Transcription
All right, everybody. We have a special treat on the channel today. One of my favorite scientists of all time, Professor Michael Snyder. I'm going to give a longer intro than normal, and you'll understand why. I want you to understand why I'm so enthusiastic about this interview.
So, you know, on the academic credentials, Professor Snyder served as the chair of genetics at Stanford School of Medicine, is currently the director of Stanford Center for Genomic and Personalized Medicine. His lab is literally the world leader in advanced deep multiomic profile. And we'll get into what that means. And this is demonstrated by an unprecedented, unparalleled publication record, cell, science, nature, basically monthly. For those of you who know what an H index is, I did look up his this morning. It is a grotesque 220. Now, for reference, there's no like threshold for what excellent is, but 20 is considered good. 40 is considered outstanding. 60 is considered exceptional. I got this from an AI summary. 220 is like the highest I've ever seen.
But those are just the check boxes. I've known a lot of academics in my time, especially senior academics as I've gone through my PhD, my MD, and it's very rare that someone's at the pinnacle of the career like this and is still so engaged with the work. I think a lot of PIs often, you know, shift shift off the thinking to postdocs and there's only a few people that I've corresponded with who are deep in the work themselves. Actually, just as a quick tangent, I believe you have like an N equals one experiment in one of the Cell Press journals. Is that correct?
That's correct. Yeah. Oh, yeah. Which is insane. Like blows my mind. So, you know, for me, I don't know how to describe this other than like this is the Thanos of PubMed cell in nature. For me as a dorky scientist, this is like a Swifty meeting Taylor. So, uh, sorry to embarrass you, but it's true. Um, I have an embarrassing story I could tell, but maybe I'll I'll leave that for later and we might have to clip it because it gets a little bit edgy. But anyway, today we're going to delve into some of his incredible studies. We could go on for many hours, but I want to hit on two. One about the four types of type 2 diabetes and then what was the most popular paper ever published in Nature Aging.
So, with that, uh, Professor Snder, welcome to the channel. Um, I'd like to give you a chance to respond and tell us a little about yourself, your lab, um, and what you do.
Sure. Well, Nick, it's great to be here. My lab is a big data lab, as you probably figure it out. Uh, I guess it started with trying to look at problems in biology with, you know, holistic approaches, collect as much data as possible to learn how the biological systems work. And then I moved to Stanford about 16 years ago. And then really the goal was apply it to medicine. So collect big data around systems and and in this case individuals to try and understand and track disease and and that's really where it started.
I you just step back a minute and you probably would agree everybody agrees with us. Our health care systems broken. Um we you know practice sick care rather than health care meaning we don't work on keeping people healthy. We treat them rather when they're ill. uh and even the way we practice health care, it's all broken. And so certainly with the revolution in data technology, collect data collection, all these new technologies like genome sequencing, mass spectrometry, wearables, you know, you can now collect data around people at a level that's never been possible. We can really transform the way, you know, healthc care is practiced. And that's really what's driving us. And so that's what our lab does. We do deep data profiling on people. We'll sequence their genome. will make as many measurements out of their blood, urine, and poop, if you will, their microbiome. Uh, along with these physiological measurements with wearables to try and collect data while they're healthy and track their health.
Yeah, very well said. Um, you know, I often say like medicine is siloed, metabolism is not. And if you know, people want to know who's doing the leg work behind the scenes to make personal like true personalized medicine mainstream, it's it's people like you. So, I want to thank you for that. And I also want to describe a term that I know will come up in this interview which is multiomix um and longitudinal multiomics. Can you unpack what that is because that's kind of at uh the heart of a lot of the work you do.
It is. Yeah. So omixes of course is a collection of molecules. So your genomes collection of your genes if you will. Your transcriptto is collection of RNA transcripts. Proteomics collection of proteins. And so multiomics is where we actually do all of these things. Our lab was a little unique at the time we launched this because we were doing a lot of proteomics and we were doing a lot of transcripttoics. In fact, our lab for what it's worth invented RNA seek. Uh, and so we've along with other labs but anyway we um yeah we're we're collecting a lot of data on these different systems and really the goal on modomics profiling is to make as many measurements as possible out of people's again blood and urine especially blood because that is a window into your health and that turns out to be very very informative. You only get part of the picture with your transcriptto. You get more with your proteome. And we think you get a ton with your metabolum. It's one of the better ohms of study actually.
