Look & Feel Younger: How To Increase Lifespan & Healthspan With Age | Drs. Nathan Price & Lee Hood

Are you transitioning into a disease? Are you heading toward wellness?

Can you talk about the origins of this concept of systems biology and scientific wellness? Let's hear from both of you on this.

I think the essential feature to understand is that humans are incredibly complex. They have complex systems that interact with one another. They interact with one another at many different levels from an informational point of view, and from an organizational point of view. And in thinking about this many, many years ago, it became obvious that there were a number of things that we had to do to deal with complexity. And the most important of it was really what's come to be known now as big data. What is essential in a human being to deciphering their complexity, whether it be a complexity associated with wellness, or with disease, is having information about many different systems, being able to understand what the organism is, what the human is on the outside as well as the inside. And one major principle that we've really formulated is the idea blood is a window into health and disease. your blood bathes every organs, they secrete molecules into it. If you can analyze those molecules, you can in a sense begin to understand how all those different organs are with regard to their health state and so forth. I think another thing that's become very important is when you make the measurement on many different blood analytes, proteins and metabolites and clinical chemistries and everything, you get an enormous amount of complex data. The question is, how do we deal with that data? That's where systems biology comes in. The idea is exactly as you expressed it, Mark. The idea is that our body is a multitude of different biological networks that carry out the physiology of a normal individual. In fact, when you become diseased, those networks also become diseased. What we have to do with systems biology is take the big data information that we can now gather from your blood and so forth and to place it into those networks and to do it so in a dynamical way so that over time we know how the networks are changing. Those networks and their change give us an idea of the slope of your health. Are you transitioning into a disease? Are you heading toward wellness? The integration then of big data of systems biology. I'd say the final initiative is the understanding of how AI is going to be able to deal with this complexity and translate it into meaningful information for physicians. For example, we have the hope in the future that with large language models and digital twins and knowledge graphs, these are all fundamental tools of AI, we'll be able to take the very complex information that comes from your genome, and from your phenome and feed it into this system and have it identify your deficiencies and for each individual, give us an ordered list of the actionable possibilities that can move us to greater wellness or back from disease to a wellness state and so forth. So those are the fundamental principles. and you enunciated them very clearly when you articulated the introduction.

Thank you. Nathan, what are your thoughts on this sort of revolution in scientific wellness and how did you kind of come up with this concept? Because it's kind of a beautiful way of expressing the hard science behind health, which is something we've not really looked at in medicine.

Yeah, and that's kind of how we thought about it because Lee and I both come in from a hard hardcore science background into this. But you mentioned earlier, the way that people think about wellness, you tend to think about yoga and you think about.

Dr. Acupuncture, right.

Acupuncture, exactly, all these kind of things. And so, there's this really interesting nexus between the way we think about medicine or healthcare in the West and how it's thought about in the East. And, there's this very interesting intersection about a philosophy of wellness. but taking some of the scientific rigor of the principles of what we have that has defined a lot of Western medicine. We get into this in the beginning of the book. And so, you know, that's where we came up with this, you know, the term for better or worse that we chose scientific wellness was kind of to indicate wellness, which is the orientation towards not disease, but health. But the scientific moniker was supposed to kind of differentiate or give a sense, you know, that's what we're talking about. And so, scientific wellness in that sense then is really just the same principles that we might think of in precision medicine. That's trying to take a very deep view into how you leverage all these molecular data and tools. But our feeling is that that is too narrow because the way that that's getting set up, it's still set up around this paradigm of wait till you have some horrible symptom. get diagnosed with a disease, and you get this drug. And we're trying to give those drugs more precisely. But Scientific Wellness says, no, that's not enough. We want to shift the whole orientation so that we're still being precise and deep, but do it early and focus on let's extend your health span as long as possible, reverse disease in its earliest transitions if it arises, and try to never get into those later states to the degree that that's possible. And then the effort around scientific wellness is really to just drive how do we have as deep of a scientific enterprise underneath the wellness paradigm that we've developed under the disease paradigm.

It's amazing. It reminds me of a patient I had years ago that came in and this blood sugar, I think it was 120 or something. And I said, Hey, has your doctor seen this? And have you gotten any advice from your doctor about what to do? And he says, well, yeah, my doctor said my blood sugar is going off, but wait till I get in the diabetic range and then he'll give me a medication. It's sort of the opposite of what we're talking about is like, well, if your blood sugar is 120, you're already screwed. If, you know, when you look at the data, anything over 85, which, you know, 100 is considered now normal, it used to be 110. But if you're 85 to 100, you're still having an increasing risk in a linear way for cardiovascular disease and other bad events. And, you know, when you think about what you're talking about as a doctor, you know, what I learned to do was take a medical history, what I could remember to ask, you know, do a physical exam, look for things that I could see crudely that were wrong, maybe get some imaging, a few blood tests, not that money, maybe 20, 30, 40 analytes in your basic chem screen, CBC, and then, you know, assume that I could kind of figure things out. And, you know, when things go really bad, you know, those things are off, right? When you're kind of in the hospital at the end stage of these processes, you get to see abnormalities, but most of the time you don't see much. And it's like, It's kind of a joke because there's billions and billions of chemical reactions every second. And what you are both talking about is really a revolution where we're not just taking, you know, a few physical signs and symptoms, a few lab tests, we're talking about literally billions and billions of data points that are going on in your body from your genes to your microbiome, to your metabolome, to your transcriptome, to your proteome, all these things that we're looking at in the omics revolution. And, and biometrics that we're now gonna be able to put in through wearables, you know, and we're gonna be doing, you know, analyze the things that we, we haven't been able to measure before. And so we're, we're, yeah, all of a sudden, we'll have enormous amounts of data, but there's no way a doctor can make sense of that because they, we have one brain and it's, it's kind of not AI, but it's pretty good eye, but it's not, it's not able to sort of measure and track and interpret and analyze a list. And, you know, you've talked before about these dense Dynamic data clouds. You've gave a talk at Cleveland Clinic for Grand around, Nathan was really great. And you talked about the future of this and shared, you know, what you've been learning through some of the work you've done. And so rather than just taking, you know, a simple view, you're taking this complex view and mapping out, you know, where are people in the Continuum of health and wellness? So you, you've done a number of projects at Pioneer 100 group. You're now trying to do a much bigger one with a million people. And, you know, you've, you've created air, which is an attempt to kind of create a clinical model for this. Can you talk about your learnings from the A Pioneer 100, which is where you try to collect all this data and make sense of it for people and then give them a plan that's as you call it, P4P or P4, which is personalized, it's preventive, it's predictive, and it's participatory, meaning the patient has to do some of the work.

