FoundMyFitness - #064 Dr. Michael Snyder on Continuous Glucose Monitoring and Deep Profiling for Personalized Medicine

Episode Date: May 27, 2021

Michael Snyder Dr. Michael Snyder is the director for the Center for Genomics and Personalized Medicine at Stanford and a pioneer and advocate of "deep profiling." Deep profiling seeks to apply intelligent analysis to large data sets to yield specialized clinical insight, ranging from common consumer-grade wearables like Apple Watches to whole-body MRI, continuous glucose monitoring, and metabolomics. In this episode, we discuss: (00:00) Introduction to Dr. Michael Snyder (12:51) Continuous glucose monitor use in people without diabetes (31:04) A smartwatch helped diagnose Dr. Snyder's Lyme disease (34:00) Predicting other illnesses with smartwatches (40:41) Detecting airborne pollutants in the exosome  (51:04) Genetics and metabolism tell us our Ageotypes (58:05) Exercise is most important for longevity (01:03:11) Dr. Snyder's lifestyle habits If you're interested in learning more, you can read the full show notes. Join over 300,000 people and get the latest distilled information on personalized medicine straight to your inbox weekly: https://www.foundmyfitness.com/newsletter Become a FoundMyFitness premium member to get access to exclusive episodes, emails, live Q+A's with Rhonda and more: https://www.foundmyfitness.com/crowdsponsor

Today's episode features a self-confessed believer in the philosophy that more data is usually better, an enthusiast for all things wearable technology, including and particularly continuous glucose monitors, but also sleep trackers, fitness trackers, exosome trackers, and more. In addition, he is the chairman of the Department of Genetics and Director of the Center for Genomics and Personalized Medicine at Stanford University. That man is Dr. Michael Snyder. Today we talk a lot, including how some of these technologies, such as the more lab clinical analytic varieties like measuring metabolomics, transcriptomics, proteomics, and genomics

might change medicine for the better, making it more personal, making it more preventative. Many of you, I'm sure, will be particularly interested in our discussion in the overall utility of continuous glucose monitors or CGMs from a preventative medicine standpoint. While CGMs like the Dexcom and the Freestyle Libre are still considered a medical product, and for the most part are prescribed under the care of a physician, they have been fairly commoditized with companies like Nutrisense, January AI, and levels, making them widely and easily available to lifestyle optimizers through a physician network, usually around a couple hundred dollars per month. In this episode, Dr. Snyder and I discuss how his genomic analysis revealed

he was at risk for type 2 diabetes and how ultimately this coincided with an eventual diagnosis. How some of Dr. Snyder's data suggests that nine out of 10 people with pre-diabetes are unaware they have it. This is important because even pre-diabetes can be clinically relevant. How a person's blood glucose response to a specific type of food can drastically differ from another person's. How Dr. Michael Snyder used wearable devices to help diagnose his Lyme disease. How Dr. Snyder's ongoing study used wearable devices to help identify elevated heart rate as one of the first symptoms in many illnesses, including COVID-19. How smart watches that can detect heart rate variability may be able to help detect some heart

conditions such as atrial fibrillation. How measuring a person's exosome can identify what airborne pathogens they've been exposed to and how Dr. Snyder is trying to determine what this means for disease risk. How children exposed to high levels of air pollution have biomarkers of Alzheimer's disease in their brains. How certain lifestyle modifications such as sauna use in sulfurophane can help rid the body of some airborne pollutants. How Dr. Snyder's data suggests that different organs, such as the heart, liver, and kidneys age at different rates in different people, and how this may define not only how people age at different rates, but what diseases they are more susceptible to, how the microbiome in the gut influences glucose regulation and cholesterol and so much more.

But before we jump in, I want to mention just a couple of things. If you've been enjoying our podcast, you don't want to miss out on our newsletter. I send out an email newsletter when we have a new fully research topic page, when we have exciting new podcast episodes, or if there's been new research on some of my favorite topics. It's the best way to get curated information from me and my team on topics I think you should know about. It's also a place where I open up a little more freely than I do on social media. I'd love to have you join our email list and get our informative updates. To sign up, just head over to foundmyfitness.com forward slash newsletter. That's foundmyfitness.com forward slash N-E-W-S-L-E-T-E-R newsletter. One last reminder. Anything discussed in this podcast

is not intended in any way as qualified medical advice. If you need qualified medical advice, please seek it out. With that said, enjoy the podcast with Dr. Michael Snyder. Welcome back, everyone, to another episode of the Family Fitness podcast. I'm super excited to be sitting here with Dr. Michael Snyder. He is the chairman of the Department of Genetics and director of the Center for Genomics and Personalized Medicine at Stanford. He runs a team of over 100 scientists at Innovation Lab at Stanford as well. He, him and his collaborators are doing some really exciting research that will really change the health care system, shift it from one that's focused on treatment only to one

that's really able to be focused on prevention in addition to treatment. He is also the founder of QBio, which is a company that gathers quite a bit of data to give people a better understanding of their health status, as well as the founder of a company called January AI, which is involved in glucose monitoring. So I'm super excited to have you here today, Mike. Thanks for having me. I kind of wanted to kick off this podcast, Mike, with a quote from William Thompson, who is a famous, he was a famous mathematician and physicist. He basically formulated the first and second laws of thermodynamics. And he's got this quote that I've often referred to because I just think it's a great quote. And the quote is, if you can't measure it, you can't improve it. And I feel

like that's so relevant to your work and particularly even to yourself. I mean, you are probably one of the most extensively monitored humans there are in modern day. I mean, you've measured everything. I mean, people can't even imagine all the things you've measured on yourself. Maybe you could talk a little bit about what sort of parameters you've measured in yourself and what you've learned from that data. Yeah, sure. So we're all about collecting data. I'm a believer you can't have enough data.

More information is always better than less information. So what do we collect? Well, we do deep molecular measurements on me. We'll, for example, sequence, first of all, my DNA, so I know what genes, what kinds of risk factors I might have, genetic risk factors. We also do very, very deep molecular measurements on me, meaning we'll draw my blood and urine and profile literally tens of thousands of molecules. We'll study my poop for the microbiome. And we do a lot with wearables that will, I'm sure, talking about today with smart watches and other devices. In fact, I have eight of those devices I use every day.

You've actually got a pretty interesting story. So you've sequenced your entire genome, and you learned some really interesting predispositions that you have. And that actually turned out to be a pretty interesting story. Sure, yeah. No, actually, this big data collection has helped me several times. And the first was, in fact, from my genome sequence. So I sequenced my genome. It told me things I was at risk for.

One of them was quite surprising. It said type 2 diabetes. and I'm not overweight. I exercise pretty regularly, and I thought, well, you know, how can that be? I don't have a family history of type 2 diabetes, so I wasn't too sure it was right, but I was, in fact, tracking my sugar levels, which is what happens when you get type 2 diabetes, your sugar levels go up, as well as many other things. And what we discovered actually about nine months into the study, after I sequenced my DNA,

saw this risk for type 2 diabetes, my sugar. actually was shooting up through the roof. And I was only following it closely because of the fact my genome told me I was at high risk. And in fact, when I first discovered this, I was going in getting a test for something called insulin resistance, which is associated with type 2 diabetes. And the doctor actually, you know, she was skeptical. Why are you here? You don't look like you have diabetes because I'm not overweight.