Yeah. No, it's it's really phenomenal. And I really want to hammer home how important this is. I'll use an analogy. You can correct me if this is an inappropriate analogy, but I imagine like when I go to my PCP and get a panel of labs, that's like them. They're taking a cross-section at a point in time doing like a handdrawn doodle of what I am. Now, multiomix is taking, like you said, your genome, your transcriptum, your proteom, your your fecal microbiome, and then integrating it into a highresolution 4K image of you. And then longitudinal is you do it over time. So, this is like the difference between a handdrawn doodle, standard medicine, versus like watching Avatar in 4K. That's how it feels to me. I don't know if anybody's used that sort of analogy. Do you think it's a fair analogy?
I agree. Yeah. I use a related one. And I always say, you know, if your health is a thousand piece jigsaw puzzle, we're trying to get 700 pieces, whereas in today's world, they get about five or six. They really get an incomplete picture. Yeah. So, it's the exact same analogy, I think. Um, and we're capable of getting these much better pictures and and you know, maybe to go in the story a little bit, we started this just to get the technology going on me, uh, and then later applied it to 109 people and now we have a few more on, some have dropped off, but we've been doing this study for about 12 years now, uh, on most people, me longer. Um, and by actually doing these deep data dives, uh, 49 of the first 109 actually learned something pretty important about their health. in the first three and a half years, meaning we saw something was off with them preymptomatically. They didn't have any symptoms, but we could see that something uh was not right. In fact, we caught someone with early lymphoma, two people with precancers, two people with serious heart issues by getting these deep data dives. So, we think that's very very important. Um, and so, yeah, it's definitely going to transform medicine.
But let's get into specifics. So, the first paper I wanted to talk about was the the many flavors of of diabetes. So you published a paper. It was in nature biomedical engineering last year. It was truly fascinating because basically what it showed was that type 2 diabetes the most common type typically adult onset associated with obesity is thought of as a single disease because of how we diagnose it now. But in fact it's not one disease. It's closer to being four diseases. We're going to get into that. But what was the motivation behind this study?
Well, a couple things. One is one I'm type two diabetic. I'm I'm a weird one. And in fact, that was really what triggers this whole thing. I have a beta cell defect. And everybody looks at me and says there's no way you're diabetic because I'm pretty thin. Uh, and so that was very unexpected certainly at the time. It's actually predicted for my genome, believe it or not, by a tool who passed away recently. Yeah, I have a I'm very very high on the polygenic risk score. Polygenic risk scores don't work. That's a whole another thing for most people. But I'm cardiologist that. But anyway, that's yeah, they do a little better uh than most fields. Uh, anyway, I'm at the extreme end and that's why it worked for me. It's the first time someone predicted their gene their disease from apologenic risk score. Wow. But anyway, backing up. Um, so, uh, if you think about diabetes, there's another motivation is the fact that it's so rampant. You probably know, just to set the stage a little here, 11.6% of people are diabetic, 20% don't know it, and 33% of US citizens are pre-diabetic and 90% 80 or 90% of those don't know it. So, we have this problem out there that a lot of people aren't even aware of. And yet, it's getting worse and worse and worse. I argue it's worse than the pandemic, the diabetes endemic. And then you think about diabetes, it's it's not just one condition. You've got a lot of different organ systems. You've got your liver, your muscle, your even your brain's a major glucose consumer. U your pancreas of course. Uh, and then you have all these biochemical pathways. You have insulin. You have something called incrretin. That's these GLPs that are getting a lot of attention. Um, so you have all these biochemical pathways, very complex process. And then of course with me getting diabetic and we we didn't realize what was going on at first. I'm not muscle resistance. We can dive in on that a little bit. It's pretty clear. And actually, by the way, South Asians, there's a lot of them that are thin diabetics as well. Uh, so if you just look at the people out there, we're all not overweight. And that's this visceral fat is not what's triggering our diabetes in many cases. And so that was the motivation for this.