Well, I think the Pioneer 100, which occurred around 2014 was a study in which we put together essentially 108 of our friends and we carried out a whole genome analysis. We did the blood analytes and we used a Fitbit and we analyzed the gut microbiome. All of these data were integrated together. It was done in the context of people who understood medicine. So from these data, we identified 3,500 statistical associations and took the most interesting of those associations to the medical literature. And they led us to actionable possibilities to something concrete the individual that had that particular perturbation could do either to improve their wellness and or let them avoid disease. And during the course of this program, we came up with hundreds of actionable possibilities. And it was obvious with a larger study, we'd end up coming up with thousands of these kinds of things. What we did learn that was very important was that the fourth P, participatory, the need for the patient to take their own health was absolutely critical. In this pilot study, we had coaches who actually could take these actionable possibilities, prioritize them and discuss them with each patient, encouraging them to move on and carry these things out. We found that the coaching was enormously beneficial In one, the retainment of these people for the full year that we did it, or nine months that we did it. And two, in causing people to actually carry out these actions and so forth. And that was foundation for us moving to Ayraville, a program that lasted for four years, did exactly the same thing with the same general approach. but generated an enormous amount of data that gave us deep insights into scientific wellness, into healthy aging, into our ability to detect transitions years before the actual disease showed up clinically with the hope that we'll be able to use prevention and or therapy to reverse it at that early and very simple stage. stage. And it's that that's where we stand today. We have from these two demonstrations, one of 108 and one of about 5,000, an enormously compelling picture of what we can do for an individual if we analyze the genome and the phenome. And I just add that the phenome really turns out to be three interesting things. It's one, your personal behavior and the choices you make, and two, it's your environment and how they impinge upon yourself. And I think a really apt analogy for doing this is a composer writes a tune, right? And it's the player that takes the tune and gives it life and puts passion in it and changes it, and different players change it in different ways. It's exactly the same with the genome and the phenome. The genome is your basic construction kit for how human develops, but the phenome is the artist, and that's your behavior and your environment that play on this genome. And the three of those things together give you your phenotype at any given point in time. And essentially these measurements are attempting to determine the influence your environment and your behavior have had in making what you are and more important in being able to take you to where we want to go to superb wellness. And I'll just say one more thing. Most people who are well don't realize they've probably exerted only 20 or 30% of their potential for wellness. They can do much, much more in many different ways. And it comes back to how do we persuade patients to actively participate in this process and commit themselves to a future where your health span will equal your lifespan. And we think we can move it out into the 90s or 100s, where we can give you an extra 20 or 30 years of healthy life. And of course, the fascinating question is, what are you going to do with it?

Exactly. Well, and we know what you're doing with it, Ali. You're 85 and going strong and just building new companies and businesses and writing new books. It's very inspiring. You know, I think, you know, Nathan, I think we, you know, when you looked at the Pioneer 100 study, it was very early on, right, in the in this sort of space and it was, you know, using lots of data information. It was mostly filtered through the human mind to come up with a set of sort of recommendations and guidelines. But now we're talking about, you know, something that's even beyond that. So how do you see this progressing where we use these big data analytics, and AI machine learning behind the scenes to actually make sense of this enormous amount of data. And how, you know, what's the next iteration of the, you know, the phenome study or the Pioneer 100? Because, you know, as a, you know, as a physician, I've been doing this for so long, I see all the patterns in the data that I recognize. And I can be very good at it because I've had, you know, tens of thousands of hours of experience, seeing millions and millions of data points, and I'm sort of, you know, 30 years in. But it's kind of locked in my head. I can't really share with anybody easily. And I know I'm missing probably 90% or 99% of what actually really is true also, even though I probably see more than most. So, you know, I know that there's so much in there and I've seen patterns in when I do these, you know, really extensive phenotyping of people through all the diagnostic testing. And I can see these patterns that I are emerging that I've never seen described. For example, I see someone with heavy metal toxicity and I can guess that they have it because I see mitochondrial dysfunction on organic acids. I see for example, methylation problems, low glutathione, low zinc, low amino acids, oxidative stress, and it gives me a clue that there's something damaging these various pathways and it's likely heavy metals. So I can infer that, but I've never seen that written up in a paper. And I'm sure that's just like the tip of the iceberg. So how do we take the insights that now we're gleaning from this phenotyping, these dense dynamic data clouds of all these biomarkers and actually make sense of it? and create a predictive model for people to create a personalized program that optimizes their health and not just as treating disease.

It's a great question, Mark. So at the very beginning, you're exactly right. There was, you know, you were just pinpricks of information relative to now that you could really deal with. But over the last several years, you know, and maybe we'll come back to this in a little bit, we learned a tremendous amount about the data, published a ton of papers, insights, things we can talk about. you can come back to that in a minute, but I want to address your forward question of what do we do with all these different kinds of data? And this is where, as we talk about in the age of scientific wellness, the reason we think this is so ripe for pushing now is because of these two big features that are happening. There's this massive growth in the amount that you can measure, but at the same time, there's massive increase in how you can interpret those data and deliver it back to people. So for example, one of the things that we've done recently is to get into digital twins. And so a digital twin is a representation of your body's physiology and we've done this first for brain health. And so what we can actually do in this case is, and we're going to release a test on this and a product based on this next year. But basically what you can do is you can monitor for a number of these blood measures, your genetics, cognitive assessments, and so forth. And you can then run a simulation based on your particular biology, and it's based around the, you know, understanding from a physiologic and molecular level what's driving brain health. And you can actually forecast the likely amount of time that you have with a healthy brain given your current state. More importantly, you can go to personalized recommendations for different kinds of things that people can do, some of which are exercise to keep your oxygenation in your brain high, you can get into things like phosphatidylcholine, turns out that that becomes rate limiting under low oxygen conditions. Latest people are developing dementia, hugely important. Vitamin D, very simple one, we could talk a lot more about that one. turns out to be very important. There's many, many of these. But the point is that what you can actually do with the digital twins is you can get a representation of a person's individual risk profile and then tailor the precise recommendations. These recommendations are very different person to person. Once you get to four recommendations, only 1% of people actually benefit from what's the best thing, the best four in the population. we just did those simulations. So it's very interesting when you do that. And so you get this intense personalization and you can get into the physiology and you can start to make sense of this. Because you have to take the complexity of all these measures, you can't place that on a person. You have to put that into the algorithms and deliver back simple actionable information. And then the other side of the coin, which I'll just mention here briefly, is the ChatGPT and all these things that have shocked the world over the last year. The ability now to deliver personalized insights that give you a lot of context and that you can have a back and forth with and you can get access to a dialogue even with what your digital twin is saying or what you're learning about your body. The capability for us to develop personalization on that front is just radically better than any of us thought it was going to be a couple of years ago. And so those things together are really pushing us into this new world of where we're going to be able to harness so much more of this complexity than we could have even thought about before.