You don't have family history. You know, it doesn't make any sense. And I said, well, my genome said I have this going on. And so she actually drew blood and my sugar levels were high, actually. We were both surprised. In fact, she repeated the measurements. And sure enough, they were quite high. And then it turns out this insulin resistance, I wasn't insulin resistant.

I was insulin sensitive. But my sugar was high. So we measured it a week later and did some other tests, something called hemoglobin A1C. And sure enough, I'd cross the threshold and it was classified as type 2 diabetic. So my genome tipped me off and then these other measurements actually, you know, basically discovered it. What was pretty unusual about is that it came up after a very nasty viral infection, actually. About three weeks later is when I was getting measured and that's when my sugar levels were rising. It's very relevant to the current pandemic, in fact, because a lot of people think that COVID might trigger type two diabetes.

In fact, there's some evidence for that already. But this is the first demonstration that a viral infection could actually. trigger type 2 diabetes. And so in my case, I caught it because I was doing this deep profiling. I typically, you know, when you see someone who's healthy, active, you know, looks lean, how common is it to see that they may be metabolically unhealthy in terms of at least, you know, their glucose regulation? Yeah, well, on the glucose side, it's quite frequent, actually. So we have some studies going on with something called continuous glucose monitoring. But backing up a little bit, it's pretty clear that actually nine out of ten people have

pre-diabetes, so not yet diabetes, actually have no idea. What that means is their glucose is starting to go out of control. They're not officially classified as diabetic yet, but they will be. And it turns out that 9% of the U.S. population is diabetic, but 33% are pre-diabetic. And most of those pre-diabetic will go on to become diabetic. and yet they have no idea they're pre-diabetic. And so we think actually capturing that information is pretty darn important so that they can start getting their glucose under control

long in advance of getting full-blown type 2 diabetes. Now this is something that I would imagine a routine checkup that people do, I mean, maybe they don't do this once a year or twice a year, whether you're getting something like, I guess, would that even be reflected on HBA-1C, which is your sort of long-term glucose measurements. And also, you know, if there's so many people that have what are pre-diabetes, and maybe you can sort of, I don't know if you can tell us what those levels are,

and if it's a pretty hard, I mean, is there a scale that's kind of sliding or is it like, this is like for sure if you're within this range, you're pre-diabetic? And also I kind of want to get into, you know, which what you've been doing and with your work at Innovation Lab in terms of like measuring, using continuous glucose monitors, which a lot of people are now using these days to actually inform people about their glucose regulation. Continuous glucose monitors are still considered a medical device. For that reason, they are prescribed under the care of a physician. However, increasingly, they're fairly commoditized with companies like Nutrisense,

January AI, and levels making them available to virtually anyone through a physician network. and selling them usually for around a couple hundred dollars per month, with the most well-known CGM brands being the Dexcoms and the Freestyle Libre. Yeah. So, well, as far as there is a range, yeah, there is. So normally people want to have their glucose at 90 or below, but, and when it gets over 120, then you're typically classified as type 2 diabetic. And so in between is typically called pre-diabetes.

They are arbitrary numbers because it's a scale. You can be anywhere in that range. So the goal is to keep your glucose numbers down. But it's actually even more complicated than that because people will spike the foods and different foods can spike you very much out of control. So for example, if I eat pulled pork, believe it or not, that'll send my glucose over 350. It goes totally out of control. So you do want to know what foods do that to you.

It turns out that is different for different people. So different foods spike different people. And these continuous glucose monitors, which is one of the things we are using in our study, is a great way to measure those. So we can actually dive deeper in on that if you want. Turns out everybody spikes the different foods differently. Some people spike the bread, other people to bananas, other people to pasta. And it's just different with different people. It's thought that at least part of that's due to what's called your microbiome, the microbes in your gut

that digest your food differently. And so we're all different. And so that's why it's really important to get these personal measurements. And that's a big theme of ours. Try to click big data because everybody's different and those data will be different for different people. And so understanding people's baseline and then how they're shifting from that baseline is absolutely critical for understanding your health. What do you think about people that are, I mean, obviously using continuous glucose monitors

for people that are diabetic, type 1 or type 2, or for pre-diabetic. What do you think about for just the general population? I mean, there's a lot of companies now popping up that are doing, you know, just that, allowing, you know, just normal people. Like I, myself, where one, as you and I have discussed previously in another conversation. And I've gathered a lot of data, but I sort of want to know what your thoughts are on. Well, I think everybody should wear one. Now I'm biased.

I'm a guy who likes to collect a lot of data around these things. But what's really clear comes back to what I was saying before. For pre-diabatics, they have no idea most of the time. They have no idea they're spiking away. And so putting these monitors on, first of all, they discover that. But even normal people, it turns out, will be spiking as well. And it has to do with the way we currently measure glucose dysregulation. look at final glucose levels like we started talking about or at this hemoglobin A1C, these final

levels. But there's actually different ways to have different glucose dysregulation. My own belief is diabetes is probably 50 different diseases. We lump it all together into one or two, basically, whether you have type one or type two. And the reality is there's many subtypes. I'm a very unusual type two diabetic, actually. So these monitors are great for discovering people who have glucose dysregulation, but it doesn't show up by normal means. They're also phenomenal because, as I say, people spike to different foods.

So you get to see what foods you will spike to. And then you can actually personalize your habits. If you spike to bread, for example, well, maybe you want to avoid bread and eat other things. And so one of the companies that was involved in founding, that's exactly what they do. They have a food recommender that says if you, you know, this is what you normally spike to. Don't eat that when you go to a restaurant, eat something else that won't cause this. And I think getting taught those habits early before your diabetic is absolutely huge because I think the key is to get things under control before you have diseases. And that's a big theme of errors.

Try to catch disease early before symptoms. So you can manage it and keep people in, keep people healthy, basically. So you mentioned something very interesting that you think diabetes is like 50 different diseases. Can you just elaborate a little bit about that? That's very interesting. Yeah, so most people think, oh, you're diabetic because your cells don't respond to insulin. This is what's called insulin resistance. And that's true for a lot of people. But I'm an exception and I'm not alone. I know there are others. My cells actually respond fine to insulin. Other people are diabetic because they don't make insulin. The classic case is type one, but there are cases of type

too where they don't make insulin. Turns out I make insulin fine. So I'm making insulin, my cells respond to it. So what's wrong with me? Well, turns out I don't release it from my pancreas, which we only found by doing some follow-up, some pretty simple tests, actually. And so what's needed to treat me is very different from what's needed to treat others. Turns out I'm not a metformin responder. So I don't respond to the most common type two drug, type two diabetes drug that's out there. But I respond really great to this other drug, it's called Repenolide, that actually promotes release of insulin from your pancreas. So this is a classic case of knowing exactly what's wrong with you can actually suggest the right therapy, and therefore I can take medicines that will work on me, not ones that

don't work on me. So we think that's the power of all of this, knowing exactly what's wrong. So I don't know what a cut type of diabetic I am. I'm probably type X. Right now, I'm lumped in as type 2, but that's only because they put people in these broad categories. And I imagine there's other types as well. If you look at it, some people have high, other kinds, like high lipids and other people don't. And that's clearly associated with more total metabolic dysregulation. And so what's probably going on with them is a little bit different from others who may not

have lipid dysregulation. So it's going to vary from person to person. And so I think understanding exactly what's wrong with people will help let them manage their health much, much better. Absolutely. So you wear a continuous glucose monitor. Have you found certain dietary and or lifestyle decisions make a big impact on your glucose regulation? Yeah, totally. So certain foods I avoid, like the pooled pork I mentioned earlier.