Wow. Yeah. So, we'll get into some more details. Basically the spoiler alert is you you identified four different types. We'll get into the methodology, but just to describe what those were. I'll go over them quickly. There's muscle insulin resistance. So your muscles are your biggest glucose sync. And if your muscles aren't listening to insulin, you can't sync glucose. So then you have glucose regulation. That's, you know, ingredient one. You could say there's beta cell dysfunction. So as you mentioned, I guess that's what's predominant in you where your pancreas, the beta cells that make insulin, they're dysfunctional. That's flavor two. Impaired incrretin action. So as you mentioned, you know, incrretin hormones, they're released from your gut. Some of them synergize with insulin to amplify the effect. One's called GLP-1, very well-known hormone. Now, we'll get into a little bit more of that later because there was a a pretty viral study that I want to talk about that's been in the news recently with respect to some uh other popular medications. And then finally, hippatic glucose dysregulation. So, your liver also handles glucose. It produces glucose and if that's a it can lead to high blood glucose. So, say you're insulin resistant at your liver, then the insulin is not good. It's telling the liver to put the brakes on making more glucose. Um, and just as kind of an aside and there more by the way, but those are the four main ones we study, but there there'll be some other um diabetes subfotypes as well.
Yeah. So, let's get into why is it so important to dissect these subtypes? We will get into more specifics on yet a second paper in nature medicine this year, but at a high level like why is this important for everybody in everyday life?
because we're all different and um we and and in fact that gets set probably early in life. You may know your microbiome gets set in the first three years of your life. So we're all very very different and we all then respond to foods very very differently. Some people and we'll talk about this more later I'm sure where some people most people spike to white rice but a lot of people spike to bread, some to pasta, some to potatoes. It's all over the map. It's clear we're we're all reacting differently. we respond to medications very differently. So something is heterogeneous out there. And so it's reasonable to think that this heterogeneity is due to these diabetes sub phenotypes which people haven't really looked at. And that's what we did. Now I should emphasize we spend a lot of time on so-called normal people who often have glucose dysregulation. That was one of the first things we learned and and pre-diabetics in fact that these continuous glucose monitors are very very powerful for measuring this of I'm sure your audience probably knows about these and I'm wearing one right now. They're these patches that measure your glucose every five minutes and they now um they're just very revolutionary for following your glucose patterns and that's when you can see you react differently to different foods. So there's got to be an underlying basis for this and that's what we dug into. We started measuring sub phenotypes on people uh and then we'll talk a little bit about ways you can do that now with machine learning and AI and then we basically uh started matching it up with foods and seeing what sub phenotypes put you off for certain foods if you will.
Yeah. No, it really is the frontier of of personalized medicine. And we will get into what you were able to do with CGMs which was quite remarkable uh going forward. You're going to learn very quickly. I love analogies. So, just to kind of hammer this point home, I'm going to write a letter on this paper, it really caught me. And the analogy that came to mind because now I'm an influencery person. So, I think in thumbnails was um pizza. So, as I read the paper, I was imagining like a recipe for disease. Um, say one pathology is bread, another is vegetables, another is cheese. You can mix them together in different ratios and get different dishes. So you can get a Caesar salad, lots of vegetables, a little bit of bread in the form of croutons and then topping with cheese or a pizza, different amount ratios of, you know, bread and vegetable, tomato sauce and cheese. And depending if you're like a metabolic salad or metabolic pizza, you're going to have different well one metabolic impacts you're but also like now I'm mixing analogies, but you're going to consume the food differently. You're going to address it differently in your real life. So um hopefully that's a reasonable analogy. I don't mean to make a light of this but just to get people to think about like we think of type two diabetes as one thing but it is diverse and pizzas. Yeah.
Um, so my next question for you is why has nobody looked at type two diabetes like you did in this study? Why has nobody looked at it like this before?
Yeah. I think there are probably two reasons. One is medicine's very conservative, very um if you will simplistic and I don't think there's they and probably from the pharma standpoint it's nice if you can just give one drug for everybody but uh as I mentioned before that doesn't work. Turns out I'm a former nonresponder. So I don't respond to the most popular medication out there. Um anyway and I respond to certain ones but not others. And again it made a lot of sense for my my sub phenotypes. We can talk about that as well, but I think people haven't got into it because also then they didn't really know how to act upon it. And I think now with these glucose monitors, it's pretty clear you can bring lifestyle into glucose management, which wasn't possible before. And now with lifestyle, we see that different foods do spike different people as they say. So basically, you should be able to match people's foods with what spikes them. And knowing their phenotype, you can get predictive. And we'll talk about that, I'm sure. So meaning that if you measure some phenotype, it's very actionable information now. And I don't think people realize that before. It's only in recent years too that a lot of these new drugs have come out. They're called SGLT2 inhibitors is one kind of drug and the GLPs that are out there. Those are gaining a lot of popularity. So So some of these drugs do work pretty globally. Uh, some of them are very specific. One that works well on me is called Rapinolide. uh that is actually um it promotes insulin release from your pancreas and I'm a great responder to that. So actually that works pretty well on me although these days I am on the GLPs and the SLT2s because they work even better. But um anyway that but the point is that I can match the drug with my phenotype and get a response. And I mentioned before I'm metformin nonresponder and I can tell you other things as well. We'll probably get in this as well, that I made lifestyle changes, some of which worked, some of which didn't work. And in hindsight, now knowing my sub phenotype, my beta cell defect, it it's pretty obvious why some worked and some didn't. So, this basically allows you to target lifestyle approaches with precision on the N equals one level rather than like, you know, shooting in the dark. So, it's like trying to hit a bullseye on uh a target with a bow and arrow if you're like a professional archer versus if you're blindfolded an amateur. Like, it really is gamechanging.