I mean, there's ChatGPT there, like now, for example, if I put in all my symptoms, I enter in all my lab data, and I hit, you know, tell me what's wrong and what to do about it, would it give me anything useful at this point, or is this still far off.

So I've played with this a lot, so maybe I'll jump in on that. But it's pretty much what I do in my free time. I don't do anything else.

You have a contract. You put it on your symptoms. My stomach hurts. I got a headache.

Yeah. It's partially there. If you use earlier versions, like the GPT 3.5, for example, you'll get lots of hallucinations. It's sometimes useful, sometimes not. GPT-4 is pretty good, except anyway, there's this weird trend. It's not as good as it used to be. And there's a lot of chatter around that on it. It doesn't let you go as deep as it used to. I don't know if it's legal or.

If they're not really- yeah, how they.

Put guardrails on it. Yeah. They put guardrails and various kinds on it and so forth. But as long as your, if your question is reasonably well dealt with in available text that it's generating from, it can be quite good. And I've had, and I've used it. not just on medical issues, but explain statistical analysis of this kind of data or something like that. And it actually gives back really reasonable kinds of information. Now, it's not fully to where it wants to, oh, and I did see a survey, maybe you saw this as well, they polled doctors and apparently 60% of doctors are using GPT today right now in the background on things that they do.

Really?

So I saw that survey.

But actually, but it was interesting.

Not totally ready for prime time, but.

Just to say that, yeah, go ahead. Well, no, I was at this big medical conference in Lake Nona, and they had this guy from Microsoft with, I think, Prometheus, which was kind of a new version of like ChatGPT that was like, you know, four doctors. And they had a case report that they were sharing and they were entering in this case study. And it got it totally wrong. And I guessed it immediately. Like I wasn't guessing, I just knew what it was because I listened to the story, but, you know, it was basically a patient who had frequent urination, fever, chills, had had, I think maybe had had a history of rheumatoid arthritis of strep long ago or something like that, or had a murmur, maybe had a murmur as a sort of part of the exam. It was just a murmur. And I'm like, oh, this guy has endocarditis. This guy has bacterial endocarditis. And the chat, the Prometheus thing said, oh, he's got a, you know, kidney infection. And I'm like, no, he said he had a kidney infection. And it was wrong. And it was like in front of like 500 people. So, you know, I kind of wonder. But I do think that, you know, the things are changing. So as you've gotten into sort of looking at the these sort of enormous amounts of data through the phenome typing of people, you know, when that goes into these machine learning AI models, like, Where is the next step in this in medicine? Are we all kind of moving towards this? Are doctors going to become in some ways obsolete? Or are they just going to be helping to kind of implement some of the decision support that these tools give? Because personally, I would love to be able to put all the data for my patients in and instead of spending hours and hours muddling over and thinking about it, trying to remember every study I ever read and what to do in my medical school training, this is going to give me kind of a roadmap to start with and then implement it. How far are we away from that?

Well, I'll make a couple of comments. I think a really important thing about these large language models, which is what GPT and the other things we've talked about are, is that they have to be educated properly. So if you take a large language model and you expose it to the internet, you expose it to the conspiracy theories and the lying and all of those other things, you have an enormous susceptibility in that device. My argument is for health, we ought to have a GPT that has only been educated with biomedical data. We're actually collaborating with a group that has one of those. and what our hope is, is wealth. And part of the education has been to put PubMed into the device, which gives you an enormous amount of data. Now some is right and some is wrong, and you'll still have to make judgments. But what we plan to do is we have access, for example, to Google's knowledge graph. And this is a graph that connected roughly 50 different features from the literature. So it's assembled from the PubMed literature, all of the relationships between genes and proteins and diseases and drugs and on and on and on.

PubMed for those listening is just the entire body of peer-reviewed published medical literature.

Biological information.

Yeah, it's a lot, it's millions of millions of studies.

Well, this knowledge graph has 50 million nodes and 850 million edges, which means an enormous number of relationships. So we're going to put this knowledge graph in this medically educated GTP. And we're going to put in, we're building now a knowledge graph for the kidney. we'd certainly like to put in the knowledge graph for brain health. All of the knowledge graphs and digital twins that we have should go into educating this thing. Then my hope is the following. We'll be able to take the data, genome and phenome from each individual enormously more complicated than what we did in Airville, maybe 10 times as much data as we had initially, and put it in there and ask it to generate from tens of thousands of actionable possibilities, the ordered priority of actionable possibilities that you as an individual can use to optimize your health or avoid disease or whatever. What the AI will actually do is send this information to a doctor and there'll be two things the information will have to do. One, clearly explain the actionable possibility and what the doctor and the patient will be expected to do. But two, it's to give the physician the medical evidence for this actionable possibility to assure an or her, it's bonafide. And the dramatic result of this is you will be able to take a family practitioner and make him a domain expertise expert in virtually every field of medicine. It gives you this global reach that you were talking about and the capacity to handle virtually anything. that democratizes medicine in an incredible way. And I'll argue, we'll never ever get rid of the physician because they're in the end still an integrative factor that we're a long ways from being able to replicate and so forth. But he will have the tools to become a world expert in every field of medicine. really quite a remarkable promise for the future. And what it promises for patients, that is the optimization of this wellness and prevention Nathan and I have talked about, I think is really dramatic.

So how far away from this are we?

So I think we'll begin to see the effects of this within the next year or so as these things get, I mean, we won't have them in the full glory for, you know, who knows? Maybe 10 years is way too long to say, 'cause look, what, I mean, that 60% of the doctors would use a tool like this. I would have said there's no way in the world that that conservative group of people would ever go into AI like this.

And yet, So they're putting their patient's history in there and saying, Hey, what's wrong? Is that what they're doing?

Yeah.

That's right.

