenic diet. And again, it was pretty interesting to me that we were able to see really substantial changes in how the immune system was responding. First of all, both diets again seem to have improved immune function, both adaptive and innate immune function as compared to their baseline measurements when they came into the study. So when they're reading their habitual diet, whatever that is typically high in ultra-processed foods, they switched to both of these diets.

Kevin Hall (17:39):

We saw market changes in their immune system even compared to baseline. But when we then went and compared the two diets, they were actually divergent also, in other words, the vegan diet seemed to stimulate the innate immune system and the ketogenic diet seemed to stimulate the adaptive immune system. So these are the innate immune system can be thought of. Again, I'm not an immunologist. My understanding is that this is the first line defense against pathogens. It happens very quickly and then obviously the adaptive immune system then adapts to a specific pathogen over time. And so, this ability of our diet to change the immune system is intriguing and how much of that has to do with influencing the gut microbiota, which obviously the gut plays a huge role in steering our immune system in one direction versus another. I think those are some really intriguing mechanistic questions that are really good fodder for future research.

Eric Topol (18:42):

Yeah, I think it may have implications for treatment of autoimmune diseases. You may want to comment about that.

Kevin Hall (18:51):

Yeah, it's fascinating to think about that the idea that you could change your diet and manipulate your microbiota and manipulate your gut function in a way to influence your immune system to steer you away from a response that may actually be causing your body damage in your typical diet. It's a fascinating area of science and we're really interested to follow that up. I mean, it kind of supports these more anecdotal reports of people with lupus, for example, who've reported that when they try to clean up their diet for a period of time and eliminate certain foods and eliminate perhaps even ultra-processed food products, that they feel so much better that their symptoms alleviate at least for some period of time. Obviously, it doesn't take the place of the therapeutics that they need to take, but yeah, we're really interested in following this up to see what this interaction might be.

Eric Topol (19:46):

Yeah, it's fascinating. It also gets to the fact that certain people have interesting responses. For example, those with epilepsy can respond very well to a ketogenic diet. There's also been diet proposed for cancer. In fact, I think there's some even ongoing trials for cancer of specific diets. Any comments about that?

Kevin Hall (20:10):

Yeah, again, it's a really fascinating area. I mean, I think we kind of underappreciate and view diet in this lens of weight loss, which is not surprising because that's kind of where it's been popularized. But I think the role of nutrition and how you can manipulate your diet and still you can have a very healthy version of a ketogenic diet. You can have a very healthy version of a low-fat, high carb diet and how they can be used in individual cases to kind of manipulate factors that might be of concern. So for example, if you're concerned about blood glucose levels, clearly a ketogenic diet is moderating those glucose levels over time, reducing insulin levels, and that might have some positive downstream consequences and there's some potential downsides. Your apoB levels might go up. So, you have to kind of tune these things to the problems and the situations that individuals may face. And similarly, if you have issues with blood glucose control, maybe a high carbohydrate diet might not be for you, but if that's not an issue and you want to reduce apoB levels, it seems like that is a relatively effective way to do that, although it does tend to increase fasting triglyceride levels.

Kevin Hall (21:27):

So again, there's all of these things to consider, and then when you open the door beyond traditional metabolic health markers to things like inflammation and autoimmune disease as well as some of these other things like moderating how cancer therapeutics might work inside the body. I think it's a really fascinating and interesting area to pursue.

Eric Topol (21:55):

No question about it. And that also brings in the dimension of the gut microbiome, which obviously your diet has a big influence, and it has an influence on your brain, brain-gut axis, and the immune system. It's all very intricate, a lot of feedback loops and interactions that are not so easy to dissect, right?

Kevin Hall (22:16):

Absolutely. Yeah, especially in humans. That's why we rely on our basic science colleagues to kind of figure out these individual steps in these chains. And of course, we do need human experiments and carefully controlled experiments to see how much of that really translates to humans, so we need this close sort of translational partnership.

On the Pathogenesis of Obesity, Calories In and Calories Out

Eric Topol (22:35):

Yeah. Now, you've also written with colleagues, other experts in the field about understanding the mechanisms of pathogenesis of obesity and papers that we'll link to. We're going to link to everything for what we've been discussing about calories in, calories out, and that's been the longstanding adage about this. Can you enlighten us, what is really driving obesity and calories story?

Kevin Hall (23:05):

Well, I co-organized a meeting for the Royal Society, I guess about a year and a half ago, and we got together all these experts from around the world, and the basic message is that we have lots of competing theories about what is driving obesity. There's a few things that we all agree on. One is that there is a genetic component. That adiposity in a given environment is somewhere between 40% to 70% heritable, so our genes play a huge role. It seems like there's certain genes that can play a major role. Like if you have a mutation in leptin, for example, or the leptin receptor, then this can have a monogenic cause of obesity, but that's very, very rare. What seems to be the case is that it's a highly polygenic disease with individual gene variants contributing a very, very small amount to increased adiposity. But our genes have not changed that much as obesity prevalence has increased over the past 50 years. And so, something in the environment has been driving that, and that's where the real debates sort of starts, right?

Kevin Hall (24:14):

I happen to be in the camp that thinks that the food environment is probably one of the major drivers and our food have changed substantially, and we're trying to better understand, for example, how ultra-processed foods which have risen kind of in parallel with the increased prevalence of obesity. What is it about ultra-processed foods that tend to drive us to overconsume calories? Other folks focus maybe more on what signals from the body have been altered by the foods that we're eating. They might say that the adipose tissue because of excess insulin secretion for example, is basically driven into a storage mode and that sends downstream signals that are eventually sensed by the brain to change our appetite and things like that. There's a lot of debate about that, but again, I think that these are complementary hypotheses that are important to sort out for sure and important to design experiments to try to figure out what is more likely. But there is a lot of agreement on the idea that there's something in our environment has changed.

Kevin Hall (25:17):

I think there's even maybe a little bit less agreement of exactly what that is. I think that there's probably a little bit more emphasis on the food environment as opposed to there are other folks who think increased pollution might be driving some of this, especially endocrine disrupting chemicals that have increased in prevalence. I think that's a viable hypothesis. I think we have to try to rank order what we think are the most likely and largest contributors. They could all be contributing to some extent and maybe more so in some people rather than others, but our goal is to try to, maybe that's a little simple minded, but let's take the what I think is the most important thing and let's figure out the mechanisms of that most important thing and we'll, number one, determine if it is the most important thing. In my case, I think something about ultra-processed foods that are driving much of what we're seeing. If we could better understand that, then we could both advise consumers to avoid certain kinds of foods because of certain mechanisms and still be able to consume some degree of ultra-processed foods. They are convenient and tasty and relatively inexpensive and don't require a lot of skill and equipment to prepare. But then if we focus on the true bad guys in that category because we really understand the mechanisms, then I think that would be a major step forward. But that's just my hypothesis.

Eric Topol (26:43):

Well, I’m with you actually. Everything I've read, everything I've reviewed on ultra-processed food is highly incriminating, and I also get frustrated that nothing is getting done about it, at least in this country. But on the other hand, it doesn't have to be either or, right? It could be both these, the glycemic index story also playing a role. Now, when you think about this and you're trying to sort out calories in and calories out, and let's say it's one of your classic experiments where you have isocaloric proteins and fat and carbohydrate exactly nailed in the different diets you're examining. Is it really about calories or is it really about what is comprising the calorie?

Kevin Hall (27:29):

Yeah, so I think this is the amazing thing, even in our ultra-processed food study, if we asked the question across those people, did the people who ate more calories even in the ultra-processed diet, did they gain more weight? The answer is yes.

Kevin Hall (27:44):

There's a very strong linear correlation between calorie intake and weight change. I tend to think that I started my career in this space focusing more on the metabolism side of the equation, how the body's using the calories and how much does energy expenditure change when you vary the proportion of carbs versus fat, for example. The effect size is there, they might be there, but they're really tiny of the order of a hundred calories per day. What really struck me is that when we just kind of changed people's food environments, the magnitude of the effects are like we mentioned, 500 to 700 calories per day differences. So I think that the real trick is to figure out how is it that the brain is regulating our body weight in some way that we are beginning to understand from a molecular perspective? What I think is less well understood is, how is that food intake control system altered by the food environment that we find ourselves in?

The Brain and GLP-1 Drugs

Kevin Hall (28:42):

There are a few studies now in mice that are beginning to look at how pathways in the brain that have been believed to be related to reward and not necessarily homeostatic control of food intake. They talk to the regions of the brain that are related to homeostatic control of food intake, and it's a reciprocal sort of feedback loop there, and we're beginning to understand that. And I think if we get more details about what it is in our foods that are modulating that system, then we'll have a better understanding of what's really driving obesity and is it different in different people? Are there subcategories of obesity where certain aspects of the food environment are more important than others, and that might be completely flipped in another person. I don't know the answer to that question yet, but it seems like there are certain common factors that might be driving overall changes in obesity prevalence and how they impact this reward versus homeostatic control systems in the brain, I think are really fascinating questions.

Eric Topol (29:43):

And I think we're getting much more insight about this circuit of the reward in the brain with the food intake, things like optogenetics, many ways that we're getting at this. And so, it's fascinating. Now, that gets me to the miracle drug class GLP-1, which obviously has a big interaction with obesity, but of course much more than that. And you've written about this as well regarding this topic of sarcopenic obesity whereby you lose a lot of weight, but do you lose muscle mass or as you referred to earlier, you lose body fat and maybe not so much muscle mass. Can you comment about your views about the GLP-1 family of drugs and also about this concern of muscle mass loss?

Kevin Hall (30:34):

Yeah, so I think it's a really fascinating question, and we've been trying to develop mathematical models about how our body composition changes with weight gain and weight loss for decades now. And this has been a long topic, one of the things that many people may not realize is that people with obesity don't just have elevated adiposity, they also have elevated muscle mass and lean tissue mass overall. So when folks with obesity lose weight, and this was initially a pretty big concern with bariatric surgery, which has been the grandfather of ways that people have lost a lot of weight. The question has been is there a real concern about people losing too much weight and thereby becoming what you call sarcopenic? They have too little muscle mass and then they have difficulties moving around. And of course, there are probably some people like that, but I think what people need to realize is that folks with obesity tend to start with much higher amounts of lean tissue mass as well as adiposity, and they start off with about 50% of your fat-free mass, and the non-fat component of your body is skeletal muscle.

Kevin Hall (31:45):

So you're already starting off with quite a lot. And so, the question then is when you lose a lot of weight with the GLP-1 receptor agonist or with bariatric surgery, how much of that weight loss is coming from fat-free mass and skeletal muscle versus fat mass? And so, we've been trying to simulate that using what we've known about bariatric surgery and what we've known about just intentional weight loss or weight gain over the years. And one of the things that we found was that our sort of expectations for what's expected for the loss of fat-free mass with these different drugs as well as bariatric surgery, for the most part, they match our expectations. In other words, the expected amount of fat loss and fat free mass loss. The one outlier interestingly, was the semaglutide study, and in that case, they lost more fat-free mass than would be expected.

Kevin Hall (32:44):

Now, again, that's just raising a little bit of a flag that for whatever reason, from a body composition perspective, it's about a hundred people underwent these repeated DEXA scans in that study sponsored by Novo Nordisk. So it's not a huge number of people, but it's enough to really get a good estimate about the proportion of weight loss. Whether or not that has functional consequences, I think is the open question. There's not a lot of reports of people losing weight with semaglutide saying, you know what? I'm really having trouble actually physically moving around. I feel like I've lost a lot of strength. In fact, it seems to be the opposite, right, that the quality of the muscle there seems to be improved. They seem to have more physical mobility because they've lost so much more weight, that weight had been inhibiting their physical movement in the past.

Kevin Hall (33:38):

So it's something to keep an eye on. It's an open question whether or not we need additional therapies in certain categories of patients, whether that be pharmacological, there are drugs that are interesting that tend to increase muscle mass. There's also other things that we know increase muscle mass, right? Resistance exercise training, increase this muscle mass. And so, if you're really concerned about this, I certainly, I'm not a physician, but I think it's something to consider that if you go on one of these drugs, you might want to think about increasing your resistance exercise training, maybe increasing the protein content of your diet, which then can support that muscle building. But I think it's a really interesting open question about what the consequences of this might be in certain patient populations, especially over longer periods of time.

Dietary Protein, Resistance Exercise, DEXA Scans

Eric Topol (34:30):

Yeah, you've just emphasized some really key points here. Firstly, that resistance exercise is good for you anyway. And get on one of these drugs, why don't you amp it up or get it going? The second is about the protein diet, which it'd be interesting to get your thoughts on that, but we generally have too low of a protein diet, but then there are some who are advocating very high protein diets like one gram per pound, not just one gram per kilogram. And there have been studies to suggest that that very high protein diet could be harmful, but amping up the protein diet, that would be a countering thing. But the other thing you mentioned is a DEXA scan, which can be obtained very inexpensively, and because there's a variability in this muscle mass loss if it's occurring, I wonder if that's a prudent thing or if you just empirically would just do the things that you mentioned. Do you have any thoughts about that?

Kevin Hall (35:32):

Yeah, that's really a clinical question that I don't deal with on a day-to-day basis. And yeah, I think there's probably better people suited to that. DEXA scans, they're relatively inexpensive, but they're not readily accessible to everyone. I certainly wouldn't want to scare people away from using drugs that are now known to be very effective for weight loss and pretty darn safe as far as we can tell, just because they don't have access to a DEXA scanner or something like that.

Eric Topol (36:00):

Sure. No, that makes a lot of sense. I mean, the only reason I thought it might be useful is if you're concerned about this and you want to track, for example, how much is that resistant training doing?

Kevin Hall (36:13):

But I think for people who have the means to do that, sure. I can't see any harm in it for sure.

Continuous Glucose Sensors?

Eric Topol (36:19):

Yeah. That gets me to another metric that you've written about, which is continuous glucose tracking. As you know, this is getting used, I think much more routinely in type one insulin diabetics and people with type 2 that are taking insulin or difficult to manage. And now in recent months there have been consumer approved that is no prescription needed, just go to the drugstore and pick up your continuous glucose sensor. And you've written about that as well. Can you summarize your thoughts on it?

Kevin Hall (36:57):

Yeah, sure. I mean, yeah, first of all, these tools have been amazing for people with diabetes and who obviously are diagnosed as having a relative inability to regulate their glucose levels. And so, these are critical tools for people in that population. I think the question is are they useful for people who don't have diabetes and is having this one metric and where you target all this energy into this one thing that you can now measure, is that really a viable way to kind of modulate your lifestyle and your diet? And how reliable are these CGM measurements anyway? In other words, do they give the same response to the same meal on repeated occasions? Does one monitor give the same response as another monitor? And those are the kinds of experiments that we've done. Again, secondary analysis, these trials that we talked about before, we have people wearing continuous glucose monitors all the time and we know exactly what they ate.

Kevin Hall (37:59):

And so, in a previous publication several years ago, we basically had two different monitors. One basically is on the arm, which is the manufacturer's recommendation, the other is on the abdomen, which is the manufacturer's recommendation. They're wearing them simultaneously. And we decided just to compare what were the responses to the same meals in simultaneous measurements. And they were correlated with each other thankfully, but they weren't as well predictive as you might expect. In other words, one device might give a very high glucose reading to consuming one meal and the other might barely budge, whereas the reverse might happen for a different meal. And so, we asked the question, if we were to rank the glucose spikes by one meal, so we have all these meals, let's rank them according to the glucose spikes of one device. Let's do the simultaneous measurements with the other device.

Kevin Hall (38:53):

Do we get a different set of rankings? And again, they're related to each other, but they're not overlapping. They're somewhat discordant. And so, then the question becomes, okay, well if I was basically using this one metric to kind of make my food decisions by one device, I actually start making different decisions compared to if I happen to have been wearing a different device. So what does this really mean? And I think this sort of foundational research on how much of a difference you would need to make a meaningful assessment about, yeah, this is actionable from a lifestyle perspective, even if that is the one metric that you're interested in. That sort of foundational research I don't think has really been done yet. More recently, we asked the question, okay, let's ignore the two different devices. Let's stick to the one where we put it on our arm, and let's ask the question.

Kevin Hall (39:43):

We've got repeated meals and we've got them in this very highly regimented and controlled environment, so we know exactly what people ate previously. We know the timing of the meals, we know when they did their exercise, we know how much they were moving around, how well they slept the night before. All of these factors we could kind of control. And the question that we asked in that study was, do people respond similarly to the same meal on repeated occasions? Is that better than when you actually give them very different meals? But they match overall for macronutrient content, for example. And the answer to that was surprisingly no. We had as much variability in the glucose response to the same person consuming the same meal on two occasions as a whole bunch of different meals. Which suggests again, that there's enough variability that it makes it difficult to then recommend on for just two repeats of a meal that this is going to be a meal that's going to cause your blood glucose to be moderate or blood glucose to be very high. You're going to have to potentially do this on many, many different occasions to kind of figure out what's the reliable response of these measurements. And again, that foundational research is typically not done. And I think if we're really going to use this metric as something that is going to change our lifestyles and make us choose some meals other than others, then I think we need that foundational research. And all we know now is that two repeats of the same meal is not going to do it.

Eric Topol (41:21):

Well, were you using the current biosensors of 2024 or were you using ones from years ago on that?

Kevin Hall (41:27):

No, we were using ones from several years ago when these studies were completed. But interestingly, the variability in the venous measurements to meal tests is also very, very different. So it's probably not the devices per se that are highly variable. It's that we don't really know on average how to predict these glucose responses unless there's huge differences in the glycemic load. So glycemic load is a very old concept that when you have very big differences in glycemic load, yeah, you can on average predict that one kind of meal is going to give rise to a much larger glucose excursion than another. But typically these kind of comparisons are now being made within a particular person. And we're comparing meals that might have quite similar glycemic loads with the claim that there's something specific about that person that causes them to have a much bigger glucose spike than another person. And that we can assess that with a couple different meals.

Eric Topol (42:31):

But also, we know that the spikes or the glucose regulation, it's very much affected by so many things like stress, like sleep, like exercise. And so, it wouldn't be at all surprising that if you had the exact same food, but all these other factors were modulated that it might not have the same response. But the other thing, just to get your comment on. Multiple groups, particularly starting in Israel, the Weizmann Institute, Eran Segal and his colleagues, and many subsequent have shown that if you give the exact same amount of that food, the exact same time to a person, they eat the exact same amount. Their glucose response is highly heterogeneous and variable between people. Do you think that that's true? That in fact that our metabolism varies considerably and that the glucose in some will spike with certain food and some won’t.

Kevin Hall (43:29):

Well, of course that's been known for a long time that there's varying degrees of glucose tolerance. Just oral glucose tolerance tests that we've been doing for decades and decades we know is actually diagnostic, that we use variability in that response as diagnostic of type 2 diabetes.

Eric Topol (43:49):

I'm talking about within healthy people.

Kevin Hall (43:53):

But again, it's not too surprising that varying people. I mean, first of all, we have a huge increase in pre-diabetes, right? So there's various degrees of glucose tolerance that are being observed. But yeah, that is important physiology. I think the question then is within a given person, what kind of advice do we give to somebody about their lifestyle that is going to modulate those glucose responses? And if that's the only thing that you look at, then it seems like what ends up happening, even in the trials that use continuous glucose monitors, well big surprise, they end up recommending low carbohydrate diets, right? So that's the precision sort of nutrition advice because if that's the main metric that's being used, then of course we've all known for a very long time that lower carbohydrate diets lead to a moderated glucose response compared to higher carbohydrate diets. I think the real question is when you kind of ask the issue of if you normalize for glycemic load of these different diets, and there are some people that respond very differently to the same glycemic load meal compared to another person, is that consistent number one within that person?

Kevin Hall (45:05):

And our data suggests that you're going to have to repeat that same test multiple times to kind of get a consistent response and be able to make a sensible recommendation about that person should eat that meal in the future or not eat that meal in the future. And then second, what are you missing when that becomes your only metric, right? If you're very narrowly focused on that, then you're going to drive everybody to consume a very low carbohydrate diet. And as we know, that might be great for a huge number of people, but there are those that actually have some deleterious effects of that kind of diet. And if you're not measuring those other things or not considering those other things and put so much emphasis on the glucose side of the equation, I worry that there could be people that are being negatively impacted. Not to mention what if that one occasion, they ate their favorite food and they happen to get this huge glucose spike and they never eat it again, their life is worse. It might've been a complete aberration.

Eric Topol (46:05):

I think your practical impact point, it's excellent. And I think one of the, I don't know if you agree, Kevin, but one of the missing links here is we see these glucose spikes in healthy people, not just pre-diabetic, but people with no evidence of glucose dysregulation. And we don't know, they could be up to 180, 200, they could be prolonged. We don't know if the health significance of that, and I guess someday we'll learn about it. Right?

Kevin Hall (46:36):

Well, I mean that's the one nice thing is that now that we have these devices to measure these things, we can start to make these correlations. We can start to do real science to say, what a lot of people now presume is the case that these spikes can't be good for you. They must lead to increased risk of diabetes. It's certainly a plausible hypothesis, but that's what it is. We actually need good data to actually analyze that. And at least that's now on the table.

Eric Topol (47:04):

I think you're absolutely right on that. Well, Kevin, this has been a fun discussion. You've been just a great leader in nutrition science. I hope you'll keep up your momentum because it's pretty profound and I think we touched on a lot of the uncertainties. Is there anything that I didn't ask you that you wish I did?

Kevin Hall (47:23):

I mean, we could go on for hours, I'm sure, Eric, but this has been a fascinating conversation. I really appreciate your interest. Thank you.

Eric Topol (47:30):

Alright, well keep up the great stuff. We'll be following all your work in the years ahead, and thanks for joining us on Ground Truths today.

**************************************

Footnote, Stay Tuned: Julia Belluz and Kevin Hall have a book coming out next September titled “WHY WE EAT?

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Below is a brief video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! This one has embedded one of my favorite TikTok’s from Will. There are several links to others in the transcript. The audios are also available on Apple and Spotify.

Transcript with links to both audio and videos, commencement addresses, NEJM article coverage

Eric Topol (00:06):

Hi, it's Eric Topol from Ground Truths, and I've got an amazing couple with me today. It's Will Flanary and Kristin Flanary, the Glaucomfleckens. I've had the chance to get to know them a bit through Knock Knock, Hi! which is their podcast. And of course, everyone knows Dr. Glaucomflecken from his TikTok world and his other about 4 million followers on Instagram and Twitter and all these other social media, and YouTube. So welcome.

Will Flanary (00:43):

Thanks for having us.

Kristin Flanary (00:44):

Thank you. Happy to be here.

By Way of Background

Eric Topol (00:45):

Yeah. Well, this is going to be fun because I'm going to go a quick background so we can go fast forward because we did an interview back in early 2022.

Kristin Flanary (00:56):

Yes.

Eric Topol (00:57):

And what you've been doing since then is rocking it. You're like a meteoric, right. And it was predictable, like rarefied talent and who couldn't love humor, medical humor, but by way of background, just for those who are not up to speed. I guess you got your start, Will, as a class clown when your mother was a teacher in the sixth grade.

Will Flanary (01:22):

Yep, yep. I misbehaved a little bit. It helped that I still made good grades, but I cut up a bit in class.

Eric Topol (01:32):

And then you were already in the comedy club circuits doing standup in Houston as an 18-year-old.

Will Flanary (01:40):

It was all amateur stuff, nothing, just dabble in it and trying to get better. I was always kind of naturally funny just with my friend group and everything. I loved making people laugh, but doing standups is a whole different ball game. And so, I started doing that around Houston as a high school senior and kept that going through college and a little bit into med school.

Kristin Flanary (02:02):

Houston was a good training ground, right? That where Harris Wittels was also coming up.

Will Flanary (02:07):

Yeah. A lot of famous comedians have come through Houston. Even going back to Bill Hicks back in the, was that the 80s, I think? Or 90s?

Eric Topol (02:17):

Well, and then of course, it was I think in 2020 when you launched Dr. Glaucomflecken, I think. Is that right?

Will Flanary (02:28):

That's when it really started to take off. I was on Twitter telling jokes back in 2016.

Kristin Flanary (02:39):

GomerBlog before that, that's actually where it was born.

Will Flanary (02:41):

I was doing satire writing. I basically do what I'm doing now, but in article form, trying to be The Onion of medicine. And then the pandemic hit, started doing video content and that's really with lockdown. That's when, because everybody was on social media, nobody had anything else to do. So it was right place, right time for me and branching out into video content.

On to Medical School Commencement Addresses

Eric Topol (03:11):

Alright, so that's the background of some incredible foundation for humor. But since we last got together, I'll link the Medicine and the Machine interview we did back then. What has been happening with you two is nothing short of incredible. I saw your graduation speeches, Will. Yale in 2022, I watched the UCSF in 2023 and then the University of Michigan in 2024. Maybe there's other ones I don't even know.

Kristin Flanary (03:45):

There’s a few others.

Will Flanary (03:45):

There's a few. But I feel like you've done, I'm sure your fair share of commencement addresses as well. It's kind of hard to come up with different ways to be inspirational to the next generation. So fortunately, we have together, we have some life experiences and learned a thing or two by doing all of this social media stuff and just the things we've been through that I guess I have enough things to say to entertain an interest.

Eric Topol (04:18):

Well, you're being humble as usual, but having watched those commencement addresses, they were the best medical commencement addresses I've ever seen. And even though you might have told us some of the same jokes, they were so great that it was all right. Yeah, and you know what is great about it is you've got these, not the students, they all love you of course, because they're probably addicted to when's your next video going to get posted.

(04:44):

But even the old professors, all the family members, it's great. But one of the things I wanted to get at. Well, I'll start with the graduation speeches, because you were such an inspiration, not just with humor, but your message. And this gets back to you as a couple and the tragedies you've been through. So you really, I think, got into this co-survivor story and maybe Kristin, since you are the co-survivor of two bouts of Will’s testicular cancer, and then the sudden cardiac death. I mean, people don't talk about this much, so maybe you could help enlighten us.

Tragedies and Being a Co-Survivor

Kristin Flanary (05:26):

Yeah, it’s funny because the experience of being a co-survivor is nothing new. It’s as long as we've had human beings, we've had co-survivors. But the concept around it and giving it a name and a label, a framework to be able to think about it, that is what I think is new and what people haven't talked about before. So co-survivor is just this idea that when a medical trauma happens to a patient, the patient has their experience and if they survive it, they are a survivor and they have a survivor experience. And also, most people are closely attached to at least one other person, if not many. And those people are co-surviving the medical event along with the survivor. That event is happening in their lives as was happening to them too. If someone comes in with a patient to the hospital, that person, you can just assume by default that their lives are pretty intimately or profoundly intertwined or else why would that person be there? And so, thinking of it as there's the patient and then there's also a co-patient, that family members in the past have only been thought of as caregivers if they've been thought of at all. And that is certainly one aspect of the role, but it's important to remember that whatever it is that's happening to the patient is also affecting the family members' lives in a really deep and profound way.

Eric Topol (07:04):

That's really helpful. Now, the fact that you recognize that in your graduation speech, Will, I think is somewhat unique. And of course, some of the other things that you touched on like playing to your creativity and the human factors, I mean, these are so important messages.

Will Flanary (07:23):

Well, in the discussion about co-survivorship and because I talk about that whenever I do my keynotes and when I do the commencement addresses, but all credit goes to Kristin for really being the driving force of this idea for me and for many others because as a physician, we take care of patients. Our focus is always on the patient. And it really wasn't until this happened to me and my family and Kristin in particular that I started to understand exactly what she's talking about and this idea. And so, Kristin gets a lot of credit for just really bringing that term and that idea to the forefront.

Eric Topol (08:09):

Yeah, well, you saved his life. It's just not many have that bond. And then the other thing I just want to mention now, you've been recognized by the American Heart Association and a whole bunch of other organizations awarded because of your advocacy for CPR. And you even mentioned that I think in one of your commencement addresses.

Will Flanary (08:31):

Yeah, I tried to get the crowd to do CPR. Like team up, partner up, and it kind of fell flat. It wasn't quite the right time, I think, to try to do a mass class on CPR. So maybe next time.

Eric Topol (08:47):

Right. Well, so you had this foundation with the Glaucomflecken General Hospital and taking on 37 specialties and all these incredible people that became part of the family, if you will, of spoof on medicine and your alter ego and these videos that you would do. And sometimes you have three or four different alter egos in there playing out, but now you've branched into new things. So one which is an outgrowth of what we were just talking about. You've been on this country tour, Wife & Death.

“Wife and Death,” A Nationwide Tour

Kristin Flanary (09:28):

Yes.

Eric Topol (09:29):

Wife and death. I mean, yeah, I guess we can make the connect of how you named it that, but what is it you've been selling out in cities all over the country, and by the way, I'm really upset you haven't come to San Diego, but tell us about wife and death.

Will Flanary (09:44):

Yeah. Well, we have this amazing story and all these medical challenges we've been through, and then developing the Glaucomflecken brand and universe, and we've done keynotes together for years, and then we thought, let's have more fun with it. Let's do keynotes. They're great. We can get our message out, but sometimes they're just a bit stuffy. It's an academic environment.

Kristin Flanary (10:15):

They're usually at seven in the morning also, so that's the downside.

Will Flanary (10:21):

So we thought, let's just put together our own live show. Let's put together something that we could just creatively, we can do whatever we want with it. I could dress up as characters, Kristin, who has these beautiful writing and monologues that she's put together around her experience and just to create something that people can come into a theater and just experience this wide range of emotions from just laughter to tears of all kinds, and just have them feel the story and enjoy this story. Fortunately, it has a happy ending because I'm still alive and it's been so much fun. The audiences have been incredible. Mostly healthcare, but even some non-healthcare people show up, and we've been blown away by the response. Honestly, we should have done bigger theaters. That's our lesson for the first go round.

Eric Topol (11:21):

I saw you had to do a second show in Pittsburgh.

Will Flanary (11:24):

We did.

Kristin Flanary (11:26):

That one sold out too. Something about Pittsburgh, that was a good crowd, and there was a lot of them.

Will Flanary (11:33):

It was almost like in Pittsburgh, they rarely ever get any internet comedian ophthalmologists that come through. I don't know.

Eric Topol (11:41):

Well, I see you got some still to come in Denver and Chicago. This is amazing. And I wondered who was coming and I mean, it's not at all surprising that there'd be this phenomenal popularity. So that's one thing you've done that's new, which is amazing. And of course, it's a multidimensional story. The one that shocked me, I have to tell you, shocked me, was the New England Journal partnership. The New England Journal is the most stodgy, arrogant, I mean so difficult. And not only that.

Kristin Flanary (12:17):

You said that. Not us.

Partnering with the New England Journal of Medicine!

Eric Topol (12:19):

Yeah, yeah. They'll get this too. They know we don't get along that well, but that's okay. You even made fun of journals. And now you're partnering with the New England Journal, God's greatest medical journal, or whatever. Tell us about that.

Will Flanary (12:39):

Well, so one thing that I really enjoy doing, and I've done it with my US healthcare system content is almost like tricking people into learning things. And so, if you make something funny, then people will actually sit there and listen to what you have to say about deductibles and physician-owned hospitals and all these inner workings. DIR fees and pharmacy, all these things that are really dry topics. But if you can make them funny, all of a sudden people will actually learn and listen to it. And the New England Journal of Medicine, they approached me with an idea. Basically just to take one or two of their trials per month. And I just make a skit out of that trial with the idea being to help disseminate some of the research findings that are out there, because I guess it's getting harder and harder for people to actually read, to sit down and read a journal article.

(13:43):

And so, I have to credit them for having this idea and thinking outside the box of a different way to get medical information and knowledge out to the masses. And you're absolutely right, that I have been critical of journals, and particularly I've been critical of the predatory nature of some of the larger journals out there, like Elsevier. I've specifically named Elsevier, Springer, these journals that have a 40% profit margin. And I certainly thought about that whenever I was looking into this partnership. And the reason I was okay with doing it with the New England Journal is because they're a nonprofit, first of all, so they're run by the Massachusetts Medical Society. That's the publisher for that journal. And so, I feel okay partnering with them because I feel like they're doing it in a much better way than some of the bigger journal corporations out there.

Kristin Flanary (14:54):

Well, and also part of the deal that we negotiated was that those articles that you make skits about those will be available open access.

Will Flanary (15:03):

Oh yeah. That was a prerequisite. Yes. It was like, if I'm going to do this, the articles that I'm talking about need to be free and readily available. That's part of it.

Eric Topol (15:14):

I think you've done about five already, something like that. And I watched them, and I just was blown away. I mean, the one that got me where I was just rolling on the floor, this one, the Belantamab Mafodotin for Multiple Myeloma. And when you were going on about the Bortezomib, Dexamethasone. We'll link to this. I said, oh my God.

Will Flanary (15:40):

Yeah. The joke there is, you don't have any idea how long it took me to say those things that quickly. And so, I was writing this skit and I'm like, wouldn't it be funny if somehow that triggered a code stroke in the hospital because this person is saying all these random words that don't have any meaning to anybody. Man, I tell you, I am learning. Why would I ever need to know any of this information as an ophthalmologist? So it's great. I know all this random stuff about multiple myeloma that I probably would never have learned otherwise.

Kristin Flanary (16:21):

It's the only way, you won't read a journal either.

Eric Topol (16:23):

Well, and if you read the comments on the post. These doctors saying, this is the only way they want to get journal information from now on.

Will Flanary (16:33):

Which is double-edged sword, maybe a little bit. Obviously, in a 90 second skit, there's no way I'm going to cover the ins and outs of a major trial. So it's really, in a lot of ways, it's basically like, I call it a comedy abstract. I’m not going much further than an abstract, but hopefully people that are actually interested in the topic can have their interest piqued and want to read more about it. That's kind of the idea.

Eric Topol (17:06):

Yeah. Well, they're phenomenal. We'll link to them. People will enjoy them. I know, because I sure did. And tenecteplase for stroke and all that you've done. Oh, they're just phenomenal.

Will Flanary (17:20):

Every two weeks we come out with a new one.

Eric Topol (17:24):

And that is basically between the fact that you are now on the commencement circuit of the top medical schools and doing New England Journal videos on their articles. You've crossed a line from just making fun of insurance companies and doctors of specialties.

Kristin Flanary (17:44):

Oh, he has crossed many lines, Dr. Topol.

Eric Topol (17:46):

Yeah. Oh yeah. Now you've done it, really. Back two years ago when we convened, actually it's almost three, but you said, when's it going to be your Netflix special?

Will Flanary (18:02):

Oh, gosh.

Eric Topol (18:02):

Is that in the works now?

Will Flanary (18:04):

Well, I'll tell you what's in the works now.

Kristin Flanary (18:06):

Do you know anyone at Netflix?

Will Flanary (18:09):

A New Animated Series

No. We're working on an animated series.

Eric Topol (18:12):

Oh, wow. Wow.

Will Flanary (18:13):

Yeah. All these characters. It's basically just this fictional hospital and all these characters are very cartoonish, the emergency physician that wears the bike helmet and everything. So it's like, well, what do we have together? What do we, Kristin and I have time for? And it wasn't like moving to LA and trying to make a live action with actors and do all, which is something we probably could have tried to do. So instead, we were like, let's just do an animated series.

Kristin Flanary (18:48):

Let's have someone else do the work and draw us.

Will Flanary (18:51):

So we've worked with a writer for the first time, which was a fun process, and putting together a few scripts and then also an animator. We learned a lot about that process. Kristin and I are doing the voiceovers. And yeah, it's in process.

Kristin Flanary (19:10):

We're the only actors we could afford.

Will Flanary (19:12):

Right, exactly.

Eric Topol (19:13):

I can't wait to see it. Now when will it get out there?

Will Flanary (19:17):

Well, we're hoping to be able to put it out on our YouTube channel sometime early next year. So January, February, somewhere around there. And then we can't fund the whole thing ourselves. So the idea is that we do this, we do this pilot episode, and then we'll see what kind of interest we can generate.

Eric Topol (19:37):

Well, there will be interest. I am absolutely assured of that. Wow.

Will Flanary (19:42):

Let us know if you know anybody at the Cartoon Network.

Kristin and Will Flanary (19:45):

Yeah, we're open to possibilities. Whatever, Discovery channel. I don't know.

Eric Topol (19:51):

You've gotten to a point now where you're ready for bigger things even because you're the funniest physician couple in medicine today.

Kristin Flanary (20:05):

Well, that's a very low bar, but thank you.

Will Flanary (20:08):

There are some funny ones out there, but yeah, I appreciate that.