Um, you did maybe I'll dive into this one now actually if you want, which is um I'm a beta cell defect and and I initially got my diabetes under control with lifestyle. I cut out all the cakes and sugars and all that sort of stuff and I was running and that was working pretty well. Um, meaning I was well, it went in two bouts of this thing, but in the second bout I got it down to about 57. Uh, and then I kept creeping up with age and I was running about as much as I could, you know, working to my schedule four times a week, about 45 minutes a run, and it was kind of working, but it was still creeping up. So, I shifted from running to to weightlifting to build muscle mass. And that worked actually. By the way, I gained 10 pounds of muscle mass. It had zero effect on my glucose control. Okay? And the reason is I'm a beta cell defect. Meaning I can lift weights till the cows come home, gain as much muscle mass I want, it's not going to fix the insulin release from my pancreas. I didn't know it at the time that was my defect. Yeah. Uh, so the point is if you can match your defect, you will actually you can improve things uh with with a lifestyle change. And actually we've shown in other studies that muscle resistance for folks are muscle resistant if they're more active they actually do improve their glucose control. So we think again matching people's sub phenotype with their lifestyle can be very very powerful.
I want to double click on that. I mean the examples you gave are tremendous. I think we'd once discussed though there was a I think this was back in like 2015 so I can't believe it's been a decade but there was a a letter out in Nature and it cited a figure that the top 10 grossing drugs only help between 1 in 4 and 1 in 25 I think people who take them and the reason that's so important is because what's considered now like gold standard is the human randomized control trial and what people need to understand is that is yes a randomized control trial but it's on a heterogeneous group of people so you can get a statistically significant result while helping the minority of people. And then what you end up with is a large proportion of people where, you know, to take your example, they're being given the frontline drug even though it's not helping them at all. And you can imagine where that ends us up. Huge problem.
Exactly. We're all We're all different. And I don't really care what happens to the average person. I care what happens to me when I'm trying to get my glucose under control. Exactly. And that's true of everyone. We want to get everybody treated. Right. Right. And we're all different. And again, knowing the sub phenotype then is the only way to get it right, I think. But even just zooming back without advanced multiomics. I mean, you mentioned CGMs, which is so powerful. I'm going to show my editor can bring up figures. I'm going to have him show a figure from the paper, but you showed all the different glycemic curves from participants in your study. Um, I'll put up two side by side now, but it's been S14 and S42. And what I want people to notice is often diabetes can be defined or characterized diagnosed by an average blood sugar or if you do something called an oral glucose tolerance test where you take you know a carbohydrate to glucose challenge and that at key time points measure your glucose. So say at 2 hours what is your glucose and then you can help use that to diagnose diabetes. But if you look at the two um participant curves I'm showing, you're going to see their CGM curves in blue and yellow. And at the 2hour mark, the person who is the yellow curve has a lower glucose. The person who has the blue curve actually has a higher glucose. But if you look at the shape of the curves, the yellow curve has this massive spike over 200 milligrams per deciliter. And the blue curve is like flat as a pancake. Pardon the um unlan analogy there, but the point is if you're not looking at the shape of the curve, if you're not getting continuous glucose data, you completely miss that and you would conclude, oh, the person on the blue curve has worse glycemic control even though it's way better. So, give you a chance to respond to that. The importance of just like
Yeah, I mean, you hit the nail on the head. There's two aspects of this. One is that those are single measurements and they weren't always picking the right time points. for these people. Again, these are dynamic things. We think area under the curve is probably the most important of all that. And that's missed when you pick a single time point of some people have rapid response, some have slow. So, you're spot on that one time point is not an accurate way to measure this. And the other thing that was pretty valuable to us was the shape of the curve. That's also what drew us into this heterogeneative diabetes. We all have different curves, if you will. Some people have slow rise, slow fall. Some people have a very rapid rise in their glucose. This is after they drink a glucose drink. They'll have a very rapid rise and a slow fall. Some people have multiple humps, believe it or not. They'll have multiple bumps. Some people go down below their resting glucose or if you will, uh, they they become hypoglycemic. Uh, others don't. So, what's going on? That was one of the motivations for this whole