Well, I know we should probably not over over. It means they use it to some degree because, you know, the thing about like replacing doctors, the line that I really like, I think it's Eric Topol's, which is, you know, AI won't replace doctors, but doctors who use AI will replace doctors who don't. And I think that is a really good way to put it because It is a tool and I think it's like today it's already a super useful tool. Like if you're trying to remember something or if you want to delve into the literature, it's so, you can, and especially with these particular GPTs that are based around PubMed and things like that, they're already in assist, right? So it's just already a function of how strongly that assist can be made. And I think the doctor is still going to be the quarterback. But your ability to block and tackle and just solve lots of issues with the AIs is incredible. And it's not just the LLMs. I mean, one of the really biggest uses that's straightforward right off the bat is getting rid of as many medical errors as possible. Because a doctor who's tired, it's easy to, you got a long complicated name and there's two of them that look almost exactly the same. It's pretty easy to accidentally check the wrong box. But if the AI actually knows, well, you said your patient has diabetes and that's a drug, did you actually mean this drug for multiple sclerosis? And that's already happening today, right? Hospital systems have saved millions of lives already by just implementing some of those really simple things. The kind of mistake that's easy to make as a human and a computer won't make. Now, vice versa, computers will make the kind of, you know, and AIs will make errors that a human never would because they don't understand causality, they don't understand the context. There's all kinds of stuff like the case study that you got right that the AI didn't. There's things that it doesn't know. So a hybrid or what we call Centaur AI in the book, a hybrid approach really makes a lot of sense so you can cover your bases because those two kinds of intelligence, human intelligence and AI, actually operate quite differently and the kind of errors you make are very different. combining them is powerful.

What you're talking about is definitely going to help transform the expertise of physicians and allow them to practice medicine that's more up to date, that reflects the scientific literature that is based on understanding a wide network of biological factors that they haven't been able to consider before. And that's going to be fantastic. But the truth is that wellness, health does not happen in a doctor's office.

Right.

And so 80 to 90% of the things that determine your health actually don't require a doctor and are things that you can learn about yourself and fix without a doctor's help. And so in a way, this is also going to help, I think, disintermediate people from the health care system and from doctors because we don't really have a health care system. We have a sick care system. And so what you're talking about is actually a new kind of health care system where people are going to be empowered with their own health data, Guided by, you know, these big dence data clouds of their own biological information from all their omics to their blood panels, the things we don't even measure now that we're going to measure to their wearables and biometrics. I mean, I have a Garmin watch. I mean, I know everything about myself. My pulse ox, my heart rate ability, how much I slept, how much deep sleep, how much light sleep, how much my training frequency is, you know, like how much time I need to recover. I mean, it's pretty impressive. And all that is just sitting out there ready to be kind of harvested and used. And so individuals, I think, are are in this moment where they can become more empowered to be the actors in determining their own degree of wellness and health and then know when to go to the doctor. Like, oh, well, gee, you know, your creatinine is like five. You better get your ass over to the nephrologist tomorrow. So that that's going to for sure be still there. But a lot of the stuff that actually requires a physician isn't really needed. It's really diet, lifestyle, you know, behavioral changes, supplements in other practices that they have access to. So how do you see this kind of being a tool that the individuals and patients and consumers can use in a way that is really going to disrupt healthcare.

You know, Mark, I think you made a really excellent point, and that is the importance of education for the consumer, if you will. And we're doing a number of things in that regard. For example, this past year, an educational team at the Institute for Systems Biology that that I initiated 20 years ago to deal with K through 12 science education problems, has put together a four module one year course based on two chapters several of us wrote in the Systems Biology and Systems Medicine book, one on systems medicine, one on P4 healthcare. and the essence of this module is to give them the picture that is portrayed in our book of what healthcare is going to be in the future and to clearly explain the responsibilities they'll have for their own education. And it makes very strongly the point, the core of your health is going to be diet exercise, sleep, stress, etc. And these are things you can do about it, and these are tools and devices you can use to measure it. And oh, by the way, there is this more sophisticated medicine of assaying your blood and your gut microbiome that can tell us and by the time students will get done with that year course, I'll guarantee they'll know more about what I think, what we think the future of medicine is than 95% of the physicians out there. I mean, this revolution in transforming healthcare from a disease orientation to an orientation of wellness and prevention I can't stress how important that's going to be in doing two things. One, improving the quality of health for every single individual that practices even partially. And two, it's going to lead to enormous cost savings in the healthcare system by avoiding what costs 86% of our healthcare dollars today. namely chronic diseases.

And Mark, I'd love to kind of weigh on that question as well that you know, that you asked because I think it's such an important thing because you're exactly right because the more and more of what we can call, you know, put under healthcare, especially if we start talking about wellness care, right? We like to say scientific wellness is should be the front door of the healthcare system. Most of that effort should be really beyond, you know, this maintenance, maintenance of health and then you get referred back into the disease care system when you know, hopefully early enough that really make a difference, but with some advanced warning. But the ability for us to deliver this really efficiently and low cost, I totally agree with you, is pushing this more and more to the home remotely, making it easier. So some of the things that we've done, you know, for example, you know, we've spent the last few years developing a essentially painless, you know, at home blood collection device. It used to be called the OneDraw, now called the NanoDrop. you know, but that's like one feature of it.

You're not going to go to jail like Elizabeth Holmes with this, are you?

Not at all. Yes, exactly. That was my objection to the name change, honestly.

Sounds like very familiar.

It will be real science.

Yeah, I have gotten into her story.

The nanotainer.

Yeah, I read the book. I watched the documentary, like, 12 times. I watched the the dramatization what they did of it. It's a fascinating story in many ways. But you can move to home, right? Microbiome testing, right? You can do that in your home. You can get access to this with AIs. We developed something called the Microbiome Wipe to make that as easy as possible for people and so forth. But the whole idea is that we should be able to deliver health information to people in ways that are much more efficient, much more user friendly, not nearly as expensive, and that people can have a real control over that kind of, over their health and be informed by really deep data. You know, I think that's really the key. Oh, and on the, you know, coming back to, you know, some of these, you know, like small measurements, and you brought up Elizabeth Holmes and so forth. One of the things that's important is that a lot of people have failed in trying to take traditional measures and miniaturizing them, at least doing a lot of them at the same time. But the kind of things that we're talking about in terms of omics, like a metabolome where you can make thousands of measures, which we're going to do on this device, a proteome that you can do again, thousands of measurements. Those are only ever done on small amounts of blood. If Lee and I are running something on that in our lab or any of the top labs in the world, you only ever run those things on time. If you gave them a huge vat of blood, all they would do is take a tiny amount out of it and run it on the mass spec. There's no such thing as running this through it. So you're talking about technologies that are miniaturized already. That's the only way that's the way that they work. And so there isn't actually a technological breakthrough of any kind that's needed to use this small amount of blood to get those many measurements. The breakthrough is you have to understand how to read the information. But in the modern world, I'd much rather have an information challenge than a technology challenge because the information challenge can actually be overcome by getting access to samples, the AI is the long— and I'll give one interesting example. So think about what happened in genomics. So in the genome, initially, one of the traits that we couldn't predict from the genome was height. Now we all know height is heritable, right? If you have tall parents, you have tall kids. If you have short parents, you have short kids.

