Summary
About 33% of Americans are prediabetic but 90% don't know it. CGMs reveal that identical foods produce wildly different glucose responses across individuals, largely due to microbiome differences. Wearables tracking heart rate, temperature, and oxygen can detect infections like Lyme and COVID-19 before symptoms appear.
Key Points
- Snyder's genome sequence revealed diabetes risk despite being lean and active, leading to early detection after a viral infection
- Approximately 33% of Americans are prediabetic, yet nine out of ten are unaware due to infrequent medical monitoring
- Different individuals show dramatically different blood glucose reactions to identical foods, influenced partly by microbiome composition
- Smartwatches measuring heart rate, temperature, and oxygen levels helped Snyder diagnose Lyme disease before symptom onset
- Research suggests elevated heart rate detected via wearables identifies over 80% of COVID-19 cases in early stages
- New tools quantify cumulative environmental exposures, revealing links between childhood air pollution and Alzheimer's biomarkers
- Different body systems age at varying speeds across individuals, creating distinct ageotypes affecting disease susceptibility
Key Moments
CGM reveals hidden glucose spikes from common foods
Different people spike from different foods. CGMs help identify your personal glucose triggers and build healthier eating habits.
"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, exposome 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 9 out of 10 people with prediabetes are unaware they have it. This is important because even prediabetes 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 smartwatches that can detect heart rate variability may be able to help detect some heart conditions such as atrial fibrillation. How measuring a person's exposome 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 and sulforaphane 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 researched 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-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 Found My 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. Him and his collaborators are doing some really exciting research that will really change the healthcare 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, 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, 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 we'll, I'm sure, talk about today with smartwatches 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 times. And the first was in fact, from my genome sequence. So I sequenced my genome. It told me things that 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."