study as well. this first one you referred to where we saw these shapes of the curve and we said there's that's probably reflecting what's going wrong, right? They're all drinking the exact same drink glucose. Yeah. Yet the shape of the curve is very very different. That's got to be reflecting how they're metabolizing that glucose. And that's why we did the sub phenotyping and then that's how we started relating the sub phenotypes and the shape of the curve. And here's where we used AI and machine learning to actually figure this out. We took many features from these curves. They call these you know these characteristic features as you know and they um we took I think initially 50 ultimately zoomed in on 14 that were informative like how fast you rise after 30 minutes how fast at at two hours and you know how fast you go down whether you have two humps or one and do you go down below the the line. We take basically 14 features and then we relate them to these four types of diabetes you referred to to see if we can build predictive models and two of them worked really really well for muscle resistance and beta cell defect. We're pretty precise now about 0.9 and we think this was a big deal because they now sell these glucose monitors over the counter. Yeah. Yeah. So that means at home you could drink a glucose drink and we could tell you whether you're muscle insulin resistant or a beta cell defect from from a simple assay. If you were to do this in a clinic, it costs over $1,000, take six hours, at least the way we do it to measure muscle resistant. It's um pretty elaborate. And now you can do this in a simple at home test. It's not yet out to the world, but that's our plan. and we're going to put it up online for free so so that anybody can just do this glucose curve at home and then know what their subtype is and and we'll talk about the ramifications of that in a minute.
Yeah, you you anticipated where I was going. Um, thank you. I'm going to pull back and then direct right back where you were. Um, I did want to show and now I'm challenging Professor Schneider because this is one of many dozens of papers he's published over the last year. So, I'm going to mention a figure you guys are going to see it right now. He's not seeing, but I don't know if you remember, I think it was figure two A through E. So there's basically A is showing the average blood sugar. So the HBA1C, and it's organized with the participants going lowest HBA1C, so lowest average blood sugar to highest. And you see it kind of like building up. And then the next four columns are these four flavors. muscle insulin resistance, um, the deposition index, the beta cell function, incretin effect, and then hypatic insulin resistance, liver dysfunction. And really what I want you to take away from this is that there isn't any coupling between the average blood sugar and any of the other elements of the profile, but everybody might have some dominant effect. So when we talked about like the the recipe for diabetes, somebody might have like mostly vegetables or mostly cheese or mostly bread, but you can't predict it based on the average blood sugar. So that's what we're talking about. This is just a different graphical way of depicting it. And another point I just want to emphasize is in order to figure out what your dominant pathology is, for Professor Snyder, it's beta cell dysfunction. Right now, the kind of tests you need to do, you can't get in clinic. You need to be in a research lab. So what they did next which professor Snider already mentioned was the really revolutionary part of the study when it comes to patient care because as you described you can't just look at a glycemic curve any of these curves and like intuitit what's going on but if you have a huge data sets machine learning can figure out this particular shape of the curve how it slopes at different points actually tells us about what your dominant pathology is. And so using the data set they collected now patients in theory can have a CGM that you know directs to an app and it says oh based on the curve of your response we know this about your metabolism. We know you're insulin resistant at your muscles or not your beta cells are doing X Y and Z. That is to me absolutely mind-blowing that we can do that now in the year 2025 and that it sounds like this might be clinically available within the next several years.
I hope so. So we're going to repeat it. We're that's in progress now and then to I'm pretty confident because the the signal is so strong at least for those two muscle resistant beta cell defects and we're still working on adding other features if you will to improve those other phenotypes I mentioned. So the goal will be to get them all there and we're going to try and bring in microbiome as well which we know has and influences and so the ultimate goal would be you do this at home you drink a glucose shot you know what your sub phenotype is and you just save yourself tons of uh money and hopefully tons of time because you can do it all at home. That's the goal.
All right. Um, I'm going to ask two questions before we get to your nature medicine study. Um, which was one I know people are going to ask, so I'll just ask it. Do you need a glucose drink or can you replace it with your favorite candy of choice? Be that sour patch.