If you're short, like 5'11 and hard.

It depends part on what you're, there's some other factors, but by and large, it's fairly heritable, right? So in the early days, there's no gene for height and there's no small set of genes for height, but you fast forward to now and height is now the number one trait that we can predict with the highest accuracy. You can capture over 60% of the variance in height by a genome prediction, but that genome prediction requires over 180,000 genetic variants. So, it's distributed across this long tail. So, one of the things that we don't know yet is how many- you- mean SNPs?

You mean you're talking about SNPs?

Yeah.

SNPs, yeah.

But, you know, this is like one single nucleotide polymorphism, which in English means you substitute out one nucleotide in that gene sequence that changes the function of the gene. So, you need 180,000 of these slight little spelling Variations in order to actually predict what's going on.

That's impressive. Predict high. But you could see that there's, there was a really interesting paper. And one of the people they included was Sean Bradley. If you remember him, he was a, you know, a basketball player. He was seven six, huge outlier. And you look at this and you get a distribution and he's a massive outlier. Like if you looked at his genome at birth, you could have predicted that he was going to be crazy tall. And so you can do this in the NBA, you can do it in all these different groups. And so coming back to the blood, the thing that we don't know yet is it might be possible once we're able to make, say, tens of thousands of measurements out of the blood instead of the handful that we do in medicine, we might find that there's a lot of information in that long tail. It's a little harder because it's not as digital as the genome, but it might be there. And so it's an open question, but these are some of the things that are really fascinating as we go forward because There might be a ton of signal that will let us optimize health in many ways and look for early warning signs or clear them and so forth. And there is just an incredible amount of data you can pull out of blood that we haven't harnessed yet.

Yeah, and I think it's really important. I think people, you know, what are we gonna say, Lee?

I was gonna say one really nice example of polygenic scores and how they can be, they can apply to improving people's health. is we were able to look at the polygenic score that is a multiplicity of SNPs that explain a fraction of why you have high LDL, which is a proxy for heart disease and so forth. We were able to show that people in the upper fraction of those things could only bring down their LDL cholesterol with statins and other chemistry. whereas people in the lower 40% or so beautifully brought down high LDL with just exercise or diet. So I would argue for all of the 150 or so polygenic traits we have, many toward disease, we're going to want to treat low-risk people differently from high-risk people. And it's going to be important for everybody to know those risks.

Well, that's the personalization.

Everyone's doctors to know those risks.

Yeah, not like one size fits all right now. You've got a high LDL, you get a statin, boom. It's not very nuanced and it's very rudimentary. You know, one of the things that actually concerns me is how these AI machine learning models will be trained because, you know, when I was at Cleveland Clinic, I met this guy who developed Watson, which is the IBM AI supercomputer. And, you know, the big thing was Watson went to medical school, I read all the medical textbooks, books, I've read all the medical literature. And in my mind, I'm like, and I went up and said to the guy, I said, listen, this is great, but you're gonna be just doing the same things better. It's like, you're gonna be basically having a better mousetrap for the same kind of medicine, which is disease-based, organ-based, ICD-10 disease classification system diseases, which isn't actually a true reflection of how biology is organized, right? We're an ecosystem, we're a network of biological networks. It's not the typical diseases that we think of that we should should be looking at is what's underneath the mechanisms and the root causes and the things that go wrong, like the hallmarks of aging are much more approximate than understanding of what's really going on in the body. And so I'm like, well, this is great, but it's going to help diagnose conventional problems better and have a better application of conventional approaches, which to me is like, you know, kind of just doing kind of a, you know, a better approach to sort of, I don't know, the Earth is flat kind of thing. It's not actually changing things. So I wonder, my question is, how do we avoid that and how do we train these models in AI in the assistance biology, assistance medicine, functional medicine approach to actually know what to do with the data? Because I see the same data as traditional doctors, but I see very different things when I look at them.

I think the really important point is with phenomics, we're going to give billions of features of the individual to the AI, and it's going to sort through a dimensionality of information that's staggering. And reading textbooks is one thing that's metadata, that's descriptions at very high levels. Your data is the core of what you are, and AI is going to be able to extract that essence and translate it into the actionable possibilities that'll actually benefit you enormously. And the other point is you'll have to train the AI properly. And it isn't just reading medical textbooks or not. It's using knowledge graphs, it's using digital twins, it's using all of these kinds of things.

Yeah.

I mean, let me give you an example. Maybe we can play off this because, you know, for example, let's say a patient comes in with psoriatic arthritis. And I, and I, and I, from a traditional point of view, the dog, the diagnosis, they have these kinds of lesions. You can diagnose it with AI interpretation of skin pictures based on their medical history, joint exams, etc. Etc. Certain blood tests, and it'll say, okay, this is what you've got. psoariatic arthritis, this is an autoimmune disease. And here's the catalog of drugs you get to choose from and the therapies, steroids, creams, you know, biologics, whatever. And you're gonna get that, that recommendation. Now, when I see that patient, I think of different things. I think, what is the cause of their inflammation? And, and in psoriatic arthritis, after decades of doing this, I've learned that it's a few things that aren't typically considered one. It could be, environmental toxins like heavy metals. And I've seen completely clear up with just getting rid of heavy metals in the body. I've seen it be from, for example, gluten sensitivity, which is maybe diagnosed or undiagnosed celiac or gluten, non-celiac gluten sensitivity, and removing that helps. I've seen it be the result of an overgrowth of bad bacteria or fungus in the gut, SIBO or SIFO, and severe leaky gut and a disruption in that barrier that drives these symptoms. And my approach would be very, very different depending on which of those problems a person had. And so what I always say is just because you know the name of the disease doesn't mean you know what's wrong with you, right? And so how is this new framework going to be, you know, drawing on that kind of knowledge? And I guess some, you know, some of this knowledge around toxins or leaky gut or gluten are there in the literature, but they're things that are completely ignored by traditional doctors and health care because they don't fit in the paradigm. It's an outlier, so it's like, we don't know what to do with that. And so it's like, let's just stick with our lane. And so what I'm wondering is how do we kind of use this new phenotyping to kind of give us the right information, the right plan for the.

Person, instead of saying, oh, you have psoriatic arthritis, take this drug. You know, the really key point is all of those things will be reflected in the phenome, and you'll be able to read those things. And even in a simple way with Aravatil, we had people that had extremely high mercury levels. And we were wondering where that came from. And the most common cause turned out to be people who ate a lot of tuna sushi. And those are simple kinds of correlations that the observation in the blood says you've got high mercury, and there'll be a whole series of conclusions you can draw and and so too, having parasites, having all sorts of different environmental exposures, or your own self choices. Those will all be reflected in your phenome, and those will be read by an instrument that will, you know, in time become increasingly sophisticated. I'm not saying they'll start out that way. But they'll take in more and more data as these things are learned.