Is that, I'm sorry, the pooled pork, because like I eat, you know, protein and fat for me doesn't raise my glucose level, post-pranthal glucose levels. much at all. Is it pulled pork, is it just pulled pork or is it like sugary barbecue sauce on the pulled pork? Yeah, it turns out there was sugar in the pulled pork. You hit the nail on the head. So which everybody said to me when I showed this to someone said, well, Mike, of course, everybody knows they put sugar in the pooled pork, but I had no idea. And I think we all have those eye-opening moments when you wear a continuous school coast model that in hindsight the stuff always makes sense. But you're still surprised when you see it because you didn't realize that was going on because nobody has every food memorized. And we saw one case of a saying to

a journalist who said he was trying to eat really healthy. He had salmon on a salad every day. And he said, you know, he couldn't think of anything healthier than that. And then his sugar spiked through the roof. And it turns out he had had some blossom salt on it that had sugar in it. And same thing in hindsight. He said, oh, of course. But he didn't realize that at the time. We all have these habits that probably have some not so healthy things snuck in there, even when we think we're being perfectly healthy that you can avoid. So anyway, food avoidance, you know, or eating better foods for you is one easy thing to do. Another thing to do is exercise. I know it sounds obvious, but it's very obvious when you're wearing glucose monitor.

In fact, we can teach people as part of this app of January AI where you actually eat something. Nearly everybody spikes the rice, by the way. White rice is kind of nasty. It spikes people. Oh, totally. I avoid it like the plague. So does corn flakes of milk.

Yeah, also pretty nasty, actually. So that's good. Anyway, so as one training exercise, you'll eat white rice and then go eat white rice. do a brisk walk 15 minutes later, and you'd be amazed at how much it suppresses your glucose spikes. So you can actually learn these habits, both just either on your own by wearing a monitor or some of these apps that are out there now, and programs are meant to help teach you, help you modify your behavior. So again, you can be healthier. And then the goal was to get in

healthy habits so that you will be living a healthy life, basically. So we kind of took a deeper dive into the continuous glucose monitors, which I'm also a huge fan of. I've been measuring mine for the past, you know, three or something years, about three years and learned a lot from it as well in terms of foods that I, you know, have a bigger spike to, post-pranidial spike two and other lifestyle factors that affect my my fasting blood glucose, particularly sleep and lack of sleep. That was a big one that was very like eye-opening for me. But you, Through your research and your collaborators, you guys are collecting not only, you know, glucose, using continuous glucose monitor to get information about people's glucose regulation, but you guys

are getting a ton of data. I mean, just everything from antibodies to RNA to their proteome to metabolome. Do you mind kind of talking a little bit about this big data and how you're sort of longitudally, gathering all this data from people and learning interesting things about the health status of people that people didn't even know about before. Yeah, sure. So maybe I'll even say a word or two of how we got into this, which is, you know, I think the healthcare system's broken. I think when you're healthy, they just don't measure you very often. And when they do, they measure very, very little. So, you know, go into a doctor's office now. And it looks like the

doctor's office 40 years ago, they'll bring out the stethoscope, these few things on the measure, you know, 15 things, say, oh, you're great, go home. Yeah, if you think about where the world is today, we can measure so, so much more. And so the genome is one thing. We talked about that with the diabetes, but you can make all kinds of molecular measurements. You can measure, as you say, all your RNA, all your proteins, all your metabolites, all your lipids, in your blood, and you can do your metabolites and your lipids and your urine and proteins and your microbiome, all these things you can measure. And we don't know for sure what's important and what's not, but we do know we get a much more complete picture when we do that. It's like looking, you know, at say you're doing

a jigsaw puzzle. This is what I would say we're doing now, where you, if you put out five pieces, that's how medicine is today. And you try and guess what that puzzle is like or what is the picture in the puzzle, you really don't stand a very good chance from five pieces if it's a thousand piece puzzle. When you put all thousand pieces there, you have a pretty good idea what that picture is. And that's how we view health. We want to get this a thousand piece picture on everybody, and we want to do it over time so we can see if that picture is changing. So basically, that's what we're doing. We're profiling people very, very deeply, and then we do it over time so we can see these shifts. And one thing we've learned is that every,

Everybody's profile is different. I know that sounds obvious, but people don't realize how different they are. And in fact, and it doesn't matter which measurement we use, whether we're following your proteins or your metabolites or your lipids, your pattern will be very, very different than mine. And it'll be fairly stable over time, believe or not. There will be some things that will change. But for the most part, it's the same. And not only that, what happens is if you get sick, say you get a viral infection or something else, your profile will shift. but believe it or not, you will still look more like you than me when I'm sick.

So what that means is that it's very hard to tell the difference when somebody is ill if you're comparing them to everybody else. But it's really easy when you compare them to themselves. So that's why we think these profiles are really important while people are healthy, so you can detect that shift really, really easily. And it turns out that that's been very, very important. So in our study, we have 109 people, and just from the first little over three years of profiling, 49, almost half, learned something important about their health.

And it was all kinds of different areas. Some was in the area of cancer. Some was in the area of cardiovascular, other in the metabolic space. And just as some examples, we caught some with early lymphoma. Two people with pre-cancers. They weren't cancers yet, but they have a good chance of turning into cancer. We caught it early. They're called M-Guss and smoldering myeloma.

And then there were two people of serious heart issues. They had no idea. One was a really young guy from his genome sequence. We figured it out. And another was from wearables and so on and so forth. Caught people with diabetes who didn't even know they had diabetes. And then we saw people become diabetic. So we learned, and it was no one technology.

That's the power of this. This global profiling. Let us see the whole picture. And so sometimes it would be, for example, the genome sequence at this this. Sometimes it might be the protein markers, and sometimes it was imaging. And it was no one, one, again, technology that did it. And sometimes it's a combination. See, well, the image looks a little off. Look, these protein markers are off. Let's do some follow up there. And sure enough, you'd see the person had an underlying condition, always before symptomatic. And again, so it was

very, very powerful. Have these people learn, again, some pretty darn important things. You don't want to learn about you have a heart condition by getting a heart attack. That's not the way to learn. Do you think that the transition from, you know, the way the health care system is now to this more precision medicine system that you're talking about where it's like you're getting on, gathering all this data from people and you're making very, you know, you're making decisions based on their personal data and a lot of it. Do you think that's something, like how do you think we're going to be able to transition to that point because we will get there, right? Right. So there needs to be a change at so many different levels.