Yeah, I was trained as a chemist, so I'd prefer you do the glucose drink and you can buy these 50 gram drinks, but believe it or not, grapes and potato that ratio, grapes are like solid sugar. Um, and some people, you know, they'll spike the grapes um badly. Uh, most people spike a little bit. Um, anyway, um, but if Yeah, the grapes to potato ratio can actually pretty pin down uh whether you're insulin muscle insulin resistant, believe it or not. Wow. The problem with that is we eat different amounts of grapes and potatoes. So that's what makes me a little nervous, but we can try and tell people to eat I forgot what that number is. It might be like 20 grapes is the equivalent of what of you know the sugar. So, so there probably are ways to do it that might be a little more palatable, but um, anyway, the chemists of me would prefer. By the way, you should do this while you're fast at first here in the morning.
So, you're saying one could develop a protocol with a standardized um mass or number of grapes and amount of potatoes cooked a particular way and then use their CGM responses even now to figure out what's going on. You can do you have a protocol for that? I mean, we we can do it after the fact. I can drop it in the notes.
Yeah, you'll have to drop it in the notes. I don't have it off the top of my head. I I know what the amounts are. I believe it's 50 grams of carbs for each we used, but what I don't remember is what that translates into the number of grapes and okay, you know, the amount of potatoes that was Yeah, it does depend on the amount. So, okay, we we'll try to collect a protocol and and drop it in the notes. That's pretty awesome.
Um, and we're going to get to the nature medicine study. I did just want to pick your brain about one other thing. A study came out last year that I covered recently. It's been a little bit of a hot debate because and I'll explain, it's going to be obvious in a second. There was a human trial published uh a little bit before this study that we're talking about now in Nature Biomedical Engineering. It was published in Cell Metabolism. It was a human trial that found that um statin medications which are prescribed to about one in four adults over 40 for secondary prevention of cardiovascular disease um we already kind of know they increase HBA1C and increased type 2 diabetes risk. That's known. But they also smash down GLP1. The effect was very dramatic. Like you know a seven-year-old could could see it. I'll show the graph on screen now. But the reason I raise it is because given the nature of the study um that we just discussed, did you or would you be interested in looking back to see if patients on statins predominated in the incrretin effect um dominant pathology? I can send you the paper. This is I'm springing this on you, but I'm just curious to get your thoughts.
We should definitely look at that. And and to your point, actually, I'm textbookked this way. That is to say, when I go on statins, my glucose goes up. I know that already for me, and I wasn't aware. It might be due to the GLPS. There's another study suggesting there's a modest effect on insulin resistance when you go on statins. You increase that. So, um, but the GLP one probably makes more sense in the sense that, uh, I'm, as I say, I go up on on Yeah. uh, statin. So, I've been a little careful about that. In fact, I give my heart guy a little bit of grief on this. because he wants me to keep increasing my statins. And I said, "Well, you're just trying to prevent me from getting a heart attack. You don't care if my sugar goes way off the map." And uh we we laugh about that's where we talk about Yeah. medicine being siloed. They just care about it is. Yeah. And so he's not looking holistic. Whereas I'm trying to balance this thing. In the end, I'm on these PCSK9 inhibitors to control. So I didn't say, but I have high if I didn't take something, I would have very very high cholesterol. So these PCSK9 are amazing. I mean I went it drove mine way down. So I've So now I'm off statins to be honest and it's controlled pretty well. Yeah, there's a lot of places I could go that are controversial. I will just say though on a functional end and I'll send you this paper and I'm going to be covering it in a separate video. But the interesting thing about this study was that they actually decoded the mechanism by which statins uh lower GLP1 and it was through the microbiome. So basically they showed a change in the microbiome as a function of statins. In this case it was 20 migs of a tovvisatin. Um, and there was a decrease in the production of a secondary bile acid udca. So then what they did because udca is actually available as a medication for liver disease is they gave in a pilot trial patients on statins who had glucose dysregulation udca. And guess what? Insulin resistance dropped. Blood sugar dropped. GLP1 went up. So I'll send you the paper to see what you Yeah, very very cool. And I think this fits with this whole concept. Again, metabolism is complex. It involves a lot of different organ systems, a lot of different relationships, and you have to understand the whole thing if you want to manage it properly. And again, we're all different in how we've set up our homeostasis or lack thereof. And so we really need to control this.