So you're saying basically that the AI and machine learning itself will sort out what's true and will help to sort of distinguish Earth is flat world from an Earth is round world.

And even though- Give you symptoms, diagnoses, and it'll make hypotheses, but someone like you, that's why we need the physician as a part of the system. And the physician himself will be training AI as you say, no, wait a minute. Here are three other explanations I've come across. Yeah, but those three other explanations are.

Not things that traditional medicine now even considers. They're things that I, on the functional medicine, systems medicine folks are doing, but.

It'S just like- Functional medicine can feed into AI just as certain as traditional medicine. Interesting.

I think one of the- what do you think, Nathan?

I know this is a hard question, but you guys are smart, so I'm asking the hard questions.

I think it's a really good question. So a lot of it has to do with, with how you set these things up. So the AIs, there's many, many ways you can set up, you know, you can set up AIs, but they're, but the, but as long as it's really focused on, like you were saying, like diagnosis, you know, here's the steps, and these are the buckets, and we're just trying to optimize kind of this information we have down to these buckets. You're exactly right. It's not going know more than that. It's just going to do that. So partly, you know, what I think is really important is that we go down a layer to where we're really trying to understand the biology behind it. I think this is where you're, where you're getting. Right. Yeah. And also, and also lifestyle.

Right.

But, and so some of the things that we really want to say, and this is partly what we get into in the age of scientific Wellness, because, like, our understanding of genetics, genetics is all based around disease. So we understand all kinds of things about the genetics of disease, but we need to understand the genetics of health, and we need understand the molecules of health and things of that nature. So one of the elements, and Lee was getting at this with the kind of large scale phenomic data that you can get from metabolites and proteins and going to these much larger numbers of measures out of the blood and so forth, is we should have modules that are looking at things related to your health like systemic inflation, like how much information is passing between or molecules are getting from your gut into your blood. right? And you can look at that. How much of an integrity do you have in your blood-brain barrier? So, you're basically going down and now you're trying to develop these modules that are related to different aspects of the function of the health of all these different biological systems. And so, you're not just trying to learn, okay, what's my diagnosis? You're actually looking at the function towards homeostasis of multiple different Elements at the same time. And this can be quite surprising and insightful. You know, when we did this, you know, I mentioned we, you know, we built these models for, for brain health, for example. And it was really about that, like trying to understand how does the brain maintain homeostasis? And so you're using a certain form of AI to do that. And most of those papers and the things that we put in there and the data that we pulled was not data about Alzheimer's disease. It was data about health, was data about how does the brain stay alive? What are the things that it has to satisfy? How much energy does it have to make? How does it take out the trash? How does it do, you know, and get very much more technical if we wanted to, but, you know, there's all kinds of things that are in there. And so, and it can lead to surprising outcomes. Like one, you know, one thing that crystallized that was pretty interesting is that as your neurons die, and a big trigger for that is low oxygen, and then ApoE4, it turns out that when you put these things together, you want to keep cholesterol levels in these supporting cells, astrocytes, really low. ApoE4, which gives you high risk for Alzheimer's, will transport that cholesterol out of astrocytes, but it's slow, so that concentration stays high. ApoE2 will do it quickly so it stays low. those two facts together will predict for you the age of onset of all the different genotypes and when they get Alzheimer's. So then a surprising thing happens is as those neurons die, you have to secrete a molecule to recruit additional synapses in order to keep your brain functioning. And what's the molecule that the body uses? Amyloid beta. And so beta amyloid then Rather than being thought of as the cause of Alzheimer's, where we've spent, I got a quote from a guy the other day, I have to go verify it, that we've spent over a trillion dollars on, he was pretty good pharma source, but I, you know, I have to, I haven't verified it, but I've been using 300 billion as my super conservative estimate, but somewhere in that range. So with that amount of money, 450 failed clinical trials, we have a couple that have kind of, you know, help a little bit right now. But the point is that by mistaking cause and consequence, by not understanding homeostasis, not breaking, you know, understanding, you know, I think the, you know, the essence of that disease, you know, and putting it into a, you know, a structure that's set up to, you know, operate on, on, you know, this one drug paradigm like it does, and it does that, that well, but it's, you know, aimed at a certain thing. that causes a massive problem. So that was an area where we went into an AI and you start piecing all these different things together. It explains why statins look like they're good for the prevention of Alzheimer's in observational studies, but bad for Alzheimer's when you look at a RCT with MCI. We can get into details on that, but you can sort these kind of things out. So anyway, all that to say that we could go through examples. But as you start moving from, okay, I'm just doing a diagnosis to I'm going to break down all the different biological modules and try to understand them in depth, and then arm, depending on the, you know, which situation you're in, either the person in their home, if we're talking about wellness, or the physician in the, you know, in the hospital system, if we're talking about disease. But you can inform that by this basic biology informed by this really deep phenotyping. So it can be incredibly incredibly powerful. And we could go anyway, on and on about how you set up these different kinds of AIs, because it gets really rich, actually.

But I think your early point, Mark, was really an important one. You have to take these data and put them into a systems biology context that gives you causality. That's the really important point. And that's, as Nathan said, really nicely what's going to differentiate out what these large language models can do from, you know, what IBM did, where it had the wrong set of data. And it was, I think the other major mistake that IBM made is it was all done by engineers who thought they knew what doctors needed, and they didn't. frankly.

I think the difference between cause and consequences is really critical. You know, we say functional medicine is the medicine of why, not what. And it's based on mechanisms and causes, not symptoms and location, which is what traditional medicine is. And this is the revolution you're talking about. And, you know, I think, you know, there's, there's an important to understand what are the root causes of disease, but also what are the causes of health and what are the features of health? And I think the hallmarks of aging, which is a new heuristic to understand what goes wrong as we age, that are underlying all diseases, the things that actually are the root cause of the dysfunctions that lead to symptomatic disease, things like nutritional sensing problems, mitochondrial issues, damaged proteins, stem cell exhaustion, zombie cell formation, new senescent cells, the shortened telomere, the increase in inflammation, the microbiome changes, the altered cellular communication, inflammaging, all these things are things that are described in functional medicine as root causes. Then if we treat those, the diseases get better. And the argument is we treat diseases, we may extend life by five to seven years. If we treat causes, we might get 30 or 40 years. But there's also an interesting paper that I'm not sure if you both saw in Cell in 2021 that talked about the hallmarks of health. And they sort of break it down into a very similar framework, which is, you know, first we have to do is, is be able to maintain homeostasis. We have to be able to respond to stress. We have to actually make sure things stay in their right location and, and don't have a leaky gut or a leaky brain. And, and their Hallmarks of Health are, you know, the, the homeostatic resilience, hormetic regulation, repair and regeneration, which are a response to stress, these, the maintenance of home state homeostasis, which is how do we recycle and turnover parts that we need? How do we? integrate different circuitries of communication? How do we have a balance in a rhythmic oscillation and circadian rhythms? And how do we compartmentalize things spatially with making sure our intestinal barrier is right, our brain barrier is right, and communication systems are right? And how do we contain things that are bad things like allergens or toxins or microbes from affecting us? So, this hallmarks of health framework is really interesting, and I think it's a very different way of looking at and modeling what's going on to actually define what is scientific wellness? What is the measure of wellness? Because like, oh, I know how to diagnose diabetes. I get your blood sugar, and if it's over 126, you're diabetic. But gee, what do I measure to determine if you have a high level of scientific wellness? What are the things that you're looking.