Eric Topol (20:11):

Well, I'm a really big comedy fan. Every night I watch the night before, since I'm old now, but of Colbert and Jimmy Kimmel, just to hear the monologues. Trevor Noah, too. And I can appreciate humor. I'll go to see Sebastian Maniscalco or Jim Gaffigan. That's one of the things I was going to ask you about, because when you do these videos, you don't have an audience.

Will Flanary (20:39):

Oh yeah.

Eric Topol (20:40):

You're making it as opposed to when you are doing your live shows, commencement addresses and things like that. What's the difference when you're trying to be humorous, and you have no audience there?

Will Flanary (20:55):

Well, whenever I'm filming a skit, it's just all production. In fact, I feel like it's funny. I think it's funny, but it's really not until I see the response to it, or I show Kristin, or what I have is where I really know if it's going to work. It's great to put the content out there and see the responses, but there's nothing like live interaction. And that's why I keep coming back to performing. And Kristin's been a performer too in her life. And I think we both really enjoy just the personal interaction, the close interaction, the response from people to our story.

Kristin Flanary (21:36):

We do most of our work alone in this room. I do a lot of writing. He does a lot of playing.

Will Flanary (21:44):

Dress up.

Kristin Flanary (21:44):

All the people in his head, and we do that very isolated. And so, it's very lovely to be able to actually put names to faces or just see human bodies instead of just comments on YouTube.

Will Flanary (21:59):

Meet people.

Kristin Flanary (21:59):

It's really nice.

Will Flanary (22:01):

We’ve been doing meet and greets at the live shows and seeing people come up wearing their costumes.

Eric Topol (22:07):

Oh, wow.

Will Flanary (22:11):

Some of them talk about how they tell us their own stories about their own healthcare and talk about how the videos help them get through certain parts of the pandemic or a difficult time in their life. And so, it reinforces that this means something to a lot of people.

Kristin Flanary (22:29):

It's been really fun for me, and probably you too, but to get to see the joy that he has brought so many people. That's really fun to see in person especially.

Eric Topol (22:42):

No question. Now, when you're producing it together, do you ever just start breaking into laughter because it's you know how funny this is? Or is it just you're on kind of a mission to get it done?

Will Flanary (22:54):

Well, the skits I do by myself. And sometimes when I'm writing out the skit, when I'm writing the skit itself, I will laugh at myself sometimes. Not often, but sometimes they're like, oh, I know that's really funny. I just wrote a skit that I'm actually going to be debuting. I'm speaking at the American Academy of PM&R, so the big PM&R conference. I'm writing a skit, it's How to Ace your PM&R residency interview.

Will Flanary (23:28):

I was writing up that skit today and kind of chuckling to myself. So sometimes that happens, but whenever we do our podcast together, we definitely have outtakes.

Kristin Flanary (23:38):

Oh yeah, we've got some.

Will Flanary (23:40):

We crack each other up.

Kristin Flanary (23:41):

We do.

Will Flanary (23:42):

Sometimes we're getting a little punchy toward the end of the day.

Eric Topol (23:47):

And how is the Knock Knock, Hi! podcast going?

Will Flanary (23:51):

It's awesome. Yeah.

Kristin Flanary (23:52):

Yeah. It's a really fun project.

Will Flanary (23:54):

We still enjoy. You can work with your spouse and in close proximity and still be happily married. So it's doable everyone.

Kristin Flanary (24:06):

That's right. And we're in that phase of life that's really busy. We've got kids, we've got a gazillion jobs. House, my parents are around, and so it's like the only time all week that we actually get to sit down and talk to each other. So it's actually kind of like a part of our marriage at this point.

Will Flanary (24:28):

We’re happy to involve the public in our conversations, but we couldn't do it because we have all these things going on, all our hands and all these little places. We can't do it without a team.

Kristin Flanary (24:41):

Yeah, absolutely.

Will Flanary (24:41):

And that's the thing that I've learned, because I've always been a very loner type content creator. I just wanted to do it all myself. It's in my head and I have trouble telling others, describing what's in my head. And Kristin and our producers have helped me to be able to give a little bit of control to others who are really good at what they do. And that's really the only way that we've been able to venture out into all these different things we've talked about.

Eric Topol (25:12):

Well, I think it comes down to, besides your ability to get to people in terms of their laughter receptors, you have this incisive observer capability. And that's one of the things I don't, I can't fathom because when you can understand the nuances of each specialty or of each part of healthcare, and you haven't necessarily interacted with these specialists or at least in recent years, but you nail it every time. I don't know how you do it, really that observational, is that a central quality of a comedian, you think?

Will Flanary (25:52):

There's definitely a big part of that. You got to get the content from somewhere. But for the specialties, it's really first about just getting the personalities down. And that doesn't change over time.

Kristin Flanary (26:08):

Or around the world.

Will Flanary (26:09):

Or around the world. We hear from people from all over the world about, oh, it's the same in Guatemala as it is in the US.

Kristin Flanary (26:18):

Surgeons are the same.

Will Flanary (26:19):

Yeah.

Kristin Flanary (26:20):

Emergency is the same.

Will Flanary (26:21):

Which has been really cool to see. But so, I draw on my experience interacting with all these specialties back in my med school and intern days. You're right, as an ophthalmologist, we don't get out very much.

Eric Topol (26:33):

No.

Will Flanary (26:35):

So very few people have ever seen an ophthalmologist. We do exist. But then beyond that, I do have to include some actual medical things. And so, I actually, I do a lot of research. I find myself learning more about other fields sometimes than I do in my own field. So especially the further out I get from med school, I know less and less.

Eric Topol (27:00):

Yeah, that's what I was thinking. But you're always spot on. It's interesting to get that global perspective from both of you. Now you're still doing surgery and practicing ophthalmology. Have you reduced it because this has just been taking off so much more over the recent years or keeping it the same?

Will and Kristin Flanary (27:21):

Nope, I’m still. Do you know how many years I had to come along on all of this medical training? He is not allowed to give this up.

Will Flanary (27:29):

I know there's something called a sunk cost fallacy, but this is no fallacy. There's enough of a sunk cost. I got to stick with it. No, I still enjoy it. That's the thing. It actually, it informs my comedy, it grounds me. All of the social media stuff is built upon this medical foundation that I have. And if I stopped practicing, I guess I could maybe cut back. But I'm not planning on doing that. If I stop practicing medicine, I feel like it would make my content less.

Kristin Flanary (28:07):

Authentic.

Will Flanary (28:08):

Less authentic, yeah. That's a good way to put it.

Eric Topol (28:09):

Yeah, no, that makes a lot of sense. That's great you can get that balance with all the things you're doing.

Will Flanary (28:17):

And if I stop practicing medicine, they're not going to invite me to any more commencement addresses, Dr. Topol. So I got to draw the line somewhere.

Eric Topol (28:28):

One of the statements you made at some point earlier was, it was easier to go to become a doctor than to try to be a comedian. And yeah, I mean you proven that.

Will Flanary (28:38):

A lot of ways. That's true.

Eric Topol (28:40):

Wow. I am pretty awestruck about the rarefied talent that you bring and what you both have done for medicine today. And the thing is, you're so young, you have so much time ahead to have an impact.

Will Flanary (28:57):

You hear that Kristin, we're young. Look at that.

Kristin Flanary (29:00):

That's getting less and less true.

Will Flanary (29:01):

Kristin, she just turned 40. It's right around the corner for me. So I don't know.

Will Flanary (29:11):

We got some years left.

Eric Topol (29:12):

You're like young puppies. Are you kidding? You're just getting started. But no, I think that what you brought to medicine in terms of comedy, there's no other entity, no person or people like you have done. And just the last thing I want to ask you about is, you have a platform for advocacy. You've been doing that. We talked about co-survivor. We talked about nurturing the human qualities in physicians like creativity and also taking on the insurance companies, which are just monstrous. I'll link a couple of those, but the brain MRI one or the Texaco.

Will Flanary (29:54):

Texaco Mike.

Eric Topol (29:55):

Yeah, that one is amazing. But there is so many. I mean, you've just taken them apart and they deserve every bit of it. Do you have any other targets for advocacy or does that just kind of come up as things go?

Will Flanary (30:08):

It kind of comes up as things go. There's things I keep harping on. The prior authorization reform, which I've helped in a couple of different states. There's a lot of good people around the country doing really good work on prior authorization and reforming that whole process. And I've been able to just play a small part in that in a couple of different ways. And it's been really fun to do that. And so, I do plan on continuing that crusade as it were. There's certain things I'd like to see. I've been learning more about what pharmacists are dealing with as well as a physician. Unfortunately, we are very separate in a lot of ways and just how we come up in medicine. And so, I have had my eyes opened a lot to what community pharmacists are dealing with. For all the terrible things that we have to deal with as physicians in the healthcare system. Pharmacists have just as much, if not more of the things that they're doing that are threatening their livelihoods. And so, I had love to see some more reform on the PBM side of things, pharmacy benefit managers, Caremark, Optum, all of them. They're causing lots of problems.

Eric Topol (31:24):

I couldn't agree with you more. In fact, I'm going to have Mark Cuban on in a few weeks and we're going to get into that. But the pharmacists get abused by these chains.

Will Flanary (31:33):

Oh, it's bad. It's really bad.

Eric Topol (31:35):

Horrible, horrible. I feel, and every time I am in a drugstore working with one of them, I just think what a tough life they have to deal with.

Will Flanary (31:45):

I guess from an advocacy standpoint, the good news is that there's never a shortage of terrible injustices that are being foisted upon the public and physicians and healthcare workers.

Kristin Flanary (31:59):

Yes. The US healthcare system is ripe for advocacy.

Will Flanary (32:01):

Yes. And that's a lesson that I tell people too, and especially the med students coming up, is like, there's work to be done and get in touch with your state societies and there's always work to be done.

Eric Topol (32:18):

Now you've stayed clear of politics. Totally clear, right?

Will Flanary (32:24):

For the most part, yeah. Yeah. It depends on what you consider politics. It depends on what you consider politics.

Eric Topol (32:32):

It being election day, you haven't made any endorsements.

Will Flanary (32:36):

I haven't. And I don't know. I can only handle so much. I've got my things that I really care about. Of course I'm voting, but I want to talk on the things that I feel like I have the expertise to talk about. And I think there's nothing wrong with that. Everybody can't have an opinion on everything, and it means something. So I am happy to discuss the things that I have expertise about, and I'm always on the side of the patient and wanting to make life better for our patients. And that's the side I'm on.

Kristin Flanary (33:25):

I think also he never comes out and explicitly touches on certain topics, but it's not hard to tell where he falls.

Will Flanary (33:34):

If you really want read into it all.

Kristin Flanary (33:38):

It's not like it's a big secret.

Eric Topol (33:40):

I thought that too. I'm glad you mentioned it, Kristin. But it doesn't come out wide open. But yeah, it's inferred for sure.

Eric Topol (33:49):

I think the point being there is that because you have a reach, I think there's no reach that it has 4 million plus people by your posts and no less the tours and keynotes and everything else. So you could go anywhere but sticking to where you're well grounded, it makes a lot of sense. And anyway, I am going to be staying tuned. This is our two-year checkup. I'm hoping you're going to come to San Diego on your next tour.

Kristin Flanary (34:21):

We're working on 2025 plans.

Will Flanary (34:23):

Oh, we got more shows coming up. And we'll hit up other parts of the country too.

Eric Topol (34:28):

I feel like I got to meet you in person, give you a hug or something. I just feel like I'm missing out there. But it's just a joy to have had a chance to work with you on your podcast. And thanks for coming back on one of mine. There's lots of podcasts out there, but having you and joining you is such fun. So thank you.

Will Flanary (34:54):

This has been great. Thank you for having us.

Kristin Flanary (34:55):

Yeah, thank you.

*****************************************

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Note on Exodus from X/twitter:

Many of you have abandoned the X platform for reasons that I understand. While I intend to continue to post there because of its reach to the biomedical community, I will post anything material here in the Notes section of Ground Truths on a daily basis and cover important topics in the newsletter/analyses.

Above is a brief video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to audio and external citations

Eric Topol (00:06):

Well, hello it's Eric Topol with Ground Truths, and I'm really delighted to welcome Dr. Rachael Bedard, who is a physician geriatrician in New York City, and is actually much more multidimensional, if you will. She's a writer. We're going to go over some of her recent writings. She's actually quite prolific. She writes in the New Yorker, New York Magazine, New York Times, New York Review of Books. If it has New York in front of it, she's probably writing there. She's a teacher. She works on human rights, civil rights, criminal justice in the prison system. She's just done so much that makes her truly unique. That's why I really wanted a chance to meet her and talk with her today. So welcome, Rachael.

Rachael Bedard (00:52):

Thank you, Dr. Topol. It's an honor to be here.

Eric Topol (00:55):

Well, please call me Eric and it's such a joy to have a chance to get acquainted with you as a person who is into so many different things and doing all of them so well. So maybe we'd start off with, because you're the first geriatrician we've had on this podcast.

Practicing Geriatrics and Internal Medicine

Eric Topol (01:16):

And it’s especially apropos now. I wanted maybe to talk about your practice, how you got into geriatrics, and then we'll talk about the piece you had earlier this summer on aging.

Rachael Bedard (01:32):

Sure. I went into medicine to do social justice work and I was always on a funny interdisciplinary track. I got into the Mount Sinai School of Medicine through what was then called the Humanities and Medicine program, which was an early acceptance program for people who were humanities focused undergrads, but wanted to go into medicine. So I always was doing a mix of politics and activist focused work, humanities and writing, that was always interested in being a doctor. And then I did my residency at the Cambridge Health Alliance, which is a social medicine program in Cambridge, Massachusetts, and my chief residency there.

(02:23):

I loved being an internist, but I especially loved taking care of complex illness and I especially loved taking care of complex illness in situations where the decision making, there was no sort of algorithmic decision-making, where you were doing incredibly sort of complex patient-centered shared decision making around how to come up with treatment plans, what the goals of care were. I liked taking care of patients where the whole family system was sort of part of the care team and part of the patient constellation. I loved running family meetings. I was incredibly lucky when I was senior resident and chief resident. I was very close with Andy Billings, who was one of the founders of palliative care and in the field, but also very much started a program at MGH and he had come to work at Cambridge Hospital in his sort of semi-retirement and we got close and he was a very influential figure for me. So all of those things conspired to make me want to go back to New York to go to the Sinai has an integrated geriatrics and palliative care fellowship where you do both fellowships simultaneously. So I came to do that and just really loved that work and loved that medicine so much. There was a second part to your question.

Eric Topol (03:52):

Is that where you practice geriatrics now?

Rachael Bedard (03:55):

No, now I have ever since finishing fellowship had very unusual practice settings for a geriatrician. So right out of fellowship, I went to work on Rikers Island and then New York City jail system, and I was the first jail based geriatrician in the country, which is a sort of uncomfortable distinction because people don't really like to think about there being a substantial geriatric population in jails. But there is, and I was incredibly lucky when I was finishing fellowship, there was a lot of energy around jail healthcare in New York City and I wrote the guy who was then the CMO and said, do you think you have an aging problem? And he said, I'm not sure, but if you want to come find out, we'll make you a job to come find out. And so, that was an incredible opportunity for someone right out of fellowship.

(04:55):

It meant stepping off the sort of academic track. But I went and worked in jail for six years and took care of older folks and people with serious illness in jail and then left Rikers in 2022. And now I work in a safety net clinic in Brooklyn that takes care of homeless people or people who have serious sort of housing instability. And that is attached to Woodhull Hospital, which is one of the public hospitals in New York City. And there I do a mix of regular internal medicine primary care, but I preferentially see the older folks who come through, which is a really interesting, painful, complicated patient population because I see a fair amount of cognitive impairment in folks who are living in the shelter system. And that's a really hard problem to address.

Frailty, The Aged, and Longevity

Eric Topol (05:54):

Well, there's a theme across your medical efforts. It seems to me that you look after the neglected folks, the prisoners, the old folks, the homeless people. I mean that's kind of you. It's pretty impressive. And there's not enough of people like you in the medical field. Now, no less do you do that, but of course you are a very impressive author, writer, and of many topics I want to get into with you, these are some recent essays you've written. The one that piqued my interest to start to understand who you were and kind of discover this body of work was the one that you wrote related to aging and President Biden. And that was in New York Times. And I do want to put in a quote because as you know very well, there's so much interest in longevity now.

Eric Topol (06:51):

Interrupting the aging process, and this one really stuck with me from that op-ed, “Time marches forward, bodies decline, and the growing expectation that we might all live in perfect health until our 100th birthdays reflects a culture that overprizes longevity to the point of delusion.” So maybe if you could tell us, that was a rich piece, you got into frailty, you related it to the issues that were surrounding President Biden who at that time had not withdrawn from the race. But what were you thinking and what are your thoughts about the ability to change the aging process?

Rachael Bedard (07:36):

I am very interested in, I mean, I'm incredibly interested in the science of it. And so, I guess I think that there are a few things.

(07:49):

One thing is that the framework that, the part that gives me pause the most is this framework that anything less than perfect health is not a life worth living. So if you're going to have a long life, life should not just be long and sort of healthy in relative terms to your age cohort, but healthy that when you're 80 you should feel like you have the health of a 45-year-old is my understanding of the culture of longevity science. And while I understand why that's aspirational and everybody worry about my body's decline, I think it's a really problematic thing to say that sick bodies are bodies that have disability or people who have cognitive difference are somehow leading lesser lives or lives that are not meaningful or not worth living. I think it's a very, very slippery slope. It puts you in a place where it sort of comes up against another trend or another emerging cultural trend, which is really thinking a lot about physician-assisted suicide and end of life choices.

(09:04):

And that in some ways that conversation can also be very focused on this idea that there's just no way that it's worth living if you're sick. And that's just not true, I think, and that's not been true for many, many, many of my patients, some of whom have lived with enormous disability and incredible burden of illness, people who are chronically seriously ill and are still leading lives that for them and for the people who love them are filled with meaning. So that's my concern about the longevity stuff. I'm interested in the science around the longevity stuff for sure. I'm interested in, I think we're living in this really interesting moment where there's so much happening across so many of the chronic disease fields where the things that I think have been leading to body decay over the last several decades for the majority of the population, we're sort of seeing a lot of breakthroughs in multiple fronts all at once. And that's really exciting. I mean, that's really exciting. And so, certainly if it's possible to make it to 100 in wonderful health, that's what I'd wish for all of us. But to hold it up as the standard that we have to achieve, I think is both unrealistic and a little myopic.

Eric Topol (10:28):

Yeah. Well, I certainly agreed with that and I think that that particular essay resonated so well and you really got into frailty and the idea about how it can be potentially prevented or markedly delayed. And I think before we move on to one of those breakthroughs that you were alluding to, any comments about the inevitability of frailty in people who are older, who at some point start to get the dwindles, if you will, what do you have to say about that?

Rachael Bedard (11:11):

Well, from a clinical standpoint, I guess the caveat versus that not everybody becomes frail and dwindles exactly. Some people are in really strong health up until sort of their final years of life or year of life and then something happens, they dwindle quickly and that's how they die. Or some people die of acute events, but the vast majority of us are going to become more frail in our final decades than we are in our middle decades. And that is the normal sort of pattern of wear and tear on the body. And it is an extraordinary framework, I think frailty because the idea of this sort of syndrome of things where it's really not a disease framework, it is a syndrome framework and it's a framework that says many, many small injuries or stressors add up to create a lot of stress and change in a body and trauma for our body. And once you are sort of past a tipping point of an amount of stress, it's very hard to undo those things because you are not sort of addressing one pathologic process. You're addressing, you're trying to mitigate many processes all at once.

(12:31):

When I wrote that piece, it was inspired by the conversation surrounding President Biden's health. And I was particularly struck by, there was a huge amount of clinical speculation about what was going on with him, right? I'm sure you remember there were people, there was all of this talk about whether he had Parkinson's and what his cognitive status was. And it felt to me like there was an opportunity to do some public education around the idea that you need not have one single sort of smoking gun illness to explain decline. What happens to most of us is that we're going to decline in many small ways sort of simultaneously, and it's going to impact function when it tips over a little bit. And that pattern of decline is not going to be steady day over day worsening. It's going to be up and down. And if you slept better the night before, you might have a better day the next day. And if you slept badly, you might have a worse day. And without knowing anything specific about his clinical situation, it felt like a framework that could explain so much of what we were seeing in public. And it was important also, I think to say that nothing was necessarily being hidden from anybody and that this is the kind of thing that, this has accumulated stress over time that then presents suddenly all at once after having been submerged.

Eric Topol (14:01):

Yeah, you reviewed that so well about the wear and tear and everything related to that. And before I move on to the second topic, I want to just circle back to something you alluded to, which is when Peter Attia wrote about this medicine 3.0 and how you would be compressed and you'd have no comorbidities, you'd have no other illnesses and just fall off the cliff. As a geriatrician, do you think that that is even conceivable?

Rachael Bedard (14:35):

No. Do you think it is?

Eric Topol (14:37):

No, but I just wanted to check the reality. I did challenge on an earlier podcast and he came up with his pat answer. But no, there's no evidence of that, that maybe you can delay if there ever was a way to do that. But I think there's this kind of natural phenomena that you just described, and I'll refer people also to that excellent piece that you get into it more.

Rachael Bedard (15:06):

Peter Attia, I mean, he is certainly the sort of standard bearer in my mind of that movement and that science or that framework of thinking about science. And there's stuff in there that's really valuable. The idea of thinking about lifestyle in your middle decades is having meaningful impact on how you will age, what your final years will look like. That seems intuitively true, I think. And so, thinking about his emphasis on exercise, I mean, his emphasis on exercise is particularly intense and not super achievable for the average person, but the idea that you should sort of be thinking about keeping your body strong because it will decline eventually. And so, you want to do that from a higher peak. That makes a lot of sense to me. The idea that where we sort of draw pathologic disease cutoffs is obviously a little bit arbitrary. And so, wanting to think about optimizing pre-disease states and doing prevention, that's obviously, I think pretty appealing and interesting. It's just really in an evidence free zone.

Ozempic for the Indigent

Eric Topol (16:18):

Yeah, that's what I confronted him with, of course, he had a different perspective, but you summed that up really well. Now let's switch to a piece you had in New York magazine. It was entitled, What If Ozempic Is Just a Good Thing? And the reason, of course, this ties into the first thing we're discussing. There's even talk now, the whole GLP-1 family of drugs with the dual triple receptors, pills to come that we're going to be able to interrupt a path towards Alzheimer's and Parkinson's. Obviously you've already seen impact in heart disease, liver disease, kidney disease way before that, diabetes and obesity. So what are your thoughts? Because you wrote a very interesting, you provided a very interesting perspective when you wrote that one.

Rachael Bedard (17:11):

So that piece I wrote because I have this unbelievably privileged, interesting clinical practice. In New York City, there is public health insurance basically available to anybody here, including folks who are undocumented. And the public hospital system has pharmacies that are outpatient pharmacies that have, and New York Medicaid is very generous and they arranged through some kind of brilliant negotiating. I don't quite know how to make Ozempic to make semaglutide available to people who met criteria which meant diabetes plus obesity, but that we could prescribe it even for our very, very poor patients and that they would be able to get it reliably, that we would have it in stock. And I don't know how many other practices in the country are able to reliably provide GLP-1s to marginalized folks like that. I think it feels like a really rare opportunity and a very distinct perspective.

(18:23):

And it has just been the most amazing thing, I think to have this class of drugs come along that, as you say, addresses so many problems all at once with at least in my prescribing experience, a relatively mild tolerable side effect profile. I have not had patients who have become incredibly sick with it. And for folks where making that kind of impact on their chronic illness is so critical to not just their longevity, but their disease status interacts so much with their social burden. And so, it's a very meaningful intervention I think around poverty actually.

(19:17):

I really feel that almost all of the popular press about it has focused very much on use amongst the wealthy and who's getting it off label and how are they getting it and which celebrities are taking it, and what are the implications for eating and diet culture and for people who have eating disorders. And that's a set of questions that's obviously sort of interesting, but it's really interesting in a very rarefied space. There's an unbelievable diabetes epidemic in this country, and the majority of people who have diabetes are not the people who are getting written about over and over again in those pieces. It's the patients that I take care of, and those people are at risk of ending up on dialysis or getting amputations. And so, having a tool this effective is really miraculous feeling to me.

Eric Topol (20:10):

Well, it really gives me some hope because I don't know any program like that one, which is the people who need it the most. It's getting provided for them. And we have been talking about a drug that costs a thousand dollars a month. It may get down to $500 a month, but that's still a huge cost. And of course, there's not much governmental coverage at this point. There might be some more for Medicare, Medicaid, whatever in the future, but it's really the original criteria of diabetes, and it took almost 20 years to get to where we are right now. So what's so refreshing here is to know that there's at least one program that is helping to bridge the inequities and to not make it as was projected, which was, as you say, for celebrities and wealthy people more exclusively, so that's great. And we still don't know about the diverse breadth of these effects, but as you well know, there's trials in Alzheimer's. I spoke to Steve Horvath recently on the podcast and he talked about how it's reset the epigenetic clock, GLP-1.

Rachael Bedard (21:24):

Does he think so?

Eric Topol (21:26):

Whoa. Yeah, there was evidence that was just presented about that. I said, well, if that does correspond to aging, the thing that we spoke about first, that would be very exciting.

Rachael Bedard (21:37):

It’s so wild. I mean, it's so exciting. It's so exciting to me on so many levels. And one of them is it's just exploding my mental model of disease pathogenesis, and it's making me think, oh my goodness, I have zero idea actually how metabolism and the brain and sort of cardiovascular disease, all of those things are obviously, what is happening in the interplay between all of those different systems. It's really so much more complicated and so much more interdependent than I understood it to be. I am really optimistic about the Alzheimer's trial. I am excited for those results, and I think we're going to keep seeing that it prevents different types of tumors.

Eric Topol (22:33):

Yeah, no, and that's been shown at least certainly in obese people, that there’s cancers that gets way reduced, but we never had a potent anti-inflammatory that works at the brain and systemically like this before anyone loses the weight, you already see evidence.

Long Covid and ME/CFS

(22:50):

It is pretty striking. Now, this goes back to the theme that was introduced earlier about looking after people who are neglected, who aren't respected or generally cared for. And I wanted to now get into Long Covid and the piece you wrote in the New Yorker about listening to patients, called “what would it mean for scientists to listen to patients?” And maybe you can talk about myalgic encephalitis/chronic fatigue (ME/CFS), and of course Long Covid because that's the one that is so pervasive right now as to the fact that these people don't get respect from physicians. They don't want to listen to their ailments. There's no blood tests, so there's no way to objectively make a diagnosis supposedly. And they're basically often dismissed, or their suffering is discounted. Maybe you can tell us again what you wrote about earlier this year and any updated thoughts.

Rachael Bedard (24:01):

Have you had my friend Harlan Krumholz on the show to talk about the LISTEN study?

Eric Topol (24:04):

Not yet. I know Harlan very well. Yes.

Eric Topol (24:11):

I know Akiko Iwasaki very well too. They’re very, very close.

Rachael Bedard (24:14):

So, Akiko Iwasaki and Harlan Krumholz at Yale have been running this research effort called the LISTEN study. And I first learned about it sometime in maybe late 2021. And I had been really interested in the emerging discourse around chronic illness in Long Covid in the 2021. So when we were past the most acute phase of the pandemic, and we were seeing this long tail of sequelae in patients, and the conversation had really shifted to one that was about sort of trying to define this new syndrome, trying to understand it, trying to figure out how you could diagnose it, what were we seeing sort of emerge, how are we going to draw boxes around it? And I was so interested in the way that this syndrome was really patient created. It came out of patients identifying their own symptoms and then banning together much, much faster than any kind of institutional science can ever work, getting into message boards together or whatever, and doing their own survey work and then coming up with their own descriptive techniques about what they were experiencing.

(25:44):

And then beyond that, looking into the literature and thinking about the treatments that they wanted to try for themselves. Patients were sort of at the forefront of every step of recognizing, defining, describing this illness presentation and then thinking about what they wanted to be able to do for themselves to address it. And that was really interesting to me. That was incredibly interesting to me. And it was also really interesting because by, I don't know exactly when 2021 or 2022, it was already a really tense landscape where it felt like there were real factions of folks who were in conflict about what was real and what wasn't real, how things ought to be studied, who ought to be studying them, what would count as evidence in this realm. And all of those questions were just really interesting to me. And the LISTEN study was approaching them in this really thoughtful way, which was Harlan and Akiko sort of partnering really closely with patients who enrolled.

(26:57):

And it's a decentralized study and people could enroll from all over the world. There's a portion of patients who do have their blood work evaluated, but you can also just complete surveys and have that data count towards, and those folks would be from anywhere in the world. Harlan did this amazing, amazing work to figure out how to collect blood samples from all over the country that would be drawn at home for people. So they were doing this decentralized study where people from their homes, from within the sort of circumstances of their lives around their chronic illness could participate, which that was really amazing to me. And then they were partnering really thoughtfully with these patients just to figure out what questions they wanted to ask, how they wanted to ask them, and to try to capture a lot of multimodal data all at once.

(27:47):

Survey data, journaling so people could write about their own experience in a freeform journal. They were collecting blood samples, and they were holding these town halls. And the town halls were on a regular basis, Harlan and Akiko, and anybody who was in the study could come on, could log onto a Zoom or whatever, and Harlan and Akiko and their research staff would talk about how things were going, what they were working on, what questions they had, what the roadblocks were, and then they would answer questions from their participants as the study was ongoing. And I didn't think that I had ever heard of something quite like that before. Have you ever heard of anything?

Eric Topol (28:32):

No. I mean, I think this is important to underscore, this was the first condition that was ever patient led, patient named, and basically the whole path was laid by the patient. So yes, and everything you summarize is so well as to the progress that's been made. Certainly, Harlan and Akiko are some of the people that have really helped lead the way to do this properly as opposed to, unfortunately one and a half billion dollars that have been put to the NIH for the RECOVER efforts that haven't yet led to even a significant clinical trial, no less a validated treatment. But I did think it was great that you spotlighted that just because again, it's thematic. And that gets me to the fourth dimension, which is you're the first prison doctor I've ever spoken to. And you also wrote a piece about that called, “the disillusionment of a Rikers Island Doctor” in the New Yorker, I think it was. And I wonder if you could tell us, firstly, now we're four years into Covid, you were for a good part of that at Rikers Island, I guess.

The Rikers Island Prison Doctor During Covid

Rachael Bedard (30:00):

I was, yeah.

Eric Topol (30:00):

Yeah. And what could be a more worrisome spot to be looking after people with Covid in a prison? So maybe you could just give us some insight about all that.

Rachael Bedard (30:17):

Yeah, it was really, I mean, it was the wildest time, certainly in my career probably that I'll ever have. In the end of February and beginning of March of 2020, it became very apparent to my colleagues and I that it was inevitable that this virus that was in Wuhan and in Italy was coming to the US. And jails are, we sort of jokingly described them as the worst cruise ships in the world. They are closed systems where everybody is eating, sleeping, going to the bathroom, everything on top of each other. There's an incredible amount of excess human contact in jails and prisons because people don't have freedom of movement and they don't get to do things for themselves. So every single, somebody brings you your mail, somebody brings you your meals, somebody brings you your medications. If you're going to move from point A to point B, an officer has to walk you there. So for a virus that was going to spread through what we initially thought was droplets and then found out was not just droplets but airborne, it was an unbelievably high-risk setting. It's also a setting where folks tend to be sicker than average for their age, that people bring in a lot of comorbidity to the setting.

(31:55):

And it's not a setting that does well under stress. I mean, jails and prisons are places that are sort of constitutionally violent, and they're not systems that adapt easily to emergency conditions. And the way that they do adapt tends to be through repressive measures, which tends to be violence producing rather than violence quelling. And so, it was just an incredibly scary situation. And in mid-March, Rikers Island, the island itself had the highest Covid prevalence of anywhere in the country because New York City was the epicenter, and Rikers was really the epicenter within New York. It was a wild, wild time. Our first seriously ill patient who ended up getting hospitalized. That was at that time when people were, we really didn't understand very much about what Covid looked like. And there was this guy sitting on the floor and he said, I don't know. I can't really get up.

(32:59):

I don't feel well. And he had an O2 stat of 75 or something. He was just incredibly hypoxic. It's a very scary setting for that kind of thing, right? It's not a hospital, it's not a place where you can't deliver ICU level care in a place like that. So we were also really worried about the fact that we were going to be transferring all of these patients to the city hospitals, which creates a huge amount of extra burden on them because an incarcerated patient is not just the incarcerated patients, the officers who are with that person, and there are special rules around them. They have to be in special rooms and all of these things. So it was just a huge systems crisis and really painful. And we, early on, our system made a bunch of good guesses, and one of our good guesses was that we should just, or one of our good calls that I entirely credit my bosses with is that they understood that we should advocate really hard to get as many people out as we could get out. Because trying to just manage the population internally by moving people around was not going to be effective enough, that we really need to decant the setting.

(34:18):

And I had done all of this work, this compassionate release work, which is work to get people who are sick out of jail so that they can get treatment and potentially die in a free setting. And so, I was sort of involved in trying to architect getting folks who were sort of low enough security risks out of jail for this period of time because we thought that they would be safer, and 1500 people left Rikers in the matter of about six weeks.

Rachael Bedard (34:50):

Which was a wild, wild thing. And it was just a very crazy time.

Eric Topol (34:56):

Yeah. Well, the word compassion and you go together exceptionally well. I think if we learn about you through your writings, that really shines through and what you've devoted your care for people in these different domains. This is just a sampling of your writings, but I think it gives a good cross section. What makes you write about a particular thing? I mean, obviously the Rikers Island, you had personal experience, but why would you pick Ozempic or why would you pick other things? What stimulates you to go after a topic?

Rachael Bedard (35:42):

Sometimes a lot of what I write about relates to my personal practice experience in some way, either to geriatrics or death and dying or to the criminal justice system. I've written about people in death row. I've written about geriatrics and palliative care in sort of a bunch of different ways. I am interested in topics in medicine where things are not yet settled, and it feels very of the moment. I'm interested in what the discourse is around medicine and healthcare. And I am interested in places where I think the discourse, not just that I'm taking a side in that discourse, but where I think the framework of the discourse is a little bit wrong. And I certainly feel that way about the Ozempic discourse. And I felt that way about the discourse around President Biden, that we're having not just a conversation that I have a strong opinion about, but a conversation that I think is a little bit askew from the way that we ought to be thinking about it.

Eric Topol (36:53):

And what I love about each of these is that you bring all that in. You have many different points of view and objective support and they're balanced. They're not just trying to be persuasive about one thing. So, as far as I know, you're extraordinarily unique. I mean, we are all unique, but you are huge standard deviations, Rachael. You cover bases that are, as I mentioned, that are new to me in terms of certainly this podcast just going on for now a couple of years, that is covering a field of both geriatrics and having been on the corrections board and in prison, particularly at the most scary time ever to be working in prison as a physician. And I guess the other thing about you is this drive, this humanitarian theme. I take it you came from Canada.

Rachael Bedard (37:59):

I did.

Eric Topol (37:59):

You migrated to a country that has no universal health.

Rachael Bedard (38:03):

That's right.

Eric Topol (38:03):

Do you ever think about the fact that this is a pretty pathetic situation here?

Rachael Bedard (38:08):

I do. I do think about it all the time.

Eric Topol (38:10):

In our lifetime, we'll probably never see universal healthcare. And then if you just go a few miles up north, you pretty much have that.

Rachael Bedard (38:18):

Yeah, if you've lived in a place that has universal healthcare and you come here, it's really sort of hard to ever get your mind around. And it has been an absolute possessing obsession of my entire experience in the US. I've now been here for over 20 years and still think it is an unbelievably, especially I think if you work with marginalized patients and how much their lack of access compounds the difficulty of their lives and their inability to sort of stabilize and feel well and take care of themselves, it's really frustrating.

Advice for Bringing Humanities to Medicine in a Career

Eric Topol (39:14):

Yeah, yeah. Well, I guess my last question to you, is you have weaved together a career that brings humanities to medicine, that doesn't happen that often. What's your advice to some of the younger folks in healthcare as to how to pull that off? Because you were able to do it and it's not easy.