Yeah. Yeah. And the lipid stuff's super interesting. We'll have to talk more about that at length. I know I have a study I'd love to do with you, but um, you know what my LVL is on last check? This is This came up on Huberman Lab, so it's already out in the open. Mark Heyman and Andrew brought it up, but just give a wild guess.
Oh, what? Uh, I probably as high as you possibly could think. 300 574.
Holy cow. Wow. And I'm not saying I'm comfortable with that, but it is an area of interest. Right now I have no cardiovascular plaque on coronary CT and geography. Um, we can delve into that. It's an area of interest. I'm not endorsing this for your average person. Let's be clear. But the theme of this conversation between us is n equals one. Uh, and people shouldn't be threatened by other people's narratives. I'm certainly in in certain ways anxious. But there's a lot of information and a lot of nuance around every individual's choice. Um, we can go into that another time, but hopefully this will be a good time capsule because you're the guy I'd like to work with to figure out precisely what's going on in my body because I can change my LDL between 100 and 500 and back with lifestyle, which is not something cardiology even recognizes. But anyway, no, they don't bring lifestyle into medicine in general at all anymore. There's a few, you know, these integrative medicine institutes and things are doing this. I actually just did a sbatical for two months at one at UC Irvine. That was incredibly uh interesting and valuable. Actually, I believe it or not, I got my hypertension under control through acupuncture. Yeah, it was pretty wild.
I do want to add one last thing though, back on this point while we're on the topic of of subphenotypes and foods that just to hammer home. It it turns out your subfotype does determine what food you'll spike to. Meaning if you are um insulin resistant, muscle in resistant, you will spike to potatoes and pasta, but not if you're um insulin sensitive. And likewise, if you have a beta cell defect, you'll spike to potatoes. So, we can match foods with these sub phenotypes. And again, we think this is why this is very important, not just for, you know, exercise also affects your sub phenotypes, but we think the foods you eat will will your response to the foods you eat are very important. Yeah. So, we just think this whole thing, you know, we can match this stuff up and do a lot of control through lifestyle changes and and again, I hope that'll be very valuable for controlling this diabetes endemic. The bottom line is that again based on your sub phenotypes is going to determine what food you spike to. Yeah. So, really knowing your sub phenotype is a big deal. People with with again who are insulin musculins resistant, they will spike to potatoes and pasta. Those who are um I should back up. Those who are insulin resistant or insulin sensitive spike the rice. Everybody spikes the rice. Interestingly enough, that's white rice it is. Uh, Asians spike more than non-Asians to white rice, which is something I don't fully understand. Uh, and that's been reported before. But then we also found that again muscle insulin resistance spikes to potatoes and pasta, but not if you're sensitive. Yeah, if you have a beta cell defect, you'll spike to uh potatoes. Um, yeah. So, anyway, you can match these these sub phenotypes with the foods you eat or should shouldn't eat.
I I think that actually has been an effect that's been independently replicated the rice thing. Um, you might know the work of Aaron Seagal at the Weissman Institute. They had some uh yeah, some study out I think it was nature was maybe ZV 20. I'm going to mess up on the year, but they had a paper on personalized glycemic responses. And I remember watching his TED talk uh and he said something to the effect of his wife's a nutritionist and they were shocked that everybody spiked to rice a lot more than say ice cream. So she would be like, "Oh, you know, to control your blood sugar, maybe have a little bit more ice cream instead of rice." I think he was saying it light-heartedly. But yeah, there's something about rice apparently. Um, go figure.
Well, rice, you may know, it's bred today to be for yield and it's highly glycemic. Um, so in the old days, rice, apparently, many, many moons ago, rice was 20% protein. Rice today is 8% protein. Yeah. The white rice you buy in a store. So, if you go find you can find these specialty blends of rice that are 20% protein. I bought them for my family. I'm the only one who likes them. The other members don't. So, it's you you may have some taste issues going on there. But I actually like it better because it has a lot of flavor. Uh, it's more but there are these rices that are better. And more recently, I found for me anyway, I I take these uh and I don't have any stock here, but these mission pa things, and I eat the ones that I think they're called carb balance, and they actually suppress they're I wouldn't say suppress, they give much lower spiking than the ones that don't have the carb. they have more fiber. So the car form of the carbs is different. That was another thing in that nature medicine study we gave the study you mentioned from Aaron Seagull. It's a classic where he shows a picture of someone spiking to a banana. Bananas and cookies. Yeah. I'll throw that now. I use that all the time. It's great. Yeah. Yeah. So that's a classic. And and um, yeah, we did a very controlled experiment in this recent paper where we had 55 people eat seven different carbohydrates that were in different forms. Exact same amount though. So, one was one right was white rice, one was mixed berries, one was grapes, pasta, and potatoes. And again, uh, everybody did spike the rice, but some people spike more to bread and some to pasta. And we could match again some of these things up. Bread, people of hypertension tend to spike more to the bread. That's amazing.