One is don't mistake, what we're doing is a research project. But what we've learned is that what kinds of measurements are powerful that, you know, medicine can use. So once again, we've spun off a company. I'm a believer, academics are great at proof of principle and discovering things, but they're terrible at scaling. So we formed a company in this case, QBio, that does a medical version of what I was saying. And it's not cheap. It costs $3,500, which some people can afford, but most people probably can't right now. But I hope as time goes on, we'll get it cheaper. We would still argue getting a thorough exam where they do deep molecular measurements and a whole body MRI is still very powerful because the same thing we caught a lot of diseases early,

someone with early pancreatic cancer, for example, prostate heart conditions, just from the first bunch of people we've been profiling. So we know this is powerful. Now, why won't medicine do it? Well, first of all, the system's broken once again. Nobody pays to keep you healthy. Medicine in the U.S., people typically get paid when they're ill. So, you know, you walk into a hospital, that's when the doctor gets paid. If you're healthy, nobody's going to pay you if you walk in the hospital to keep you healthy. So we need to re-incentivize the system to make it different. There are ways of doing that, I think. So for example, it probably pays employers to keep you healthy because then your productivity will be better. So I could see where if we could have,

you know, big places like Google or Facebook or what have you, have their employees covered by these sorts of things. They would have healthier workers and that would save them time. I think also economically, if we can show in certain areas, this is. been done, like for cardiovascular disease, you can probably make an argument that it is important to get people measured so they stay healthy because if they have a heart attack and go on long-term disability, that's really expensive. So I think you have to find these niches. And the other thing you have to do, you have to show, like insurers don't want to pay unless you've proven to them at works. And this was true for all new technologies.

Even when it's pretty obvious, this is an important thing to do. Nobody will. pay to implement it until you show it works and it saves them money. Otherwise, there's no reason for doing it. So I think we need to change the mindset of the insurers. We need to have physicians embrace the stuff. That's another thing. When genome sequencing first came out, we told people we're running around sequencing healthy people's genomes. A lot of physicians freaked out and told me what we're doing was wrong. They still tell me what we're doing is wrong, that we shouldn't be profiling so deeply because you're going to learn all these things, is going to break the health care system, I'm going to turn everybody into hypochondriacs.

And I think that's just very demeaning to people, because I think people, it's just very paternalistic. People are very good at deciding for themselves what they want or don't want in terms of information. And getting this information to catch disease early is a no-brainer to me. And people are going to handle it. Now actually, they're warming up to the idea of genome sequencing is okay. But I guarantee 10 years ago, most physicians thought this was a really bad. idea. Well, you mentioned, and I think that not only are people warming up to it, there's a whole

movement of people that are wearing these wearables. They're wearing their Fitbits and their aura rings to track their sleep and, you know, all the other devices that are out there, you know, what do you think is a good start for people that are interested in tracking their own health and understanding, you know, what they can, you know, about themselves through wearing some of these wearables that are available to them. For example, like heart rate or body temperature fluctuations. Yep. So great question.

So I would say for the big data as a whole, the world has not formed up to that yet. But you're right, there's a subset of people who are, you know, eager beavers to be quantified, this quantified self-movement. But I think as far as spreading into the general population, you're right, wearables are an area that's starting to have power. but even the medical establishment is still fairly resistant to wearables, I would argue. But they are gaining attraction. They started out as fitness trackers.

And the way we got involved is when they start out as fitness trackers, Apple Watch didn't exist. We said, well, gosh, he's a pretty good physiological monitors. They're measuring, as you say, heart rate, skin temperature, more limited number of things at that time. Now they'll measure all kinds of things. Heart rate, heart rate variability, respiration, even your blood oxygen, even though it's not accurate. It measures your change is pretty good. And blood pressure, yeah, all kinds of different things you can get from a smartwatch. And many of those same things from a ring, the aura ring, for

example. So they're very, very powerful. And if you think about, they're measuring you 24-7, and they're taking hundreds of thousands of measurements every day. Some of them will make millions of measurements every day. And so they're really getting a detailed view of your physiology. And we think that's super powerful. And so what we discovered, early on, we put these on folks. And once again, that's probably the second most important thing I learned from monitoring myself was when I got Lyme disease. I figured it out from a smart watch and something called a pulse ox that measures your blood oxygen. And the story there, if you'd like to hear it, was I was, you can tell, I measure everything on me. I'm wearing four smart watches right now. I can see. And I normally have a ring, but I lost it.

and glucose monitors the whole shebang. So anyway, with these smartwatches, or sorry, with the Lyme disease, I was helping my brother put up fences of rural Massachusetts. And then two weeks later was flying to Norway through Frankfurt, actually. And on this last flight, I'd measure myself as you know, I always wear pulse-hawks because it turns out your blood oxygen drops on airline flights. Most people don't know that. most pilots do, most flight attendants don't. But anyway, your blood oxygen does drop. It's not well

documented how much it drops. We've now documented all that. But it was pretty clear to me when I flew from Frankfurt to Norway, my blood oxygen dropped abnormally low. It dropped a 90 when it would normally drop on that kind of a plane to 96, and it never came back to normal. And I saw my heart rate was running high. And then when, yeah, same thing, it stayed high. And then I later learned my skin temperature was up too, all measured for my smartwatch. And then I had no symptoms at that point, but a day later, I started getting some mild symptoms, and they didn't go away. So I went to a doctor in Norway.

I warned him it might be Lyme because of the timing that's two weeks. And he drew blood, saw my immune cells called monocytes rupt, said, yep, we've got a bacterial infection. He recommended I take penicillin. I said, no, I need doxycycline, which is what you use for Lyme. He was a little tense for the moment there because, you know, doctors don't like their patients telling him what to do. And he was no exception, that's for sure. He did give in because I was about to go about the Arctic Circle and I did not want to be sick.

And he gave it in and it cleared it up right away. You do take it for two weeks. And when I got back, I got tested. And sure enough, I was Lyme positive by antibodies, even had some antigens, some proteins from the Lyme that were still there. And it's well-controlled experiment because I had given blood three days before. before I left, and sure enough, I was negative, so I was here converted during that time. So the key part of that story is I actually figured out when I was first getting sick from my smartwatch and a pulse ox, and it was how I detected my line before symptoms.

So with that, we realize that these are really powerful devices for measuring when you get ill. And so I had two years of data at that point on my smartwatch, and so we went and looked over all the data, and it turns out I was ill four times during that period. One was the line case. Two times were a viral infection, and the fourth time I was asymptomatic, but I know I was sick because there's a protein called C-reactive protein that was elevated, and it was just as high as my viral infections of wine. So there were four times I was ill. We looked at the data every single time my resting heart rate and my skin temperature was up and was up before the symptoms appeared. So we wrote an algorithm.

It works for heart rate. It doesn't work for skin temperature. We wrote an algorithm that follows your baseline. It looks for a jump up in your heart rate. Resting heart rate, I should emphasize. And it works. So retrospectively, we could show every single time I got ill, my heart rate jumped up early, and it was advanced a symptom.

It worked on me, and it worked on three other people who also were ill, and we're wearing the same smartwatch. One of them got sick twice every single time. We could see the jump up in heart rate. before symptoms. So that really showed that these are incredible health monitors. And these weren't expensive devices. These are like, especially at that time, I think it was something like a $150 watch

that was doing all this. And I know you can do it for $100 watch. So as you might imagine then when COVID came along, we had been building out and improving your algorithms, building an infrastructure to do this at scale. I'll come back to what that means in a minute. But what we can do now is, and when COVID came along, we quickly enrolled, opened up the study, partnered with Fitbit, Garmin, and launched a study to try and first show if we could detect COVID with a smart watch.