Rachael Bedard (39:39):

My main advice when people ask me about this, especially to students and to residents who are often the people who are asking is to write when you can or pursue your humanities interests, your critical interests, whatever it is that you're doing. Do it when you can, but trust that your career is long and that you have a lot of time. Because the thing that I would say is I didn't start publishing until I was in fellowship and before that I was busy because I was learning to become a doctor. And I think it's really important that my concern about being a doctor who's a hybrid, which so many of us are now. A doctor or something else is you really do want to be a good doctor. And becoming a good doctor is really hard. And it's okay if the thing that is preoccupying you for the first 10 years of your training is becoming a great clinician. I think that's a really, really important thing to do. And so, for my first 10 years for med school and residency and chief residency and fellowship, I would write privately on the side a fair amount, but not try to publish it, not polish that work, not be thinking in sort of a careerist way about how I was going to become a doctor writer because I was becoming a doctor. And that was really preoccupying.

(41:08):

And then later on, I both sort of had more time and mental space to work on writing. But also, I had the maturity, I think, of being a person who was comfortable in my clinical identity to have real ideas and insights about medicine that felt different and unique to me as opposed to, I barely understand what's going on around me and I'm trying to pull it together. And that's how I would've been if I had done it more, I think when I was younger. Some people are real prodigies and can do it right out the gate, but I wasn't like that.

Eric Topol (41:42):

No, no, I think that's really sound advice because that's kind of the whole foundation for everything else. Is there a book in the works or will there be one someday?

Rachael Bedard (41:53):

There may be one someday. There is not one now. I think about it all the time. And that same advice applies, which is I believe in being a late bloomer and taking your time and figuring out what it is you really want to do.

Eric Topol (42:10):

Yeah. Well, that's great. Have I missed anything? And obviously we only can get to know you in what, 40 minutes to some extent, but have I not touched on something that you want to bring up?

Rachael Bedard (42:23):

No, I don't think so. Thank you for this conversation. It's been lovely.

Eric Topol (42:28):

No, I really enjoyed it. I'll be following your career. It's extraordinary already and you've got decades ahead to make an impact and obviously thinking of all these patients that you look after and have in the past, it’s just extraordinary. So what a joy to talk with you, Rachael, and I hope we'll have a chance to do that again in the times ahead.

Rachael Bedard (42:51):

Me as well. Thank you so much for inviting me.

**********************************************

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Superimposed on an impressive body of work on the blood-brain-barrier and immune system, Prof Akassoglou and her collaborators just published an elegant study in Nature that centered on the direct binding os the SARS-CoV-2 spike protein to fibrin with marked downstream pro-inflammatory effects. The findings and potential treatments have implications beyond Covid, Long Covid to other neurologic diseases.

Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to audio and to relevant papers, graphics

Eric Topol (00:07):

Well, hello this is Eric Topol with Ground Truths, and with me today is Katerina Akassoglou. She is at the Gladstone Institute and she is a remarkable neuroimmunologist who has been doing extraordinary work for three decades to unravel the interactions between the brain, blood vessels and the role of inflammation. So Katerina, there's a lot to discuss, so welcome.

Katerina Akassoglou (00:40):

Thank you. Thank you so much. It's a great pleasure to join.

By Way of Background

Eric Topol (00:43):

It's really interesting going back in your career. First of all, we're thankful that you immigrated here from Greece, and you have become one of the leading scientists in this discipline of important discipline of neuroimmunology, which is not just about Covid that we're going to talk about, but Alzheimer's and neurodegenerative diseases. This is a really big hot area and you're definitely one of the leaders. And what I was impressed is that all these years that you've been working on the integrity of the blood-brain barrier, the importance of fibrinogen and fibrin, and then comes along the Covid story. So maybe what we can do is start with that, which is you've made your mark in understanding this whole interaction between what can get into the brain, through the blood-brain barrier and incite inflammation. So this has been something that you've really taken to the extreme knowledge base. So maybe we can start with your work there before we get into the important seminal Nature paper that you recently published.

Katerina Akassoglou (01:57):

Yes, of course. So since very early on, I was still a graduate student when we made the first discovery and at the time was like mid-90s, so it was really ahead of its time. That dysregulation of cytokine expression in the brain of mice was sufficient to induce the whole cascade of events, triggering neurodegeneration, demyelination in pathological alterations, very reminiscent of multiple sclerosis pathology. And it was really hard to publish that study at the time because it was not yet accepted that this regulation of the immune system modeling the brain can be linked to neurodegeneration. So that was 1995 when we made that discovery, and I became really interested, what are the pathogenic triggers that actually polarized the immune cells in the brain? So with this, of course, this transgenic animal was expressing TNF, it was an artificially made animal that we made, but naturally what were the triggers that would polarize the innate immune cells? So I looked really early on in this mice and what I found was that the very first event was leaks of blood-brain barrier. It was opening of the blood-brain barrier in this mouse before inflammation, before demyelination, before neuronal loss. And this is really what shaped the question that, is it possible that these blood leaks that happened very early in the pathology, could this be the instigators of pathogenic inflammation in the brain?

Eric Topol (03:34):

Yeah. So in a way, you got at this question because of the chicken-and-egg and what happens first, and you got to the temporal saying, which happened first as you said, the leak before you could see evidence of inflammation and being able to study this of course in the experimental model, which you couldn't really do in people. And what I love about the description of your career, which has been quite extraordinary contributions is connecting the dots between the blood, the inflammatory response and the brain. Perhaps no one has done that like you have. And before we get into the recent paper, a lot of people are not aware that a year ago, a group in the UK known as PHOSP-COVID, they published a really important paper in Nature Medicine of over 1,800 people who were hospitalized with Covid and they found that fibrinogen was the best marker for cognitive deficits at 6 and 12 months (Figure below)

(04:40):

So that's just one of many papers, but it's a particularly well done study that already before you got into this work that recently published had emphasized fibrinogen. And by the way, again, having spent a lot of years in clots in the arteries, for me, we have to just get it down to fibrinogen plus thrombin gets you to fibrin. Okay, so fibrin is a major player here when fibrinogen is cleaved. So here we have the basis that you established, which is the fibrinogen leakage into the brain, activating inflammation, activating microglia, which like the macrophages of the brain and inciting the whole process. And before we close, I want to not just talk about Covid, but Alzheimer's too. But now let's get into the study that you did, [Fibrin drives thromboinflammation and neuropathology in COVID-19] which is striking, I mean really striking. And can you kind of take us through, because you not only demonstrated the importance of fibrin in inciting neuroinflammation in this model, but also how you could reverse it or prevent it. So this, and you looked at it in many different ways, this was a systematic approach. Maybe you can take us through how you were able to make such compelling evidence.

The Multimodal Evidence

Katerina Akassoglou (06:09):

Yes, thank you. First of all, thank you for bringing up the human relevance because this was also our inspiration for the work that we did in the Covid study. So as you mentioned in Covid patients, fibrinogen unbiased mass spec analysis was identified as the predictive biomarker for cognitive impairment in Long Covid patients. And this was in addition to also neuropathology data about the abundance of fibrin deposition in the brain. And these were studies that were done by NIH that have found deposition of fibrin in the brain and the reports for the abnormal and puzzling coagulation in Covid that is not setting other infections and also in many cases not always relating with the severity of symptoms. So even mild cases of Covid also had increased coagulation. I was really intrigued by this human, all this evidence in human data, and I thought that maybe the way that we're thinking about this, that it's systemic inflammation that drives the clotting.

(07:24):

Maybe there's another aspect to this. Maybe there is a direct effect of the virus with the coagulation cascade, and in this way maybe this can be an instigator of inflammation. So this was the original idea to be able to reconcile this data from the clinic about why do we have this prevalence of coagulopathy in Covid. And of course, the second question is, could this also be a driver of the disease? And of course, we're in a unique position because we have been studying this pathway now for over 20 years to have all the toolbox, the genetic toolbox, the pharmacologic toolbox to be able to actually really address these questions with genetic loss of function studies, with a blood innate immunity multiomics pipeline that we have set up in the lab. And of course, with preclinical pharmacology in our ABSL3 facility. So we had the infrastructure in place and the source in place to actually really dissect this question with both genetic tools as well as also technology platforms.

Eric Topol (08:29):

And you had in vivo imaging, you're the director of in vivo imaging for Gladstone and UCSF. So you do have the tools to do this.

Katerina Akassoglou (08:38):

Yes. The imaging that you mentioned is really important because this is, we employed that very early in our studies over now 15 years ago. And the reason was sometimes from snapshots of histopathology, you cannot really understand the sequence of events. So by being able to image these processes, both neuronal activity, microglia activation, infiltration of peripheral cells in the brain, this is how we could see the steps that what happens early on and to be able to answer these chicken-and-egg questions that you mentioned. So these were very, they're very important experiments, especially at the beginning because they were hypothesis driving and we were able to ask the right questions to drive our research program.

Eric Topol (09:26):

Now was the binding of the spike protein to one key site in fibrinogen, was that known before? [See outstanding Figure below from Trends in Immunology]

Katerina Akassoglou (09:36):

No, this was not known. So there was evidence that there are abnormal clots in Covid, but it was not known whether the spike protein would directly bind to protein to the coagulation cascade. So one of the key discoveries in our study was to use peptide array mapping and be able to identify not only the binding, but exactly the domains on fibrin that spike binds too. And what we found was two key domains, one the inflammatory domain and the other the plasmin binding site, which is important for fibrin degradation. So this suggested a potential dual deleterious role for this interaction, both by maybe affecting inflammation, but also delaying fibrinolysis, which is the degradation of this toxic protein from the brain. And indeed, we found that this interaction was responsible for all these two aspects, including decreased degradation, more inflammation, but also at the same time increased, increased coagulation. So it was a really pathogenic interaction.

Eric Topol (10:47):

Yeah, actually it's pretty striking. You have these two sites, the plasmin cleavage site of fibrinogen, which as you say, we knew there was a problem with clots. We knew that, but we didn't know exactly the spike protein how exactly it was implicated, particularly with fibrinogen. And then this other site, the CD11b-C18, now that's fancy for surface receptors of macrophages. And basically, this is critical because it's this microglia activation in the brain, and I know you saw it in the lungs as well through this other site that spike protein activated. So you had a twofer here of things that you discovered that the SARS-CoV-2 spike protein was capable of doing. This was a really big revelation. And then you also looked at mice that were genetically manipulated. So maybe you can, because before we get to your antibody monoclonal, the ways that you proved this were, I mean, one thing after another is really systematic. So maybe you can teach us about that.

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Establishing Causality

Katerina Akassoglou (12:08):

Yeah, sure. So the first was about chemistry experiment. So this of course, we had to get to the next step to see is there any causality for this pathway. So we employed genetic loss of function studies and we had knockout mice, either fibrinogen knockout mice, this mice have all blood proteins except fibrinogen, and they have a delay in coagulation so they don't clot properly. But we also had a mutant mouse, which is a fibrinogen NK mouse. And this was a mutation only within this inflammatory domain that you mentioned, inflammatory domain that binds to C11b-C18. Other names for this is of course complement receptor 3, Mac-1 (αMβ2). It's the same, many names for this receptor, that as you mentioned, is expressed not only in microglial in the brain, but also peripheral immune cells including macrophages as well as also neutrophils which are CD11b expressing.

(13:12):

So we now have genetic models to be able to look at both complete depletion of fibrinogen, but also a very specific mutation and very selective mutation that only blocks the inflammatory properties without affecting the properties of fibrin in hemostasis. And these mice were made many years ago by a very close collaborator, Jay Degen at the University of Cincinnati. So what we found is that when we block either the inflammatory domain or we completely deplete fibrinogen, there was this profound protection after infection in internasal infection with the virus in lung inflammation. And this was both suppression of oxidative stress and this pathogenic inflammation in the lung, but also decreasing fibrosis, which has been associated with also Long Covid. And the surprise came from the transcriptomic data. So when we did transcriptomic analysis in this mice in the lungs, we found perhaps the expected decrease in the immune signatures in macrophages. This was in line with our previous work in, as you mentioned, Alzheimer's models, multiple sclerosis models. But what also was really surprising is there was that genes that are associated with activation of NK cells were upregulated. And of course this was the first time we had infected these mice, previously we had not done an infection before. So I think that maybe because of this region we had not seen before in our data this immunomodulatory role of fibrin that not only surprises the macrophage response, but also increases these NK cells that are important for viral clearance.

Eric Topol (15:00):

So again, the finding another important unique finding is the natural killer (NK) cells and effect there from the activation of this, as you said, the inflammation site or the CD11b-C18 that we've been talking about. So now another layer of this, a dimension of your Nature paper was that you tested an antibody that you already had developed so-called 5B8. A monoclonal that specifically binds to the domain of the one we're talking about this inflammation domain of fibrinogen. So can you tell us about what that showed?

Katerina Akassoglou (15:45):

Yes, so we tested this antibody in different models of Covid, which were both models with neuroinvasion and models without neuroinvasion. So we used both transgenic mice for hACE2, the human ACE2 infected with Delta, but we also use mouse adapted viruses like Beta that is just in the wild type mice with no transgenic being involved that these are without neuroinvasion. And we wanted to see if the antibody had any potential protective effects. And what we found is that the antibody protected from inflammation in the lung. So the data looked so similar with a genetic mutation of this pathway, protection from inflammation, decreased fibrosis, increased viral clearance, so decreased spike and viral proteins in the lungs. But we also found a protection in the brain. So the brains of this mice, including both the models we used with neuroinvasion and without, they both have had microglia activation in the brain. And we also found neuronal loss in the Delta infected mice and the antibody protected from both neuroinflammation but also improved neuronal survival in the mice. Showing that there can be this despite regardless of which model we used, there was this protective effect suggesting that by blocking fibrin, either the periphery or in the brain, this could be protected for these models.

Eric Topol (17:28):

Yeah, so I mean this is fascinating because until now, until this report of yours and your colleagues at Gladstone, there was knowledge that there would be neuroinflammation from Covid, both in patients from various biomarkers and imaging as well as in experimental model. But what this did was take it to the fibrin story, and I guess that's one of the questions you nailed that how important fibrin is, but that doesn't necessarily rule out other triggers of neuroinflammation, right?

Katerina Akassoglou (18:04):

Oh, absolutely not. So I think that this is one of the mechanisms that can be very important, especially in some patients. But we know that there are additional of course mechanisms of neuroinflammation including auto-antibody responses, as well as also endotheliopathy that are persistent endotheliopathy, this can be interacting also with each other. So I think that it's important for future research that we understand how do these mechanisms feed into each other? Are there a positive feedback loops between autoimmune mechanisms and coagulopathy and endothelial dysfunction with inflammation? But I think most importantly, I think that if we're thinking of this in the context of patients, can we identify patients with mechanism that might be more prevalent in specific cases of Long Covid and tailor our potential future clinical trials towards the needs of Long Covid patients?

Towards Treatment

Eric Topol (19:06):

Absolutely. I did interview some months back on Grounds Truths, Michelle Monje at Stanford, who I'm sure and interact with, and she's also works not so much on the fibrin side, but on neuroinflammation and the likeness between this condition in people and chemo brain because of the inflammation that's seen there. So we've talked about the multiple triggers that could contribute to brain inflammation, which I think most people would say in Long Covid this is one of the most, besides obviously the lack of energy, the profound fatigue and disability, but the cognitive function hit, not just brain fog is often profound. And we've just seen some reports about that, and particularly in hospitalized patients, how bad that can be. So that gets us to a potential treatment. Now, one of the things that's out there dangling, there's many things that people have talked about in terms of why can't we have a treatment for Long Covid?

(20:13):

And now of course this fibrin pathway, if you will, lends itself to many possibilities, whether it's anticoagulants or fibrinolytics like a tPA or things like nattokinase, which is a Japanese food enzyme that you could get at the nutrition centers or whatever. What are your thoughts? Because we don't have any good studies. There are all these little, tiny studies and they don't provide much conclusion, and you have an antibody that could potentially be effective. As I understand it, you set up a company some years ago, Therini Bio and used to be called MedaRed. You're the first woman scientist at Gladstone to develop a spin out company, which is another point of congratulations on that. But could the antibody be tested in patients or what do you think about these other possibilities?

Katerina Akassoglou (21:15):

Yes, yes. These are great questions. So first of all, the different approaches that you mentioned have very different mechanism of action. So degrading fibrin, the degradation products of fibrin also can have deleterious effects. The dimer, for example, can be very pro-inflammatory. So at the same time, blocking coagulation can also have a diverse effects because this can lead to excessive hemorrhage. So the approach that we took was to selectively block the inflammatory properties of fibrin without affecting beneficial effects of the molecule in normal hemostasis. So the challenge when I made the antibody was to be able to dissect these two functions of fibrin. It's our most important clotting factor, but at the same time, a molecule with profound pro-inflammatory capacity. So the observation that these two domains, the clotting domain and inflammatory domain were not overlapping, was really the foundation of this invention was that we could maybe create this antibody to be able to target them in a selective way.

Other Neurologic Conditions

(22:31):

So the antibody I developed is neutralizing blood toxicity by blocking the inflammatory domain of fibrin without adverse coagulation effects. And it's now completing phase one trials. So it has already completed the single ascending dose at 40 milligram per kilogram. It's interim data were announced already for this trial, with no safety signals. So if the antibody completes this year, the phase one trials, then it should be possible to be tested in different patient populations. You mentioned before chemo brain, and I think it's important that we think that blood-brain barrier disruption occurs among many neurological conditions, and it's an early event associated with early disease onset and worse prognosis in multiple sclerosis, Alzheimer's disease, traumatic injuries. So I think that it's by developing a strategy, therapeutic strategy to neutralize blood toxicity, this can have applications in a wide range of neurological conditions with vascular dysfunction.

Eric Topol (23:54):

Yeah, no. In your Nature Immunology 2020 piece [Figure below], you started with the 1883 identification of multiple sclerosis (MS) lesions were “engorged with blood”, the first link between blood leaks and brain inflammation. So this has enormous potential. And what I like about this Katerina is that you've dissected the clot component versus the inflammatory trigger of the fibrinogen and fibrin story. And this is so vital because if you keep throwing these things that just going to work on the clot and not deal with the pro-inflammatory consequences, then you're going to get the wrong impression that clots are not that important. And by the way, you did mention, and I want to come back to that too, endothelial inflammation, which is another feature of Long Covid is another kind of interactive part of this because when the lining of the blood vessel is inflamed, it will attract microthrombi and also be a participant in this whole affair. What do you think about Alzheimer's and the prospects of being able to interfere with Alzheimer's? We have 20 years in someone before this process takes hold and meets clinical manifestations. Would an antibody like this ever be useful along the way?

Katerina Akassoglou (25:29):

Yeah, so well, our antibody was tested first in Alzheimer's, this models when it was originally published, and we performed reversal trials in Alzheimer's models. So we dosed mice when they have established amyloid plaques, microglia activation, neuronal loss, and we could reverse this effect so it could increase cholinergic neurons in mice, reduce inflammation in a very selective way, only the neurotoxic part of inflammation and for genetic depletion of this pathway with akin mice in Alzheimer's disease. Also, improves from cognitive impairment, and we now have a new paper in Cell Press that is showing this effects also with really nice and unbiased machine learning models for behavioral segmentation [Figure below].

So I think that there is the data both from genetic studies and the antibody show projection in Alzheimer's disease. And of course, as you might have read the recent Lancet report from the Lancet committee on dementia that identified the vascular risk factors as the key contributors, especially post sporadic cases of Alzheimer's disease that is over 90% of Alzheimer's disease that is not genetically linked.

(26:58):

So I think that there is a real need in Alzheimer's disease to be able to block this vascular induced pathology. And an antibody like the fibrin neutralizing therapy could be positioned to be protective from the vascular induced immune-mediated neurodegeneration in this disease as well. I mean, ultimately, I think that we need to be thinking the terms of efficacy. So we want to have a drug that is efficacious, but we also want it to be selective. And the selectivity is really important because the immune system has so many protective functions. So if we block phagocytosis, we end up with more debris, decrease of neurorepair, anti-myelination. So by blocking a ligand here and not blocking, not eliminating a cell type or blocking a global pathway in this cell, but biologic a single ligand, I think we have been able to achieve this balance between efficacy, but also safety because we only block this neurotoxic populations and not the entire innate immune response that also has been beneficial for metastatic functions in the brain.

Blocking Neuroinflammation

Eric Topol (28:19):

So you're bringing up another critical concept about targeting the inflammation, this kind of goldilocks story of how much you interfere with the immune response and how much you are able to reduce the adverse pro-inflammatory effects. So that gets me to what if we don't know in any given patient how much fibrin is having a role in their Long Covid. Although we know it has to be a prominent feature because we saw it in, not just a hospitalized patient series that I mentioned we reviewed, but other papers as well. But what about if you just try to take on inflammation like through a GLP-1 drug or cGAS–STING or any of these really strong anti-inflammatory pathways. Do you see a difference in a generalized approach versus a specific approach that is really fibrin centered?

Katerina Akassoglou (29:22):

Yeah, so we have a focus actually on both because we wanted to dissect the downstream intracellular pathways of fibrin, and it's interesting that we can find specific inflammatory mediators that potentially can also be targeted as well, to be able to preserve that specificity, which I think is really important because if we don't preserve the specificity, we'll end up with a lot of adverse effects by eliminating major immune responses. But the point that you raised I think is really important because it's not enough to have an efficacious and selective drug if you don't know the patient population that will benefit from this drug. So I think that in addition to the drug discovery studies, it's important to develop also biomarker programs with both fluid biomarkers, but also imaging biomarkers to be able to identify the patient populations that will benefit from such treatment.

(30:25):

So if for example, a patient population has a fibrin deposition, blocking only downstream might not be enough, and it might be really important to neutralize this fibrin toxicity in the brain of patients. And with our target engagement studies, we show that at least in animal models, the antibody can be there. So I'm very encouraged by also programs that are going on now in the scientific community to develop noninvasive ligands to be able to image fibrin in the brain that are already tested in different patient populations like multiple sclerosis. Because I think we're going to learn so much from the biology as we start interrogating and asking these questions now in different patient populations.

Eric Topol (31:14):

I think that's a vital point you're making because the success of a clinical trial here in a clinical syndrome that is mosaic with lots of different types of pathways. If you can nail down the patients that would have the most to stand to benefit from a particular intervention, that the chance of you not missing the benefit that is matching the marker, what image marker or other markers is so vital. Well, we've talked, I think, about some fascinating discoveries that you and your colleagues have made. I mean, it's really extraordinary, and obviously we need this in Long Covid. But you know what, Katerina, it's almost made me think that you were warming up to this for three decades, that somehow or other you were working on all this stuff and then came Covid. Is that how you see it, that somehow or other you didn't know that all the work you were doing was going to wind up in this space?

Katerina Akassoglou (32:18):

Oh, I never thought I would work in a virology project. This collaboration started over Zoom with Warner Greene. We were both sheltering in place. It was the beginning of the pandemic, and the first reports were coming out about this puzzling coagulopathy. And our labs were hardly operational at the time, as you know, we had to close down our labs for a while. And however, this was a very big problem, and we thought that this is our role as scientists. If we feel that we can contribute and we have the tools to contribute, we felt that it's important that we pivot some part of our research, and even we wouldn't be doing this before, but it was important to pivot a part of our research and collaborate. And I think studies like this, this study would have been impossible without a team of collaborators. As you know, there were over 50 scientists involved at Gladstone, UCSF, UCLA, UCSD, Stanford University. Without collaboration, this study wouldn't be possible. So I'm really grateful to everyone who came together to solve this problem because I think that's what scientists should be doing. We should be solving problems as they arise.

Eric Topol (33:41):

Well, and also, I think a lot of people don't realize that, for example, when the Covid vaccines came along, people think, oh, well, it all got done in 10 months since the sequence of the virus, when in fact it took 30 years at least between all the factors that went into having an mRNA and sequencing virus and nanoparticles. And in many ways, your arc of this work is like that because it took three decades to have all the tools and the basic understanding, the antibody that you had developed for different reasons and this fascinating unraveling of what's going on in the model and undoubtedly in some patients at least as well. So before we wrap up, have I missed anything about this just remarkable work you've done?

Katerina Akassoglou (34:33):

Oh, thank you. I just want to thank you for this discussion and thank you for emphasizing the different areas and the different decisions that this pathway can have implications both for our understanding, our basic understanding of the blood brain immune interface, as well as also potential translation. And I think that the curiosity sometimes of how things work, I never thought it would work on Covid, like you mentioned at the beginning, but I think that basic science and curiosity driven science can sometimes lead to discoveries with translational implications that hopefully might benefit patients one day.

Eric Topol (35:21):

Yeah, well, undoubtedly it will. We're indebted to you, Katerina and all the folks that you have teamed up with, connecting the dots at the neurovascular interface. Phenomenal work and will follow the subsequent with great interest and it will likely not just a story about Long Covid, but other areas as well, so thank you.

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When I think of digital biology, I think of Patrick Hsu—he’s the prototype, a rarified talent in both life and computer science, who recently led the team that discovered bridge RNAs, what may be considered CRISPR 3.0 for genome editing, and is building new generative A.I. models for life science. You might call them LLLMs-large language of life models. He is Co-Founder and a Core Investigator of the Arc Institute and Assistant Professor of Bioengineering and Deb Faculty Fellow at the University of California, Berkeley.

Above is a brief snippet of our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Here’s the transcript with links to the audio and external links to relevant papers and things we discussed.

Eric Topol (00:06):

Well hello, it's Eric Topol with Ground Truths and I'm really delighted to have with me today Patrick Hsu. Patrick is a co-founder and core investigator at the Arc Institute and he is also on the faculty at the University of California Berkeley. And he has been lighting things up in the world of genome editing and AI and we have a lot to talk about. So welcome, Patrick.

Patrick Hsu (00:29):

Thanks so much. I'm looking forward to it. Appreciate you having me on, Eric.

The Arc Institute

Eric Topol (00:33):

Well, the first thing I'd like to get into, because you're into so many important things, but one that stands out of course is this Arc Institute with Patrick Collison who I guess if you can tell us a bit about how you two young guys got to meet and developed something that's really quite unique that I think brings together investigators at Stanford, UCSF, and Berkeley. Is that right? So maybe you can give us the skinny about you and Patrick and how all this got going.

Patrick Hsu (01:05):

Yeah, sure. That sounds great. So we started Arc with Patrick C and with Silvana Konermann, a longtime colleague and chemistry faculty at Stanford about three years ago now, though we've been physically operational just over two years and we're an independent research institute working at the interface of biomedical science and machine learning. And we have a few different aspects of our model, but our overall mission is to understand and treat complex human diseases. And we have three pillars to our model. We have this PI driven side of the house where we centrally fund our investigators so that they don't have to write grants and work on their very best ideas. We have a technical staff side of the house more like you'd see in a frontier AI lab or in biotech industry where we have professional teams of R&D scientists working cross-functionally on higher level organizational wide goals that we call our institute initiatives.

(02:05):

One focused on Alzheimer's disease experimentally and one that we call a virtual cell initiative to simulate human biology with AI foundation models. And our third pillar over time is to have things not just end up as academic papers, but really get things out into the real world as products or as medicines that can actually help patients on the translational side. And so, we thought that some really important scientific programs could be unlocked by enabling new organizational models and we are experimenting at the institutional scale with how we can better organize and incentivize and support scientists to reach these long-term capability breakthroughs.

Patrick, Patrick and Silvana

Eric Topol (02:52):

So the two Patrick’s. How did you, one Patrick I guess is a multi-billionaire from Stripe and then there's you who I suspect maybe not quite as wealthy as the other Patrick, how did you guys come together to do this extraordinary thing?

Patrick Hsu (03:08):

Yeah, no, science is certainly expensive. I met Patrick originally through Silvana actually. They actually met, so funny trivia, all three Arc founders did high school science together. Patrick and Silvana originally met in the European version of the European Young Scientist competition in high school. And Silvana and I met during our PhDs in her case at MIT and I was at Harvard, but we met at the Broad Institute sort of also a collaborative Harvard, MIT and Harvard hospitals Institute based in Kendall Square. And so, we sort of in various pairwise combinations known each other for decades and worked together for decades and have all collectively been really excited about science and technology and its potential to accelerate societal progress. Yet we also felt in our own ways that despite a lot of the tremendous progress, the structures in which we do this work, fund it, incentivize it and roll it out into the real world, seems like it's really possible that we'll undershoot that potential. And if you take 15 years ago, we didn't have the modern transformer that launched the current AI revolution, CRISPR technology, single-cell, mRNA technology or broadly addressable LNPs. That’s a tremendous amount of technologies have developed in the next 15 years. We think there's a real unique opportunity for new institutes in the 2020s to take advantage of all of these breakthroughs and the new ones that are coming to continue to accelerate biological progress but do so in a way that's fast and flexible and really focused.

Eric Topol (04:58):

Yeah, I did want to talk with you a bit. First of all before I get to the next related topic, I get a kick out of you saying you've worked or known each other for decades because I think you're only in your early thirties. Is that right?

Patrick Hsu (05:14):

I was lucky to get an early start. I first started doing research at the local university when I was 14 actually, and I was homeschooled actually until college. And so, one of the funny things that you got to do when you're homeschooled is well, you could do whatever you want. And in my case that was work in the lab. And so, I actually worked basically full time as an intern volunteer, cut my teeth in single cell patch clamp, molecular biology, protein biochemistry, two photon and focal imaging and kind of spiraled from there. I loved the lab, I loved doing bench work. It was much more exciting to me than programming computers, which was what I was doing at the time. And I think these sort of two loves have kind of brought me and us to where we are today.

Eric Topol (06:07):

Before you got to Berkeley and Arc, I know you were at Broad Institute, but did you also pick up formal training in computer science and AI or is that something that was just part of the flow?

Patrick Hsu (06:24):

So I grew up coding. I used to work through problems sets before dinner growing up. And so, it's just something that you kind of learn natively just like learning French or Mandarin.

New Models of Funding Life Science

Eric Topol (06:42):

That's what I figured. Okay. Now this model of Arc Institute came along in a kind of similar timeframe as the Arena BioWorks in Boston, where some of the faculty left to go to Arena like my friend Stuart Schreiber and many others. And then of course Priscilla and Mark formed the Chan Zuckerberg Institute and its biohub and its support. So can you contrast for one, these three different models because they’re both very different than of course the traditional NIH pathway, how Arc is similar or different to the others, and obviously the goal here is accelerating things that are going to really make a difference.

Patrick Hsu (07:26):

Yeah, the first thing I would say is zooming out. There have been lots of efforts to experiment with how we do science, the practice of science itself. And in fact, I've recently been reading this book, the Demon Under the Microscope about the history of infectious disease, and it talks about how in the 1910s through the 1930s, these German industrial dye manufacturing companies like Bayer and BASF actually launched what became essentially an early model for industrial scale science, where they were trying to develop Prontosil, Salvarsan and some of these early anti-infectives that targeted streptococcus. And these were some of the major breakthroughs that led to huge medical advances on tackling infectious disease compared to the more academic university bound model. So these trends of industrial versus academic labs and different structures to optimize breakthroughs and applications has been a through current throughout international science for the last century.

(08:38):

And so, the way that we do research today, and that's some of our core tenets at Arc is basically it hasn't always been this way. It doesn't need to necessarily be this way. And so, I think organizational experiments should really matter. And so, there's CZI, Altos, Arena, Calico, a variety of other organizational experiments and similarly we had MRC and Bell Labs and Xerox PARCS, NIBRT, GNF, Google Research, and so on. And so, I think there are lots of different ways that you can organize folks. I think at a high level you can think about ways that you can play with for-profit versus nonprofit structures. Whether you want to be a completely independent organization or if you want to be partnered with universities. If you want to be doing application driven science or really blue sky curiosity driven work. And I think also thinking through internally the types of expertise that you bring together.

(09:42):

You can think of it like a cancer institute maybe as a very vertically integrated model. You have folks working on all kinds of different areas surrounding oncology or immunotherapy and you might call that the Tower of Babel model. The other way that folks have built institutes, you might call the lily pad model where you have coverage of as many areas of biomedical research as possible. Places like the Whitehead or Salk, it will be very broad. You'll have planned epigenetics, folks looking at RNA structural biology, people studying yeast cell cycle, folks doing in vivo melanoma models. It's very broad and I think what we try to do at Arc is think about a model that you might liken more to overlapping Viking shields where there's sort of five core areas that we're deeply investing in, in genetics and genomics, computation, neuroscience, immunology and chemical biology. Now we really think of these as five areas that are maybe the minimal critical mass that you would need to make a dent on something as complicated as complex human diseases. It's certainly not the only thing that you need, but we needed a critical mass of investigators working at least in these areas.

Eric Topol (11:05):

Well, yeah, and they really converge on where the hottest advances are being made these days. Now can you work at Arc Institute without being one of these three universities or is it really that you maintain your faculty and your part of this other entity?

Patrick Hsu (11:24):

So we have a few elements to even just the academic side of the house. We have our core investigators. I'm one of them, where we have dually appointed faculty who retain their latter rank or tenured appointment in their home department, but their labs are physically cited at the Arc headquarters where we built out a lab in Stanford Research Park in Palo Alto. And so, folks move their labs there. They continue to train graduate students based on whatever graduate programs they're formally affiliated with through their university affiliation. And so, we have nearly 40 PhD students across our labs that are training on site every day.

(12:03):

So in addition to our core investigators, we also have what we call our innovation investigators, which is more of a grant program to faculty at our partner universities. They receive unrestricted funding from us to seed a new project or accelerate an existing area in their group and their labs stay at their home campus and they just get that funding to augment their work. The third way is our technical staff model where folks basically just come work at Arc and many of them also are establishing their own research groups focusing on technology R&D areas. And so, we have five of those technology centers working in molecular engineering, multi-omics, complex cellular models, in vivo models, and in machine learning.

Discovery of Bridge RNAs

Eric Topol (12:54):

Yeah, that's a great structure. In fact, just a few months ago, Patrick Collison, the other Patrick came to Stanford HAI where I'm on the board and you've summarized it really well and it's very different than the other models and other entities, companies included that you mentioned. It's really very impressive. Now speaking of impressive on June 26, this past few months ago, which incidentally is coincident with the draft genome in the year 2000, the human sequence. You and your colleagues, perhaps the most impressive jump in terms of an Arc Institute contribution published two papers back-to-back in Nature about bridge RNA: [Bridge RNAs direct programmable recombination of target and donor DNA] and [Structural mechanism of bridge RNA-guided recombination.] And before I get you to describe this breakthrough in genome editing, some would call it genome editing 3.0 or CRISPR 3.0, whatever. But what we have today in the clinic with the approval of CRISPR 1.0 for sickle cell and thalassemia is actually quite crude. I think most people will know it's just a double stranded DNA cleavage with all sorts of issues about repair and it's not very precise. And so, CRISPR 2.0 is supposed to be represented by David Liu's contributions and his efforts at Broad like prime and base editing and then comes yours. So maybe you can tell us about it and how it is has to be viewed as quite an important advance.

Patrick Hsu (14:39):

The first thing I would say before CRISPR, is that we had RNA interference. And so, even before this modern genome editing revolution with programmable CRISPRs, we had this technology that had a lot of the core selling points as well. Any target will now become druggable to us. We simply need to reprogram a guide RNA and we can get genetic access to things that are intracellular. And I think both the discovery of RNA interference by Craig Mello and Andy Fire or the invention or discovery of programmable CRISPR technologies, both depend on the same fundamental biological mechanism. These non-coding guide RNAs that are essentially a short RNA search string that you can easily reprogram to retarget a desired enzyme function, and natively both RNAi and CRISPR are molecular scissors. Their RNA or DNA nucleases that can be reprogrammed to different regions of the genome or the transcriptome to make a cut.

(15:48):

And as bioengineers, we have come up with all kinds of creative ways to leverage the ability to make site specific cuts to do all kinds of incredible things including genome editing or beyond transcriptional up or down regulation, molecular imaging and so on and so forth. And so, the first thing that we started thinking about in our lab was, why would mother nature have stopped only RNAi and CRISPR? There probably are lots of other non-coding RNAs out there that might be able to be programmable and if they did exist, they probably also do more complicated and interesting things than just guide a molecular scissors. So that was sort of the first core kind of intuition that we had. The second intuition that we had on the technology side, I was just wearing my biology hat, I’ll put on my technology hat, is the thing that we call genome editing today hardly involves the genome.

(16:50):

It's really you're making a cut to change an individual base or an individual gene or locus. So really you're doing small scale single locus editing, so you might call it gene level or locus level cuts. And what you really want to be able to do is do things at the genome scale at 100 kb, a megabase at the chromosome scale. And I think that's where I think the field will inevitably go if you follow the technology curves of longer and longer range gene sequencing, longer and longer range gene synthesis, and then longer and longer range gene editing. And so, what would that look like? And we started thinking, could there be essentially recombination technologies that allow you to do cut and paste in a single step. Now, the reason for that is the way that we do gene editing today involves a cut and then a multi-step process of cellular DNA repair that resolves the cut to make the exertion or the error prone deletion or the modification that ends up happening.