All right, my last question for you and then we're going to leave the audience on a cliffhanger for the most viral paper ever published in nature aging which is um this process we talked about you know multiomic profiling subphenotyping for diabetes can it be applied to other diseases obesity cardiovascular disease Alzheimer's it should be um I think all these diseases anything that's a complex disease is probably by its very nature personalized and we we know that for for example for cardiovascular disease. There's many different ways to get there with either high like um high lipids in your blood or uh what you're at risk for. And same is true for as you point out for Alzheimer's. There's early onset. A lot of that's genetically um induced if you will. It's not 100% clear all the things triggering dementia Alzheimer's. There's a lot to unpack there. uh but most of these autoimmune diseases they probably all are uh complex coming in different forms and so I think we do need to subtype them. I think these new molecular tools the OMIX tools that we talked about at the beginning are going to be the way to do a lot of this and at some level we have OMIX signatures for insulin resistance and such as well that's another way to do it. Yeah. uh we think the shape of a curve on a home test is the easiest way to do these things but uh it may be we need to do you know omix tests or more biochemical ones for some of these complex diseases and if you want to pick one that really needs a lot of work it's it's mental health uh all these you know anxiety depression things like this they that's almost certainly due to many different forms of of disease and condition I guess you don't say disease in that you say conditions, but you get the point. It shouldn't be unmedicalized. Like the brain is a metabolic organ like anything else. And you are so right. I'm actually part of a a startup and co-ounding a startup called Neurovital is going to try to use we should bring him on as a consultant AI and metabolic health therapy along with talk therapy for mental health. But because of that, I've been delving more and more into the literature. It is remarkable like you're looking at signatures in depression. So you can look in the brain and see autophagy is dysfunctional in certain brain regions and that different medications that are anti-depressants converge on this or and I have to send you this paper. There was one on um I don't know if you've heard of homoindilic acid. It's a dopamine um derivative. Sorry not a dopamine derivative a tyrrosene derivative. So in kind of the same catakolamine family as dopamine but unlike things like people say oh serotonin is made in your gut but that doesn't cross the blood brain barrier. Homopilic acid crosses the blood brain barrier. And what they found was that people with depression are deficient in homoanilic acid and the bugs that make it. And then they modeled depression in mice, different mouse models, so corticosterone model and chronic um unpredictable um mild stressor model. And they found that homoilic acid went low and if they supplemented homoyic acid or the bugs that make it, they were able to basically reverse the depression phenotype in mice. And this is a neurotransmitter made by the gut that can be orally supplemented that goes through the bloodline barrier. So to your point, just like you did with this diabetes paper, if you can identify subphenotypes of depression that are primarily, you know, dominance in homopanic acid deficiency, you could just give a supplement that could have a tremendous impact on mental health.
Yeah. No, I think that's incredible. So, and I think we need to do a lot more of this is very underststudied and of course you know that you know ketogenic diets are amazing for people with bipolar conditions. So, yeah, we re we really need to you know dig into this a lot more subfotype people find out what therapies work and if we could do it all by lifestyle that's by far the best way to manage this.
Yeah. So, I mean that's now my job. I'll build a platform hopefully can get some wealthy philanthropists interested in in funding projects by by you the workhorse of personalized medicine. So, you know, with that I'm going to pause here. We're going to go into our next conversation, but quickly I know you're doing so much work. If people want to engage with the research and or if you have ongoing studies that people might want to volunteer for, where can they find this information?
Yeah. Well, you can visit our website. We have a list of studies that are active. Some you need to be in the Bay Area because we do intense measurements on them and we need to collect the samples there. But some you can do anywhere around the country in the world even. And then um, you know of course we're always looking for funds to fund the research. So, same thing if you go to the website and you want to contribute, we'd be more than delighted. Um, and because we have lots of amazing studies we want to run and so some with you, Nick, would be great. And, um, yeah, so any support people want to do either by participating in our studies or financial, that'd be fantastic.
Yeah, absolutely. All right. Well, everybody should come back for part two. We're going to talk about the most viral study ever published in nature aging. Uh, thank you, Professor Snyder.
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