And then the second part, which we're in now, is alerting people if their heart rate goes up. And so what we're doing for the first part, we showed that with a smart watch, we can see people's resting heart rate jump up. We had 32 people wearing a Fitbit at the same time they were head COVID and they had a diagnosis date and a symptom date. And we showed that for 26 of the 32, we could detect the jump up and resting heart rate. And in basically in nearly all cases, it was at or before symptoms. So 81% of the time, we can see people's resting heart rate jump up with COVID. And then we have now, And it turns out, by the way, the very first case we had, it was 10 days early. Somebody, it was very clear signal.

You can see this person's heart rate jump up 10 days early. On average, it's about four days. So people's heart rate will jump up four days before their symptoms if they have COVID, it turns out, and that we can pick up with a smart watch. So we're now, we've written some algorithms to do this in real time, comes back to what I was telling you before. So you follow your baseline, and you look for this elevating. and resting heart rate. We have three different algorithms to do this. And so we'll profile you. I don't

know if you've signed up for this study yet, Rhonda. I hope you have. So anyway, it builds an hour-by-hour measurement of you. And when you jump up, you know, pretty high for an extended period, say, six, these days, to keep the false positive, it's more like 12 or 24 hours. If you're up for a while and it's statistically, you know, unlikely, it's not a random fluctuation, it's up there, we send an alert. And it turns out it works pretty well. So 70% of the time, we can detect illness. We had 63 people as of the end of January who had COVID, and 44 of them we could detect in real time and alert them before at the time of symptoms. So it's not perfect. It needs a lot of tuning still. That's why we want people to enroll in our study. We want to basically improve the

algorithms. And we don't yet, it's not just specifically for COVID. Other illnesses will trigger it and also other things will trigger it like too much alcohol, not a drink or two, but if you really tie one on, you'll send your heart rate up. Hiking in the mountains will do it too. So you have to contextualize it. But it does work in general for detecting respiratory illnesses. And so with with improvement in algorithms, I want to be able to tell the difference between, you know, drinking too much versus the respiratory viral infection. I don't yet know if we'll tell the difference between flu and COVID. We'll have to see. Where can people go to sign up for this COVID tracking study with their wearables? Yeah, go to Innovations with an S on the end.commodstamford.org

slash wearables. So innovations. dot Stanford. Dot edu slash wearables. And we'd love to have you enroll. And it does work. I can show you a picture. You'll see you'll get these red alerts.

And then we want you to respond and say, yeah, here's what I was doing if that happens. We'll put that URL in the show notes as well. So I kind of want to to shift back and ask you about heart rate variability. You mentioned that. And do you like, do you think,

what can people learn? learn from their heart rate variability data that they're getting from their whatever, fill in the blank wearable they're wearing. Yeah, great question. So heart rate variability, this might sound a little funny to you, but you're actually supposed to have a variable heart rate. If it's totally constant, there's something wrong with you, actually. So it's a measure of health. So it should be shifting around a little bit. And yeah, I mean, it's one of these parameters. It's a sign of your health. And there's other measurements too, like when things go off with some of these measurements, you're going to actually pick up atrial defibrillation, basically, AFIB, as it's

called, can get picked up with your smart watch. Right now there's a high false positive rate, but some people have picked that up from their smart watch. You can even measure, get an electrocardiogram measurement from an Apple Watch, you may know, and some other devices now. So those you can measure and they give you some information. Again, there's still some noise that people are working through, and that's why we need to tune the algorithms better and get more data. We ourselves are very focused on the infectious disease detection. Other groups are more focused on heart health.

But heart rate variability is a key measure of heart health, if you will. To kind of shift gears for a minute and talk a little bit about some of your other data, which I found really interesting. This had to do with the exosome. Am I saying that correctly? Yeah. The expose-um. I'm not sure people know what that is.

So maybe you could kind of start there just explaining what that is. Yeah, your exosome is all the things you're exposed to. And what we're measuring is your airborne exposure. So we have a device. This is the second generation. The third one's coming that's smaller. What it's doing is it's sucking up air at about one-fifth of what you breathe. And what we have, it's measuring things like PMP,

2.5 and temperature and humidity. But the power of this is it actually has a filter under the intake valve that captures all the particulates like pollen and bacteria and fungi that you're actually breathing. And under that, we have a chemical absorbent that sucks up all the chemicals that you would also be breathing. And then we analyze them. We don't analyze them in real time. We take it apart and take the cartridge out actually. We can measure all the biologicals, meaning pollen and bacteria and fungi that you're exposed to, and we can measure all the chemicals that you're exposed to too. Pesticides, turns out there are plastics everywhere, pesticides are everywhere,

but their amounts will vary quite a bit, and certain carcinogens are everywhere too. And some locations will have a ton of this stuff and others, you know, generally not so much. So that's what we're trying to measure. And the ultimate goal is to try and understand how, exposures will contribute to, you know, your disease because there's no question that your risk for disease, it depends upon genetics, but it also depends upon environment. And we think that's the

one thing that's always been very poorly measured is the environmental exposure. So that's what we're actively working on now amongst other things. So to try and capture that information. It's also the other, it's also the one thing that's not as easy to control. For example, you know, you can make a decision about what you're going to have for breakfast, lunch, or dinner, or whether you're going to fast and not eat or, you know, when you'll eat your food or if you're going to exercise. But the things you're breathing in, with the exception of, I guess, if you live somewhere close to a, you know, busier street or a highway, for example, where there's likely to be more particulate matter and air pollution and benzene, it's very difficult to control the air

you're breathing in. And there's been so many studies that have been coming out over the last few years. And of course, these are observational studies. There's all sorts of potential confounding factors that come along with observational data. But I think that, you know, there has just been a pretty, I think, clear picture that air pollution increases the risk for a variety of age-related diseases. In addition to just respiratory disease, Alzheimer's disease, you know, for one. I mean, this is even something that's been shown in children that live in, for example, Mexico City that have, you know, passed away. There's been some post-mortem studies that have looked at amyloid plaques in their brain,

and it's just like looks like an old person's brain in a young child in a place where the air pollution and the air quality is so poor. So, you know, have you guys been looking at, you know, have you guys been looking at, you know, you know, maybe where people are living or if they have a hepa filter or some kind of filter, whether or not that makes a difference. Yeah. So a great question. So the answer is that I'll say to some extent what you're talking about is the more non-specific stuff like pollution, smoking.

We know those are harmful, no question. And so we have looked at that. We actually discovered it looks like for one group we were studying that speculated that actually there was, There's some heavy metal mining upstream, and they speculated there's a high incidence of a lot of immune disease. They thought that that might be due to the mining and actually does correlate that way. That is people live along the river, we discover it actually are seeing to have a much higher incidence of autoimmune disease. So I suspect their hypothesis is correct. And so smoking, yes.