(17:59):

And so, it's very complicated and whether that's nucleases or base or prime editing, you're all generally limited to the small-scale single locus changes. However, there are natural mechanisms that have solved this cut and paste problem, right? There are these viruses or bacterial versions of viruses known as phage that have generally been trying to exert their multi kilobase genomes into bacterial hosts and specialize throughout billions of years. So our core thought was, well, if there are these new non-coding RNAs, what kind of functions would we be excited about? Can we look in these mobile genetic elements, these so-called jumping genes for new mechanisms? They're incredibly widespread. Transposons are thought to be some of the most diverse enzyme mechanisms found in nature. And so, we started computationally by asking ourselves a very simple question. If a mobile element inserts itself into foreign DNA and it's able to somehow be programmable, presumably the inside or something encoded in the inside of the element is predictive of some sequence on the outside of the element.

(19:15):

And so, that was the core insight we took, and we thought let's look across the boundaries of many different mobile genetic elements and we zoomed in on a particular sub family of these MGE known as insertion sequence (IS) elements which are the most autonomous minimal transposons. Normally transposons have all kinds of genes that they use to hitchhike around the genomic galaxy and endow the bacterial host with some fitness advantage like some ability to metabolize some copper and some host or some metal. And these IS elements have only the enzymes that they need to jump around. And if you identify the boundaries of these using modern computational methods, this is actually a really non-trivial problem. But if you solve that problem to figure out with nucleotide resolution where the element boundaries end and then you look for the open reading frame of the transposases enzyme inside of this element, you'll find that it's not just that coding sequence.

(20:19):

There are also these non-coding flanks inside of the element boundaries. And when we looked across the non-coding, the entire IS family tree, there are hundreds of these different types of elements. We found that this particular family IS110, had the longest non-coding ends of all IS elements. And we started doing experiments in the lab to try to figure out how these work. And what we found was that these elements are cut and paste elements, so they excise themselves into a circular form and paste themselves back in into a target site linearly. But the circularization of this element brings together two distal ends together, which brings together a -35 and a -10 box that create and reconstitute a canonical bacterial transcriptional promoter. This essentially is like plugging a plug into an electrical socket in the wall and it jacks up transcription. Now you would think this transcription would turn on the transposase enzyme so it can jump around more but it transcribes a non-coding RNA out of this non-coding end.

(21:30):

We're like, holy crap, are these RNAs actually involved in regulating the transposon? Now the boring answer would be, oh, it regulates the expression. It's like an antisense regulate or something. The exciting answer would be, oh, it's a new type of guide RNA and you found an RNA guided integrase. So we started zooming in bound dramatically on this and we undertook a covariation analysis where we were able to show that this cryptic non-coding RNA has a totally novel guide RNA structure, totally distinct from RNAi or CRISPR guide RNAs. And it had a target site that covaried with the target site of the element. And so we're like, oh wow, this could be a programmable transposase. The second thing that we found was even more surprising, there was a second region of complementarity in that same RNA that recognized the donor sequence, which is the circularized element itself. And so, this was the first example of a bispecific guide RNA, and also the first example of RNA guided self-recognition by a mobile genetic element.

Eric Topol (22:39):

It's pretty extraordinary because basically you did a systematic assessment of jumping genes or transposons and you found that they contain things that previously were not at all recognized. And then you have a way to program these to edit, change the genome without having to do any cuts or nicks, right?

Patrick Hsu (23:05):

Yeah. So what we showed in a test tube is when we took this, so-called bridge RNA, which we named because it bridges the target and donor together along with the recombinase enzyme. So the two component system, those are the only two things that you need. They're able to cut and paste DNA and recombine them in a test tube without any DNA repair, meaning that it's independent of cellular DNA repair and it does strand nicking, exchange, junction resolution and religation all in a single mechanism. So that's when we got super excited about its potential applications as bioengineering tool.

Eric Topol (23:46):

Yeah, it's pretty extraordinary. And have you already gone into in vivo assessment?

Patrick Hsu (23:54):

Yes, in our initial set of papers, what we showed is that these are programmable and functional or recombinases in a test tube and in bacterial cells. And by reprogramming the target and donor the right way, you can use these enzymes not just for insertion, but also for flipping and cutting out DNA. And so, we actually have in a single mechanism the ability to do bridge editing, if you will, for universal DNA recombination, insertion, excision or inversion, similar to what folks have been doing for decades with Cre recombinase, but with fully programmable recognition sequences. The work that we're doing now in the lab as you can imagine is to adapt these into robust tools for mammalian genome editing, including of course, human genomes. We're excited about this, we're making good progress. The CRISPR has had thousands of labs over the last 10, 15 years working on it to make these therapeutic level potency and selectivity. We're going to work and follow that same blueprint for getting bridge systems to get to that level of performance, but we're on the path and we're very optimistic for the future.

Exemplar of Digital Biology

Eric Topol (25:13):

Yeah, I think it's quite extraordinary and it's a whole different look to what we've been seeing in the CRISPR era for over the past decade and how that's been advancing and getting more specific and less need for repair and being able to be more versatile. But this takes it to yet another dimension. Now, this brings me to the field that when I think of this term digital biology, I think of you and now our mutual acquaintance, Jensen Huang, who everybody knows now. Back some months ago, he wrote and said at a conference, “Where do I think the next amazing revolution is going to come? And this is going to be flat out one of the biggest ones ever. There’s no question that digital biology is going to be it. For the first time in human history, biology has the opportunity to be engineering, not science.” So can you critique Jensen? Is he right? And tell us how you conceive the field of digital biology.

Patrick Hsu (26:20):

If you look at gene therapy today, the core concepts are actually remarkably simple. They're elegant. Of course, you're missing a broken gene, you need to put it back. And that can be curative. Very simple, powerful concept. However, for complex diseases where you don't have just a single gene that goes wrong, in many cases we actually have no idea what to do. And in fact, when you're trying to put in DNA, that's over more than a gene scale. We kind of very quickly run out of ideas. Is it a CAR and a cytokine, a CAR and a cytokine and another thing? And then we're kind of out of ideas. And so, we started thinking in the lab, how can we actually design genomes where it's not just let's reduce the genome into individual Lego blocks, iGem style with promoters and different genes that we just sort of shuffle the Lego blocks around, but actually use AI to design genome sequences.

(27:29):

So to do that, we thought we would have to first of all, train a model that can learn and decode the foreign language of biology and use that in order to design sequences. And so, we sort of have been training DNA foundation models and virtual cell models at Arc, sort of a major effort of ours where the first thing that we tried was to take a variance of transformer architecture that's used to train ChatGPT from OpenAI, but instead apply this to study the next DNA token, right? Now, the interesting thing about next token prediction in English is that you can actually learn a surprising amount of information by just predicting the next word. You can learn world knowledge is the capital of Azerbaijan, is it Baku or is it London, right? Or if you're walking around in the kitchen, then the next text is, I then left the kitchen or the bathroom, right?

(28:33):

Now you're learning about spatial reasoning, and so you can also learn translation obviously. And so similarly, I think predicting the next token or the next base and DNA can lead you to learn about molecular biochemistry, is the next amino acid residue, hydrophobic or hydrophilic. And it can teach you about the mechanics of some catalytic binding pocket or something. You can learn about a disease mutation. Is the next base, the sick linked base or the wild type base and so on and so forth. And what we found was that at massive scale, DNA foundation models learn about molecular function, not just at the DNA level, but also at the RNA and the protein. And indeed, we could use these to design molecular systems like CRISPR-Cas systems, where you have a protein and the guide RNA. It could also design new DNA transposons, and we could design sequences that look plausibly like real genomes, where we generate a megabase a million bases of continuous genome sequence. And it really looks and feels like it could be a blurry picture of something that you would actually sequence. This has been a wonderful collaboration with Brian Hie, a PI at Stanford and an Arc investigator, and we're really excited about what we've seen in this work because it promises the better performance with even more scale. And so, simply by scaling up these models, by adding in more compute, more training data or more powerful models, they're going to get sharper and sharper.

New A.I. Models in Life Science

Eric Topol (30:25):

Yeah. Well, this whole use of large language models for the language of life, whether it's the genome proteins and on and on, actually RNA and even cells has really taken root. And of course, this is really one of the foundations of that field of digital biology, which brings together generative AI, AI tools and trying to push forward our understanding in biology. And also, obviously what's been emphasized in drug discovery, perhaps it's been emphasized even too much because we still have a lot to learn about biology, but that gets me to these models. Like today, AlphaProteo was announced by DeepMind, as we all know, AlphaFold 1, 2, now 3. They were kind of precursors of being able to predict proteins from amino acid 3D structure. And that kind of took the field by a little bit like ChatGPT for life science, but now it's a new model all the time. So you've been working on various models and Arc Institute, how do you see this unfolding? Are we just going to have every aspect of the language of life being approached in all the different interactions? And this is going to help us get to a much more deep level of understanding.

Patrick Hsu (31:56):

I'll say two things. The first is a lot of models that you just described are what I would call task specific models. A model for de novo design of a binder, a model for protein structure prediction. And there are other models for protein fitness or for RNA structure prediction, et cetera, et cetera. And I think what we're going to move towards are more unifying models where there's different classes of models at different levels of scale. So we will have these atomic level models for looking at generative chemistry or ligand docking. We have models that can unify genomes and their molecules, and then we have models that can unify cells and tissues. And so, for example, if you took an H&E stain of some liver, there are folks building models where you can then predict what the single cell spatial transcriptome will look like of that model. And that's obviously operating at a very different level of abstraction than a de novo protein binder. But in the long run, all of these are going to get, I think unified. I think the reason why this is possible is that biology, unlike physics, actually has this unifying theory of evolution that runs across all of its length scales from atomic, molecular, cellular, organismal to entire ecosystem. And the promise of these models is no short then to make biology a predictive discipline.

Patrick Hsu (33:37):

In physics, the experimentalists win the big prizes for the theorists when they measure gravitational waves or whatever. But in biology, we're very practical people. You do something three times and do a T-test. And I think my prediction is we can actually gauge the success of these LLMs or whatever in biology by how much we respect theory in this field.

The A.I. Scientist

Eric Topol (34:05):

Yeah. Well, that's a really interesting perspective, an important perspective because the proliferation of models, which we're going to get into not just doing the things that you described, but also being able to be “pseudo” scientists, the so-called AI scientist. Maybe you could comment about that concept because that's been the idea that everything from the question that could be asked to the hypothesis and the experiment design and the analysis of data and then the feedback. So what is the role of the scientists, that seems to have been overplayed? And maybe you can put that in context.

Patrick Hsu (34:48):

So yeah, right now there's a lot of excitement that we can use AI agents not just to do software enterprise workflows, but to be a research assistant. And then over time, itself an autonomous research scientist that can read the literature, come up with an idea, maybe run a bunch of robots in the lab or do a bunch of computational analyses and then potentially even analyze data, conclude what is going on and actually write an entire paper. Now, I think the vision of this is compelling in the long term. I think the question is really about timescale. If you break down the scientific method into its constituent parts, like hypothesis generation, doing an experiment, analyzing experiment and iterating, we're clearly going to use AI of some kind at every single step of this cycle. I think different steps will require different levels of maturity. The way that I would liken this is just wet lab automation, folks have dreamed about having pipetting robots that just do their western blots and do their cell culture for them for generations.

(36:01):

But of course, today they don't actually really feel fundamentally different from the same ones that we had in the 90s, let's say. Right? And so, obviously they're getting better, but it seems to me one of the trends I'm very bullish about is the explosion of humanoid robots and robot foundation models that have a world model and a sense of physics and proportionate space loaded onto them. Within five years, we're going to have home robots that can fold your clothes, that can organize your kitchen and do all of this while you're sleeping, so you wake up to a clean home every day.

Eric Topol (36:40):

It’s not going to be just Roomba anymore. There's going to be a lot more, but it isn't just the hardware, it's also the agents playing in software, right?

Patrick Hsu (36:50):

It's the integrated loop of the hardware and the software where the ability to make the same machine generally intelligent will make it adaptable to a broad array of tasks. Now, what I'm excited about is those generally intelligent humanoid robots coming into the lab, where instead of creating a centrifuge or a new type of pipetter that's optimized for your Beckman or Hamilton device, instead you just have robot arms that you snap onto the edge of the bench and then they just work alongside you. And I do think that's coming, although it'll take a lot of hardware and software and computer vision engineering to make that possible.

A Sense of Humor

Eric Topol (37:32):

Yeah, and I think also going back to originating the question, there still is quite a debate about the creativity and the lack of any simulation of AGI, whatever that means anymore. And so, the human in the loop part of this is obviously I think it's still of critical nature. Now, the other thing I learned about you is you have a great sense of humor, which is really important by the way. And recently, which is great that you're active on X or Twitter because that's one way we get to see what you're thinking on a day-to-day basis. But I think you put out a poll which was really quite provocative , and it was about, here's what it said, “do more people in the world *truly* understand transformers or health insurance?” And interestingly, you got 49% for transformers at 51% for health insurance. Can you tell us what you're thinking when you put that poll together? Because obviously a lot of people don't understand either of these.

Patrick Hsu (38:44):

I think the core question is, there are different ways of looking at the world, some of which are very bottom up and some of which are very top down. And one of the very surprising things about transformers is they're taking something that is in principle, an incredibly simple task, which is if you have a string of text, what is the next letter? And somehow at massive, massive scale, you can unlock something that looks an awful lot like reasoning, and you've got these emergent behaviors. Now the bottoms up theory of just the linear algebra that's going on in these models couldn't possibly really help us predict that we have these emerging capabilities. And I think similarly in healthcare, there's a literal set of parts that are operating in some complex way that at massive scale becomes this incredibly confusing and dynamic system for how we can actually incentivize how we make medicines, how we actually take care of people, and how we actually pay for any of this from an economic point of view. And so, I think it was, in some sense if transformers can actually be an explainable by just linear algebra equations, maybe there will be a way to decompose the seemingly incredibly confusing world of healthcare in order to actually build a better way forward.

Computing Power and the GPU Arms Race

Eric Topol (40:12):

Yeah. Well that's great. Now the other thing I wanted to ask you about, we open source and the arms race of GPUs and this whole kind of idea is you touched on the need for coalescing a lot of these tools to exploit the synergy. But we have an issue because many academic labs like here at Scripps Research and so many others, including as I learned even at Stanford, have limited access to GPUs. So computing power of large language models is a problem. And then the models that exist today that can be adopted like Llama or others, and they're somewhat limited. And then we also have a movement towards trying to make things more open source, like for example, recently OpenCRISPR with Profluent Bio that is basically trying to use AI for CRISPR guides. And so, how do you deal with this arms race, computing power, open source, proprietary models that are not easily accessible without a lot of resources?

Patrick Hsu (41:30):

So the first thing I would say is, we are in the academic science sphere really unprepared for the level of resources that are required for doing this type of cutting edge computational work. There are top Stanford computer science professors or computational researchers who have a single GPU in their office, and that's actually what their whole lab runs off of.

(41:58):

The UC Berkeley campus, the grid runs on something like 12 megawatts of power and how are they going to build an on-premises GPU clusters, like a central question that can scale across the entire needs? And these are two of the top computer science universities in the world. And so, I think one of our kind of core beliefs at Arc is, as science both experimentally and computationally has gotten incredibly complex, not just in terms of conceptually, but also just the actual infrastructure and machines and know-how that you need to do things. We actually need to essentially support this. So we have a private GPU cloud that we use to train our models, and we have access to significantly large clusters for large burst kind of train outs as necessary. And I think infrastructurally for running genomics experiments or doing scalable brain organoid screens, right, we're also building out the infrastructure to support that experimentally.

Eric Topol (43:01):

Yeah, no, I think this is one of the advantages of the new model like the Arc Institute because not many centers have that type of plasticity with access to computing power when needed. So that's where a brilliant mind you and the Arc Institute together makes for a formidable recipe for future advances and of course building on the ones you've already accomplished.

The Primacy of Human Talent

Patrick Hsu (43:35):

I would just say, my main skill, if I have one, is to recruit really, really smart people. And so, everything that you're seeing and hearing about is the work of unbelievable colleagues who are curious, passionate, and incredible scientists.

Eric Topol (43:53):

But it also takes the person who can judge those who are in that category set as a role model. And you're certainly doing that. I guess just in closing, I mean, it's just such a delight to get to meet you here and kind of get your thoughts on what is the hottest thing in life science without question, which brings together the fields of AI and what's going on, not just obviously in genome editing, but this digital biology era that we're still in the early phases of, I mean, I think you could say that it's just going to continue to accelerate the exponential curve. We're still kind of on the bottom of that, I would imagine where we're headed. Any other things that you want to bring up that I haven't touched on that will round out this conversation?

Patrick Hsu (44:50):

I mean, I think it's very early days here at Arc.

Patrick Hsu (44:53):

When we founded Arc, we asked ourselves, how do we measure success? We don't have customers or revenue in the way that a typical startup does. And we felt sort of three things. The first was research institutes live and die by their talent. Can we actually hire incredible people when we make offers to people we want to come, do they come? The second was, when those folks do come to Arc, do they feel like they're able to work on important research programs that they couldn't do sort of at their prior university or company? And then longer term, the third thing was, and there's just no shortcut around this, you need to do important work. And I think we've been really excited that there are early signs that we're able to do all three of these things, and we're still, again, just following the same scaling laws that we're seeing in natural language and vision, but for the domain of biology. And so, we're excited about what's ahead and think if there are folks who are interested in learning more about Arc, just shoot me an email or DM.

Eric Topol (46:07):

Yeah, well I would just say, congratulations on what you've already achieved. I know you're going to keep rocking it because you already have in a short time. And for anybody who doesn't know about Arc Institute and your work and your team, I hope this is going to be putting them on notice actually what can be accomplished outside of the usual NIH funded model, which is kind of a risk-free zone where you basically have to have your results nailed down before you send in your proposal frequently, and it doesn't do great things for young people. Really, I think you actually qualify in that demographic where it's hard for them to break in for getting NIH grants and also for this type of work that you're doing. So we'll look for the next bridge beyond bridge RNAs of your just fantastic efforts. So Patrick, thanks so much for joining us today, and we'll be checking back with you and following all the great work that you'll be doing in the times ahead.

Patrick Hsu (47:14):

Thanks so much, Eric. It was such a pleasure to be here today. Appreciate the opportunity.

*******************

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Arvind Narayanan and Sayash Kapoor are well regarded computer scientists at Princeton University and have just published a book with a provocative title, AI Snake Oil. Here I’ve interviewed Sayash and challenged him on this dismal title, for which he provides solid examples of predictive AI’s failures. Then we get into the promise of generative AI.

Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to audio and external links to key publications

Eric Topol (00:06):

Hello, it's Eric Topol with Ground Truths, and I'm delighted to welcome the co-author of a new book AI SNAKE OIL and it's Sayash Kapoor who has written this book with Arvind Narayanan of Princeton. And so welcome, Sayash. It's wonderful to have you on Ground Truths.

Sayash Kapoor (00:28):

Thank you so much. It's a pleasure to be here.

Eric Topol (00:31):

Well, congratulations on this book. What's interesting is how much you've achieved at such a young age. Here you are named in TIME100 AI’s inaugural edition as one of those eminent contributors to the field. And you're currently a PhD candidate at Princeton, is that right?

Sayash Kapoor (00:54):

That's correct, yes. I work at the Center for Information Technology Policy, which is a joint program between the computer science department and the school of public and international affairs.

Eric Topol (01:05):

So before you started working on your PhD in computer science, you already were doing this stuff, I guess, right?

Sayash Kapoor (01:14):

That's right. So before I started my PhD, I used to work at Facebook as a machine learning engineer.

Eric Topol (01:20):

Yeah, well you're taking it to a more formal level here. Before I get into the book itself, what was the background? I mean you did describe it in the book why you decided to write a book, especially one that was entitled AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference.

Background to Writing the Book

Sayash Kapoor (01:44):

Yeah, absolutely. So I think for the longest time both Arvind and I had been sort of looking at how AI works and how it doesn't work, what are cases where people are somewhat fooled by the potential for this technology and fail to apply it in meaningful ways in their life. As an engineer at Facebook, I had seen how easy it is to slip up or make mistakes when deploying machine learning and AI tools in the real world. And had also seen that, especially when it comes to research, it's really easy to make mistakes even unknowingly that inflate the accuracy of a machine learning model. So as an example, one of the first research projects I did when I started my PhD was to look at the field of political science in the subfield of civil war prediction. This is a field which tries to predict where the next civil war will happen and in order to better be prepared for civil conflict.

(02:39):

And what we found was that there were a number of papers that claimed almost perfect accuracy at predicting when a civil war will take place. At first this seemed sort of astounding. If AI can really help us predict when a civil war will start like years in advance sometimes, it could be game changing, but when we dug in, it turned out that every single one of these claims where people claim that AI was better than two decades old logistic regression models, every single one of these claims was not reproducible. And so, that sort of set the alarm bells ringing for the both of us and we sort of dug in a little bit deeper and we found that this is pervasive. So this was a pervasive issue across fields that were quickly adopting AI and machine learning. We found, I think over 300 papers and the last time I compiled this list, I think it was over 600 papers that suffer from data leakage. That is when you can sort of train on the sets that you're evaluating your models on. It's sort of like teaching to the test. And so, machine learning model seems like it does much better when you evaluate it on your data compared to how it would really work out in the real world.

Eric Topol (03:48):

Right. You say in the book, “the goal of this book is to identify AI snake oil - and to distinguish it from AI that can work well if used in the right ways.” Now I have to tell you, it's kind of a downer book if you're an AI enthusiast because there's not a whole lot of positive here. We'll get to that in a minute. But you break down the types of AI, which I'm going to challenge a bit into three discrete areas, the predictive AI, which you take a really harsh stance on, say it will never work. Then there's generative AI, obviously the large language models that took the world by storm, although they were incubating for several years when ChatGPT came along and then content moderation AI. So maybe you could tell us about your breakdown to these three different domains of AI.

Three Types of AI: Predictive, Generative, Content Moderation

Sayash Kapoor (04:49):

Absolutely. I think one of our main messages across the book is that when we are talking about AI, often what we are really interested in are deeper questions about society. And so, our breakdown of predictive, generative, and content moderation AI sort of reflects how these tools are being used in the real world today. So for predictive AI, one of the motivations for including this in the book as a separate category was that we found that it often has nothing to do with modern machine learning methods. In some cases it can be as simple as decades old linear regression tools or logistic regression tools. And yet these tools are sold under the package of AI. Advances that are being made in generative AI are sold as if they apply to predictive AI as well. Perhaps as a result, what we are seeing is across dozens of different domains, including insurance, healthcare, education, criminal justice, you name it, companies have been selling predictive AI with the promise that we can use it to replace human decision making.

(05:51):

And I think that last part is where a lot of our issues really come down to because these tools are being sold as far more than they're actually capable of. These tools are being sold as if they can enable better decision making for criminal justice. And at the same time, when people have tried to interrogate these tools, what we found is these tools essentially often work no better than random, especially when it comes to some consequential decisions such as job automation. So basically deciding who gets to be called on the next level of like a job interview or who is rejected, right as soon as they submit the CV. And so, these are very, very consequential decisions and we felt like there is a lot of snake oil in part because people don't distinguish between applications that have worked really well or where we have seen tremendous advances such as generative AI and applications where essentially we've stalled for a number of decades and these tools don't really work as claimed by the developers.

Eric Topol (06:55):

I mean the way you partition that, the snake oil, which is a tough metaphor, and you even show the ad from 1905 of snake oil in the book. You're really getting at predictive AI and how it is using old tools and selling itself as some kind of breakthrough. Before I challenge that, are we going to be able to predict things? By the way, using generative AI, not as you described, but I would like to go through a few examples of how bad this has been and since a lot of our listeners and readers are in the medical world or biomedical world, I'll try to get to those. So one of the first ones you mentioned, which I completely agree, is how prediction of Covid from the chest x-ray and there were thousands of these studies that came throughout the pandemic. Maybe you could comment about that one.

Some Flagrant Examples

Sayash Kapoor (08:04):

Absolutely. Yeah, so this is one of my favorite examples as well. So essentially Michael Roberts and his team at the University of Cambridge a year or so after the pandemic looked back at what had happened. I think at the time there were around 500 studies that they included in the sample. And they looked back to see how many of these would be useful in a clinical setting beyond just the scope of writing a research paper. And they started out by using a simple checklist to see, okay, are these tools well validated? Does the training and the testing data, is it separate? And so on. So they ran through the simple checklist and that excluded all but 60 of these studies from consideration. So apart from 60 studies, none of these other studies even passed a very, very basic criteria for being included in the analysis. Now for these 60, it turns out that if you take a guess about how many were useful, I'm pretty confident most cases would be wrong.

(09:03):

There were exactly zero studies that were useful in a clinically relevant setting. And the reasons for this, I mean in some cases the reasons were as bizarre as training a machine learning model to predict Covid where all of the positive samples of people who had Covid were from adults. But all of the negative samples of people who didn't have Covid were from children. And so, essentially claiming that the resulting classifier can predict who has Covid is bizarre because all the classifier is doing is looking at the checks history and basically predicting which x-ray belongs to a child versus an adult. And so, this is the sort of error in some cases we saw duplicates in the training and test set. So you have the same person that is being used for training the model and that it is also used for evaluating the model. So simply memorizing a given sample of x-rays would be enough to achieve a very high performance. And so, for issues like these, I think all 60 of these studies prove to be not useful in a clinically relevant setting. And I think this is sort of the type of pattern that we've seen over and over again.

Eric Topol (10:14):

Yeah, and I agree with you on that point. I mean that was really a flagrant example and that would fulfill your title of your book, which as I said is a very tough title. But on page 29, and we'll have this in the post. You have a figure, the landscape of AI snake oil, hype, and harm.

And the problem is there is nothing good in this landscape. So on the y-axis you have works, hype, snake oil going up on the y-axis. And on the x-axis, you have benign and harmful. So the only thing you have that works and that's benign is autocomplete. I wouldn't say that works. And then you have works facial recognition for surveillance is harmful. This is a pretty sobering view of AI. Obviously, there's many things that are working that aren't on this landscape. So I just would like to challenge, are you a bit skewed here and only fixating on bad things? Because this diagram is really rough. I mean, there's so much progress in AI and you have in here you mentioned the predicting civil wars, and obviously we have these cheating detection, criminal risk prediction. I mean a lot of problems, video interviews that are deep fakes, but you don't present any good things.

Optimism on Generative AI

Sayash Kapoor (11:51):

So to be clear, I think both Arvind and are somewhat paradoxically optimistic about the future of generative AI. And so, the decision to focus on snake oil was a very intentional one from our end. So in particular, I think at various places in the book we outline why we're optimistic, what types of applications we think we're optimistic about as well. And the reason we don't focus on them is that it basically comes down to the fact that no one wants to read a book that has 300 pages about the virtues of spellcheck or AI for code generation or something like that. But I think I completely agree and acknowledge that there are lots of positive applications that didn't make the cut for the book as well. That was because we wanted people to come to this from a place of skepticism so that they're not fooled by the hype.

(12:43):

Because essentially we see even these positive uses of AI being lost out if people have unrealistic expectations from what an AI tool should do. And so, pointing out snake oil is almost a prerequisite for being able to use AI productively in your work environment. I can give a couple of examples of where or how we've sort of manifested this optimism. One is AI for coding. I think writing code is an application that I do, at least I use AI a lot. I think almost half of the code I write these days is generated, at least the first draft is generated using AI. And yet if I did not know how to program, it would be a completely different question, right? Because for me pointing out that, oh, this syntax looks incorrect or this is not handling the data in the correct way is as simple as looking at a piece of code because I've done this a few times. But if I weren't an expert on programming, it would be completely disastrous because even if the error rate is like 5%, I would have dozens of errors in my code if I'm using AI to generate it.

(13:51):

Another example of how we've been using it in our daily lives is Arvind has two little kids and he's built a number of applications for his kids using AI. So I think he's a big proponent of incorporating AI into children's lives as a force for good rather than having a completely hands-off approach. And I think both of these are just two examples, but I would say a large amount of our work these days occurs with the assistance of AI. So we are very much optimistic. And at the same time, I think one of the biggest hindrances to actually adopting AI in the real world is not understanding its limitations.

Eric Topol (14:31):

Right. Yeah, you say in the book quote, “the two of us are enthusiastic users of generative AI, both in our work and our personal lives.” It just doesn't come through as far as the examples. But before I leave the troubles of predictive AI, I liked to get into a few more examples because that's where your book shines in convincing that we got some trouble here and we need to be completely aware. So one of the most famous, well, there's a couple we're going to get into, but one I'd like to review with you, it's in the book, is the prediction of sepsis in the Epic model. So as you know very well, Epic is the most used IT and health systems electronic health records, and they launched never having published an algorithm that would tell when the patient was hospitalized if they actually had sepsis or risk of sepsis. Maybe you could take us through that, what you do in the book, and it truly was a fiasco.

The Sepsis Debacle

Sayash Kapoor (15:43):

Absolutely. So I think back in 2016/2017, Epic came up with a system that would help healthcare providers predict which patients are most at risk of sepsis. And I think, again, this is a very important problem. I think sepsis is one of the leading causes of death worldwide and even in the US. And so, if we could fix that, I think it would be a game changer. The problem was that there were no external validations of this algorithm for the next four years. So for four years, between 2017 to 2021, the algorithm wasn't used by hundreds of hospitals in the US. And in 2021, a team from University of Michigan did this study in their own hospital to see what the efficacy of the sepsis prediction model is. They found out that Epic had claimed an AUC of between 0.76 and 0.83, and the actual AUC was closer to 0.6, and AUC of 0.5 is making guesses at random.

(16:42):

So this was much, much worse than the company's claims. And I think even after that, it still took a year for sepsis to roll back this algorithm. So at first, Epic's claims were that this model works well and that's why hospitals are adopting it. But then it turned out that Epic was actually incentivizing hospitals to adopt sepsis prediction models. I think they were giving credits of hundreds of thousands of dollars in some cases. If a hospital satisfied a certain set of conditions, one of these conditions was using a sepsis prediction model. And so, we couldn't really take their claims at face value. And finally in October 2022, Epic essentially rolled back this algorithm. So they went from this one size fits all sepsis prediction model to a model that each hospital has to train on its own data, an approach which I think is more likely to work because each hospital's data is different. But it's also more time consuming and expensive for the hospitals because all of a sudden you now need your own data analysts to be able to roll out this model to be able to monitor it.

(17:47):

I think this study also highlights many of the more general issues with predictive AI. These tools are often sold as if they're replacements for an existing system, but then when things go bad, essentially they're replaced with tools that do far less. And companies often go back to the fine print saying that, oh, we should always deploy it with the human in the loop, or oh, it needs to have these extra protections that are not our responsibility, by the way. And I think that gap between what developers claim and how the tool actually works is what is most problematic.

Eric Topol (18:21):

Yeah, no, I mean it's an egregious example, and again, it fulfills like what we discussed with statistics, but even worse because it was marketed and it was incentivized financially and there's no doubt that some patients were completely miscategorized and potentially hurt. The other one, that's a classic example that went south is the Optum UnitedHealth algorithm. Maybe you could take us through that one as well, because that is yet another just horrible case of how people were discriminated against.

The Infamous Optum Algorithm

Sayash Kapoor (18:59):

Absolutely. So Optum, another health tech company created an algorithm to prioritize high risk patients for preemptive care. So I think it was around when Obamacare was being introduced that insurance networks started looking into how they could reduce costs. And one of the main ways they identified to reduce costs is basically preemptively caring for patients who are extremely high risk. So in this case, they decided to keep 3% of the patients in the high risk category and they built a classifier to decide who's the highest risk, because potentially once you have these patients, you can proactively treat them. There might be fewer emergency room visits, there might be fewer hospitalizations and so on. So that's all fine and good. But what happened when they implemented the algorithm was that every machine learning model needs like the target variable, what is being predicted at the end of the day. What they decided to predict was how much patient would pay, how much would they charge, what cost the hospital would incur if they admitted this patient.

(20:07):

And they essentially use that to predict who should be prioritized for healthcare. Now unsurprisingly, it turned out that white patients often pay a lot more or are able to pay a lot more when it comes to hospital visits. Maybe it's because of better insurance or better conditions at work that allow them to take leave and so on. But whatever the mechanism is, what ended up happening with this algorithm was I think black patients with the same level of healthcare prognosis were half as likely or about much less likely compared to white ones of getting enrolled in this high risk program. So they were much less likely to get this proactive care. And this was a fantastic study by Obermeyer, et al. It was published in Science in 2019. Now, what I think is the most disappointing part of this is that Optum did not stop using this algorithm after this study was released. And that was because in some sense the algorithm was working precisely as expected. It was an algorithm that was meant to lower healthcare costs. It wasn't an algorithm that was meant to provide better care for patients who need it most. And so, even after this study was rolled out, I think Optum continued using this algorithm as is. And I think as far as I know, even today this is or some version of this algorithm is still in use across the network of hospitals that Optum serves.

Eric Topol (21:31):

No, it's horrible the fact that it was exposed by Ziad Obermeyer’s paper in Science and that nothing has been done to change it, it's extraordinary. I mean, it's just hard to imagine. Now you do summarize the five reasons predictive AI fails in a nice table, we'll put that up on the post as well. And I think you've kind of reviewed that as these case examples. So now I get to challenge you about predictive AI because I don't know that such a fine line between that and generative AI are large language models. So as you know, the group at DeepMind and now others have done weather forecasting with multimodal large language models and have come up with some of the most accurate weather forecasting we've ever seen. And I've written a piece in Science about medical forecasting. Again, taking all the layers of a person's data and trying to predict if they're high risk for a particular condition, including not just their electronic record, but their genomics, proteomics, their scans and labs and on and on and on exposures, environmental.

Multimodal A.I. in Medicine

(22:44):

So I want to get your sense about that because this is now a coalescence of where you took down predictive AI for good reasons, and then now these much more sophisticated models that are integrating not just large data sets, but truly multimodal. Now, some people think multimodal means only text, audio, speech and video images, but here we're talking about multimodal layers of data as for the weather forecasting model or earthquake prediction or other things. So let's get your views on that because they weren't really presented in the book. I think they're a positive step, but I want to see what you think.

Sayash Kapoor (23:37):

No, absolutely. I think maybe the two questions are sort of slightly separate in my view. So for things like weather forecasting, I think weather forecasting is a problem that's extremely tenable for generative AI or for making predictions about the future. And I think one of the key differences there is that we don't have the problem of feedback loops with humans. We are not making predictions about individual human beings. We are rather making predictions about what happens with geological outcomes. We have good differential equations that we've used to predict them in the past, and those are already pretty good. But I do think deep learning has taken us one step further. So in that sense, I think that's an extremely good example of what doesn't really fit within the context of the chapter because we are thinking about decisions thinking about individual human beings. And you rightly point out that that's not really covered within the chapter.

(24:36):

For the second part about incorporating multimodal data, genomics data, everything about an individual, I think that approach is promising. What I will say though is that so far we haven't seen it used for making individual decisions and especially consequential decisions about human beings because oftentimes what ends up happening is we can make very good predictions. That's not in question at all. But even with these good predictions about what will happen to a person, sometimes intervening on the decision is hard because oftentimes we treat prediction as a problem of correlations, but making decisions is a problem of causal estimation. And that's where those two sort of approaches disentangle a little bit. So one of my examples, favorite examples of this is this model that was used to predict who should be released before screening when someone comes in with symptoms of pneumonia. So let's say a patient comes in with symptoms of pneumonia, should you release them on the day of?

(25:39):

Should you keep them in the hospital or should you transfer them to the ICU? And these ML researchers were basically trying to solve this problem. They found out that the neural network model they developed, this was two decades ago, by the way. The neural network model they developed was extremely accurate at predicting who would basically have a high risk of having complications once they get pneumonia. But it turned out that the model was saying essentially that anyone who comes in who has asthma and who comes in with symptoms of pneumonia is the lowest risk patient. Now, why was this? This was because when in the past training data, when some such patients would come into the hospital, these patients would be transferred directly to the ICU because the healthcare professionals realized that could be a serious condition. And so, it turned out that actually patients who had asthma who came in with symptoms of pneumonia were actually the lowest risk amongst the population because they were taken such good care of.