At that are the most important things to determine that? Yeah, I love that.

So, we've referred to these typically as metrics. Are you familiar with this paper?

Are you familiar with this paper? The Hallmarks of Health. Yeah, the Hallmarks of Health. It's interesting. and it's exactly along the lines of what we're talking about. And this is why we talked about needing this enterprise of scientific wellness to establish markers and models and the deep science behind health so that you don't go in and you just get a test for disease or not. Because this is exactly what I think the health checkup of the future, whether it's at home or in an office, should look like. which is how good is your body at dealing with oxidative stress? Are you able to—what's your glucose control actually look like? That one's easy now because we have CGMs and so forth. How robust is—yeah, exactly, the integrity of your intestinal lining, is your gut microbiome healthy, et cetera, et cetera. And so defining what those are is, I think, in many ways the central effort of scientific wellness because those are the kind of things. And I agree with your other point, which is if you can optimize those as you go along and you don't go down into the disease pathways, you're just much, much more likely to gain a lot. Because once you're down the disease pathway in a long way, coming back to Alzheimer's just as an example, Once you've lost a bunch of your neurons have died and billions of synapses are gone, there's no notion that you can have a small molecule drug that's going to regrow those back. There's no chance of that. But prevention, thinking about, okay, what do I have to do so that my brain cells don't die in the first place is a much more doable. And we wrote this piece on Alzheimer's prevention for the LA Times a few weeks ago that goes into some of that. But basically, those two problems, like the regrowth of your brain after it dies and the prevention of it from dying, are not in the same universe in terms of complexity and in terms of your likelihood of being successful. And so you've got to think about, you know, how do we stay healthy? Stay away from those boundaries where you're going to cross some irreversible threshold and stay as healthy as possible and things like those metrics for health. that's a bridge to get there.

You know, I'll make a quick point on metrics for health, and that is in the Airville program where we had people ranging in age from 91 to up from 21 to 90. We were able to determine parameters from looking at blood analytes that gave us an algorithm for biological that's the age your body says you are, as opposed to your chronologic age. The further your biologic age is below your chronologic age, the more healthy your aging. And what we showed in the AirovaL program was that for women, for every year they stayed in the AirovaL scientific wellness program, they lost a year and a half of biological age. and for men it was 0.8 years. The important point is here is a metric that allows you to assess aging. Here is a metric that in a sense allows you to assess wellness. It's the integration of a whole series of measurements in the blood that culminate in essentially one. We're going to be looking in the Million Person Project for tens, if not 20s, of these summation products that can look at integrated sets of networks and their behaviors. And of course, for aging, it's an enormously integrated set of networks you're assessing. But you can get single measurements that give you very, very deep insights. And these are the things that are going to be the gauges for assessing wellness in its many different dimensionality. Yeah, I mean, and the quantitative sophistication.

Is going to get more and more. Right now, for example, Aravelle, I'm sure you weren't using DNA methylation, using other intermediate biomarkers that you sort of aggregate and come up with a biological age estimate, right? And now we'll be able to use other biomarkers like DNA methylation or other measurements like telomere length or the age of our immune system and actually have a very much more robust picture than even what's available then. So this is accelerating so fast, it's exponential.

But methylation will never give you suggestions about how to improve your education, your aging, whereas the metabolic analyses from the blood do give you insights into things you can do to improve your aging. so these different things give you different capacities. Okay?

That's really why I created and co-founded Function Health, which is a testing platform to get your biomarkers interpreted through AI through the lens of functional medicine to help you personalize and see what's going on at this sort of rate of dysfunction in your body were things that we weren't looking at very often. So it's very powerful. Now, I know both of you are younger than your biological age. Nathan, you're in your 40s, you're about 10 years younger. Lee, you're 85 and are 70 years old biologically. I actually recently saw an article about you and read it in Popular Mechanics, which is an interesting place to read an article about you, Lee. But it talked about your own health routine. So, you've been deep in the weeds on this. What have you come to as a synthesis for your own program for how to actually live in a way that is about scientific wellness and that is about reversing your own biological age.

Well, I would say that- Because Nathan.

Still looks like he's 12. So I think I want to hear your perspective.

I'm waiting for people to say that after I'm 50, you know, which isn't so far away.

Well, I would say that I fundamentally agree that the a core set of things for health, namely diet, exercise, sleep and stress. I really believe in ways to mediate them. For exercise, I was an athlete, played football in high school and college. I've exercised my entire life, vigorous exercises. And it's a way of my life. I do it virtually every single morning. I spend 40 minutes and I do intense aerobic exercises like 150 push-ups and, and 100 sit-ups 100 deep knee bends to exercise all the deep muscles. And that's really important as you age because.

Well, you do 150 push-ups all at once.

No, I, I do them. I do 80 at the beginning of my routine and 70 at the end. my routine.

Wait, you can do 80 push-ups straight?

Absolutely.

At 85 years old, that's unbelievable. That's unbelievable. I mean, I can get to about 70, but I'm like younger than you.

Well, but you haven't done those kind of things. So exercise is a big part of my life. I'll tell you on the diet, it's all the classic things, vegetables and fruit.

And.

Low on the gluten kind of thing. But I think what has been a real.

Interest.