I mean, those are documented studies by a lot of people, you know, inner city kids at, you know, exposed pollution, have a lot of this. back up a little bit, though. I do think you can have a better control, at least some of control over this in the sense that you mentioned hepafilter. Sure, that's good. But one thing's I discover, I would argue we don't really know enough to make informed decisions about all what's going on. And as a good example, I used to have more severe allergies. They're kind of mild now. And I always assumed they were pine. They came out, you know, April, May, like everybody's. But then when I actually did the correlation, when I looked at my data, and I looked at allergies, my something called eosinophils, which are involves in your allergic response. And I correlate it with my exposures.

And guess what? It didn't correlate with pine. It correlated with eucalyptus. So why you said, well, how's that actionable? Well, I got two trees behind my house and pine and an eucalyptus. And if I'm never going to take one of them down, you can guess which one goes down first. It's going to be that eucalyptus tree. So I honestly think that having more information tends sometimes lead to more informed decisions.

And I hope that will be true about some of the other chemical things like this. Just a ton we don't know. It's been speculating. You know, why are, you know, girls reaching puberty earlier? And it's been speculated that some of the plastics look similar to, you know, estrogen. And nobody knows if that's true or not. How do you know? there's never been a proper study to do that sort of thing. We can make those measurements. We might

be able to see if any of that's true. And so I feel like we need a, everybody knows the environment is important for disease, but we don't know what aspects of the environment. Crohn's, you name a disease, Crohn's, you mentioned aging, absolutely true. It's there, and it's just never been measured, so we don't know exactly which molecules are leading to these problems. Some of it may be just general particulates like pollution, air pollution, but some of it may be very, very specific ones. And let's see if we can get those things outlawed, like, right, get those metals out of the gas was something that was that out of the paints, was something let out of paints, was only because studies took a while realize that lead was causing, you know, neurological problems. And so you have to,

you have to identify that. You have to measure it. You have to see it first to know what's going on. And I think that's what the measurements all about. You know, we're just taking tons of measurements. You're exposed to thousands of species and which ones are associated with what aspects of your health. We don't know until we measure it. Have you guys measured down the exposisome of firefighters, people that are, you know, fighting these wildfires? Yeah, believe it or not, we are. So we've been, when these fires in California have burst out, we've been trying to get our monitors out as many people as possible.

and including firefighters. You have to put them a little carefully because they obviously can't interfere with their duties. But yeah, so we are trying to do this. And I don't know what you know, but it's something you alluded to that kids in these poor areas,

like in Fresno where there's a lot of pollution where there's a lot of firefighters, there's a lot of studies that show that they have a much, much higher incidence of allergies and asthma when they're in these areas. And it does seem to be associated with firefighters. So we're trying to measure exactly what they're getting exposed to.

And maybe there's some way to help control that or figure out ways to try and extract that. I guess we can see what the harmful elements are. Maybe even if we can't control the exposure, we'll try to as much as possible, I suppose, but maybe there's a way to try and keelate it and remove it out of folks. I don't know. That's not exactly my area of expertise. I do know a little bit about not keylating, but so there's a couple of, not to get too far off topic, but you might find it interesting that.

So one, there's a compound found in some cruciferous vegetables and particularly in broccoli sprouts, the young sprout of mature broccoli called sulfurophane, which has been shown in now quite a few studies. There's been intervention trials where people are specifically given sulfurophane concentrate. And it causes, so a lot of these Chinese studies have been done in China where there's also terrible air pollution problems.

it increases the excretion of benzene, which is found in, you know, air pollution, cigarette smoke, for example, by like 60% after 24 hours. Also, aquiline is now the one that it increases the excretion of by activating the NRF2 pathway, which then downstream, a lot of these phase two detoxification enzymes get get activated, for example. The other interesting thing is sweat. And, you know, there's been a variety of studies that have looked at which kind of, for example, heavy metals or other xenobiotics like parabins or BPA, things like that, which are excreted through a variety of different, you know, mechanisms. Some are excreted, you know, more prominently through urine and others through sweat. And so things like exercise and even sauna bathing can, you know, for example, mercury, is one that really is excreted through sweat. So you might find that interesting.

I don't know if that's a little of this, but you've told me a lot of new stuff. I absolutely didn't know. So great. Thanks for sharing that. Yeah. No, I think, yeah, detoxing for mercury is definitely something that's been out there for a while. Chris, I grew up with that stuff.

Yeah, everybody had the amalgams in their, the mercury amalgams in their team. Yeah, and we used to play with that stuff when we were kids when the thermometer broke. We used to stick her hands on that. That was such a good thing to do. So I wanted to talk a little bit about the agotypes and how you've used all this data, you know, a variety of metabolites and the proteome and the genome and just other biomarkers as well to kind of look at how people are aging and what their predisposition is to, you know, a disease of the brain versus a disease of the heart versus a disease of the liver, for example.

Yeah. So, well, I mentioned before that we take tons of measurements on people, including the microbiome and all kinds of molecules. And for the most part, your molecules don't change. There are interesting times. Believe it or not, there are seasonal changes. And we argue there's two seasons in California, so you'll see changes in some molecules with seasons.

But the other is by following people over time, we can see how they're aging. And it's the first time it's been done in this kind of detail. And so normally when they study aging, they look at the difference between old people and young people and say, oh, old people have this and young people have that. And therefore, like hemoglobin A1C, a marker of diabetes, old people have, you know, more and young people don't, therefore it changes over time. And what we discovered, and I don't think it was a surprise, but it's nice to show, is that people are aging differently.

So I'm a metabolic kidney liver ager, but my immune system's not aging somebody. much. Another person, he's a cardio ageer. His cardiac pathways are changing the most rapidly over time. We think you only need about five measurements within two years and we can tell how you're aging. And again, everybody's aging differently. So we think of it like a car where, you know, you have a car, the whole thing does get older, but some parts wear out first. And so we think that's what's going on. And, you know, the cardio ager, the heart we later learned that person was stage two hypertensive, which kind of fits with the idea that cardiovascular system was going off. Yeah, and so in the end, we had 43 people had enough data, and we could group them into these

classification, agitotypes, we like to call them, what's your agotype? And it was kidney, liver, immune, and metabolic aging. Now, I know there's more than that. There's a cardio ageer, but we only had one of those. We didn't have, there'll be many categories of it. age of types. And we're about to analyze more people soon. So we'll see just how many categories we can come up with. But everybody ages differently. And so you might say, so what? And by the way, we can see some people will go up. There's clinical markers associated with these. So in the metabolic age of type, you'll see people's hemoglobin A1C and other things going up. But what's kind of cool is some people have theirs going down. So what did they do to make theirs go down? Well, you can go back

and look at the data, and some people did exercise. This is an easy one. They exercise, they lost weight, and that makes a lot of sense. But there's other things we didn't realize, like there's a molecule called creatinin, which is a mark of kidney function, and we discovered that's known to go up over time, but we discovered we had a whole bunch of individuals who were going down for the creatinins. Like, what's going on there? Well, eight out of ten of them were on statins, so our hypothesis is that the statins might be helping. It couldn't really the muscle mass, but we didn't detect any changes of muscle mass based on some of the measurements we're doing. So it may be that statins and improved kidney function, nobody really

realize that. That's possible. That's something we're going to follow up on. So by correlating, you know, these, how people are changing, we can see what's going on. I think we can do more than that. I think if you're a kidney age or maybe you drink more water, if you're liver age, or maybe you shouldn't drink so much, alcohol. You know, there's a kidney age. You know, there's, We think, again, it's actionable information. If you're immunolage, or maybe you want to take, you know, tamaric or kummer and some of the garlic, things that should, you know, help with your immune system, give you a little immune burst. So we think that's what the information does have value.