(26:38):

But now if you use this prediction that a patient comes in with symptoms of pneumonia and they have asthma, and so they're low risk, if you use this to make a decision to send them back home, that could be catastrophic. And I think that's the danger with using predictive models to make decisions about people. Now, again, I think the scope and consequences of decisions also vary. So you could think of using this to surface interesting patterns in the data, especially at a slightly larger statistical level to see how certain subpopulations behave or how certain groups of people are likely to develop symptoms or whatever. But I think when as soon as it comes to making decisions about people, the paradigm of problem solving changes because as long as we are using correlational models, I think it's very hard to say what will happen if we change the conditions, what will happen if the decision making mechanism is very different from one where the data was collected.

Eric Topol (27:37):

Right. No, I mean where we agree on this is that at the individual level, using multimodal AI with all these layers of data that have now recently become available or should be available, that has to be compared ideally in a randomized trial with standard of care today, which doesn't use any of that. And to see whether or not that decision's made, does it change the natural history and is it an advantage, that's yet to be done. And I agree, it's a very promising pathway for the future. Now, I think you have done what is a very comprehensive sweep on the predictive AI failures. You've mentioned here in our discussion, your enthusiasm and in the book about generative AI positive features and hope and excitement perhaps even. You didn't really yet, we haven't discussed much on the content moderation AI that you have discreetly categorized. Maybe you could just give us the skinny on your sense of that.

Content Moderation AI

Sayash Kapoor (28:46):

Absolutely. So content moderation AI is AI that's used to sort of clean up social media feeds. Social media platforms have a number of policies about what's allowed and not allowed on the platforms. Simple things such as spam are obviously not allowed because let's say people start spamming the platform, it becomes useless for everyone. But then there are other things like hate speech or nudity or pornography and things like that, which are also disallowed on most if not all social media platforms today. And I think a lot of the ways in which these policies are enforced today is using AI. So you might have an AI model that runs every single time you upload a photo to Facebook, for instance. And not just one perhaps hundreds of such models to detect if it has nudity or hate speech or any of these other things that might violate the platform's terms of service.

(29:40):

So content moderation AI is AI that's used to make these decisions. And very often in the last few years we've seen that when something gets taken down, for instance, Facebook deletes a post, people often blame the AI for having a poor understanding. Let's say of satire or not understanding what's in the image to basically say that their post was taken down because of bad AI. Now, there have been many claims that content moderation AI will solve social media's problems. In particular, we've heard claims from Mark Zuckerberg who in a senate testimony I think back in 2018, said that AI is going to solve most if not all of their content moderation problems. So our take on content moderation AI is basically this. AI is very, very useful for solving the simple parts of content moderation. What is a simple part? So basically the simple parts of content moderation are, let's say you have a large training data of the same type of policy violation on a platform like Facebook.

(30:44):

If you have large data sets, and if these data sets have a clear line in the sand, for instance, with nudity or pornography, it's very easy to create classifiers that will automate this. On the other hand, the hard part of content moderation is not actually just creating these AI models. The hard part is drawing the line. So when it comes to what is allowed and not allowed on platforms, these platforms are essentially making decisions about speech. And that is a topic that's extremely fraught. It's fraught in the US, it's also fraught globally. And essentially these platforms are trying to solve this really hard problem at scale. So they're trying to come up with rules that apply to every single user of the platform, like over 3 billion users in the case of Facebook. And this inevitably has these trade-offs about what speech is allowed versus disallowed that are hard to say one way or the other.

(31:42):

They're not black and white. And what we think is that AI has no place in this hard part of content moderation, which is essentially human. It's essentially about adjudicating between competing interests. And so, when people claim that AI will solve these many problems of content moderation, I think what they're often missing is that there's this extremely large number of things you need to do to get content moderation right. AI solves one of these dozen or so things, which is detecting and taking down content automatically, but all of the rest of it involves essentially human decisions. And so, this is sort of the brief gist of it. There are also other problems. For example, AI doesn't really work so well for low resource languages. It doesn't really work so well when it comes to nuances and so on that we discussed in the book. But we think some of these challenges are solvable in the medium to long term. But these questions around competing interests of power, I think are beyond the domain of AI even in the medium to long term.

Age 28! and Career Advice

Eric Topol (32:50):

No, I think you nailed that. I think this is an area that you've really aptly characterized and shown the shortcomings of AI and how the human factor is so critically important. So what's extraordinary here is you're just 28 and you are rocking it here with publications all over the place on reproducibility, transparency, evaluating generative AI, AI safety. You have a website on AI snake oil that you're collecting more things, writing more things, and of course you have the experience of having worked in the IT world with Facebook and also I guess also Columbia. So you're kind of off to the races here as one of the really young leaders in the field. And I am struck by that, and maybe you could comment about the inspiration you might provide to other young people. You're the youngest person I've interviewed for Ground Truths, by the way, by a pretty substantial margin, I would say. And this is a field where it attracts so many young people. So maybe you could just talk a bit about your career path and your advice for people. They may be the kids of some of our listeners, but they also may be some of the people listening as well.

Sayash Kapoor (34:16):

Absolutely. First, thank you so much for the kind words. I think a lot of this work is with collaborators without whom of course, I would never be able to do this. I think Arvind is a great co-author and supporter. I think in terms of my career parts, it was sort of like a zigzag, I would say. It wasn't clear to me when I was an undergrad if I wanted to do grad school or go into the industry, and I sort of on a whim went to work at Facebook, and it was because I'd been working on machine learning for a little bit of time, and I just thought, it's worth seeing what the other side has to offer beyond academia. And I think that experience was very, very helpful. One of the things, I talked to a lot of undergrads here at Princeton, and one of the things I've seen people be very concerned about is, what is the grad school they're going to get into right after undergrad?

(35:04):

And I think it's not really a question you need to answer now. I mean, in some cases I would say it's even very helpful to have a few years of industry experience before getting into grad school. That has definitely, at least that has been my experience. Beyond that, I think working in a field like AI, I think it's very easy to be caught up with all of the new things that are happening each day. So I'm not sure if you know, but AI has I think over 500-1,000 new archive papers every single day. And with this rush, I think there's this expectation that you might put on yourself on being successful requires a certain number of publications or a certain threshold of things. And I think more often than not, that is counterproductive. So it has been very helpful for me, for example, to have collaborators who are thinking long term, so this book, for instance, is not something that would be very valued within the CS community, I would say. I think the CS community values peer-reviewed papers a lot more than they do books, and yet we chose to write it because I think the staying power of a book or the longevity of a book is much more than any single paper could do. So the other concrete thing I found very helpful is optimizing for a different metric compared to what the rest of the community seems to be doing, especially when it comes to fast moving fields like AI.

Eric Topol (36:29):

Well, that last piece of advice is important because I think too often people, whether it's computer scientists, life scientists, whoever, they don't realize that their audience is much broader. And that reaching the public with things like a book or op-eds or essays, varied ways that are intended for public consumption, not for, in this case, computer scientists. So that's why I think the book is a nice contribution. I don't like the title because it's so skewed. And also the content is really trying to hammer it at home. I hope you write a sequel book on the positive sides of AI. I did want to ask you, when I read the book, I thought I heard your voice. I thought you had written the book, and Arvind maybe did some editing. You wrote about Arvind this and Arvind that. Did you write the first draft of the book and then he kind of came along?

Sayash Kapoor (37:28):

No, absolutely not. So the way we wrote the book was we basically started writing it in parallel, and I wrote the first draft of half the chapters and he wrote the first draft of the other half, and that was essentially all the way through. So we would sort of write a draft, pass it to the other person, and then keep doing this until we sent it to our publishers.

Eric Topol (37:51):

Okay. So I guess I was thinking of the chapters you wrote where it came through. I'm glad that it was a shared piece of work because that's good, because that’s what co-authoring is all about, right? Well, Sayash, it's really been a joy to meet you and congratulations on this book. I obviously have expressed my objections and my disagreements, but that's okay because this book will feed the skeptics of AI. They'll love this. And I hope that the positive side, which I think is under expressed, will not be lost and that you'll continue to work on this and be a conscience. You may know I've interviewed a few other people in the AI space that are similarly like you, trying to assure its safety, its transparency, the ethical issues. And I think we need folks like you. I mean, this is what helps get it on track, keeping it from getting off the rails or what it shouldn't be doing. So keep up the great work and thanks so much for joining.

Sayash Kapoor (39:09):

Thank you so much. It was a real pleasure.

************************************************

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Francis Collins is a veritable national treasure. He directed the National Institutes of Health from 2009 to 2021. Prior to that he led the National Human Genetics Research Institute (NHGRI) from 1997-2009, during which the human genome was first sequenced. As a physician-scientist, he has made multiple seminal discoveries on the genetic underpinnings of cystic fibrosis, Huntington’s disease, neurofibromatosis, progeria, and others. This brief summary is barely scratching the surface oh his vast contributions to life science and medicine.

A video clip from our conversation on hepatitis C. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with external inks and links to audio

Eric Topol (00:06):

Well, I am really delighted to be able to have our conversation with Francis Collins. This is Eric Topol with Ground Truths and I had the chance to first meet Francis when he was on the faculty at the University of Michigan when I was a junior faculty. And he gave, still today, years later, we're talking about 40 years later, the most dazzling Grand Rounds during his discovery of cystic fibrosis. And Francis, welcome, you inspired me and so many others throughout your career.

Francis Collins (00:40):

Well, Eric, thank you and you've inspired me and a lot of other people as well, so it's nice to have this conversation with you in the Ground Truths format.

Eric Topol (00:49):

Well, thank you. We're at the occasion of an extraordinary book you put together. It's the fifth book, but it stands out quite different from the prior books as far as I can tell. It's called The Road to Wisdom: On Truth, Science, Faith and Trust, these four essential goods that build upon each other. And it's quite a book, Francis, I have to say, because you have these deep insights about these four critical domains and so we'll get into them. But I guess the first thing I thought I'd do is just say, how at some point along the way you said, “the goal of this book is to turn the focus away from hyperpartisan politics and bring it back to the most important sources of wisdom: truth, science, faith and trust, resting upon a foundation of humility, knowledge, morality, and good judgment.” So there's a lot there. Maybe you want to start off with what was in the background when you were putting this together? What were you really aiming at getting across?

Reflections on Covid

Francis Collins (02:06):

I'm glad to, and it's really a pleasure to have a chance to chat with you about this. I guess before Covid came along, I was probably a bit of a naive person when it came to how we make decisions. Yeah, I knew there were kind of wacky things that had gone out there from time to time, but I had a sort of Cartesian attitude that we were mostly rational actors and when presented with evidence that's been well defended and validated that most people will say, okay, I know what to do. Things really ran off the rails in the course of Covid. It was this remarkable paradox where, I don't know what you would say, but I would say the development of the vaccines that were safe and highly effective in 11 months using the mRNA platform was one of the most stunning achievements of science in all of history up until now.

Francis Collins (03:02):

And yet 50 million Americans decided they didn't want any part of it because of information that came to them that suggested this was not safe or there was conspiracies behind it, or maybe the syringes had chips that Bill Gates had put in there or all manner of other things that were being claimed. And good honorable people were distracted by that, lost their trust in other institutions like the CDC, maybe like the government in general like me, because I was out there a lot trying to explain what we knew and what we didn't know about Covid. And as a consequence of that, according to Kaiser Family Foundation, more than 230,000 people died between June of 2021 and April of 2022 because of a decision to reject the opportunity for vaccines that were at that time free and widely available. That is just an incredibly terribly tragic thing to say.

Francis Collins (04:03):

More than four times the number of people who died, Americans who died in the Vietnam War are in graveyards unnecessarily because we lost our anchor to truth, or at least the ability to discern it or we couldn't figure out who to trust while we decided science was maybe not that reliable. And people of faith for reasons that are equally tragic were among those most vulnerable to the misinformation and the least likely therefore, to take advantage of some of these lifesaving opportunities. It just completely stunned me, Eric, that this kind of thing could happen and that what should have been a shared sense of working against the real enemy, which was the SARS-CoV-2 virus became instead a polarized, divisive, vitriolic separation of people into separate camps that were many times driven more by politics than by any other real evidence. It made me begin to despair for where we're headed as a country if we can't figure out how to turn this around.

Francis Collins (05:11):

And I hadn't really considered it until Covid how serious this was and then I couldn't look away. And so, I felt if I have a little bit of credibility after having stepped down after 12 years as the NIH Director and maybe a chance to influence a few people. I just have to try to do something to point out the dangers here and then to offer some suggestions about what individuals can do to try to get us back on track. And that's what this book is all about. And yeah, it's called The Road to Wisdom because that's really how I want to think of all this in terms of truth and science and faith and trust. They all kind of give you the opportunities to acquire wisdom. Wisdom is of course knowledge, but it's not just knowledge, it's also understanding it has a moral character to it. It involves sophisticated judgment about difficult situations where there isn't an obvious answer. We need a lot more of that, it seems we’re at short supply.

Deconvoluting Truth

Eric Topol (06:13):

Well, what I really loved about the book among many things was how you broke things down in just a remarkably thoughtful way. So truth, you have this great diagram like a target with the four different components.

in the middle, necessary truth. And then as you go further out, firmly established facts, then uncertainty and then opinion, and truth is not a dichotomous by any means. And you really got that down and you explained each of these different facets of truth with great examples. And so, this among many other things that you broke down, it wasn't just something that you read somewhere, you really had to think this through and perhaps this experience that we all went through, but especially you. But because you bring so much of the book back to the pandemic at times with each of the four domains, so that and the spider web. The spider web of where your core beliefs

are and then the ones further out on the web and you might be able to work on somebody out further periphery, but it's pretty hard if you're going to get to them in the middle where their main thing is science is untrustworthy or something like that.

Eric Topol (07:36):

So how did you synthesize these because the graphics are quite extraordinary?

Francis Collins (07:44):

Well, I will say the artist for the graphics is a remarkable graphic design student at the University of Michigan who happens to be my granddaughter. So it was nice having that ability to have my scratches turned into something actually looks like artwork. The concepts I got to say, Eric, I was feeling pretty unsure of myself. I never took a course in philosophy. I know there are people who've spent their entire careers going all the way back to Socrates and on up until now about what does truth mean and here's this scientist guy who's trying to say, well, let me tell you what I think about it. I'm glad to hear that you found these circles useful. They have been very useful for me and I hadn't thought about it much until I tried to put it in some sort of framework and a lot of the problems we have right now where somebody says, well, that might be true for you, but it's not true for me, that's fine if you're talking about an opinion, like whether that movie was really good or not.

Francis Collins (08:43):

But it's not fine if it's about an established fact, like the fact that climate change is real and that human activity is the main contributor to the fact that we've warmed up dramatically since 1950. I'm sorry, that's just true. It doesn't care how you feel about it, it's just true. So that zone of established facts is where I think we have to re-anchor ourselves again when something's in that place. I'm sorry, you can't just decide you don't like it, but in our current climate and maybe postmodernism has crept in all kinds of ways we're not aware of, the idea that there is such a thing as objective truth even seems to be questioned in some people's minds. And that is the path towards a terrible future if we can't actually decide that we have, as Jonathan Rauch calls it, a constitution of knowledge that we can depend on, then where are we?

Eric Topol (09:37):

Well, and I never heard of the term old facts until the pandemic began and you really dissect that issue and like you, I never had anticipated there would be, I knew there was an anti-science, anti-vaccine sector out there, but the fact that it would become so strong, organized, supported, funded, and vociferous, it's just looking back just amazing. I do agree with the statement you made earlier as we were talking and in the book, “the development of mRNA vaccines for Covid in record time as one of the greatest medical achievements in human history.” And you mentioned besides the Kaiser Family Foundation, but the Commonwealth Fund, a bipartisan entity saved three million lives in the US, eighteen million hospitalizations. I mean it's pretty extraordinary. So besides Covid, which we may come back to, but you bring in everything, you bring in AI. So for example, you quoted the fellow from Google who lost his job and you have a whole conversation with Blake Lemoine and maybe you can give us obviously, where is AI in the truth and science world? Where do you stand there and what were you thinking when you included his very interesting vignette?

Perspective on A.I.

Francis Collins (11:17):

Well, I guess I was trying to talk about where are we actually at the point of AGI (artificial general intelligence) having been achieved? That is the big question. And here's Blake Lemoine who claimed based on this conversation that I quote in the book between him and the Google AI apparatus called LaMDA. Some pretty interesting comments where LaMDA is talking about having a soul and what its soul looks like and it's a portal to all sorts of other dimensions, and I can sort of see why Blake might've been taken in, but I can also see why a lot of people said, oh, come on, this is of course what an AI operation would say just by scanning the internet and picking out what it should say if it's being asked about a soul. So I was just being a little provocative there. My view of AI, Eric, is that it's applications to science and medicine are phenomenal and we should embrace them and figure out ways to speed them up in every way we can.

Francis Collins (12:17):

I mean here at NIH, we have the BRAIN Initiative that's trying to figure out how your brain works with those 86 billion neurons and all their connections. We're never going to sort that out without having AI tools to help us. It's just too complicated of a problem. And look what AI is doing and things like imaging radiologists are going to be going out of business and the pathologists may not be too far behind because when it comes to image analysis, AI is really good at that, and we should celebrate that. It's going to improve the speed and accuracy of all kinds of medical applications. I think what we have to worry about, and I'm not unique in saying this, is that AI when applied to a lot of things kind of depends on what's known and goes and scrapes through the internet to pull that out. And there's a lot of stuff on the internet that's wrong and a lot of it that's biased and certainly when it comes to things like healthcare, the bias in our healthcare system, health disparities, inadequacies, racial inequities are all in there too, and if we're going to count on AI to fix the system, it's building on a cracked foundation.

Francis Collins (13:18):

So we have to watch out for that kind of outcome. But for the most part, generative AI it’s taking really exciting difficult problems and turning them into solutions, I'm all for it, but let's just be very careful here as we watch how it might be incorporating information that's wrong and we won't realize it and we'll start depending on it more than we should.

Breathtaking Advances

Eric Topol (13:42):

Yeah, no, that's great. And you have some commentary on all the major fronts that we're seeing these days. Another one that is a particularly apropos is way back when you were at Michigan and the years before that when you were warming up to make some seminal gene discoveries and cystic fibrosis being perhaps the first major one. You circle back in the book to CRISPR genome editing and how the success story to talk about some extraordinary science to be able to have a remedy, a cure potentially for cystic fibrosis. So maybe you could just summarize that. I mean that's in your career to see that has to be quite remarkable.

Francis Collins (14:32):

It is breathtaking, Eric. I mean I sort of like to think of three major developments just in the last less than 20 years that I never dreamed would happen in my lifetime. One was the ability to make stem cells from people who are walking around from a skin biopsy or a blood sample that are pluripotent. My whole lab studies diabetes, our main approach is to take induced pluripotent stem cells from people whose phenotypes we know really well and differentiate them into beta cells that make insulin and see how we can figure out how the genetics and other aspects of this determine whether something is going to work properly or not. I mean that's just astounding. The second thing is the ability to do single cell biology.

Francis Collins (15:16):

Which really 15 years ago you just had to have a bunch of cells and studying diabetes, we would take a whole eyelid and grind it up and try to infer what was there, ridiculous. Now we can look at each cell, we even can look at each cell in terms of what's its neighbor, does the beta cell next to an alpha cell behave the same way as a beta cell next to a duct? We can answer those questions, and of course the third thing is CRISPR and gene editing and of course the first version of CRISPR, which is the knockout of a gene was exciting enough, but the ability to go in and edit without doing a double stranded break and actually do a search and replace operation is what I'm truly excited about when it comes to rare genetic diseases including one that we work on progeria, which is this dramatic form of premature aging that is caused almost invariably by a C to T mutation in exon 11 of the LMNA gene and for which we have a viable strategy towards a human clinical trial of in vivo gene editing for kids with this disease in the next two years.

Eric Topol (16:24):

Yeah, it's just the fact that we were looking at potential cures for hundreds and potentially even thousands of diseases where there was never a treatment. I mean that's astounding in itself, no less, the two other examples. The fact that you can in a single cell, you can not only get the sequence of DNA and RNA and methylation and who would've ever thought, and then as you mentioned, taking white cells from someone's blood and making pluripotent stem cells. I mean all these things are happening now at scale and you capture this in the book.

On Humility and Trust

Now the other thing that you do that I think is unique to you, I don't know if it's because of your background in growing up in Staunton, Virginia, a very different type of world, but you have a lot of humility in the book. You go over how you got snickered by Bill Maher, how you had a graduate student who was fabricating images and lots of things, how you might not have communicated about Covid perhaps as well as could. A lot of our colleagues are not able to do that. They don't ever have these sorts of things happening to them. And this humility which comes across especially in the chapter on trust where you break down who do you trust, humility is one of the four blocks as you outlined, competence, integrity, and aligned value

So maybe can you give us a little brief lesson on humility?

Eric Topol (18:06):

Because it's checkered throughout the book and it makes it this personal story that you're willing to tell about yourself, which so few of us are willing to do.

Francis Collins (18:17):

Well, I don't want to sound proud about my humility. That would not be a good thing because I’m not, but thanks for raising it. I do think when we consider one of the reasons we decide to trust somebody, that it does have that humility built into it. Somebody who's willing to say, I don't know. Somebody's willing to say I'm an expert on this issue, but that other issue you just asked me about, I don't know any more than anybody else and you should speak to someone else. We don't do that very well. We tend to plunge right in and try to soak it up. I do feel when it comes to Covid, and I talk about this in the book a bit, that I was one of those trying to communicate to the public about what we think are going to be the ways to deal with this worst pandemic in more than a century.

Francis Collins (19:06):

And I wish Eric, I had said more often what I'm telling you today is the best that the assembled experts can come up with, but the data we have to look at is woefully inadequate. And so, it very well could be that what I'm telling you is wrong, when we get more data, I will come back to you as soon as we have something better and we'll let you know, but don't be surprised if it's different and that will not mean that we are jerking you around or we don't know what we're talking about. It's like this is how science works. You are watching science in real time, even though it's a terrible crisis, it's also an opportunity to see how it works. I didn't say that often enough and neither did a lot of the other folks who were doing the communicating. Of course, the media doesn't like to give you that much time to say those things as you well know, but we could have done a better job of preparing people for uncertainty and maybe there would've been less of a tendency for people to just decide, these jokers don't know what they're talking about.

Francis Collins (20:10):

I'm going to ignore them from now on. And that was part of what contributed to those 230,000 unnecessary deaths, it was just people losing their confidence in the information they were hearing. That's a source of grief from my part.

His Diagnosis And Treatment for Prostate Cancer

Eric Topol (20:24):

Well, it's great and a lesson for all of us. And the other thing that along with that is remarkable transparency about your own health, and there's several things in there, but one that coincides. You mentioned in the book, of course, you wrote an op-ed in the Washington Post back in April 2024 about your diagnosis of prostate cancer. So you touched on it in the book and maybe you could just update us about this because again, you're willing to tell your story and trying to help others by the experiences that you've been through.

Francis Collins (21:00):

Well, I sure didn't want to have that diagnosis happen, but once it did, it certainly felt like an opportunity for some education. We men aren't that good about talking about issues like this, especially when it involves the reproductive system. So going out and being public and saying, yep, I had a five year course of watching to see if something was happening, and then the slow indolent cancer suddenly decided it wasn't slow and indolent anymore. And so, I'm now having my prostate removed and I think I'm a success story, a poster boy for the importance of screening. If I hadn't gone through that process of PSA followed by imaging by MRI followed by targeted biopsies, so you're actually sampling the right place to see if something's going on. I probably would know nothing about it right now, and yet incubating within me would be a Gleason category 9 prostate cancer, which has a very high likelihood if nothing was done to become metastatic.

Francis Collins (22:03):

So I wanted that story to be out there. I wanted men who were squeamish about this whole topic to say, maybe this is something to look into. And I've heard a bunch of follow-ups from individuals, but I don't know how much of it impact it hit. I'm glad to say I'm doing really well. I'm four months out now from the surgery, it is now the case I'm pretty much back to the same level of schedule and energy that I had beforehand, and I'm very happy to say that the post-op value of PSA, which is the best measure to see whether you in fact are now cancer free was zero, which is a really nice number.

Eric Topol (22:45):

Wow. Well, the prostate is the curse of men, and I wish we could all have an automated prostatectomy so we don't have to deal with this. It's just horrible.

Francis Collins (22:58):

It was done by a robot. It wasn't quite automated, I have stab wounds to prove that the robot was actually very actively doing what it needed to do, but they healed quickly.

The Promise of Music As Therapy in Medicine

Eric Topol (23:11):

Right. Well, this gets me to something else that you're well known for throughout your career as a musician, a guitarist, a singer, and recently you hooked up with Renée Fleming, the noted opera singer, and you've been into this music is therapy and maybe you can tell us about that. It wasn't necessarily built up much in the book because it's a little different than the main agenda, but I think it's fascinating because who doesn't like music? I mean, you have to be out there if you don't enjoy music, but can you tell us more about that?

Francis Collins (23:53):

Yeah, I grew up in a family where music was very much what one did after dinner, so I learned to play keyboard and then guitar, and that's always been a source of joy and also a source of comfort sometimes when you were feeling a bit down or going through a painful experience. I think we all know that experience where music can get into your heart and your soul in a way that a lot of other things can't. And the whole field of music therapy is all about that, but it's largely been anecdotal since about World War II when it got started. And music therapists will tell you sometimes you try things that work and sometimes they don't and it's really hard to know ahead of time what's going to succeed. But now we have that BRAIN Initiative, which is pushing us into whole new places as far as the neuroscience of the brain, and it's really clear that music has a special kind of music room in the brain that evolution has put there for an important reason.

Francis Collins (24:47):

If we understood that we could probably make music therapy even more scientifically successful and maybe even get third parties to pay for it. All of this became opportunity for building a lot more visibility because of making friends withRenée Fleming, who I hadn't really known until a famous dinner party in 2015 where we both ended up singing to a trio of Supreme Court justices trying to cheer them up after a bent week. And she has become such an incredible partner in this. She's trained herself pretty significantly in neuroscience, and she's a convener and an articulate spokesperson. So over the course of that, we built a whole program called Sound Health that now has invested an additional $35 million worth NIH research to try to see how we can bring together music therapy, musician performers and neuroscientists to learn from each other, speak each other's language and see what we could learn about this particularly interesting input to the human brain that has such power on us and maybe could be harnessed to do even more good for people with chronic pain or people with PTSD, people with dementia where music seems to bring people back to life who'd otherwise seem to have disappeared into the shadows.

Francis Collins (26:09):

It's phenomenal what is starting to happen here, but we're just scratching the surface.

The Big Miss vs Hepatitis C

Eric Topol (26:14):

Well, I share your enthusiasm for that. I mean, it's something that you could think of that doesn't have a whole lot of side effects, but could have a lot of good. Yeah. Well, now before I get back to the book, I did want to cover one other relatively recent op-ed late last year that you wrote about Hepatitis C. Hepatitis C, one of the most important medical advances in the 21st century that we're squandering. Can you tell us about that? Because I think a lot of people don't realize this is a big deal.

Francis Collins (26:47):

It's a really big deal, and I confess I'm a little obsessed about it. So yes, you may regret bringing it up because I'm really going to want to talk about what the opportunity is here, and I am still the lead for the White House in an initiative to try to find the 4 million Americans who are already infected with this virus and get access to them for treatment. The treatment is fantastic, as you just said, one of the most major achievements of medical research, one pill a day for 12 weeks, 95% cure in the real world, essentially no side effects, and yet the cost is quite high and the people who need it many times do not have great healthcare and maybe also in difficult circumstances because you get hepatitis C from infected blood. And the many ways that happens these days are from shared needles from people who are experimenting with intravenous drugs, but they are family too, and many of them now recovering from that, face the irony of getting over their opioid addiction and then looking down the barrel of a really awful final couple of years dying of liver failure. I watched my brother-in-law die of hepatitis C, and it was just absolutely gruesome and heartbreaking.

Francis Collins (28:04):

So this isn't right. And on top of that, Eric, the cost of all this for all those folks who are going to get into liver failure need a transplant or develop liver cancer, this is the most common cause now of liver cancer it is astronomical in the tens of billions of dollars. So you can make a very compelling case, and this is now in the form of legislation sponsored by Senators Cassidy and Van Hollen that in a five-year program we could find and cure most of those people saving tens of thousands of lives and we would save tens of billions of dollars in just 10 years in terms of healthcare that we will not have to pay for. What's not to love here? There's a lot of things that have to be worked out to make it happen. One thing we've already done is to develop, thanks to NIH and FDA, a point of care viral RNA finger stick test for Hep C. You get an answer in less than an hour.

Francis Collins (29:00):

FDA approved that the end of June. That was a big crash program so you can do test and treat in one visit, which is phenomenally helpful for marginalized populations. The other thing we need to do is to figure out how to pay for this and this subscription model, which was piloted in Louisiana, looks like it ought to work for the whole nation. Basically, you ask the companies Gilead and AbbVie to accept a lump sum, which is more than what they're currently making for Medicaid patients and people who are uninsured and people in the prison system and Native Americans and then make the pills available to those four groups for free. They do fine. The companies come out on this and the cost per patient plummets and it gives you the greatest motivation you can imagine to go and find the next person who's infected because it's not going to cost you another dime for their medicine, it's already paid for. That's the model, and I would say the path we're on right now waiting for the congressional budget office to give the final score, it's looking pretty promising we're going to get this done by the end of this year.

The Pledge

Eric Topol (30:04):

Yeah, that's fantastic. I mean, your work there alone is of monumental importance. Now I want to get back to the book the way you pulled it all together. By the way, if anybody's going to write a book about wisdom, it ought to be you, Francis. You've got a lot of it, but you had to think through how are we going to change because there's a lot of problems as you work through the earlier chapters and then the last chapter you come up with something that was surprising to me and that was a pledge for the Road to Wisdom. A pledge that we could all sign, which is just five paragraphs long and basically get on board about these four critical areas. Can you tell us more about the pledge and how this could be enacted and help the situation?

Francis Collins (31:03):

Well, I hope it can. The initial version of this book, I wrote a long piece about what governments should do and what institutions should do and what universities should do and what K through 12 education should do. And then I thought they're not reading this book and I'm not sure any of those folks are really that motivated to change the status quo. Certainly, politicians are not going to solve our current woes. It seems that politics is mostly performance these days and it's not really about governance. So if there's going to be a chance of recovering from our current malaise, I think it's got to come from the exhausted middle of the country, which is about two thirds of us. We're not out there in the shrill screaming edges of the left and the right we're maybe tempted to just check out because it just seems so discouraging, but we're the solution.

Francis Collins (31:56):

So the last chapter is basically a whole series of things that I think an individual could start to do to turn this around. Beginning with doing a little of their own house cleaning of their worldview to be sure that we are re-anchoring to things like objective truths and to loving your neighbor instead of demonizing your neighbor. But yeah, it does go through a number of those things and then it does suggest as a way of making this not just a nice book to read, but something where you actually decide to make a commitment. Look at this pledge. I've tried the pledge out on various audiences so far and I haven't yet really encountered anybody who said, well, those are ridiculous things to ask of people. They're mostly things that make a lot of sense, but do require a commitment. That you are, for instance, you're not going to pass around information on social media in other ways unless you're sure it's true because an awful lot of what's going on right now is this quick tendency for things that are absolutely wrong and maybe anger inducing or fear inducing to go viral where something that's true almost lands with a thud.

Francis Collins (33:07):

Don't be part of that, that's part of this, but also to make an honest effort to reach out to people who have different views from you. Don't stay in your bubble and try to hear their concerns. Listen, not that you're listening in order to give a snappy response, but listen, so you're really trying to understand. We do far too little of that. So the pledge asks people to think about that, and there is a website now which will be as part of the book up on the Braver Angels website and Braver Angels is a group that has made its mission trying to bring together these divided parties across our country and I'm part of them, and you can then go and sign it there and make a public statement that this is who I am, and it will also give you a whole lot of other resources you could start to explore to get engaged in being part of the solution instead of just shaking your head. I think what we're trying to do is to get people to go beyond the point of saying, this isn't the way it should be to saying, this isn't the way I should be. I'm going to try to change myself as part of fixing our society.

Eric Topol (34:14):

Well, I'm on board for this and I hope it creates a movement. This is as you tell the stories in the book, like the fellow that you wrangled with about the pandemic and how you listened to him and it changed your views and you changed his views and this is the health of different opinions and perspectives and we got to get back there. It used to be that way more at least it wasn't always perfect, and as you said in the book, we all have some entrenched biases. We're never going to get rid of all of them, but your wisdom about the road, the pledge here is I think masterful. So I just want to pass on along and I hope listeners will go to the Brave for Angels website and sign up because if we got millions of people to help you on this, that would say a lot about a commitment to a renewed commitment to the way it should be, not the way it is right now. Well, I've covered a bunch of things, of course, Francis, but did I miss something that you're passionate about or in the book or anything that you want to touch on?

Francis Collins (35:32):

Oh my goodness, yeah. You did cover a lot of ground here, including things that I didn't pay much attention to in the book, but I was glad to talk to you about. No, I think we got a pretty good coverage. The one topic in the book that will maybe appeal particularly to believers is a whole chapter about faith because I am concerned that people of faith have been particularly vulnerable to misinformation and disinformation, and yet they stand on a foundation of principles that ought to be the best antidote to most of the meanness that's going on, and just trying to encourage them to recall that and then build upon the strength that they carry as a result of their faith traditions to try to be part of the solution as well.

Eric Topol (36:12):

I'm so glad you mentioned that. It's an important part of the book, and it is also I think something that you were able to do throughout your long tenure at NIH Director that you were able to connect to people across the aisle. You had senators and the Republicans that were so supportive of your efforts to lead NIH and get the proper funding, and it's a unique thing that you're able to connect with people of such different backgrounds, people of really deep commitment to religion and faith and everything else. And that's one of the other things that we talk about Francis here, and many times I gather is we don't have you at the helm anymore at NIH, and we're worried. We're worried because you're a unique diplomat with all this heavy wisdom and it's pretty hard to simulate your ability to keep the NIH whole and to build on it. Do you worry about it at all?

Francis Collins (37:23):

Well, I was privileged to have those 12 years, but I think it was time to get a new perspective in there, and I appreciate you saying those nice things about my abilities. Monica Bertagnolli is also a person of great skill, and I think on the hill she rapidly acquired a lot of fans by her approach, by some of her background. She's from Wyoming, she's a cancer surgeon. She's got a lot of stories to tell that are really quite inspiring. I think though it's just a very difficult time. She walked in at a point where the partisan attitudes about medical research, which we always hoped would kind of stay out of the conversation and become so prominent, a lot of it politically driven, nasty rhetoric on the heels of Covid, which spills over into lots of other areas of medical research and is truly unfortunate. So she's got a lot to deal with there, but I'm not sure I would be much better than she is in trying to continue stay on message, tell the stories about how medical research is saving lives and alleviating suffering, and we're just getting started, and she does that pretty well.

Francis Collins (38:34):

I just hope the people who need to listen are in a listening mood.

Eric Topol (38:38):

Yeah. Well, that's great to hear your perspective. Well, I can't thank you enough for our conversation and moreover for a friendship that's extended many decades now. We're going to be following not just your progeria research and all the other things that you're up to because juggling a bunch of things still, it isn't like you're slowed down at all. And thanks so much for this book. I think it's a gift. I think it's something that many people will find is a pretty extraordinary, thoughtful and easy read. I mean, it's something that I found that you didn't write it for in technical jargon. You wrote it for the public, you wrote it for non-scientists, non-medical people, and I think hopefully that's what's going to help it get legs in terms of what's needed, which is a sign the darn pledge. Thank you.

Francis Collins (39:42):

Eric, thank you. It has been a privilege being your friend for all these years, and this was a really nice interview and I appreciate that you already had carefully read the book and asked some great questions that were fun to try to answer. So thanks a lot.

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Professor Joseph Allen directs the Healthy Buildings Program at Harvard Chan School of Public Health. His expertise extends far beyond what makes buildings healthy. He has been a leading voice and advocate during the Covid pandemic for air quality and ventilation. He coined the term “Forever Chemicals” and has written extensively on this vital topic, no less other important exposures, which we covered In our wide-ranging conversation. You will see how remarkably articulate and passionate Prof Allen is about these issues, along with his optimism for solutions.

A video snippet of our conversation: buildings as the 1st line of defense vs respiratory pathogens. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with External Links and Links to Audio

Eric Topol (00:00:06):

Well, hello. It's Eric Topol from Ground Truths and I am just delighted to have with me, Joseph Allen from the Harvard School of Public Health, where he directs the Healthy Buildings Program that he founded and does a whole lot more that we're going to get into. So welcome, Joe.

Joseph Allen (00:00:24):

Thanks. It's great to be here. I appreciate the invitation.