I I ballooned up to 195 pounds at one point in my earlier career, and I've used intermittent fasting to painlessly bring me down to. I weigh just about what I weighed when I played high school football now. and I'm in terrific shape. So I think intermittent fasting, where you eat it in the evening and not again until noon the next day and, and then dinner. So you do your eating in an eight hour window rather than 16 hour. But the other things I do balance out my bloods, all these chemistries that you've talked about in the blood, I try and I have two genes that block the uptake of vitamin D, so I have to take mega doses, 5,000 international units just to keep it at a normal level and everything. And there are other chemistries that are being explored that block aging and so forth that I think are going to be very intriguing to look at in the future. But, and all the things we've learned, the actionable possibilities where they're relevant, I've tried to adopt those and use those. Keeping your blood cholesterol down. I have a terrible family history of heart attacks in the 50s for my three previous generations. And so I've made it well past the 50s. with the subset of the genes my ancestors had anyway.

That's amazingly, I mean, I think you're a testament to what's possible. I mean, you're clearly sharpest ever. You're doing 150 push-ups a day. That's a lot. And you're continuing to produce your life's greatest work. And I think you're the best is yet to come. I think for you guys.

That's how I look at it. With Nathan, I think we both look at it exactly the same way. and Nathan is in his infancy. He's got a long ways to go.

He's a baby. So how did you get 10 years younger, Nathan? Yeah.

So the routine that I've been following, it's been working for me pretty well. I mean, there's all the usual things that you have to do, right? There's a mix of aerobic and, and weight training and so forth. The things that I've really found that have been working well for me, especially lately, and this is the kind of advice you hear all the time, but just focus on things that you think you can do forever. Because I've, I've had a number of times when I've gone on, you know, I tend to go really intense and then I hurt something. And so, so the things that I've been doing that have worked really well for me. So one on diet. So I also do the intermittent fasting. So, you know, I rarely eat before noon. Work days, I tend to fast till about two o'clock. If I'm at home, I tend to fast till 11:30 or so I usually eat a little earlier if I'm at home. So I'll eat a little bit later. I tend to have a relatively small meal and I'm very consistent about lunch or breakfast. If I'm at home, I pretty much every day will eat plain Greek yogurt with blueberries and a little bit of oats in there. And I put a little collagen in coffee. And so basically what that does for me is it gives me 80 grams of protein to start. start the day and, you know, with a little bit of, of other things and I'll, I'll take my various supplements, maybe I'll talk about in a moment. And so, so that works well for me. And then I like, for me, it works well because I save about, you know, and then I save my larger meal for dinner. Data's probably, it's that it's probably better to do it the opposite, but I much prefer just from preference to do it that way. And so, yeah. And so I'm generally in a calorie deficit because I'm I'm trying to lose a little bit of weight. So it's a, you know, so I'll eat 500-ish calories for lunch and about a thousand-ish for dinner, typically. And then the thing that's been really helpful to me that I got from a nutritionist, it's been very helpful, is to try to take a, is to use like this rule of thumb on protein. And this is especially, you know, it's probably a little higher than you need on protein, but especially if you're in a calorie deficit. I think that's good, is that if you take the number of calories and you drop a zero, then that should be the, you know, that's kind of your target for the amount of protein you'd like to see. So if I, so in my Greek yogurt, so anyway, so if you do, if you have 300 calories, you'd like to get 30 grams of protein as part of that 300 calories. It's a little on the high side, but for me that works really well. That's partly because I'm in a calorie deficit. and partly because I probably don't track every single thing that I eat. So you probably have a little bit of, you know, so it gives you a little bit of leeway there. But like Greek yogurt for 300 calories of Greek yogurt is about 60 grams of protein. So I actually start much above that level and it gives me some room to come down as I eat a little bit of fruit or something like that during the day. So, so that, that has helped a lot. And so far this year, I'm down about 20 pounds. And I've also gained 5% of muscle at the same time. So it's been working pretty well for me. By exercising.

By also exercising.

By also exercising. Yeah, so I do resistance training. So, I work out, I do my weight training on days that I stay at home. So, I work in the office three days a week. I have exercise bands in my office. So, when I get a little tired in the afternoon, I do just kind of some extra resistance training in the office when I'm there. And then in the morning, you know, I'll do yoga on days that I go in. So, I do something kind of easier. and I don't do super long yoga. I do, I like to do yoga with Adriene, you know, on YouTube. So, you know, like, I just do, like, her routine. So I do, you know, it's that in the morning before going in. And then when I'm home, I alternate between weightlifting and running. And I find that to be really easy because I, you know, and then if I'm, if I'm commuting in, I, you know, I walk for an hour, so I at least get that. And then I'll do running on days I'm home.

So my, my routine is set up in such a way that it's very enjoyable for me.

And it's pretty easy for me to do.

It's easy to stick to.

Well, it happens. It's just kind of reinforcement. I can see it forever. Like, it's just, oh, and I like in between my bedroom and my bathroom, I have a, a pull-up bar. And so I do pull-ups there. And so I, you know, so I can now, you know, so I can do, I can do pull-ups in between. So if I need to go to the bathroom, then I do a few pull-ups and so that's working. So where I watch, you know, if I'm going to watch a show or something in the evening, the other thing I keep by, by the different. There's two places in my house. I'll do this, and I keep forearm exercises there, you know, so there's, like, this device that, you know, you can, you kind of push your fingers out, and then there's, you know, like a hand device. And those are just sitting there. So if I'm sitting there doing that. So I do those every day.

And then, and then in the two. Yeah. Grip strength is a huge thing.

Correlate with longevity, believe it or not. Yeah, I know that. That's why I started it. So, you know, so I do grip strength exercises pretty, you know, almost every day because they're just sitting there. and the other thing I do is if I'm in one of those rooms, we also have a small trampoline in each of those rooms. So, rather than sit, I can just do whatever on a small trampoline and so forth. So what I've tried to do is just build as many little things into my daily routine such that a little is better than nothing. And then on days I stay home, I reserve those for my bigger workout days because I have the extra time because I'm not commuting for well, I.

Think that's what that's key. It's making the healthy choice and the default choice, the default choice and the easy choice, right? If it's hard, you're not going to do it.

I'll add one more thing about what I do. I do walk to work a mile each way back and forth, and it's actually a reasonable uphill slog going home. But what I take in a vest are two grippers and the entire two miles I'm walking, I'm doing the gripper thing. so I, I'm, I'm a firm believer in if your hand strength is great, you're aging well.

There you go. Well, you guys are both Inspirations. I mean, both in terms of your own personal routine and what you're doing, but more importantly, you know, Paving the way for a radically new, different way of thinking about health and medicine and disease and pioneering something that I think is going to be the future of Healthcare. If you love that last video, you're gonna love the next one. Check it out here. Imagine if there was a drug that cost pennies, that had no side effects, that literally worked in days to cure illness. I mean, that would be pretty amazing. But that's what vitamins do. If you take someone who's vitamin deficient and you give them a vitamin, within a few days they're better and everything's gone. So.