And now we can run trials on the stuff. And here's another interesting way to look at it. In today's world, if you walk into a drugstore, you'll see a whole aisle on supplements. that, you know, a lot of them purporting to, you know, give you longevity and what, and you'd have no way of knowing if that's working. Almost any thing that's put out there that tells you, you know, this works for increasing your lifespan, how do you know? There's no way of measuring it. So we think by measuring these people's age types, if you will, it's in a time frame that's actionable. You can see how people are aging.

You can try an intervention and see if you can reverse it or at least slow it down. Was there any biomarker that, or biomarkers plural, that correlated with, you know, just aging in general? So like, let's say your HBA-A-1-C was really good as you were getting older, but that wasn't necessarily going to tell you whether or not your immune system was still aging or your liver or, you know, was there something that was a little more universal, like an inflammatory marker? Was there something that kind of could maybe affect all these organs? Well, yeah, you're getting at an issue that's of high interest to the longevity field, the aging field, is, you know, are there some, you know, panaceas that can actually slow down all these things? And it's most people, if they want to find the systems level control of aging, they're trying to do it through metabolic health. And so people like the idea of this drug metformin might be able. way to systematically slow aging and their trials now running for that out there to see the metformans

have been a widely used drug. It comes out, the first observation was that diabetics on metformin seemed to live a little bit longer than normal people. They never did the right control showing normal view with metformin would maybe live even longer too. So that's what's kind of what going on now. So I that and so there are there is interest in that. I. know what to say about that. I think it is worth trying. I think there probably are some general metabolic control. A lot of people like the idea that your unfolded proteins are going off so if you can do things that might promote, you know, better proteostasis, if you will. You can actually get people to live longer. There's a lot of interesting things out there.

So we'll have to see. It's a very active area. A lot of people are now starting to invest in this. So it went from being almost a bizarre phenomenological field to Tzu's a bit phenomenalogical, but people are really trying to do active treatments to control aging, I guess in part, because we have an aging population and people want to do something about it. Yeah, just kind of speak to the Metformin, you know, that field and that sort of arena of people that think that might improve longevity. I was sort of very interested in it as well. That study that you mentioned was probably the main.

major thing that caught my attention. But then there's been a couple of randomized controlled trials that have come out since then showing that healthy, so people that don't have type 2 diabetes, healthy people that are actively, physically active doing resistance training and or even aerobic exercise when they take metformin, it blunts some of the benefits that, your exercise-induced benefits that are improving a variety of things. And so that was really discouraging for me, at least, in turn, like what you'd want to see is it synergized or, you know, you know, there'd be an additive effect or something. I saw the same study and had the same reaction, yeah.

And like you, I exercise every day. So it was very disappointing to see that because it aggregates the effects of exercise and that form and does. And I think wasn't there a study that compared exercise to metformin use and found exercise was actually the better better at, I don't remember what their market. was if it was insulin sensitivity, if it was some other marker of glucose regulation. I don't know what it was. But that exercise was actually better, I think.

Well, I didn't see that, but I can imagine that to be the case. Exercise induces a lot of stuff that's beneficial, myostatins, all kinds of useful things, endorphins. And the other thing, especially is for aging. In fact, if you talk to anyone in the aging field, almost all of them will say exercise is number one for longevity, food number two. And, you know, I can see why they would say that when people get older, you know, one of the number one problems they have is sarcompania. So, you know, loss of muscle mass. And exercise certainly keeps that up. And now there's studies.

They used to tell people who are 80, you know, oh, take it easy, relax. So you don't hurt yourself. But actually, what you really want them to do is just the opposite. You want them to get up out of the chair and move around because they'll actually live longer and be healthier. than if they just sit around in their chair. So now there's studies out there like that. So exercise, there's no question. It's very beneficial.

We don't even understand how it works. It's pretty cool. There's a study run by NIH that's launching now, or launched a few years ago, but we're in the middle of it. It's a $70 million study to try and understand the benefits of exercise, both aerobic training, so running, biking versus resistance training, and see what promotes, what benefits, and how does it work? It'll be very fun to see what comes out of that study.

Totally. And you had a study that was published not long ago on some of the metabolic changes of exercise. We did, yeah. So we did these same deep molecular measurements on people who did exercise. We discovered half your molecules change when you run to what's called your VO2 Mac. Your VO2 Max is also an indicator of longevity. It's actually one of the best indicators of longevity. So we did. We saw changes. We could see that, you know, a lot of immune molecules shifted, and that makes sense. You boost your, you know, you remodel your muscle mass, to be honest, when you exercise, especially with resistant training. And then all kinds of metabolic control markers changed too. And I'm sure all that stuff's very, very powerful in the context of the current pandemic, the COVID-19, being able to have a, you know, a, you know, A strong immune system is really key, I think, to longevity. You may not know, but you're getting cancer your entire life, and you're clearing it your entire life. Why is it that old people get cancer?

Well, what happens is your immune system declines, especially in the people in their 60s? And so suddenly, these cancer cells that you've been clearing are getting hold, and then they can actually, you know, burst out and you get cancer. And so I think keeping immune health is in my mind, probably the number one thing. And exercise definitely promotes immune health. All kinds of beneficial cytokines and other factors are induced by exercise. Do you think that there's a bigger component for genetics or lifestyle in some of that, like how your immune system's aging versus your heart or your liver?

Is it a combination of them or is there one over the other? For sure, it's a combination. Yeah. How much of aging is due to genetics? There's no question. a strong genetic component. You look at centenarians, they run in families, but there's also a strong environmental component. You don't have to look past smoking or some of the things you described earlier living your eyewaste to see how to decrease people's lifespans. And so a lot of

risk factors of all sorts have been associated with lifespan. So the one thing about the environmental factors, the hope is that we can control that and lifestyle factors. Those are things we do have control over our genetics is a lot tougher. Well, being mindful of your time, I kind of just wanted to wrap up. Maybe, you know, since you are monitoring all these things and not only studying other people, but yourself, maybe kind of it'd be interesting to hear your lifestyle routine, like your exercise routine, maybe like your meals or, you know, like a sample of what your general meals are or how much, if you do fasting, time for the eating.

So I do exercise every day, as you can probably tell. In fact, I do weight training. You can't tell, but I actually gained 10 pounds of muscle mass since I started this. I do a whole body MRI, too, from QBio. So I, yeah, so I do exercise every day, even though it didn't help me with my glucose control. That's how I started it. I thought muscle mass would be better for glucose homeostasis. That failed on me.