Joe Allen’s Background As A Detective

Eric Topol (00:00:28):

Well, you have been, as I've learned, rocking it for many years long before the pandemic. There's quite a background about you having been a son of a homicide detective, private eye agency, and then you were going to become an FBI agent. And the quote from that in the article that's the Air Investigator is truly a classic. Yeah, you have in there, “I guarantee I'm the only public health student ever to fail an FBI lie detector polygraph in the morning and start graduate school a few hours later.” That's amazing. That's amazing.

Joseph Allen (00:01:29):

All right. Well, you've done your deep research apparently. That's good. Yeah, my dad was a homicide detective and I was a private investigator. That's no longer my secret. It's out in the world. And I switched careers and it happened to be the day I took the polygraph at the FBI headquarters in Boston, was the same day I started graduate studies in public health.

Sick vs Healthy Buildings (Pre-Covid)

Eric Topol (00:01:53):

Well, you're still a detective and now you're a detective of everything that can hurt us or help us environmentally and my goodness, how grateful we are that you change your career path. I don't know anyone who's had more impact on buildings, on air, and we're going to get into chemicals as well. So if we go back a bit here, you wrote a book before the pandemic, talk about being prescient. It’s called Healthy Buildings: How Indoor Spaces Can Make You Sick - or Keep You Well with John Macomber, your co-author. What was it that gave you the insight to write a book before there was this thing called Covid?

Joseph Allen (00:02:41):

Yeah, well, thanks for making the connection too, my past career to current career. For many years, I thought there wasn't a connection, but I agree. There's actually a lot of similarities and I also am really appreciative. I am lucky I found the field of Public Health, it's clearly where I belong. I feel like I belong here. It's a place to make an impact that I want to make in my career. So yeah, the Healthy Buildings book, we started writing years before the pandemic and was largely motivated by, I think what you and others and other people in my field have known, is that buildings have an outsized impact on our health. Yet it's not something that comes to the forefront when you ask people about what matters for their health. Right, I often start presentations by asking people that, what constitutes healthy living? They'll say, I can't smoke, I have to eat well.

(00:03:30):

I have to exercise. Maybe they'll say, outdoor pollution’s bad for you. Very few people, if any, will say, well, the air I breathe inside my building matters a lot. And over the years I had started my public health career doing forensic investigations of sick buildings. People really can get sick in buildings. It can be anything from headaches and not being able to concentrate all the way to cancer clusters and people dying because of the building. And I've seen this in my career, and it was quite frustrating because I knew, we all knew how to design and operate buildings in a way that can actually keep people healthy. But I was frustrated like many in my field that it wasn't advancing. In other words, the science was there, but the practice wasn't changing. We were still doing things the wrong way around ventilation, materials we put in our building, and I would lecture over and over and give presentations and I decided I want to try something new.

(00:04:22):

I do peer-reviewed science. That's great. I write pieces like you for the public, and I thought we'd try a longer form piece in a book, and it's published by Harvard Press. John Macomber for those who know is a professor at Harvard Business School who's an expert in real estate finance. So he'd been talking about the economic benefits of healthier buildings and some hand waving as he describes around public health. I've been talking about the public health benefits and trying to wave an economic argument. We teamed up to kind of use both of our strengths to, I hope make a compelling case that buildings are good for health and they're also just good business. In other words, try to break down as many barriers as we can to adoption. And then the book was published right as Covid hit.

Indoor Air Quality and Cognition

Eric Topol (00:05:05):

Yeah. I mean, it's amazing. I know that typically you have to have a book almost a year ahead to have it in print. So you were way, way ahead of this virus. Now, I'm going to come back to it later, but there were two things beyond the book that are pretty striking about your work. One is that you did all these studies to show with people wearing sensors to show that when the levels of CO2 were high by sensors that their cognition indoors was suffering. Maybe you could just tell us a little bit about these sensors and why aren't we all wearing sensors so that we don't lose whatever cognitive power that we have?

Joseph Allen (00:05:56):

Well, yeah. First I think we will start having these air quality sensors. As you know, they're starting to become a lot more popular. But yeah, when I first joined the faculty full-time at Harvard, one of the first studies I conducted with my team was to look at how indoor air quality influences cognitive function. And we performed a double-blind study where we took people, office workers and put them in a typical office setting. And unbeknownst to them, we started changing the air they were breathing in really subtle ways during the day, so they didn't know what we were doing. At the end of the day, we administered an hour and a half long cognitive function battery, and like all studies, we control for things like caffeine intake, baseline cognitive performance, all the other factors we want to account for. And after controlling for those factors in a double-blind study, we see that indoor air quality, minor improvements to indoor air quality led to dramatic increases in cognitive function test scores across domains that people recognize as important for everyday life.

(00:06:59):

How do you seek out and utilize information? How do you make strategic decisions? How do you handle yourself during a crisis and importantly recover after that crisis? I don't mean the world's ending crisis. I mean something happens at work that's stressful. How do you handle that and how do you respond? Well, it turns out that amongst all the factors that influence how we respond there, indoor air quality matters a lot. We call that study the COGfx Study for cognitive function. We replicated it across the US, we replicated it across the world with office workers around the world, and again, always showing these links, the subtle impact of indoor air quality on cognitive function performance. Now, that also then starts to be the basis for some of the economic analysis we perform with my colleague at Harvard Business School. We say, well, look, if you perform this much better related to air quality, what would happen if we implemented this at scale in a business?

(00:07:51):

And we estimate that there are just massive economic gains to be had. On a per person basis, we found and published on this, that's about $6,000 to $7,000 per person per year benefit across a company. It could lead to 10% gains to the bottom line performance of the company. And again, I'm a public health professor. My goal is to improve people's health, but we add a lens, mental health, brain health is part of health, and we add the economic lens to say, look, this is good for a worker of productivity and the costs are downright trivial when you compare it against the benefits, even just including the cognitive function benefits, not even including the respiratory health benefit.

Eric Topol (00:08:33):

And I mean, it's so striking that you did these studies in a time before sensors were, and they still are not widely accepted, and it really helped prove, and when we start to fall asleep in a group session indoors, it may not just be because we didn't have enough sleep the night before, right.

Joseph Allen (00:08:56):

It's funny you say that. I talk about that too. It's like, do we actually need the study to tell us to quantify what we've all experienced these bad conference rooms, you get tired, you can't concentrate, you get sleepy while you're driving your car. Yeah, a whole bunch of other factors. Maybe the speaker's boring, but a key factor is clearly indoor air quality and things like good ventilation, the chemical load in the space are all contributing.

Eric Topol (00:09:20):

Yeah. No, it's pretty darn striking. Now we're going to get into the pandemic, and this of course is when your work finally crystallized that you've been working on this for years, and then finally your collaboration with some of the aerosol experts. It was a transdisciplinary synergy that was truly extraordinary. And when you were on 60 Minutes last October, you said, “Think about the public health gains we've made over the past hundred years. We've made improvements to water quality, outdoor air pollution, our food safety, we've made improvements to sanitation: absolute basics of public health. Where has indoor air been in that conversation?” You brought it to us. I mean, you led the Lancet Commission on this. You've done a White House Summit keynote. You had a lot of influence. Why did it take us to finally wake up to this issue that you've been working on for years?

Covid is Airborne, Denial

Joseph Allen (00:10:31):

Yeah. Well, I appreciate that, but I also liked what you started with. I mean, there's been a lot of us pulling on this, and I think one of the magical moments, if you could say that when the pandemic happened was that it forced these collaborations and forced a lot of us in our field to be a bit more vocal. And even that comment about the gains we made in public health, that comes from an article that we co-authored with 40 plus scientists around the world in science, trying to drive home the point that we've ignored one of the key factors that determines our health. We were all frustrated at the beginning of the pandemic. The first piece I wrote was January 2020, talking about healthy buildings as the first line of defense, airborne spread, ventilation, filtration. I could not get it published. I could not get it published.

(00:11:20):

So I moved it to an international paper. I wrote it in the Financial Times in early February, but it wasn't until mid-March that the Times took my piece on this airborne spread buildings ventilation. At the same time, we know people like Linsey Marr, Rich Corsi, many others, Shelly Miller out there publishing, doing the fundamental research, all trying to elevate, and I think we started to find each other and say, hey, someone's trying to hit the medical journals. We're not landing there. I'm trying to hit the Times, and we’re not landing there. We're trying to get the reporters to pay attention. It's not landing there. Let's team up. Let's write these joint pieces. And I think what happened was you saw the benefit of the collective effort and interdisciplinary expertise, right? We could all start to come together, start instead of having these separate voices, a little bit of a unified voice despite important scientific minor disagreements, but start to say, hey, we started elevate each other and said, this is really important. It's the missing component of the messaging in the early days of the pandemic, and to know how to defend yourself.

Eric Topol (00:12:20):

Well, I think a lot of people think the big miss, and I know you agree, was the lack of recognition of aerosol transmission instead of just liquid droplets. But what you brought to this was really your priors on the buildings themselves and the ventilation systems and air quality that was highly, I mean, critical to it isn't just the aerosol, it's obviously how buildings are set up. Now, there's an amazing piece of course that appeared in the summer of 2021 called the Air Investigator, which profiled you, and in it brings up several things that finally are, we're starting to get our act together. I mean, ultimately there was in May 2023 years later, the CDC says, we're going to do something about this. Can you tell us what was this very distinct new path that the CDC was at least saying? And also couple that with whatever action if or not action has been taken.

Joseph Allen (00:13:33):

Yeah. So there really was a monumental shift that took, it was years in development, but we finally won the argument, collectively that airborne spread was the dominant mode of transmission. Okay, we got that. Then the question is, well, what changes? Do we actually get guidance here? And that took a little bit longer. I give Rochelle Walensky a lot of credit when she came into the CDC, we talked with her about this. That's when you start to first see ventilation starts showing up and the guidance, including guidance for schools. So I think that was a big win, but still no one was willing to set an official target or standard around higher ventilation rates. So that's important. Early in the pandemic, some people started to hear a message, yes, ventilation is important. What's the obvious next question, well, how much, what do I need? So in the summer of 2020, actually Shelly Miller and I collaborated on this.

(00:14:23):

We published some guidance on ventilation targets for schools. We said four to six air changes per hour (ACH) and target that. Well, it wasn't until 2023, spring of 2023 that you mentioned that CDC published target ventilation rates, and they went with five air changes per hour, which is right where we were talking about in summer 2020. It's what the Lancet of COVID-19 Commission adopted, but it's momentous in this way. It's the first time in CDCs history they've ever published a ventilation rate target for health. Now, I know this seems slow at the time, and it was, but if we think about some of the permanent gains that will come out of the pandemic. Pandemic changes society and science and policy and practice this, we are never going back. Now buildings will be a first line of defense for respiratory pathogens going forward that can no longer be ignored. And now we have the published target by CDC. That's a big deal because it's not just a recognition, but there's actually something to shoot for out there. It's a target I happen to like, I think there are differences between different scientists, but ultimately we've lifted the floor and said, look, we actually have to raise ventilation rates and we have something to shoot for. The public needed that kind of guidance a lot earlier, of course, but it was a big deal that it happened. It’s just too bad it took until spring 2023.

Eric Topol (00:15:46):

Yeah, I certainly agree that it was momentous, but a year plus later, has there been any change as a result of this major proclamation, if you will?

Joseph Allen (00:15:59):

Well, I actually see a lot of change from a practitioner level, but I want to talk about it in two aspects. I see a lot of schools, universities, major companies that have made this shift. For example, in the 60 Minutes piece, I talk that I advised Amazon and globally they're measuring indoor air quality with real-time sensors in their buildings. I've worked with hundreds of school districts that have made improvements to indoor air quality. I know companies that have shifted their entire approach to how they design and operate their buildings. So it's happening. But what really needs to happen, Eric, if this movement is going to benefit everyone, is that these targets need to be codified. They need to go into building codes. It can't just be, oh, I've heard about this. So I made the decision. I have the resources and the money to make this improvement.

(00:16:44):

To create a healthy building or a healthy school, we need to be sure this gets built into our code. So it just becomes the way it's done. That is not happening. There are some efforts. There are some bills at the national level. Some states are trying to pass bills, and I have to say, this is why I'm optimistic. It feels very slow. I'm as frustrated as anybody. I wanted this done before the pandemic. As soon as the pandemic hit, we saw it. We knew what we needed to get done. It didn't happen. But if we think about the long arc here and the public health gains we're actually, it's remarkable to me that we actually have bills being introduced around indoor air quality that ASHRAE has set a new health focused target for the first time really in their history. CDC, first time. New buildings going up in New York City designed to these public health targets. That's really different. I've been in this field for 20 plus years. I've never seen anything like it. So the pace is still slow, but it really is happening. But it has to reach everybody, and the only way that's going to happen is really this gets into building codes and performance standards.

The Old Efficient Energy Buildings

Eric Topol (00:17:52):

Yeah. Well, I like your optimistic perspective. I do want to go back for a second, back decades ago there was this big impetus to make these energy efficient buildings and to just change the way the buildings were constructed so that there was no leak and it kind of set up this problem or exacerbated, didn't it?

Joseph Allen (00:18:19):

Yeah. I mean, I've written about this a lot. I write in the book our ventilation standards, they've been a colossal mistake. They have cost the public in terms of its health because in the seventies, we started to really tighten up our building envelopes and lower the ventilation rates. The standards were no longer focused on providing people with a healthy indoor space. As I write in the book, they were targeted towards minimally acceptable indoor air quality, bare minimums. By the way that science is unequivocal, is not protective of health, not protective against respiratory pathogens, doesn't promote good cognitive function, not good for allergies. These levels led to more illness in schools, more absences for teachers and students, an absolute disaster from a public health standpoint. We've been in this, what I call the sick building era since then. Buildings that just don't bring in enough clean outdoor air. And now you take this, you have a building stock for 40 years tighter and tighter and tighter bumps up against a novel virus that spread nearly entirely indoors. Is it any wonder we had, the disaster we had with COVID-19, we built these bills. They were designed intentionally with low ventilation and poor filtration.

Optimal Ventilation and Filtration

Eric Topol (00:19:41):

Yeah. Well, it's extraordinary because now we've got to get a reset and it's going to take a while to get this done. We'll talk a bit about cost of doing this or the investment, if you will, but let's just get some terms metrics straight because these are really important. You already mentioned ACH, the number of air changers per hour, where funny thing you recommended between four and six and the CDC came out with five. There's also the minimum efficiency reporting value (MERV). A lot of places, buildings have MERV 8, which is insufficient. We need MERV 13. Can you tell us about that?

Joseph Allen (00:20:23):

Yeah, sure. So I think when we think about how much, you have two ways to capture these respiratory particles, right? Or get rid of them. One is you dilute them out of the building or you capture them on filters. You can inactivate them through UV and otherwise. But let's just stay on the ventilation and filtration side of this. So the air changing per hour is talking about how often the air is change inside. It's an easy metric. There are some strengths to it, there's some weaknesses, but it's intuitive and I'll you some numbers so you can make sense of this. We recommended four to six air changes per hour. Typical home in the US has half an air change per hour. Typical school designed to three air changes per hour, but they operate usually at one and a half. So we tried to raise this up to four, five, or six or even higher. On the filtration side, you mentioned MERV, right? That's just a rating system for filters, and you can think about it this way. Most of the filters that are in a building are cheap MERV 8 filters, I tend to think of them as filters that protect the equipment. A MERV 13 filter may capture 80 or 90% of particles. That's a filter designed to protect people. The difference in price between a MERV 8 and a MERV 13 is a couple of bucks.

(00:21:30):

And a lot of the pushback we got early in the pandemic, some people said, well, look, there's a greater resistance from the better filter. My fan can't handle it. My HVAC system can't handle it. That was nonsense. You have low pressure drop MERV 13 filters. In other words, there really wasn't a barrier. It was a couple extra bucks for a filter that went from a MERV 8 might capture 20 or 30% to a filter, MERV 13 that captures 80 or 90% with very little, if any impact on energy or mechanical system performance. Absolute no-brainer. We should have been doing this for decades because it also protects against outdoor air pollution and other particles we generate indoors. So that was a no-brainer. So you combine both those ventilation filtration, some of these targets are out there in terms of air change per hour. You can combine the metric if we want to get technical to talk about it, but basically you're trying to create an overall amount of clean air. Either you bring in fresh outdoor air or you filter that air. It really is pretty straightforward, but we just didn't have some of these targets set and the standards we're calling for these minimum acceptable levels, which we're not protective of health.

Eric Topol (00:22:37):

So another way to get better air quality are these portable air cleaners, and you actually just wrote about that with your colleagues in the Royal Society of Chemistry, not a journal that I typically read, but this was an important article. Can you give us, these are not very expensive ways to augment air quality. Can you tell us about these PACs ?

Joseph Allen (00:23:06):

These portable air cleaners (PACs), so the same logic applies if people say, well, I can't upgrade my system. That's not a problem for very low cost, you could have, these devices are essentially a fan and a filter, and the amount of clean air you get depends on how strong the fan is and how good the filter is. Really pretty simple stuff here, and you can put one of these in a room if it's sized right. My Harvard team has built tools to help people size this. If you're not quite sure how to do it, we have a technical explainer. Really, if you size it right, you can get that four, five or six air changes per hour, very cheap and very quickly. So this was a tool I thought would be very valuable. Rich Corsi and I wrote about this all through the summer of 2020 to talk about, hey, a stop gap measure.

(00:23:50):

Let's throw out some of these portable air cleaners. You increase the air changes or clean air delivery pretty effectively for very low cost, and they work. And now the paper we just published in my team a couple of days ago starts to advance this more. We used a CFD model, so computational fluid dynamics. Essentially, you can look at the tracers and the airflow patterns in the room, and we learn a couple things that matter. Placement matters, so we like it in the center of the room if you can or as close as possible. And also the airflow matters. So the air cleaners are cleaning the air, but they're also moving the air, and that helps disperse these kind of clouds or plumes when an infected person is breathing or speaking. So you want to have good ventilation, good filtration. Also a lot of air movement in the space to help dilute and move around some of these respiratory particles so that they do get ventilated out or captured in a filter.

Eric Topol (00:24:40):

Yeah. So let me ask you, since we know outdoors are a lot safer. If you could do all these things indoors with filtration, air changing the quality, can you simulate the outdoors to get rid of the risk or markedly reduce the risk of respiratory viruses like SARS-CoV-2 and others?

Joseph Allen (00:25:04):

Yeah, you can't drop it to zero. There's no such thing as zero risk in any of these environments. But yeah, I think some of the estimates we've seen in my own team has produced in the 60-70% reduction range. I mean, if you do this right with really good ventilation filtration, you can drop that risk even further. Now, things like distancing matter, whether or not somebody's wearing a mask, these things are all going to play into it. But you can really dramatically drop the risk by handling just the basics of ventilation and filtration. And one way to think about it is this, distance to the infector still matters, right? So if you and I are speaking closely and I breathe on you, it's going to be hard to interrupt that flow. But you can reduce it through good ventilation filtration. But really what it's doing also is preventing super spreading events.

(00:25:55):

In other words, if I'm in the corner of a room and I'm infectious and you're on the other side, well if that room is sealed up pretty good, poor ventilation, no filtration, the respiratory aerosols are going to build up and your risk is going to increase and we're in there for an hour or two, like you would be in a room or office and you're exposed to infectious aerosol. With good ventilation filtration, those respiratory particles don't have a chance to reach you, or by the time they do, they're much further diluted. Linsey Marr I think was really great early in the pandemic by talking about this in terms of cigarette smoke. So a small room with no ventilation filtration, someone smoking in the corner, yeah, it's going to fill up over time with smoke you're breathing in that secondhand smoke. In a place with great ventilation filtration, that's going to be a lot further reduced, right? You're not going to get the buildup of the smoke and smoke particles are going to operate similarly to respiratory particles. So I think it's intuitive and it's logical. And if you follow public health guidance of harm reduction, risk reduction, if you drop exposure, you drop risk.

(00:26:58):

The goal is to reduce exposure. How do we do that? Well, we can modify the building which is going to play a key role in exposure reduction.

Eric Topol (00:27:06):

Now, to add to this, if I wear a sensor or have a sensor in the room for CO2, does that help to know that you're doing the right thing?

Joseph Allen (00:27:17):

Yeah, absolutely. So people who are not familiar with these air quality sensors. They're small portal air quality sensors. One of the things they commonly measure is carbon dioxide. We're the main source of CO2 inside. It's a really good indicator of ventilation rate and occupancy. And the idea is pretty simple. If the CO2 is low, you don't have a buildup of particles from the respiratory tract, right? And CO2 is a gas, but it's a good indicator of overall ventilation rate. This room I'm in right now at the Harvard School of Public Health has air quality sensors. We have this at Harvard Business School. We have it at the Harvard Health Clinics. Many other places are doing it, Boston Public schools have real-time air quality monitors. Here's the trick with CO2. So first I'll say we have some guidance on this at the Harvard Healthy Buildings page, if people want to go look it up, how to choose an air quality sensor, how to interpret CO2 levels.

Carbon Dioxide Levels

(00:28:04):

But here's a way to think about it. We generally would like to see CO2 levels less than 800 parts per million. Historically, people in my field have said under 1,000 is okay. We like to see that low. If your CO2 is low, the risk is low. If your CO2 is high, it doesn't necessarily mean your risk is high because that's where filtration can come in. So let me say that a little bit better. If CO2 is low, you're diluting enough of the respiratory particles. If it's high, that means your ventilation is low, but you might have excellent filtration happening. Either those MERV 13 filters we talked about or the portable air cleaners. Those filters don't capture CO2. So high CO2 just means you better have a good filter game in place or the risk is going to be high. So if you CO2 is low, you're in good shape. If it's high, you don't quite know. But if you have bad filtration, then the risk is going to be much higher.

Eric Topol (00:29:01):

I like that 800 number because that's a little lower than some of the other thresholds. And why don't we do as good as we can? The other question about is a particulate matter. So we are worried about the less than 5 microns, less than 2.5 microns. Can you tell us about that and is there a way that you can monitor that directly?

Joseph Allen (00:29:25):

Sure. A lot of these same sensors that measure CO2 also measure PM 2.5 which stands for particular matter. 2.5 microns is smaller, one of the key components of outdoor air pollution and EPA just set new standards, right? WHO has a standard for 5 microgram per cubic meter. EPA just lowered our national outdoor limit from 12 to 9 microgram per cubic meter. So that's a really good indicator of how well your filters are working. Here again, in a place like this or where you are, you should see particle levels really under 5 microgram per cubic meter without any major source happening. What's really interesting about those like the room I'm in now, when the wildfire smoke came through the East coast last year, levels were extraordinary outside 100, 200, 300 microgram per cubic meter. But because we have upgraded our filters, so we use MERV 15 here at Harvard, the indoor levels of particles stayed very low.

(00:30:16):

So it shows you how the power of these filters can actually, they do a really good job of capturing particles, whether it be from our lungs or from some other source. So you can measure this, but I'll tell you what's something interesting, if you want to tie it into our discussion about standards. So we think about particles. We have a lot of standards for outdoor air pollution. So there's a national ambient air quality standard 9 microgram per cubic meter. We don't have standards for indoor air quality. The only legally enforceable standard for indoor particles is OSHA's standard, and it's 5,000 microgram per cubic meter 5,000.

(00:30:59):

And it's absurd, right? It's an absurdity. Here we are EPAs, should it be 12, should it be 9, or should it be 8? And for indoors, the legally enforceable limit for OSHA 5,000. So it points to the big problem here. We talked about earlier about the need for these standards to codify some of this. Yes, we have awareness from the public. We have sensors to measure this. We have CDC now saying what we were saying with the Lancet COVID-19 Commission and elsewhere.

This is big movement, but the standards then need to come up behind it and get into code and new standards that are health focused and health based. And we have momentum, but we can't lose it right now because it's the first time in my career I felt like we're on the cusp of really getting this and we are so close. But of course it's always in danger of slipping through our fingers.

Regulatory Oversight for Air

Eric Topol (00:31:45):

Well, does this have anything to do with the fact that in the US there's no regulatory oversight over air as opposed to let's say Japan or other places?

Joseph Allen (00:31:57):

Yeah, I mean, we have regulatory oversight of outdoor air. That's EPA. There's a new bill that was introduced to give EPA more resources to deal with indoor air. EPA has got a great indoor air environments division, but it doesn't have the legally enforceable mandate or statute that we have for outdoor. So they'd give great guidance and have for a long time. I really like that group at EPA, but there's no teeth behind this. So what we have is worker health protections at OSHA to its own admission, says its standards are out of date. So we need an overhaul of how we think about the standards. I like the market driven approach. I think that's being effective, and I think we can do it from voluntary standards that can get adopted into code at the municipal level. I think that's a real path. I see it happening. I see the influence of all this work hitting legislators. So that's where I think the most promising path is for real change.

The Risks of Outdoor Air Pollution

Eric Topol (00:33:03):

Yeah, I think sidestepping, governmental teeth, that probably is going to be a lot quicker. Now, before we get to the cost issue, I do want to mention, as you know very well, the issue of air pollution in Science

a dedicated issue just a few weeks ago, it brought up, of course, that outdoor air pollution we've been talking about indoor is extraordinary risk for cancer, dementia, diabetes, I mean everything. Just everything. And there is an interaction between outdoor pollution and what goes on indoor. Can you explain basically reaffirm your concern about particulate matter outdoors, and then what about this interaction with what goes on indoors?

Joseph Allen (00:33:59):

Yeah, so it's a great point. I mean, outdoor pollution has been one of the most studied environmental pollutants we know. And there's all of these links, new links between Alzheimer's, dementia, Parkinson's disease, anxiety, depression, cardiovascular health, you named it, right? I've been talking about this and very vocal. It's in the book and elsewhere I called the dirty secret of outdoor air pollution. The reality is outdoor air pollution penetrates indoors, and the amount depends on the building structure, the type of filters you have. But let's take an infiltration value of say 50%. So you have a lot of outdoor air pollution, maybe half of that penetrates inside, so it's lower, the concentration is lower, but 90% of the breaths you take are indoor. And if you do the math on it, it's really straightforward. The majority of outdoor air pollution you breathe happens inside.

(00:34:52):

And people, I think when they hear that think, wait, that can't be right. But that's the reality that outdoor pollution comes inside and we're taking so many breaths inside. Your total daily dose of outdoor air pollution is greater from the time you spend inside. I talk about this all the time. You see any article about outdoor air pollution, what's the cover picture? It's someone outside, maybe they're wearing a mask you can't really see. It's smoky hazy. But actually one of the biggest threats is what's happening inside. The nice thing here, again, the solutions are pretty simple and cost-effective. So again, upgrade from MERV 8 to MERV 13, a portable air cleaner. We are just capturing particles on a filter basic step that can really reduce the threat of outdoor air pollution inside. But it's ignored all the time. When the wildfire smoke hit New York City. New York City's orange, I called colleagues who are in the news business.

(00:35:48):

We have to be talking about the indoor threat because the guidance was good, but incomplete. Talk about Mayor Adams in New York City. Go inside, okay, that's good advice. And go to a place that has good filtration or they should have been giving out these low cost air cleaners. So just going inside isn't going to protect your lungs unless you're actually filtering a lot more of that air coming in. So trying to drive home the point here that actually we talk about these in silos. Well, wildfire smoke and particles, Covid and respiratory particles, we're all talking about these different environmental issues that harm our health, but they're all happening through or mediated by the building performance. And if we just get the building performance right, some basics around good ventilation, good filtration, you start to address multiple threats simultaneously. Outdoor air pollution, wildfire smoke, allergens, COVID-19, influenza, RSV, better cognitive function performance, anxiety. You start addressing the root cause or one of the contributors and buildings we can then start to leverage as a true public health tool. We have not taken advantage of the power of buildings to be a true public health tool.

Eric Topol (00:36:59):

Oh, you say it so well, and in fact your Table on page 44 in Healthy Buildings , we’ll link it because it shows quantitatively what you just described about outdoor and indoor cross fertilization if you will. Now before leaving air pollution outdoors, indoors, in order for us to affect this transformation that would markedly improve our health at the public health individual level, we're talking about a big investment. Can you put that in, you did already in some respects, but if we did this right in every school, I think in California, they're trying to mandate that in schools, in the White House, they're mandating federal buildings. This is just a little piece of what's needed. This would cost whatever trillions or hundreds of billions of dollars. What would it take to do this? Because obviously the health benefits would be so striking.

What’s It Gonna Cost?

Joseph Allen (00:38:04):

Well, I think one of the issues, so we can talk about the cost. A lot of the things I'm talking about are intentionally low cost, right? You look at the Lancet of COVID-19 Commission, our report we wrote a report on the first four healthy building strategies every building should pursue. Number one commission your building that's giving your building a tune-up. Well, guess what? That not only improves air quality, it saves energy and therefore saves money. It actually becomes cost neutral. If not provides an ROI after a couple of years. So that's simple. Increase the amount of outdoor air ventilation coming in that has an energy cost, we've written about this. Improved filtration, that's a couple bucks, really a couple bucks, this is small dollars or portable air cleaners, not that expensive. I think one of the big, and Lawrence Berkeley National Lab has written this famous paper people like to cite that shows there's $20 billion of benefits to the US economy if we do this.

(00:38:59):

And I think it points to one of the problems. And what I try to address in my book too, is that very often when we're having this conversation about what's it going to cost, we don't talk about the full cost benefit. In other words, we say, well, it's going to cost X amount. We can't do that. But we don't talk about what are the costs of sick buildings? What are the costs of kids being out of school for an entire year? What are the costs of hormonal disruption to an entire group of women in their reproductive years due to the material choices we make in our buildings? What are the costs to outdoor air pollution and cardiovascular disease, mental health? Because we don't have good filters in our buildings that cost a couple dollars. So in our book, we do this cost benefit analysis in the proforma in our book, we lay out what the costs are to a company. We calculate energy costs. We say these are the CapEx costs, capital costs for fixed costs and the OpEx costs for operating expenditures. That's a classic business analysis. But we factor in the public health benefits, productivity, reduced absenteeism. And you do that, and I don't care how you model it, you are going to get the same answer that the benefits far outweigh the cost by orders of magnitude.

Eric Topol (00:40:16):

Yeah, I want to emphasize orders of magnitude. Not ten hundred, whatever thousand X, right?

Joseph Allen (00:40:23):

What would be the benefit if we said we could reduce influenza transmission indoors in schools and offices by even a small percent because we improve ventilation and filtration? Think of the hospitalization costs, illness costs, out of work costs, out of school costs. The problem is we haven't always done that full analysis. So the conversation gets quickly to well, that's too much. We can't afford that. I always say healthy buildings are not expensive. Sick buildings are expensive. Totally leave human health out of that cost benefit equation. And then it warps this discussion until you bring human health benefits back in.

Forever Chemicals

Eric Topol (00:40:58):

Well, I couldn't agree more with you and I wanted to frame this by giving this crazy numbers that people think it's going to cost to the reality. I mean, if there ever was an investment for good, this is the one that you've outlined so well. Alright, now I want to turn to this other topic that you have been working on for years long before it kind of came to the fore, and that is forever chemicals. Now, forever chemicals, I had no idea that back in 2018 you coined this term. You coined the term, which is now a forever on forever chemicals. And basically, this is a per- and polyfluoroalkyl substances (PFAS), but no one will remember that. They will remember forever chemicals. So can you tell us about this? Because this of course recently, as you know well in May in the New Yorker, there was an expose of 3M, perhaps the chief offender of these. They're everywhere, but especially they were in 3M products and continue to be in 3M products. Obviously they've been linked with all kinds of bad things. What's the story on forever chemicals?

Joseph Allen (00:42:14):

Yeah, they are a class of chemicals that have been used for decades since the forties. And as consumers, we like them, right? They're the things that make your raincoat repel rain. It makes your non-stick pan, your scrambled eggs don't stick to the pan. We put them on carpets for stain resistance, but they came with a real dark side. These per- and polyfluoroalkyl substances, as I say, a name only a chemist could love have been linked with things like testicular cancer, kidney cancer, interference with lipid metabolism, other hormonal disruption. And they are now a global pollutant. And one of the reasons I wrote the piece to brand them as forever chemicals was because I'm in the field of environmental health. We had been talking about these for a long time and I just didn't hear the public aware or didn't capture their attention. And part of it, I think is how we talk about some of these things.

(00:43:14):

I think a lot about this. Per- and polyfluoroalkyl substances, no one's going to, so the forever chemicals is actually a play on their defining feature. So these chemicals, these stain repellent chemicals are characterized by long chains of the carbon fluorine bond. And when we string these together that imparts this and you put them on top of a product that imparts the property of stain resistance, grease resistance, water resistance, but the carbon fluorine bond is the strongest in all of organic chemistry. And these chains of the carbon fluorine bond never fully break down in the environment. And when we talk in my field about persistent organic pollutants, we talk about chemicals that break down on the order of decades. Forever chemicals don't break down. They break down the order of millennia. That's why we're finding them everywhere. We know they're toxic at very low levels. So the idea of talking about forever chemicals, I wanted to talk about their foreverness.

(00:44:13):

This is permanent. What we're creating and the F and the C are the play on the carbon-fluorine bond and I wrote an article trying to raise awareness about this because some companies that have produced these have known about their toxicity for decades, and it's just starting the past couple of years, we're just starting to pay attention to the scale of environmental pollution. Tens of millions of Americans have forever chemicals in their drinking water above the safe limit, tens of millions. I worked as an expert in a big lawsuit for the plaintiffs that were drinking forever chemicals in their water that was dumped into the drinking water supply by a manufacturing company. I met young men with testicular cancer from drinking forever chemicals in their water. These really has escaped the public's consciousness, it wasn't really talked about. Now of course, we know every water body, we use these things in firefighting foams or every airport has water pollution.

(00:45:17):

Most airports do. Firefighters are really concerned about this, high rates of cancer in the firefighter population. So this is a major problem, and the cleanup is not straightforward or easy because they're now a global pollutant. They persist forever. They're hard to remediate and we're stuck with them. So that's the downside, I can talk about the positives. I try to remain an optimist or things we're doing to try to solve this problem, but that's ultimately the story. And my motivation was I just to have people have language to be able to talk about this that didn't require a degree in organic chemistry to understand what they were.

Eric Topol (00:45:52):

Yeah, I mean their pervasiveness is pretty scary. And I am pretty worried about the fact that we still don't know a lot of what they're doing in terms of clinical sequela. I mean, you mentioned a couple types of cancer, but I don't even know if there is a safe threshold.

Joseph Allen (00:46:16):

Eric, I'll tell you one that'll be really interesting for you. A colleague of mine did a famous study on forever chemicals many years ago now and found that kids with higher levels of forever chemicals had reduced vaccine effectiveness related to these chemicals. So your point is, right, a lot of times we're using these industrial chemicals. We know a couple endpoints for their affecting our bodies, but we don't know all of them. And what we know is certainly alarming enough that we know enough to know we shouldn't be using them.

Eric Topol (00:46:51):

And you wrote another masterful op-ed in the Washington Post, 6 forever chemical just 10,000 to go. Maybe you could just review what that was about.

Joseph Allen (00:47:02):

Yeah, I've been talking a lot about this issue I call chemical whack-a-mole. So forever chemical is the perfect example of it. So we finally got people's attention on forever chemicals. EPA just regulated 6 of them. Well, guess what? There are 10,000 if not many more than that. Different variants or what we call chemical cousins. Now that's important for this reason. If you think about how we approach these from a regulatory standpoint, each of the 10,000 plus forever chemicals are treated as different. So by the time EPA regulates 6, that's important. It does free up funding for cleanup and things like this. But already the market had shifted away from those 6. So in other words, in the many thousand products that still use forever chemicals, they're no longer using those 6 because scientists have told people these things are toxic years ago. So they switch one little thing in the chemical, it becomes a new chemical from a regulatory perspective.

(00:47:57):

But to our bodies, it's the same thing. This happens over and over. This has happened with pesticides. It happens with chemicals and nail polish. It happens in chemicals in e-cigarettes. It happens with flame retardant chemicals. I wrote a piece in the Post maybe six years ago talking about chemical whack-a-mole, and this problem that we keep addressing, these one-off, we hit one, it changes just slightly. Chemical cousin pops up, we hit that one. Five years later, scientists say, hey, the next one doesn't look good either. We're doing this for decades. It's really silly. It's ineffective, it's broken, and there are better ways to handle this going forward.

Eric Topol (00:48:31):

And you know what gets me, and it's like in the pharma industry that I've seen the people who run these companies like 3M that was involved in a multi-decade coverup, they're never held accountable. I mean, they know what they're doing and they just play these games that you outlined. They're still using 16,000 products, according to the New Yorker, the employee that exposed them, the whistleblower in the New Yorker article.

Joseph Allen (00:48:58):

That was an amazing article by Sharon Lerner talking to the people who had worked there and she uncovered that they knew the toxicity back in the seventies, and yes, they were still making these products. One of the things that I think has gotten attention of some companies is while the regulations have been behind, the lawsuits are piling up.

Joseph Allen (00:49:21):

The lawsuit I was a part of as an expert for that was about an $800 million settlement in favor of the plaintiffs. A couple months later is another one that was $750 million. So right there, $1.5 billion, there's been several billion dollars. This has caught the attention of companies. This has caught the attention of product manufacturers who are using the forever chemicals, starting to realize they need to reformulate. And so, in a good way now, that's not the way we should be dealing with this, but it has started to get companies to wake up that maybe they had been sleeping on it, that this is a major problem and actually the markets have responded to it.

Eric Topol (00:50:02):

Well, that's good.

Joseph Allen (00:50:03):

Because these are major liabilities on the books.

Eric Topol (00:50:05):

Yeah, I mean, I think what I've seen of course with being the tobacco industry and I was involved with Vioxx of course, is the companies just appeal and appeal and it sounds really good that they've had to pay $800 million, but they never wind up paying anything because they basically just use their muscle and their resources to appeal and put it off forever. So I mean, it's one way to deal with it is a litigation, but it seems like that's not going to be enough to really get this overhauled. I don't know. You may be more sanguine.

Joseph Allen (00:50:44):

No, no, I agree with you. It's the wrong way. I mean, we don't want to, the solution here is not to go after companies after people are sick. We need get in front of this and be proactive. I mentioned it only because I know it has made other companies pay attention how many billion does so-and-so sue for. So that's a good signal that other companies are starting to move away from forever chemicals. But I do want to talk about one of the positive approaches we're doing at Harvard, and we have a lot of other partners in the private sector doing this. We're trying to turn off the spigot of forever chemicals entering the market in the first place. As a faculty advisor to what we call the Harvard Healthier Building Materials Academy, we publish new standards. We no longer buy products that have forever chemicals in them for our spaces.

(00:51:31):

So we buy a chair or carpet. We demand no forever chemicals. What's really neat about this is we also say, we treat them as a whole class. We don't say we don't want PFOA. That's one of the regulated chemicals. We say we don't want any of the 10,000. We are not waiting for the studies to show us they act like the other ones. We've kind of been burned by this for decades. So we're actually telling the suppliers we don't want these chemicals and they're delivering products to us without these chemicals in them. We have 50 projects on our campus built with these new design standards without forever chemicals and other toxic chemicals. We've also done studies that a doctoral student done the study. When we do this, we find lower levels of these chemicals in air and dust, of course. So we're showing that it works.

(00:52:19):

Now, the goal is not to say, hey, we just want to make Harvard a healthier campus and the hell with everybody else. The goal is to show it can be done with no impact to cost, schedule or product performance. We get a healthier environment, products look great, they perform great. We've also now partnered with other big companies in the tech industry in particular to try and grow or influence the market by saying, look how many X amount of purchasing dollars each year? And it's a lot, and we're demanding that our carpets don't have this, that our chairs don't have it, and the supply chain is responding. The goal, of course, is to just make it be the case that we just have healthy materials in the supply chain for everybody. So if you or I, or anybody else goes to buy a chair, it just doesn't have toxic chemicals in it.

Eric Topol (00:53:06):

Right, but these days the public awareness still isn't there, nor are the retailers that are selling whether it's going to buy a rug or a chair or new pots and pans. You can't go in and say, does this have any forever chemicals? They don't even know, right?

Joseph Allen (00:53:24):

Impossible. I study this and it's hard for me when I go out to try and find and make better decisions for myself. This is one of the reasons why we're working, of course, trying to help with the regulatory side, but also trying to change the market. Say, look, you can produce the similar product without these chemicals, save yourself for future lawsuits. Also, there's a market for healthy materials, and we want everybody to be a part of that market and just fundamentally change the supply chain. It's not ideal, but it's what we can do to influence the market. And honestly, we're having a lot of impact. I've been to these manufacturing plants where they have phased out these toxic chemicals.

Eric Topol (00:54:03):

That’s great to hear.

Joseph Allen (00:54:06):

And we see it working on our campus and other companies’ campuses.

Eric Topol (00:54:10):

Well, nobody can ever accuse you of not taking on big projects, okay.

Joseph Allen (00:54:15):

You don't get into public health unless you want to tackle the big ones that are really going to influence.

Micro(nano) Plastics

Eric Topol (00:54:20):

Well, that's true, Joe, but I don't know anybody who's spearheading things like you. So it's phenomenal. Now before we wrap up, there's another major environmental problem which has come to the fore, which are plastics, microplastics, nanoplastics. They're everywhere too, and they're incriminated with all the things that we've been talking about as well. What is your view about that?

Joseph Allen (00:54:48):

Well, I think it's one, well, you see the extent of the pollution. It's a global pollutant. These are petrochemicals. So it's building up, and these are fossil fuel derivatives. So you can link this not just to the direct human health impacts, the ecosystem impacts, but also ecosystem and health impacts through climate change. So we've seen our reliance on plastics grow exponentially over the past several decades, and now we're seeing the price we're paying for that, where we're seeing plastics, but also microplastics kind of everywhere, much like the forever chemicals. Everywhere we look, we find them and we're just starting to scratch a surface on what we know about the environmental impacts. I think there's a lot more that can be done here. Try to be optimistic again, at least if you find a problem, you got to try and point to some kind of solution or at least a pathway towards solutions.

(00:55:41):

But I like some of the stuff from others colleagues at Yale in particular on the principles of green chemistry. I write about them in my book a little bit, but it's this designing for non-permanence or biodegradable materials so that if we're using anything that we're not leaving these permanent and lasting impacts on our ecosystem that then build up and they build up in the environment, then they build up in all of us and in our food systems. So it seems to me that should be part of it. So think about forever chemicals. Should we be using chemicals that never break down in the environment that we know are toxic? How do we do that? As Harvard, one of the motivating things here for forever chemicals too, is how are we ignoring our own science? Everyone's producing this science, but how do we ignore even our own and we feel we have responsibility to the communities next to us and the communities around the world. We're taking action on climate change. How are we not taking action on these chemicals? I put plastics right in there in terms of the environmental pollutants that largely come from our built environment, food products and the products we purchase and use in our homes and in our bodies and in all the materials we use.

Eric Topol (00:56:50):

When you see the plastic show up in our arteries with a three, four-fold increase of heart attacks and strokes, when you see it in our testicles and every other organ in the body, you start to wonder, are we ever going to do something about this plastic crisis? Which is somewhat distinct from the forever chemicals. I mean, this is another dimension of the problem. And tying a lot of this together, you mentioned, we are not going to get into it today, but our climate crisis isn't being addressed fast enough and it's making all these things exacerbating.

Joseph Allen (00:57:27):

Yeah, let me touch on that because I think it is important. It gets to something I said earlier about a lot of these problems we treat as silos, but I think a lot of the problems run through our buildings, and that means buildings are part of the solution set. Buildings consume 40% of global energy.

(00:57:42):

Concrete and steel count for huge percentages of our global CO2 emissions. So if we're going to get climate solved, we're going to have to solve it through our buildings too. So when you start putting this all together, Eric, right, and this is why I talk about buildings as healthy buildings could potentially be one of the greatest public health interventions we have of this century. If we get it right, and I don't mean we get the Covid part, right. We get the forever chemicals part, right. Or the microplastics part, right. If you start getting this all right, good ventilation, better filtration, healthy materials across the board, energy efficient systems, so we're not drawing on the energy demand of our buildings that are contributing to the climate crisis. Buildings that also address climate adaptation and resilience. So they protect us from extreme heat, wildfire smoke, flooding that we know is coming and happening right now.

(00:58:37):

You put that all together and it shows the centrality of buildings on our collective health from our time spent indoors, but also their contribution to environmental health, which is ultimately our collective human health as well. And this is why I'm passionate about healthy buildings as a real good lens to put this all under. If we start getting these right, the decisions we make around our buildings, we can really improve the human condition across all of these dimensions we're talking about. And I actually don't think it's all that hard in all of these. I've seen solutions.

Eric Topol (00:59:12):

I'm with you. I mean, there's innovations that are happening to take the place of concrete, right?

Joseph Allen (00:59:20):

Sure. We have low emission concrete right now that's available. We have energy recovery ventilation available right now. We have real time sensors. We can do demand control ventilation right now. We have better filters right now. We have healthy materials right now.

(00:59:33):

We have this, we have it. And it's not expensive if we quantify the health benefits, the many, many multiple benefits. So it's all within our reach, and it's just about finding these different pathways. Some of its market driven, some of it's regulatory, some of it's at the local level, some of it's about raising awareness, giving people the language to talk about these things. So I do think it's the real beginning of the healthy buildings era. I really, truly believe it. I've never seen change like this in my field. I've been chasing sick buildings for a long time.

Joseph Allen (01:00:11):

And clearly there's pathways to do better.

Eric Topol (01:00:13):

You're a phenom. I mean, really, you not only have all the wisdom, but you articulate it so well. I mean, you’re leading the charge on this, and we're really indebted to you. I'm really grateful for you taking an hour of your busy time to enlighten us on this. I think what you're doing is it's going to keep you busy for your whole career.

Joseph Allen (01:00:44):

Well, the goal here is for me to put myself out of business. We shouldn't have a healthy buildings program. It just should be the way it's done. So I'm looking forward to the time out of business, hopefully have a healthy building future, then I can retire, be happy, and we'll be onto the next big problem.

Eric Topol (01:00:57):

We'll all be following your writings, which are many, and fortunately not just for science publications, but also for the public though, they're so important because the awareness level as I can't emphasize enough, it's just not there yet. And I think this episode is going to help bring that to a higher level. So Joe, thank you so much for everything you're doing.

Joseph Allen (01:01:20):

Well, I appreciate it. Thanks for what you're doing too, and thanks for inviting me on. We can't get the word out unless we start sharing it across our different audiences, so I appreciate it. Thanks so much.

Eric Topol (01:01:28):

You bet.

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Steve Horvath made the seminal discovery of the—Horvath Clock— an epigenetic clock based on DNA methylation, which is now being used extensively in medical research and offered commercially for individuals (←we talk about that!). He was on the faculty at UCLA from 2000-2022 as a Professor of Human Genetics and Biostatistics, and now works on anti-aging research at Altos Labs.

A perspective on the importance of epigenetic clocks this week’s Nature”This insight is crucial for deriving reliable biological markers of ageing in tissues or blood. Such a feat has been accomplished through the ingenious identification of epigenetic clocks in our genome. But these insights are even more important for revealing targets that enable intervention in the ageing process.”

A video snippet on vegetable intake and epigenetic clocks. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to Audio and External Links

Eric Topol (00:06):

Hello, it's Eric Topol with Ground Truths, and I've got a terrific guest with me today, Steve Horvath. He's a geneticist, a statistician, a mathematician. He's got a lot of background that has led to what is a landmark finding in biomedicine, the Horvath clock. So Steve, welcome.

Steve Horvath (00:30):

Thank you for having me.

Eric Topol (00:33):

Well, it's really fascinating. I followed your work for well over a decade since you introduced the pan-tissue clock in 2013, and it's fascinating to go back a bit on that finding, which initially, I guess was in saliva a couple of years prior, and then you found it everywhere you looked, wherever cells had a nucleus and tissues. And what gave you the sense that these markers of methylation on the DNA would give us some clues about the aging process? How did you even come about to make this discovery?

Serendipity

Steve Horvath (01:17):

It was an accidental discovery because before the methylation clock, I had worked very hard on a gene expression clock, a transcriptomic biomarker. I mean, I was at the height of my energy levels. I worked really on weekends, really eight hour days during the week. But all the weekends I had collected a large set of gene expression data and I dredged the data. And for two years and I couldn't get anywhere, there was nothing I could do. But nowadays, of course, you see various publications where people built transcriptomic clocks. But back in the day when we had these arrays, I just couldn't see a signal. And then at some point I got roped into a study of homosexuality where my collaborator at UCLA wanted to see whether there's an epigenetic correlate of sexual orientation in saliva. And so yeah, being a biostatistician, I said, sure, I analyzed the data and I couldn't find any signal for homosexuality.

(02:48):

But then I just looked for an aging signal in the same, and really within an hour of analyzing the data, I knew that I have to completely drop gene expression. I need to go after methylation. And the signal is so profound, and as you said initially we looked at saliva samples and we thought, isn't it curious? You spit in a cup and you can measure someone's age. And we were of course, hoping that this could become a valuable readout of biologic age, but it took, of course, many years to realize that potential. Nowadays, there's several companies that offer a saliva based methylation clock test. But yeah, many years passed, and it was important to fill in the details and to build the case that methylation clocks are predictive of things we care about time to death or time to various forms of morbidity. So it took many, many years to analyze large cohort studies and to accumulate the evidence that it actually works.

Eric Topol (04:16):

Yeah, I mean, it was pretty amazing back almost a decade ago when I would see, we would take tissue or blood sample and look at your clock and it would say, age of the person is 75 years. And then we look at the actual age of the person who is 75 years to say, wait a minute, how can this be? So I mean, the plausibility of this discovery, if you look back, I mean you say, well, this is just kind of the rust of the pipes, or how do you process that the methylation is such a marker potentially of a person's biologic age? Of course, we're going to get into how it could be a way to intervene to change the aging process. But would it be fair to say that its epigenetic clocks are not the same as biologic aging or how do you put all that together?

Epigenetic Age vs Biologic Age

Steve Horvath (05:21):

Yes, for sure. An epigenetic age estimate is certainly not the same as a biologic age estimate. And the reason why I say it is because biologic age is really determined by so many things and by so many organs. And as I mentioned initially, we had a clock for saliva later for blood and so on. And so, if you only have an epigenetic readout of a certain cell type, it's really too limited to assess the whole organismal state. And arguably you would want to measure also proteomics, readouts and many other data modalities. So I typically avoid the terminology biologic age, because to begin with, we don't have a definition of it. Decades of discussions, nobody really has a precise definition of it.

Second Generation Epigenetic Clocks

Eric Topol (06:35):

Well, from the first generation Horvath clock then became this newer second generation, GrimAge, PhenoAge, the DunedinPACE of aging. How has that helped to advance the field? Because as you touched on, they're measuring different things and what is it meant by kind of a second generation clock?

Steve Horvath (07:03):

Yeah, so a second generation clock truly aims to predict mortality or morbidity risk. As opposed to simply chronologic age or what is known as calendar age. And fortunately, there's no doubt that the second generation clocks can do that. I often finish a talk on GrimAge by telling the audience that I give them a money back guarantee, that it will be predictive of mortality in their cohort study. I'm 100% certain that it works if you analyze a hundred people or so. The question is more whether an individual could benefit from such a test. And there are now many providers of various epigenetic clock tests. These biomarkers have different names, but they're quite pricey. A couple of hundred dollars are needed to get such a measurement. And the question is, is it helpful for the individual should you get such a test? And I would say we are not quite there yet for a variety of reasons. The main reason being we don't have good interventions against accelerated epigenetic age. So because when you think about it, why does a doctor order a test for you? For example, cholesterol levels. Well, because they have a drug against elevated cholesterol levels, the statin. And at the moment, we don't have validated interventions against accelerated epigenetic age. So that's kind of missing.

Eric Topol (09:13):

Yeah, we're going to get to that because obviously a lot of things are in the pipeline there, but are you saying then that these people that are getting these consumer tests, that they're getting a test that really wasn't validated at an individual level, so it predicts their mortality that it may be good at a cohort or population level, but maybe it's not so helpful, accurate, or would you say it is accurate? I mean, GrimAge is a good name because since it says when you're going to die. How do you make the differentiation between the individual level or beyond?

Steve Horvath (09:59):

Yeah, I think it's good to compare to other biomarkers. So take glucose levels, hemoglobin A1C, nobody doubts that these levels predict mortality risk when you study couples a hundred people. But how accurate is such a test for an individual? Clearly there is substantial noise associated with a prediction. Two people could have exactly the same hemoglobin A1C levels, but live very different lifespans. And the same holds for epigenetic clocks. They do predict how long you live. In theory, one could arrive at an estimate of age and death. There's a complicated mathematical formula that allows you to do that, but there would be a substantial error bar associated with it, an order of magnitude plus minus five years. And so, for the individual, such an estimate is not that important because the error bar is substantial. But I want to add that these second generation clocks, they do predict mortality risk. There's no question.

Maximal Lifespan

Eric Topol (11:35):

Well, as you know, the longevity space is now very crowded with all sorts of clubs, and it's like a circus out there. And some of these things are being promoted that really don't have the basis or have a false sense to consumers who want to live forever and be healthy forever. But maybe these markers are not really helping guide them so much. Now, you recently published you and your group a fascinating paper, so getting away from the individual for a second, but now at the species level and in Science Advances, and we'll put this diagram with the podcast, but you looked at 348 mammal species for the maximal lifespan with DNA methylation. And it was amazing to see the display from the desert hamster all the way to the humpback whale with somewhere along the way, the humans. So you could predict maximal lifespan pretty well, right?

Steve Horvath (12:43):

Yes. So I collected this very large dataset over seven years, and one of the reasons was to understand the mystery of maximum lifespan. The bowhead whale can live over 211 years, whereas certain mice only three or four years. And my question was, can methylation teach us something about maximum lifespan? And the answer is a resounding, yes. The methylation profiles very much predict the maximum lifespan of a species. And maybe to use a metaphor to explain the patterns. So one can visualize methylation around the DNA molecule, like a landscape. You want that certain regions exhibit high levels of methylation. These regions must be really shut down and other parts of the DNA as opposed to exhibit very low methylation, for example, a transcriptional start sites. And long lived species have a very hilly landscapes, high hills of methylation and steep valleys of low methylation. Where shorter lived species have flatter landscapes. So that was one of the insights of that study. The other perhaps paradoxical insight was that the locations in our DNA that gain methylation with chronologic age, these regions often differ from regions that determine the maximum lifespan of our species. So that's a bit perhaps paradoxical and counterintuitive, but it just shows that the DNA encodes our species characteristics at different locations from our mortality risk.

The Other Clocks

Eric Topol (15:13):

Right. No, and I mean it's fascinating. I can imagine how it could take seven years to pull all that data together. It's amazing. Now, one of the issues of course, is if you're trying to gauge the biologic age, which we already established is somewhat different than epigenetic age or a clock, there are many different ways to do that. And you mentioned transcriptome clocks, which are not as well perhaps developed. Obviously, none of these others are developed like the Horvath clock and newer generation clocks, but there's immuno aging clocks like iAge, there's proteomic clocks, there's organ clocks with high-throughput proteomics, thousands of proteins. Do you see these as complimentary, like orthogonal where they each add to the story? Or do you really see the methylation as distinct?

Steve Horvath (16:20):

Well, I think ideally you measure all of the above to really get a very granular understanding of different facets of aging. And however, scientists always like to find deep connections between different readouts. For example, it would be wonderful if we could use proteomics instead of methylation, or my group has worked on the opposite. So we can actually estimate protein levels in the plasma based on methylation for about 10% of all plasma proteins, you can estimate their levels based on methylation. So yeah, people who are interested in these deeper programs that ideally link everything, some sort of aging program that underlies these different manifestations of aging, they will want to reduce everything. But until we have a deeper understanding, I think let's air on the side of measuring too much.

Eric Topol (17:45):

Well, what's interesting, as you mentioned, I didn't realize you could basically impute the protein story from the methylation, but one of the issues is if you want to do 11,000 plasma proteins, it could cost a thousand dollars. But if you want to do a bisulfite methylation, you might do that for very inexpensively. So there's a practical part of this too, and the immune characterization is even more expensive and difficult from a practical standpoint. So we go back to that initial work that you did and how you got into an area that is practical, inexpensive compared to some of the alternatives. But as you say, they may have features that are also helpful. Now, this is now the craze, this epigenetic clocks, and I want to mention you probably didn't see it because it's not a journal that you would look at, but just yesterday, July 29th, there were 12 papers published in JAMA Network Open.

Modulating Your Epigenetic Clock

(18:51):

Everything from how loss of loved ones changes your epigenetic clock to PTSD, to vegan diets, to inequities. I mean, just incredible. So it is the rage now. It's taken the biomedical community some years to catch up to where you were. And one of the things of course that we know that from your prior work that is an intervention that helps give a less accelerated epigenetic clock is exercise. And in fact, that was highlighted in our Lancet essay in the first week August issue. But can you comment on that and anything else that we know like plant diets and anything that favorably influence our DNA methylation pattern?

Steve Horvath (19:52):

Yes. So interestingly, vegetable intake really has a strong effect on GrimAge and many other epigenetic clocks. And maybe this is obvious to the listener, everybody knows that vegetable intake is healthy. However, it's very surprising to me as a scientist to contemplate how is it that vegetable intake affects the methylation levels of your blood? How does it affect the hematopoietic stem cells? I just don't understand the mechanism behind it, and however, the effect is very strong. So we studied postmenopausal women in the women's health initiative, and for these women, we had blood measures of carotenoid levels. So this is an objective measure of vegetable intake, and the correlations were substantial. So that's one intervention I'm quite certain about. Other intervention that have a strong effect relate to metabolic syndrome, anything that relates to type 2 diabetes such as obesity, high glucose levels, that part of the biology very much affects our epigenetic clocks. So disturbed metabolism has a strong effect.

Eric Topol (21:37):

Has these findings changed your diet or made you exercise more or anything like that?

Steve Horvath (21:44):

. So I eat a lot of frozen vegetables. My freezer as full frozen vegetables.

Eric Topol (21:56):

That's great. Well, there's a lot of uses today as we touched on in the Lancet piece as we're waiting for more benchmarking and more work on this. But for example, we have a shortage of donor organs, and there are people who might be of calendar age advanced, but their epigenetic clock might put them at a much younger age. Is that ready for use in the transplant world as one application?

Steve Horvath (22:37):

I haven't seen that yet. I've seen several studies that have explored that idea. The idea is rather obvious, but I haven't seen it implemented in practicum.

Eric Topol (22:53):

Another one is that we don't, as you've seen from some of these studies on organ clocks, our organs age at different paces and some people are accelerated heart agers or brain agers. If you had access to tissue to get methylation, would you see the same thing or this is of course of interest because we're trying to understand high risk individuals for age related diseases, whether it's dementia or heart disease or cancer. So is the second generation clocks like PhenoAge just good enough, or would you think that the organ clocks would give you some added insight?

Steve Horvath (23:47):

Yeah, I would say this is literally the frontier of research. Several groups attempt to use blood methylation or saliva or skin or fat adipose as surrogates for various other organs. And I've seen very encouraging results. So I do think this idea makes scientific sense, and which comes back to one of the miracles of methylation that this is even possible because if you had written a grant 10 years ago where you said, I will measure blood methylation to assess cognitive functioning, for example, you wouldn't have received any score, not in no funding, but however, interestingly, blood methylation does relate to cognitive functioning and many other organ functions. And so, the proof of concepts have been established. Blood methylation relates to fatty liver disease, kidney disease, lung disease. It has all been done in epidemiological studies. However, the question is how much could a blood methylation measurement help an individual? Should I measure my blood methylation to learn about my liver? And I would say we are not there yet because arguably there are wonderful plasma biomarkers to assess organ functions. And in certain ways, one needs to provide evidence that a methylation measurement is superior or compliments plasma based biomarker. And that's a hard hurdle to take.

Eric Topol (26:02):

Right. I imagine someday it may become the norm of assessing people's risk, but as you say, we're not there yet because it's a tough bar to meet, for sure. Now, you were a Professor from year 2000 at UCLA in multiple departments in genetics and biostats, and then in more recent times you joined the Cambridge unit of Altos, which is one of the companies that has gotten the most attention for its diverse efforts towards modulating, rejuvenating the aging process. So you and many top scientists around the world were recruited to Altos. I know some here at the San Diego campus. Was this thinking that it could help accelerate the whole idea of modulating aging in a favorably way or where do you see that the biotech world can play a role?

Can We Change the Pace of Aging?

Steve Horvath (27:15):

Yes. I mean, speaking for myself, I was getting tired of writing scientific papers and not affecting clinical care. I felt I needed to help identify or validate rejuvenating interventions because of the great promise, and this is perhaps best done in the setting of a biotech that is focused on translation. And that's why I joined. I'm moving away from biomarker development towards finding interventions that move the needle and ideally rejuvenate multiple organs and cell types at the same time.

Eric Topol (28:09):

Right. Now, there's lots of ideas of how we could do that from senolytics that would get rid of specific senescent cells that are bad actors to epigenetic reprogramming or chemical reprogramming or so many anti-inflammatory, like the recent paper of IL-11 that I'm sure you saw in Nature just a couple of weeks ago and many, many other ways to get there. What are you thinking? Is this going to be possible? Obviously, there's lots of naysayers. Is it going to be possible body wide or only for specific ways? For example, maybe we could bring back the thymus from its involution or we could stop ovarian failure in women so that their loss of advantage is delayed many years. Or do you think we're going to get to body wide anti-aging?

Steve Horvath (29:13):

Yeah, I think of it as divide and conquer. So ultimately I do believe that we can rejuvenate most cell types and tissues. The question is how do you roll out this program? Do you look for this one silver bullet that does it? For example, this idea of interrupted reprogramming based on Yamanaka factor combinations that looks of course very promising and rodent models. But then such silver bullet treatments could be risky for patient keyword malignant transformation, cancer risk, and it could be far safer to focus on one organ system or one tissue. For example, David Sinclair's company Life Biosciences looks at optic nerve regeneration for a reason. It could be safer. And so yeah, I'm very happy that companies explore different strategies. Certain companies focus on one condition, fatty liver disease or NASH. Other companies focus on immune system restoration. But I think many people think of one condition as really a first step to establish safety and efficacy, and then hopefully they could translate it to other body systems and organ systems.

Eric Topol (31:02):

But is it fair to say you're optimistic that we will be able to change the aging pace in people?

Steve Horvath (31:10):

Yes, I think yes. I'm very optimistic and there are several reasons for this optimism. The first is that dramatic results can be achieved in mice and rats. So we and others have published studies that show that you can reduce the epigenetic age by 30% or so and you can extend the lifespan, and you cited this very exciting paper by Stuart Cook on IL-11 inhibition that just came out in Nature. So I keep seeing these kinds of headlines, and then I want to think that one of these will actually work for humans. So the second thing that makes me optimistic is really this combination of artificial intelligence and biomedical research. Then going forward, robotics. So I can see several ways of accelerating biomedical research. So I'm quite optimistic.

The Role of A.I.

Eric Topol (32:24):

Maybe go a little deeper on the AI potential to help here. How does AI come into play?

Steve Horvath (32:33):

So AI can help in so many different ways. The first topic is biomarker development. I of course spent 10 years on a certain statistical model for building biomarkers, which is known as penalized regression. It works well, but AI allows the community to build imaging based biomarkers. So for example, based on MRI images, but also cells growing in a dish, we can say this treatment aged the cells growing in the dish or rejuvenated them. So that's one topic, biomarker discovery. The second is, of course, to design small molecules, keyword, these protein design where it has greatly accelerated drug discovery. And there are several companies working in this space, and again, there's wonderful case studies that look very convincing to me. And the third aspect of AI is another obvious one. AI can read many papers. I mean, you could be a 50-year-old professor who has read papers their entire life, but an AI can really read far better and summarize insights better.

Eric Topol (34:27):

Yeah, the complimentary in terms of the reasoning of that information. So absolutely right now, one of the problems we have here is that aging is not seen as a disease. Of course, we can remember when obesity was not considered a disease and then there was a drug and everything changed. But here we don't have a classification it's a disease. It's considered a natural process that is highly variable in people. But the question is, we can't do studies that are going to wait 20, 30 years to find out if we promoted health span and lifespan. And so, we have to rely on these clocks. So how do you see this playing out? Do you think that we might see a regulatory approval on a surrogate proxy, like an advanced Horvath clock, or do you think that's not going to cut it, that you're going to have to show more to get a anti-aging treatment across the regulatory threshold?

Steve Horvath (35:42):

Yeah, that's a very good question. So I believe that the biomarker community has already assembled enough evidence to offer a battery of tests that could be used as surrogate endpoints of interventional study. And we could discuss the components of this battery. But I would say we already have biomarkers beyond just methylation. One could have the readouts of walking speed or muscle function, many readouts, and they could be aggregated into an index to summarize the biologic age, perhaps, of the individual. So that already exists. At the same time, this field is undergoing explosive growth. You mentioned every day new papers come out in the relatively small field of epigenetic clocks. There's so many papers that it's hard to keep track, but I embrace it. I think it's wonderful because clocks get ever more powerful.

(37:11):

So yeah, I would say there should be different versions. Ideally, a regulatory agency would make an executive decision and say, for the next three years, use the following five biomarkers. Then a few years later, as the science advances, they could come up with an updated version, but even a 90% solution would very much accelerate progress in the whole field of rejuvenating interventions. So I would very much embrace a top down decision on which biomarkers should be made, because the bottom up approach, by the way, simply doesn't work. The minute you put three professors in the room to come up with a decision, which biomarker is best, there will be three different opinions. We need impartial arbiter that makes a decision.

GLP-1 Drugs and Aging

Eric Topol (38:23):

Now, the drug class that's come on the scene, of course it was in incubating for decades for diabetes, but now obesity and so of the obesity related. But now we're seeing the GLP-1 drugs that are showing potential effects in Parkinson's and Alzheimer's and cardiovascular disease, and even in obesity related cancers. And I mean across the board. And you mentioned metabolic derangement as one of the things that accelerate aging. Do you think these class of drugs that has greatly passed our expectations already and it's being tested of course, with even more potent drugs or triple receptors and pills and whatnot, will that be a candidate as one of the anti-aging interventions in the future?

Steve Horvath (39:19):

Yeah, for sure. A couple of months ago, I participated in a conference and one of the speakers showed unpublished results from a study, and they looked good to me. I mean, they registered on epigenetic clocks. This is all unpublished, but it made perfect sense to me because I mentioned the clocks do relate to metabolic health. So I was quite pleased that they registered that intervention.

Eric Topol (39:56):

It's fascinating because we could all be taking GLP-1 drugs someday, not for obesity or not for sleep apnea, but for things that are more far reaching. I didn't know about that unpublished result. That's fascinating.

Steve Horvath (40:15):

Yeah, I have a joke, which is I wish I was chubby because I would be using these drugs, but I'm relatively slender, so I don't have any good reason to take them.

Eric Topol (40:28):

That says a lot. I don't know anybody who knows more about this process than you and is very candid and frank about it. So Steve, this has been terrific to have your insights, the body of work that you should be so proud of that extends over many years and many great years and more contributions to come undoubtedly. So thank you for joining us today, and we will follow this continued evolution of our ability, not just to track the aging process, but also to modulate. So thanks very much.

Steve Horvath (41:06):

Thank you. I really like your podcast Ground Truths, it’s very informative. So thank you for this.

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Pradeep is a brilliant geneticist and Director of Preventive Cardiology, holds the Paul & Phyllis Fireman Endowed Chair in Vascular Medicine at Mass General Hospital and on faculty at Harvard Medical School and the Broad Institute. His prolific research has been illuminating for the field of improving our approach to reduce the risk of heart disease. That’s especially important because heart disease is the global (and US) #1 killer and is on the increase. We didn’t get into lifestyle factors here since there was so much ground to cover on new tests. drugs, and strategies.

A video snippet of our conversation on ApoB. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to key publications and audio

Eric Topol (00:06):

Well, welcome to Ground Truths. I'm Eric Topol and with me is Pradeep Natarajan from Harvard. He's Director of Preventative Cardiology at the Mass General Brigham Health System and he has been lighting it up on the field of cardiovascular. We're going to get to lots of different parts of that story and so, Pradeep welcome.

Pradeep Natarajan (00:31):

Thanks Eric, really delighted and honored to be with you and have this discussion.

Eric Topol (00:36):

Well, for years I've been admiring your work and it's just accelerating and so there's so many things to get to. I thought maybe what we'd start off with is you recently wrote a New England Journal piece about two trials, two different drugs that could change the landscape of cardiovascular prevention in the future. I mean, that's one of the themes we're going to get to today is all these different markers and drugs that will change cardiology as we know it now. So maybe you could just give us a skinny on that New England Journal piece.

Two New Lipid Targets With RNA Drugs

Pradeep Natarajan (01:16):

Yeah, yeah, so these two agents, the trials were published at the same time. These phase two clinical trials for plozasiran, which is an siRNA against APOC3 and zodasiran, which is an siRNA against ANGPTL3. The reason why we have medicines against those targets are based on human genetics observations, that individuals with loss of function mutations and either of those genes have reduced lipids. For APOC3, it's reduced triglycerides for ANGPTL3 reduced LDL cholesterol and reduced triglycerides and also individuals that have those loss of function mutations also have lower risk for coronary artery disease. Now that's a very similar parallel to PCSK9. We have successful medicines that treat that target because people have found that carriers of loss of function mutations in PCSK9 lead to lower LDL cholesterol and lower coronary artery disease.

(02:11):

Now that suggests that therapeutic manipulation without significant side effects from the agents themselves for APOC3 and ANGPTL3 would be anticipated to also lower coronary artery disease risk potentially in complementary pathways to PCSK9. The interesting thing with those observations is that they all came from rare loss of function mutations that are enriched in populations of individuals. However, at least for PCSK9, has been demonstrated to have efficacy in large groups of individuals across different communities. So the theme of that piece was really just the need to study diverse populations because those insights are not always predictable about which communities are going to have those loss of function mutations and when you find them, they often have profound insights across much larger groups of individuals.

Eric Topol (03:02):

Well, there's a lot there that we can unpack a bit of it. One of them is the use of small interfering RNAs (siRNA) as drugs. We saw in the field of PCSK9, as you mentioned. First there were monoclonal antibodies directed against this target and then more recently, there’s inclisiran which isn't an RNA play if you will, where you only have to take it twice a year and supposedly it's less expensive and I’m still having trouble in my practice getting patients covered on their insurance even though it's cheaper and much more convenient. But nonetheless, now we're seeing these RNA drugs and maybe you could comment about that part and then also the surprise that perhaps is unexplained is the glucose elevation.

Pradeep Natarajan (03:53):

Yeah, so for medicines and targets that have been discovered through human genetics, those I think are attractive for genetic-based therapies and longer interval dosing for the therapies, which is what siRNAs allow you to do because the individuals that have these perturbations, basically the naturally occurring loss of function mutations, they have these lifelong, so basically have had a one-time therapy and have lived, and so far, at least for these targets, have not had untoward side effects or untoward phenotypic consequences and only reduce lipids and reduce coronary artery disease. And so, instead of taking a pill daily, if we have conviction that that long amount of suppression may be beneficial, then longer interval dosing and not worrying about the pill burden is very attractive specifically for those specific therapeutics. And as you know, people continue to innovate on further prolonging as it relates to PCSK9.

(04:57):

Separately, some folks are also developing pills because many people do feel that there's still a market and comfort for daily pills. Now interestingly for the siRNA for zodasiran at the highest dose, actually for both of them at the highest doses, but particularly for zodasiran, there was an increase in insulin resistance parameters actually as it relates to hyperglycemia and less so as it relates to insulin resistance, that is not predicted based on the human genetics. Individuals with loss of function mutations do not have increased risks in hyperglycemia or type 2 diabetes, so that isolates it related to that specific platform or that specific technology. Now inclisiran, as you'd mentioned, Eric is out there. That's an siRNA against PCSK9 that's made by a different manufacturer. So far, the clinical trials have not shown hyperglycemia or type 2 diabetes as it relates inclisiran, so it may be related to the specific siRNAs that are used for those targets. That does merit further consideration. Now, the doses that the manufacturers do plan to use in the phase three clinical trials are at lower doses where there was not an increase in hyperglycemia, but that does merit further investigation to really understand why that's the case. Is that an expected generalized effect for siRNAs? Is it related to siRNAs for this specific target or is it just related to the platform used for these two agents which are made by the same manufacturer?

Eric Topol (06:27):

Right, and I think the fact that it's a mystery is intriguing at the least, and it may not come up at the doses that are used in the trials, but the fact that it did crop up at high doses is unexpected. Now that is part of a much bigger story is that up until now our armamentarium has been statins and ezetimibe to treat lipids, but it's rapidly expanding Lp(a), which for decades as a cardiologist we had nothing to offer. There may even be drugs to be able to lower people who are at high risk with high Lp(a). Maybe you could discuss that.