But I still do it anyway because, quite frankly, it makes me feel good and all those other benefits we talked about earlier, is archipenia. And then, yeah, I, because I am type two diabetic, I try and avoid carbs. I tend to eat a lot of protein. So, you know, eggs and protein-rich foods. One problem with me is that I don't like vegetables, and that's a terrible thing. It's something I've never gotten over. All kids have that. So I eat a lot of salads and things I do like, carrots and that sort of stuff. But so I am trying to make up for it. You may know that most people are very fiber poor, meaning you're supposed to get,

depends on whether you're a woman or a man, but you're supposed to get about 30 grams of fiber a day. And most people get about 12, sometimes 15. So I'm trying to be conscious about eating more fiber as well from carrots and things like that. But I will be doing fiber supplementation as well to try and get those. numbers up. So, and I eat a lot of nuts, they have omega-3s, things like that. So I guess that's my lifestyle. And I don't meditate. I was meditating. That probably is good. That's supposed to be

good for your, you know, your synaptic neurons and things. So yeah, I need to get back into that. That's something I just haven't found time for, but should. Yeah, me too. I always, if it's like, if it's between exercise and meditation, I always go for the exercise because I feel like I'm getting, I can get a meditative state from that as well. But just real quick on your lack of vegetables, interested in your microbiome data. Is that something, have you experimented with how your personal microbiome looks, you know, versus like if you're eating more vegetables or not? A little bit. Your microbiome, believe it or not, doesn't shift that much. You really have to make a long-term commitment to shift it. If you take an antibiotic, you'll shift it. But then it

bounces right back, believe we're not, as soon as you go off the antibiotic. Now, some of your strains will change, but the general composition is pretty similar if you take an animal. But some of the substrains, which may be important. Some people are still trying to figure that out. For me, I can tell one thing that works, lactobacillus ruteri, actually, you know, maybe a little gross, but gives you the stool composition you're looking for. I did try that. And sure enough, it does work. These days with my fiber diets and various things, I haven't worried about it because I do seem to be pretty well set up that way. But I have done that.

I'm about to try some other experiments. I've been doing all kinds. Our lab is doing all kinds of fiber experiments to see what fibers do what to what microbes. And we have seen some very interesting shifts. You hear fiber is good for you, but you may not realize it. But there's all kinds of fibers out there. There are, what are called long chain and short chain and hydrophobic meaning greasy and hydrophilic meaning water, you know, loving.

And, you know, positive, negative. It's like saying, oh, animals are the same. They're just all different. And so we're trying to see which fibers do what. So we have some academic studies going on there. Yeah, we have a good one that is known already, but the literature is very, very confused on these. But this one's very, very clear, which is in Rabina's Island, which is. is found in Physium Huss will actually drop your cholesterol.

That one's pretty clear. Oh, that's cool. Yeah. So there is a, like you mentioned earlier, there's associations between microbiome composition and metabolic health, type 2 diabetes, for example. Right. And there's also autoimmune connections, if I remember correctly as well. There are, yeah, there is a lot of connections between your microbiome.

And in our case, by the way, we know what microbes correlate with that drop in cholesterol. So we think they're an important part of that. In fact, it's the way they're dealing with the bioacids. We think they suck out your cholesterol. Oh, really? Yeah, it's a very different mechanism that people appreciate that we've learned by doing the steep profiling. And so we think your microbe.

And what was the strain again? I don't remember the exact strains that were doing it, but they were these, there were microbes that seemed to be elevated. And what we think they're doing is they're actually making more secondary bioacids, which bind cholesterol and actually remove it. from your blood, and that's how we think it lowers. That's our model. We haven't proven that.

And that's how we think you can lower your cholesterol with the fiber, which I don't think anybody appreciated before because they hadn't seen all these different components. Have you heard about people? There's a lot of anecdotes out there and might even be published data about L. Routreye actually lowering cholesterol as well. Be familiar with that. Yeah, that didn't do it for me, but that could be a personal thing.

It's possible. Yeah. Lactinacillus in general. You know, the bifidobacter and lactobacinicillus, those are your beneficial microbes, as you probably know. And so in general things and fibers generally increase both of those. So that's one way in which they promote this. They're thought to actually make something called short chain fatty acids, which give your immune system a boost. So that's one way in which fibers are thought to do it.

But it probably is the case that different people react to different fibers. It's very clear they do. and that depends on your microbiome because your microbiome will degrade the fibers differently. And so the microbes you have will degrade a certain set of fibers. That's different from the microbes I have in my gut. And so that's one of the reasons we are different in our metabolic health. Do you think there's a good reason to measure your personal microbiome?

Like, for example, you're in your fecal. There's quite a few companies now that will, you know, that are available to consumers, you know, where people can give a little sample and see what's in their, you know, gut, basically. Right, right. So the answer is, yes, microbiome, it's one of these things everybody knows is super, super important. But the actual showing something with clinical value has been really, really tough. So a lot of companies have formed and a lot of gone away.

And we know that there is something there, and it's a matter of getting the right understanding, I think, to go with this. Because I think ultimately, knowing your microbiome is really going to be important for knowing what food you should be eating. And there is, so some of the companies that are forming around this area, there is going to be value in all that. But it's not all worked out yet, but stay tuned. I think it's coming very, very soon, actually. and how it relates immune health and such. So it's kind of interesting because you may not know, but you have more immune cells in your gut than anywhere else in your body.

And it's because they're actually in this interplay with your microbiome that's in your colon and your feces. So basically there's this interplay between your immune system, your microbiome, and the food you eat. And it's a three-way axis. And so it's a direct link, if you will, between food, what's going on in your gut and your immune health. And then obviously your metabolic health,

since your microbiome is making all these metabolites that show up in your bloodstream and are critical for your whole metabolism. So it's a whole interplan. To us, this is why the big data is so important because it gives your better understanding of your whole health picture, not just some five or ten little analytes

that give you, again, just a very, very narrow view of your health. So this is why we think these big picture. And ultimately, food logging's a pain. You probably have done it. We need better ways to food log because I think that will help. And they're coming. They're better than they used to be.

But I think that's going to be critical, knowing your food health, again, and your microbiome and all these things. So you can put all the pieces together and really make healthy people without having to take drugs, quite frankly. I wish I didn't have to take any drugs, but I have high cholesterol and I have high glucose, so I'm taking two, which I'd prefer not to if I can control other ways. Well, Mike, I am so excited about the research you guys have been doing and are continuing to do. I think that this big data approach of just measuring as much as you can, I think you're going to find unique associations and biomarkers and, you know, not just looking at the microbiome,

but looking at the microbiome and the metabolome and the genome and the proteome and looking at the whole picture, it's very exciting time. So I look forward to follow and continue to follow your work. For people that want to find out more about what you're doing and also just follow you in general. So you are on social media. You're on Twitter. Yeah. Snyder shot is my Twitter feed, so to speak.

And then, yeah, if you want to sign a friend of any of our very, studies, there's innovations. That's Stanford.edu slash wearables for the COVID one or just go to innovations.comford.edu. And you'll see we have other studies going there as well. And then we have spun off like the continuous glucose monitoring.

January AI is a company that's trying to improve metabolic health using that kind of technology and QBio's doing the deep data profiling on people for, again, trying to get a very complete picture. It's much more medically relevant.

as I say, than the research one we're doing in the lab. So both are a lot of fun. Very cool. Well, thank you so much, Mike. And it was really great having you on the show. Thanks for having me. Hope you enjoyed this episode. Thank you to Dr. Snyder for coming on the podcast.

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