What About Lp(a)?

Pradeep Natarajan (07:13):

Yeah, I mean, Eric, as you know, Lp(a) has been described as a cardiovascular disease risk factors for quite so many years and there are assays to detect lipoprotein(a) elevation and have been in widespread clinical practice increasing widespread clinical practice, but we don't yet have approved therapies. However, there is an abundance of literature preclinical data that suggests that it likely is a causal factor, meaning that if you lower lipoprotein(a) when elevated, you would reduce the risk related to lipoprotein(a). And a lot of this comes from similar human genetic studies. The major challenge of just relating a biomarker to an outcome is there are many different reasons why a biomarker might be elevated, and so if you detect a signal that correlates a biomarker, a concentration to a clinical outcome, it could be related to that biomarker, but it could be to the other reasons that the biomarker is elevated and sometimes it relates to the outcome itself.

(08:10):

Now human genetics is very attractive because if you find alleles that strongly relate to that exposure, you can test those alleles themselves with the clinical outcome. Now the allele assignment is established at birth. No other factor is going to change that assignment after conception, and so that provides a robust, strong causal test for that potential exposure in clinical outcome. Now, lipoprotein(a) is unique in that it is highly heritable and so there are lots of different alleles that relate to lipoprotein(a) and so in a well powered analysis can actually test the lipoprotein(a) SNPs with the clinical outcomes and similar to how there is a biomarker association with incident myocardial infarction and incident stroke, the SNPs related to lipoprotein(a) show the same. That is among the evidence that strongly supports that this might be causal. Now, fast forward to many years later, we have at least three phase three randomized clinical trials testing agents that have been shown to be very potent at lowering lipoprotein(a) that in the coming years we will know if that hypothesis is true. Importantly, we will have to understand what are the potential side effects of these medicines. There are antisense oligonucleotides and siRNAs that are primarily in investigation. Again, this is an example where there's a strong genetic observation, and so these genetic based longer interval dosing therapies may be attractive, but side effects will be a key thing as well too. Those things hard to anticipate really can anticipate based on the human genetics for off target effects, for example.

(09:52):

It's clearly a risk signal and hopefully in the near future we're going to have specific therapies.

Eric Topol (09:57):

Yeah, you did a great job of explaining Mendelian randomization and the fact the power of genetics, which we're going to get into deeper shortly, but the other point is that do you expect now that there's these multiple drugs that lower Lp(a) efficiently, would that be enough to get approval or will it have to be trials to demonstrate improved cardiovascular outcomes?

Pradeep Natarajan (10:24):

There is a great regulatory path at FDA for approval just for LDL cholesterol lowering and inclisiran is on the market and the phase three outcomes data has not yet been reported because there is a wide appreciation that LDL cholesterol lowering is a pretty good surrogate for cardiovascular disease risk lowering. The label will be restricted to LDL cholesterol lowering and then if demonstrated to have clinical outcomes, the label could be expanded. For other biomarkers including lipoprotein(a), even though we have strong conviction that it is likely a causal factor there hasn't met the bar yet to get approval just based on lipoprotein(a) lowering, and so we would need to see the outcomes effects and then we would also need to understand side effects. There is a body of literature of side effects for other therapies that have targeted using antisense oligonucleotides. We talked about potential side effects from some siRNA platforms and sometimes those effects could overtake potential benefits, so that really needs to be assessed and there is a literature and other examples.

(11:31):

The other thing I do want to note related to lipoprotein(a) is that the human genetics are modeled based on lifelong perturbations, really hard to understand what the effects are, how great of an effect there might be in different contexts, particularly when introduced in middle age. There's a lot of discussion about how high lipoprotein(a) should be to deliver these therapies because the conventional teaching is that one in five individuals has high lipoprotein(a), and that's basically greater than 75 nanomoles per liter. However, some studies some human genetic studies to say if you want to get an effect that is similar to the LDL cholesterol lowering medicines on the market, you need to start with actually higher lipoprotein(a) because you need larger amounts of lipoprotein(a) lowering. Those are studies and approaches that haven't been well validated. We don't know if that's a valid approach because that's modeling based on this sort of lifelong effect. So I'm very curious to see what the overall effect will be because to get approval, I think you need to demonstrate safety and efficacy, but most importantly, these manufacturers and we as clinicians are trying to find viable therapies in the market that it won't be hard for us to get approval because hopefully the clinical trial will have said this is the context where it works. It works really well and it works really well on top of the existing therapies, so there are multiple hurdles to actually getting it directly to our patients.

How Low Do You Go with LDL Cholesterol?

Eric Topol (13:02):

Yeah, no question about that. I'm glad you've emphasized that. Just as you've emphasized the incredible lessons from the genetics of people that have helped guide this renaissance to better drugs to prevent cardiovascular disease. LDL, which is perhaps the most impressive surrogate in medicine, a lab test that you already touched on, one of the biggest questions is how low do you go? That is Eugene Braunwald, who we all know and love. They're in Boston. The last time I got together with him, he was getting his LDL down to close to zero with various tactics that might be extreme. But before we leave these markers, you're running preventive cardiology at man's greatest hospital. Could you tell us what is your recipe for how aggressive do you go with LDL?

Pradeep Natarajan (14:04):

Yeah, so when I talk to patients where we're newly getting lipid lowering therapies on, especially because many people don't have a readout of abnormal LDL cholesterol when we're prescribing these medicines, it's just giving them a sense of what we think an optimal LDL cholesterol might be. And a lot of this is based on just empirical observations. So one, the average LDL cholesterol in the modern human is about 100 to 110 mg/dL. However, if you look at contemporary hunter gatherers and non-human primates, their average LDL is about 40 to 50 and newborn babies have an LDL cholesterol of about 30. And the reason why people keep making LDL cholesterol lowering medicines because as you stack on therapies, cardiovascular disease events continue to reduce including down to these very low LDL cholesterol values. So the population mean for LDL cholesterol is high and everybody likely has hypercholesterolemia, and that's because over the last 10,000 years how we live our lives is so dramatically different and there has not been substantial evolution over that time to change many of these features related to metabolism.

(15:16):

And so, to achieve those really low LDL cholesterol values in today's society is almost impossible without pharmacotherapies. You could say, okay, maybe everybody should be on pharmacotherapies, and I think if you did that, you probably would reduce a lot of events. You'll also be treating a lot of individuals who likely would not get events. Cardiovascular disease is the leading killer, but there are many things that people suffer from and most of the times it still is not cardiovascular disease. So our practice is still rooted in better identifying the individuals who are at risk for cardiovascular disease. And so, far we target our therapies primarily in those who have already developed cardiovascular disease. Maybe we'll talk about better identifying those at risk, but for those individuals it makes lots of sense to get it as low as possible. And the field has continued to move to lower targets.

(16:07):

One, because we've all recognized, at least based on these empirical observations that lower is better. But now increasingly we have a lot of therapies to actually get there, and my hope is that with more and more options and the market forces that influence that the cost perspective will make sense as we continue to develop more. As an aside, related aside is if you look at the last cholesterol guidelines, this is 2018 in the US this is the first time PCSK9 inhibitors were introduced in the guidelines and all throughout that there was discussions of cost. There are a lot of concerns from the field that PCSK9 inhibitors would bankrupt the system because so many people were on statins. And you look at the prior one that was in 2013 and cost was mentioned once it’s just the cost effectiveness of statins. So I think the field has that overall concern.

(17:01):

However, over time we've gotten comfortable with lower targets, there are more medicines and I think some of this competition hopefully will drive down some of the costs, but also the overall appreciation of the science related to LDL. So long-winded way of saying this is kind of the things that we discussed just to give reassurance that we can go to low LDL cholesterol values and that it's safe and then we think also very effective. Nobody knows what the lower limit is, whether zero is appropriate or not. We know that glucose can get too low. We know that blood pressure can be too low. We don't know yet that limit for LDL cholesterol. I mean increasingly with these trials we'll see it going down really low and then we'll better appreciate and understand, so we'll see 40 is probably the right range.

Eric Topol (17:49):

40, you said? Yeah, okay, I'll buy that. Of course, the other thing that we do know is that if you push to the highest dose statins to get there, you might in some people start to see the hyperglycemia issue, which is still not fully understood and whether that is, I mean it's not desirable, but whether or not it is an issue, I guess it's still out there dangling. Now the other thing that since we're on LDL, we covered Lp(a), PCSK9, the siRNA, is ApoB. Do you measure ApoB in all your patients? Should that be the norm?

Measuring ApoB

Pradeep Natarajan (18:32):

Yeah, so ApoB is another blood test. In the standard lipid panel, you get four things. What's measured is cholesterol and triglycerides, they're the lipids insoluble in blood to get to the different tissues that get packaged in lipoprotein molecules which will have the cholesterol, triglycerides and some other lipids and proteins. And so, they all have different names as you know, right? Low density lipoprotein, high density lipoprotein and some others. But also in the lipid panel you get the HDL cholesterol, the amount of cholesterol in an HDL particle, and then most labs will calculate LDL cholesterol and LDL cholesterol has a nice relationship with cardiovascular disease. You lower it with statins and others. Lower risk for cardiovascular disease, turns out a unifying feature of all of these atherogenic lipoproteins, all these lipoproteins that are measured and unmeasured that relate to cardiovascular disease, including lipoprotein(a), they all have an additional protein called ApoB. And ApoB, at least as it relates to LDL is a pretty good surrogate of the number of LDL particles.

(19:37):

Turns out that that is a bit better at the population level at predicting cardiovascular disease beyond LDL cholesterol itself. And where it can be particularly helpful is that there are some patients out there that have an unexpected ratio between ApoB and LDL. In general, the ratio between LDL cholesterol and ApoB is about 1.1 and most people will have that rough ratio. I verify that that is the expected, and then if that is the expected, then really there is no role to follow ApoB. However, primarily the patients that have features related to insulin resistance have obesity. They may often have adequate looking LDL cholesterols, but their ApoB is higher. They have more circulating LDL particles relative to the total amount of LDL cholesterol, so smaller particles themselves. However, the total number of particles may actually be too high for them.

(20:34):

And so, even if the LDL cholesterol is at target, if the ApoB is higher, then you need to reduce. So usually the times that I just kind of verify that I'm at appropriate target is I check the LDL cholesterol, if that looks good, verify with the ApoB because of this ratio, the ApoB target should be about 10% lower. So if we're aiming for about 40, that's like 36, so relatively similar, and if it's there, I'm good. If it's not and it's higher, then obviously increase the LDL cholesterol lowering medicines because lower the ApoB and then follow the ApoB with the lipids going forward. The European Society of Cardiology has more emphasis on measuring ApoB, that is not as strong in the US guidelines, but there are many folks in the field, preventive cardiologists and others that are advocating for the increasing use of ApoB because I think there are many folks that are not getting to the appropriate targets because we are not measuring ApoB.

Why Aren’t We Measuring and Treating Inflammation?

Eric Topol (21:37):

Yeah, I think you reviewed it so well. The problem here is it could be part of the standard lipid panel, it would make this easy, but what you've done is a prudent way of selecting out people who it becomes more important to measure and moderate subsequently. Now this gets us to the fact that we're lipid centric and we don't pay homage to inflammation. So I wrote a recent Substack on the big miss on inflammation, and here you get into things like the monoclonal antibody to interleukin-6, the trial that CANTOS that showed significant reduction in cardiovascular events and fatal cancers by the way. And then you get into these colchicine trials two pretty good size randomized trials, and here the entry was coronary disease with a high C-reactive protein. Now somehow or other we abandon measuring CRP or other inflammatory markers, and both of us have had patients who have low LDLs but have heart attacks or significant coronary disease. So why don't we embrace inflammation? Why don't we measure it? Why don't we have better markers? Why is this just sitting there where we could do so much better? Even agents that are basically cost pennies like colchicine at low doses, not having to use a proprietary version could be helpful. What are your thoughts about us upgrading our prevention with inflammation markers?

Pradeep Natarajan (23:22):

Yeah, I mean, Eric, there is an urgent need to address these other pathways. I say urgent need because heart disease has the dubious distinction of being the leading killer in the US and then over the last 20 years, the leading killer in the world as it takes over non-communicable diseases. And really since the early 1900s, there has been a focus on developing pharmacotherapies and approaches to address the traditional modifiable cardiovascular disease risk factors. That has done tremendous good, but still the curves are largely flattening out. But in the US and in many parts of the world, the deaths attributable to cardiovascular disease are starting to tick up, and that means there are many additional pathways, many of them that we have well recognized including inflammation. More recently, Lp(a) that are likely important for cardiovascular disease, for inflammation, as you have highlighted, has been validated in randomized controlled trials.

(24:18):

Really the key trial that has been more most specific is one on Canakinumab in the CANTOS trial IL-1β monoclonal antibody secondary prevention, so cardiovascular disease plus high C-reactive protein, about a 15% reduction in cardiovascular disease and also improvement in cancer related outcomes. Major issues, a couple of issues. One was increased risk for severe infections, and the other one is almost pragmatic or practical is that that medicine was on the market at a very high price point for rare autoinflammatory conditions. It still is. And so, to have for a broader indication like cardiovascular disease prevention would not make sense at that price point. And the manufacturer tried to go to the FDA and focus on the group that only had C-reactive protein lowering, but that's obviously like a backwards endpoint. How would you know that before you release the medicine? So that never made it to a broader indication.

(25:14):

However, that stuck a flag in the broader validation of that specific pathway in cardiovascular disease. That pathway has direct relevance to C-reactive protein. C-reactive protein is kind of a readout of that pathway that starts from the NLRP3 inflammasome, which then activates IL-1β and IL-6. C-reactive protein we think is just a non causal readout, but is a reliable test of many of these features and that's debatable. There may be other things like measuring IL-6, for example. So given that there is actually substantial ongoing drug development in that pathway, there are a handful of companies with NLRP3 inflammasome inhibitors, but small molecules that you can take as pills. There is a monoclonal antibody against IL-6 that's in development ziltivekimab that's directed at patients with chronic kidney disease who have lots of cardiovascular disease events despite addressing modifiable risk factors where inflammatory markers are through the roof.

(26:16):

But then you would also highlighted one anti-inflammatory that's out there that's pennies on the dollar, that's colchicine. Colchicine is believed to influence cardiovascular disease by inhibiting NLRP3, I say believed to. It does a lot of things. It is an old medicine, but empirically has been shown in at least two randomized controlled trials patients with coronary artery disease, actually they didn't measure C-reactive protein in the inclusion for these, but in those populations we did reduce major adverse cardiovascular disease events. The one thing that does give me pause with colchicine is that there is this odd signal for increased non-cardiovascular death. Nobody understands if that's real, if that's a fluke. The FDA just approved last year low dose colchicine, colchicine at 0.5 milligrams for secondary prevention given the overwhelming efficacy. Hasn’t yet made it into prevention guidelines, but I think that's one part that does give me a little bit pause. I do really think about it particularly for patients who have had recurrent events. The people who market the medicine and do research do remind us that C-reactive protein was not required in the inclusion, but nobody has done that secondary assessment to see if measuring C-reactive protein would be helpful in identifying the beneficial patients. But I think there still could be more work done on better identifying who would benefit from colchicine because it's an available and cheap medicine. But I'm excited that there is a lot of development in this inflammation area.

Eric Topol (27:48):

Yeah, well, the development sounds great. It's probably some years away. Do you use colchicine in your practice?

Pradeep Natarajan (27:56):

I do. Again, for those folks who have had recurrent events, even though C-reactive protein isn't there, it does make me feel like I'm treating inflammation. If C-reactive protein is elevated and then I use it for those patients, if it's not elevated, it's a much harder sell from my standpoint, from the patient standpoint. At the lower dose for colchicine, people generally are okay as far as side effects. The manufacturer has it at 0.5 milligrams, which is technically not pennies on the dollar. That's not generic. The 0.6 milligrams is generic and they claim that there is less side effects at the 0.5 milligrams. So technically 0.6 milligrams is off label. So it is what it is.

CHIP and Defining High Risk People for CV Disease

Eric Topol (28:40):

It's a lot more practical, that's for sure. Now, before I leave that, I just want to mention when I reviewed the IL-1β trial, you mentioned the CANTOS trial and also the colchicine data. The numbers of absolute increases for infection with the antibody or the cancers with the colchicine are really small. So I mean the benefit was overriding, but I certainly agree with your concern that there's some things we don't understand there that need to be probed more. Now, one of the other themes, well before one other marker that before we get to polygenic risk scores, which is center stage here, defining high risk people. We've talked a lot about the conventional things and some of the newer ways, but you've been one of the leaders of study of clonal hematopoiesis of indeterminate potential known as CHIP. CHIP, not the chips set in your computer, but CHIP. And basically this is stem cell mutations that increase in people as we age and become exceptionally common with different mutations that account in these clones. So maybe you can tell us about CHIP and what I don't understand is that it has tremendous correlation association with cardiovascular outcomes adverse as well as other system outcomes, and we don't measure it and we could measure it. So please take us through what the hell is wrong there.

Pradeep Natarajan (30:14):

Yeah, I mean this is really exciting. I mean I'm a little bit biased, but this is observations that have been made only really over the last decade, but accelerating research. And this has been enabled by advances in genomic technologies. So about 10 years or plus ago, really getting into the early days of population-based next generation sequencing, primarily whole exome sequencing. And most of the DNA that we collect to do these population-based analyses come from the blood, red blood cells are anucleate, so they're coming from white blood cells. And so, at that time, primarily interrogating what is the germline genetic basis for coronary artery disease and early onset myocardial infarction. At the same time, colleagues at the Broad Institute were noticing that there are many additional features that you can get from the blood-based DNA that was being processed by the whole exome data. And there were actually three different groups that converged on that all in Boston that converged on the same observation that many well-established cancer causing mutations.

(31:19):

So mutations that are observed in cancers that have been described to drive the cancers themselves were being observed in these large population-based data sets that we were all generating to understand the relationship between loss of function mutations in cardiovascular disease. That's basically the intention of those data sets for being generated for other things. Strong correlation with age, but it was very common among individuals greater than 70; 10% of them would have these mutations and is very common because blood cancer is extremely, it's still pretty rare in the population. So to say 10% of people had cancer causing driver mutations but didn't have cancer, was much higher than anyone would've otherwise expected. In 2014, there were basically three main papers that described that, and they also observed that there is a greater risk of death. You'd say, okay, this is a precancerous lesion, so they're probably dying of cancer.

(32:17):

But as I said, the absolute incidence rate for blood cancer is really low and there's a relative increase for about tenfold, but pretty small as it relates to what could be related to death. And in one of the studies we did some exploratory analysis that suggested maybe it's actually the most common cause of death and that was cardiovascular disease. And so, a few years later we published a study that really in depth really looked at a bunch of different data sets that were ascertained to really understand the relationship between these mutations, these cancer causing mutations in cardiovascular disease, so observed it in enrichment and older individuals that had these mutations, CHIP mutations, younger individuals who had early onset MI as well too, and then also look prospectively and showed that it related to incident coronary artery disease. Now the major challenge for this kind of analysis as it relates to the germline genetic analysis is prevalence changes over time.

(33:15):

There are many things that could influence the presence of clonal hematopoiesis. Age is a key enriching factor and age is the best predictor for cardiovascular disease. So really important. So then we modeled it in mice. It was actually a parallel effort at Boston University (BU) that was doing the same thing really based on the 2014 studies. And so, at the same time we also observed when you modeled this in mice, you basically perturb introduce loss of function mutations in the bone marrow for these mice to recapitulate these driver mutations and those mice also have a greater burden of atherosclerosis. And Eric, you highlighted inflammation because basically the phenotype of these cells are hyper inflamed cells. Interestingly, C-reactive protein is only modestly elevated. So C-reactive protein is not fully capturing this, but many of the cytokines IL-1β, IL-6, they're all upregulated in mice and in humans when measured as well.

(34:11):

Now there've been a few key studies that have been really exciting about using anti-inflammatories in this pathway to address CHIP associated cardiovascular disease. So one that effort that I said in BU because they saw these cytokines increased, we already know that these cytokines have relationship with atherosclerosis. So they gave an NLRP3 inflammasome inhibitor to the mice and they showed that the mice with or without CHIP had a reduction in atherosclerosis, but there was a substantial delta among the mice that are modeled as having CHIP. Now, the investigators in CANTOS, the manufacturers, they actually went back and they survey where they had DNA in the CANTOS trial. They measured CHIP and particularly TET2 CHIP, which is the one that has the strongest signal for atherosclerosis. As I said, overall about 15% reduction in the primary outcome in CANTOS. Among the individuals who had TET2 CHIP, it was a 64% reduction in event.

(35:08):

I mean you don't see those in atherosclerosis related trials. Now this has the caveat of it being secondary post hoc exploratory, the two levels of evidence. And so, then we took a Mendelian randomization approach. Serendipitously, just so happens there is a coding mutation in the IL-6 receptor, a missense mutation that in 2012 was described that if you had this mutation, about 40% of people have it, you have a 5%, but statistically significant reduction in coronary artery disease. So we very simply said, if the pathway of this NLRP3 inflammasome, which includes IL-6, if you have decreased signaling in that pathway, might you have an even greater benefit from having that mutation if you had CHIP versus those who didn't have CHIP. So we looked in the UK Biobank, those who didn't have CHIP 5% reduction, who had that IL-6 receptor mutation, and then those who did have CHIP, if they had that mutation, it was about a 60% reduction in cardiovascular disease.

(36:12):

Again, three different lines of evidence that really show that this pathway has relevance in the general population, but the people who actually might benefit the most are those with CHIP. And I think as we get more and more data sets, we find that not all of the CHIP mutations are the same as it relates to cardiovascular disease risk. It does hone in on these key subsets like TET2 and JAK2, but this is pretty cool as a preventive cardiologist, new potential modifiable risk factor, but now it's almost like an oncologic paradigm that is being applied to coronary artery disease where we have specific driver mutations and then we're tailoring our therapies to those specific biological drivers for coronary artery disease. Hopefully, I did that justice. There's a lot there.

Why Don’t We Measure CHIP?

Eric Topol (36:57):

Well, actually, it's phenomenal how you've explained that, but I do want to review for our listeners or readers that prior to this point in our conversation, we were talking about germline mutations, the ones you're born with. With CHIP, we're talking about acquired somatic mutations, and these are our blood stem cells. And what is befuddling to me is that with all the data that you and others, you especially have been publishing and how easy it would be to measure this. I mean, we've seen that you can get it from sequencing no less other means. Why we don't measure this? I mean, why are we turning a blind eye to CHIP? I just don't get it. And we keep calling it of indeterminate potential, not indeterminate. It's definite potential.

Pradeep Natarajan (37:51):

Yeah, no, I think these are just overly cautious terms from the scientists. Lots of people have CHIP, a lot of people don't have clinical outcomes. And so, I think from the lens of a practicing hematologists that provide some reassurance on the spectrum for acquired mutation all the way over to leukemia, that is where it comes from. I don't love the acronym as well because every subfield in biomedicine has its own CHIP, so there's obviously lots of confusion there. CH or clinical hematopoiesis is often what I go, but I think continuing to be specific on these mutations. Now the question is why measure? Why aren't we measuring it? So there are some clinical assays out there. Now when patients get evaluated for cytopenias [low cell counts], there are next generation sequencing tests that look for these mutations in the process for evaluation. Now, technically by definition, CHIP means the presence of these driver mutations that have expanded because it's detectable by these assays, not a one-off cell because it can only be detected if it's in a number of cells.

(38:55):

So there has been some expansion, but there are no CBC abnormalities. Now, if there's a CBC abnormality and you see a CHIP mutation that's technically considered CCUS or clonal cytopenia of unknown significance, sometimes what is detected is myelodysplastic syndrome. In those scenarios still there is a cardiovascular disease signal, and so many of our patients who are seen in the cancer center who are being evaluated for these CBC abnormalities will be detected to have these mutations. They will have undergone some risk stratification to see what the malignancy potential is. Still pretty low for many of those individuals. And so, the major driver of health outcomes for this finding may be cardiovascular. So those patients then get referred to our program. Dana-Farber also has a similar program, and then my colleague Peter Libby at the Brigham often sees those patients as well. Now for prospective screening, so far, an insurance basically is who's going to pay for it.

(39:51):

So an insurance provider is not deemed that appropriate yet. You do need the prospective clinical trials because the medicines that we're talking about may have side effects as well too. And what is the yield? What is the diagnostic yield? Will there actually be a large effect estimate? But there has been more and more innovation, at least on the assay and the cost part of the assay because these initial studies, we've been using whole exome sequencing, which is continuing to come down, but is not a widely routine clinical test yet. And also because as you highlighted, these are acquired mutations. A single test is not necessarily one and done. This may be something that does require surveillance for particular high risk individuals. And we've described some risk factors for the prevalence of CHIP. So surveillance may be required, but because there are about 10 genes that are primarily implicated in CHIP, that can substantially decrease the cost of it. The cost for DNA extraction is going down, and so there are research tests that are kind of in the $10 to $20 range right now for CHIP. And if flipped over to the clinical side will also be reasonably low cost. And so, for the paradigm for clinical implementation, that cost part is necessary.

Eric Topol (41:10):

I don't know the $10 or $20 ones. Are there any I could order on patients that I'm worried about?

Pradeep Natarajan (41:17):

Not yet clinical. However, there is a company that makes the reagents for at least the cores that are developing this. They are commercializing that test so that many other cores, research cores can develop it. I think it's in short order that clinical labs will adopt it as well too.

Eric Topol (41:36):

That's great.

Pradeep Natarajan (41:37):

I will keep you apprised.

What About Polygenic Risk Scores?

Eric Topol (41:39):

I think that's really good news because like I said, we're so darn lipid centric and we have to start to respect the body of data, the knowledge that you and others have built about CHIP. Now speaking of another one that drives me nuts is polygenic risk score (PRS) for about a decade, I've been saying we have coronary disease for most people is a polygenic trait. It's not just a familial hypercholesterolemia. And we progressively have gotten better and better of the hundreds of single variants that collectively without a parental history will be and independently predict who is at double, triple or whatever risk of getting heart disease, whereby you could then guide your statins at higher aggressive or pick a statin, use one or even go beyond that as we've been talking about. But we don't use that in practice, which is just incredible because it's can be done cheap.

(42:45):

You can get it through whether it's 23andMe or now many other entities. We have an app, MyGeneRank where we can process that Scripss does for free. And only recently, Mass General was the first to implement that in your patient population, and I'm sure you were a driver of that. What is the reluctance about using this as an orthogonal, if you will, separate way to assess a person's risk for heart disease? And we know validated very solidly about being aggressive about lipid lowering when you know this person's in the highest 5% polygenic risk score. Are we just deadheads in this field or what?

Pradeep Natarajan (43:30):

Yeah, I mean Eric, as you know, lots of inertia in medicine, but this one I think has a potential to make a large impact. Like CHIP mutations, I said news is about 10% in individuals greater than 70. The prospect here is to identify the risk much earlier in life because I think there is a very good argument that we're undertreating high risk individuals early on because we don't know how to identify them. As you highlighted, Dr. Braunwald about LDL cholesterol. The other part of that paradigm is LDL cholesterol lowering and the duration. And as we said, everybody would benefit from really low LDL cholesterol, but again, you might overtreat that if you just give that to everybody. But if you can better identify the folks very early in life, there is a low cost, low risk therapy, at least related to statins that you could have a profound benefit from the ones who have a greater conviction will have future risk for cardiovascular disease.

(44:21):

You highlighted the family history, and the family history has given the field of clues that genetics play a role. But as the genome-wide association studies have gotten larger, the polygenic risk scores have gotten better. We know that family history is imperfect. There are many reasons why a family member who is at risk may or may not have developed cardiovascular disease. A polygenic risk score will give a single number that will estimate the contribution of genetics to cardiovascular disease. And the thing that is really fascinating to me, which is I think some of a clinical implementation challenge is that the alleles for an individual are fixed. The genotyping is very cheap. That continues to be extremely cheap to do this test. But the weights and the interpretation of what the effects should be for each of the SNPs are continually being refined over time.

(45:18):

And so, given the exact same SNPs in the population, the ability to better predict cardiovascular diseases getting better. And so, you have things that get reported in the literature, but literally three years later that gets outdated and those hypotheses need to be reassessed. Today, I'll say we have a great relative to other things, but we have a great polygenic risk score was just reported last year that if you compare it to familial hypercholesterolemia, which has a diagnostic yield of about 1 in 300 individuals, but readily detectable by severe hypercholesterolemia that has about threefold risk for cardiovascular disease. By polygenic risk score, you can find 1 in 5 individuals with that same risk. Obviously you go higher than that, it'll be even higher risk related to that. And that is noble information very early in life. And most people develop risk factors later in life. It is happening earlier, but generally not in the 30s, 40s where there's an opportunity to make a substantial impact on the curve related to cardiovascular disease.

(46:25):

But there is a lot of momentum there. Lots of interest from NIH and others. The major challenge is though the US healthcare system is really not well set up to prevention, as you know, we practice healthcare after patient's developed disease and prevent the complications related to progression. The stakeholder incentives beyond the patient themselves are less well aligned. We've talked a lot here today about payers, but we don't have a single payer healthcare system. And patients at different times of their lives will have different insurers. They'll start early in life with their parents, their first employer, they'll move on to the next job and then ultimately Medicare. There's no entity beyond yourself that really cares about your longevity basically from the beginning and your overall wellness. That tension has been a major challenge in just driving the incentives and the push towards polygenic risk scores. But there are some innovative approaches like MassMutual Life Insurance actually did a pilot on polygenic risk scoring.

(47:33):

They're in the business of better understanding longevity. They get that this is important data. Major challenges, there are federal protections against non-discrimination in the workplace, health insurance, not necessarily life insurance. So I think that there are lots of things that have to be worked out. Everybody recognizes that this is important, but we really have to have all the incentives aligned for this to happen at a system-wide level in the US. So there's actually lots of investment in countries that have more nationalized healthcare systems, lots of development in clinical trials in the UK, for example. So it's possible that we in the US will not be the lead in that kind of evidence generation, but maybe we'll get there.

The GLP-1 Drugs

Eric Topol (48:16):

Yeah, it's frustrating though, Pradeep, because this has been incubating for some time and now we have multi ancestry, polygenic risk scores, particularly for heart disease and we're not using it, and it's not in my view, in the patient's best interest just because of these obstacles that you're mentioning, particularly here in the US. Well, the other thing I want to just get at with you today is the drugs that we were using for diabetes now blossoming for lots of other indications, particularly the glucagon-like peptide 1 (GLP-1) drugs. This has come onto the scene in recent years, not just obviously for obesity, but it's anti-inflammatory effects as we're learning, mediated not just through the brain but also T cells and having extraordinary impact in heart disease for people with obesity and also with those who have heart failure, about half of heart failure for preserved ejection fraction. So recently you and your colleagues recently published a paper with this signal of optic neuropathy. It was almost seven eightfold increase in a population. First, I wanted to get your sense about GLP-1. We're also going to get into the SGLT2 for a moment as well, but how do you use GLP-1? What's your prognosis for this drug class going forward?

Pradeep Natarajan (49:55):

As it relates to the paper, I can't claim credit as one of my former students who is now Mass Eye and Ear resident who participated, but we can talk about that. There's obviously some challenges for mining real world data, but this was related to anecdotes that they were observing at Mass Eye and Ear and then studied and observed an enrichment. In general though, I feel like every week I'm reading a new clinical trial about a new clinical outcome benefit as it relates to GLP-1 receptor agonists. This is kind of one thing that stands out that could be interrogated in these other clinical trials. So I would have that caveat before being cautious about ocular complications. But the data has been overwhelmingly beneficial, I think, because at minimum, obesity and inflammation are relayed to myriad of consequences, and I'm really excited that we have therapies that can address obesity that are safe.

(50:52):

There's a legacy of unsafe medicines for obesity, especially related to cardiovascular disease. So the fact that we have medicines that are safe and effective for lowering weight that also have real strong effects on clinical outcomes is tremendous. We in cardiology are increasingly using a range of diabetes medicines, including GLP-1 receptor agonists and SGLT2 inhibitors. I think that is also the secular changes of what influences cardiovascular disease over time. I talked about over the last 10 years or so with this increase in deaths attributable to cardiovascular disease. If you look at the influences of traditional clinical risk factors today, many of them have decreased in importance because when abnormal, we recognize them, in general we modify them when recognized. And so, many of the things that are unaddressed, especially the features related to insulin resistance, obesity, they start rising in importance. And so, there is a dramatic potential for these kinds of therapies in reducing the residual risks that we see related to cardiovascular disease. So I'm enthusiastic and excited. I think a lot more biology that needs to be understood of how much of this is being influenced specifically through this pathway versus a very effective weight loss medicine. But also interesting to see the insights on how the effect centrally on appetite suppression has profound influences on weight loss as well too. And hopefully that will lead to more innovations in weight management.

The SGLT-2 Drugs

Eric Topol (52:25):

And likewise, perhaps not getting near as much play, but when it came on the cardiovascular scene that an anti-diabetic drug SGLT2 was improving survival, that was big, and we still don't know why. I mean, there's some ideas that it might be a senolytic drug unknowingly, but this has become a big part of practice of cardiology in patients with diabetes or with preserved ejection fraction heart failure. Is that a fair summary for that drug?

Pradeep Natarajan (53:00):

Yeah, I totally agree. I mean, as there has been increased recognition for heart failure preserved ejection fraction, it has been almost disheartening over the last several years that we have not had very specific effective therapies to treat that condition. Now, it is a tremendous boon that we do have medicines interestingly focused on metabolism that are very helpful in that condition for heart failure with preserved ejection fraction. But there is still much more to be understood as far as that condition. I mean, the major challenge with heart failure, as you know, especially with heart failure preserved ejection fraction, it likely is a mix of a wide variety of different etiologies. So in parallel with developing effective therapies that get at some aspect is really understanding what are the individual drivers and then targeting those specific individual drivers. That requires a lot of unbiased discovery work and further profiling to be done. So lot more innovation, but relative to heart failure itself, it is not had widespread recognition as heart failure reduced ejection fraction. So much more to innovate on, for sure.

Eric Topol (54:07):

Right, right. Yeah, I am stunned by the recent progress in cardiovascular medicine. You have been center stage with a lot of it, and we've had a chance to review so much. And speaking of genetics, I wanted to just get a little insight because I recently came across the fact that your mother here at the City of Hope in Southern California is another famous researcher. And is that, I don't know what chromosome that is on regarding parental transmission of leading research. Maybe you can tell me about that.

Pradeep Natarajan (54:41):

Yeah, I mean, I guess it is a heritable trait when a parent has one profession that there is a higher likelihood that the offspring will have something similar. So both of my parents are PhDs, nonphysicians. There is a diabetes department at the City of Hope, so she's the chair of that department. So very active. We do overlap in some circles because she does investigate both vascular complications and renal complications. And then sometimes will ask my advice on some visualization. But she herself has just had a science translational medicine paper, for example, just a couple of months ago. So it's fun to talk about these things. To be honest, because my parents are researchers, I was not totally sure that I would be a researcher and kind of wanted to do something different in medicine. But many of my early observations and just how common cardiovascular disease is around me and in my community and wanting to do something useful is what got me specifically into cardiology.

(55:45):

But obviously there are numerous outstanding, important questions. And as I went through my career, really focused on more basic investigations of atherosclerosis and lipids. What got me excited sort of after my clinical training was the ability to ask many of these questions now in human populations with many new biological data sets, at least first centered on genetics. And the capabilities continue to expand, so now I teach first year Harvard medical students in their genetics curriculum. And when I talk to them just about my career arc, I do remind them they're all doing millions of things and they're exploring lots of things, but when they get to my shoes, the capabilities will be tremendously different. And so, I really advise them to take the different experiences, mainly in an exercise for asking questions, thoughtfully addressing questions, connecting it back to important clinical problems. And then once they start to understand that with a few different approaches, then they'll totally take off with what the opportunities are down the road.

Eric Topol (56:51):

No, it's great. I mean, how lucky somebody could be in the first year of med school with you as their teacher and model. Wow. Pradeep, we've really gone deep on this and it's been fun. I mean, if there's one person I'm going to talk to you about cardiovascular risk factors and the things that we've been into today, you would be the one. So thank you for taking the time and running through a lot of material here today, and all your work with great interest.

Pradeep Natarajan (57:24):

Thanks, Eric. I really appreciate it. It's tremendous honor. I'm a big fan, so I would be glad to talk about any of these things and more anytime.

***************

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