The Weirdest, Most Shocking Things You Can Learn About Your Body From A Blood Glucose Monitor with Josh Clemente

Ben Greenfield [00:00:00]: My name is Ben Greenfield, and on.

Ben Greenfield [00:00:02]: This episode of the Boundless Life podcast.

Josh C [00:00:04]: For the average person, you've got to take action on most of the health measurements you get in your life. If you get an MRI and there's like, some finding, your doctor's going to be like, I don't know, we might have to biopsy this. We might have to, like, cut you open to figure out what this is. And I think that's a problem because.

Ben Greenfield [00:00:20]: Welcome to the Boundless Life with me, your host, Ben Greenfield. I'm a personal trainer, exercise physiologist, and.

Ben Greenfield [00:00:28]: Nutritionist, and I'm passionate about helping you.

Ben Greenfield [00:00:30]: Discover unparalleled levels of health, fitness, longevity, and beyond. Hey, what's up? Just had a great podcast on all things blood glucose monitoring with Josh Clementi, former podcast guest. We talked about these crazy, surprising things that affect your blood glucose. You're going to love this one. The Shownotes are at BenGreenfieldLife.com Glucose Surprises. That's BenGreenfieldLife.com glucose surprises. Let's dive in. For those of you who are constantly asking me about the little patch on the back of my arm, which I've mentioned a lot for tracking blood glucose, I have the guy who's actually responsible for it as my guest today.

Ben Greenfield [00:01:17]: His name is Josh Clementi and he's the founder of CEO of Levels. So Josh is a systems engineer, He's a metabolic health enthusiast. He led a team at Space X, and of course, now he's into making sure everybody feels guilty about eating a Snickers bar. And Josh was actually on the show, like, back when these things were, like, early days of CGMs. Josh, like way back in the. In the. In the CAVEMAN Days of CGMs, like four years ago, right? Yeah.

Josh C [00:01:48]: Yeah. It's great to be back and chatting with you and your audience, Ben. It's been crazy how fast time can go when you're working on something fun. I'll say.

Ben Greenfield [00:01:56]: Oh, yeah. For those of you who missed our first podcast, which was pretty fun, I'll link to it if you want to listen to it. If you go to BenGreenfieldLife.com glucosesurprises that's the URL for today's show, BenGreenFullife.com glucose surprises. You learn that Josh, like me, was homeschooled K through 12. I think I recall you having even more siblings than I did, Josh. I had five.

Josh C [00:02:21]: Yeah. I'm one of nine. So I got you there.

Ben Greenfield [00:02:23]: Oh, geez.

Josh C [00:02:23]: Or rather my parents.

Ben Greenfield [00:02:24]: Yeah, yeah. You you're not the oldest, are you?

Josh C [00:02:27]: Number two.

Ben Greenfield [00:02:28]: Okay, all right, cool. Yeah, I'm number two. Yeah. Yeah. And so I'm, I was looking at your bio before the show, and of course I, I, I saw all the usual stuff that I know about you, but it also said you're into coffee. Tell me about that.

Josh C [00:02:47]: Yeah, you know, I was never a coffee drinker growing up. My dad, I think this, I blame my dad for this. He and his siblings, he's one of six, are weirdly not out. None of them are coffee drinkers. And I, I kind of like my dad was my example growing up. So I, I never got into it. And then once I got out of college and needed a way to synthetically stay alive in the SpaceX days when we were doing 20 hours, 20 hours was kind of like the average workday for a while. Coffee just became, they had this really nice breeze to set up and like, they would, they would, there was no limit to the caffeine you could get as face X.

Josh C [00:03:21]: And so I just kind of fell in love with it, first by necessity and then later by just pure appreciation of, of the ritual. I wish I was more of a craftsperson about it. You know, I, I, right now I'm, I've kind of gone through all the different brewing methods and right now I'm really obsessed with espresso. I, I don't roast my own beans or do anything. None of the really cool stuff. But I just, you know, espresso is amazing. You can tune how much caffeine you get. Cause it's a smaller volume and just the rich flavor and the morning ritual with my wife, it's, I, I, there's nothing I don't love about, about coffee.

Ben Greenfield [00:03:52]: Watch yourself, man. You're gonna gaslit. You put stuff like that in your bio and eventually you will be roasting your own beans and, you know, using special water filtration systems for the espresso machines and yes, yes, then doing latte art. Then you'll be the latte art guy.

Josh C [00:04:08]: That's part of the reason I have it in there is I want that social pressure to keep honing the craft, you know, get deeper into it.

Ben Greenfield [00:04:13]: Yeah, yeah, that's a good point. That's a good point. Paint yourself into the corner. So this is actually kind of interesting. There's all sorts of stuff I would love to hear your take on, because I know you just have access to tons of big data on people and what affects their blood sugar, which is what I'm super curious about. But coffee is one that a lot of people I think see affects their blood sugar. So what, what's the link that you guys have seen between coffee and blood sugar? And, and also kind of like as a two parter, is it the coffee or is it the caffeine?

Josh C [00:04:43]: Yeah, I love this question because I'm, I'm kind of an outlier on this one in probably what you would call a, a bad way. So I have a really aggressive blood sugar response to caffeine specifically. Um, and the, so the first, first like what does the science and the data show us? Caffeine is a stimulant. It, it drives up cortisol and epinephrine. That's like just one of the immediate byproducts which we all can feel. So it puts you in fight or flight mode. And in fight or flight mode, your body wants to make available, you know, energy for the muscles to escape the threat, whatever it is. And so that the main mech mechanism is, is driving up circulating blood sugar.

Josh C [00:05:22]: So we see this in nearly everyone. But the degree of elevation can be either like, roughly zero to, to like plus one, say milligram per deciliter. So to, in some people like myself, 20 to 30 point blood sugar elevation elevations from just black brewed coffee, um, which is, is really interesting. And that caffeine, that's probably a caffeine enzyme that determines how quickly you can clear the caffeine that's circulating. That's my guess as to like what's driving that difference. But the, I think what's, what's really interesting is actually below that, below the hood, it's not just glucose that's affected. And if you look at oral glucose tolerance test data, there are a couple studies that, that show kind of like if you do an oral glucose tolerance test, which is where you like drink a big bolus of sugar while someone is heavily caffeinated, you'll see not just a blood sugar elevation. I think the, the study that I'm, I'm thinking about in my head was roughly 20, I think like a 25% elevation in blood sugar during the glucose tolerance test, but there was actually a 60% elevation in, in circulating insulin to clear that glucose during that timeframe.

Josh C [00:06:27]: So there's very clearly like, yeah, there's, there's an insulin sensitivity or insulin resistance that sets in when you have a lot of circulating caffeine. Um, I don't think this is acute. It doesn't, or I'm sorry, I don't think this is chronic. It doesn't seem like caffeine is making people less healthy. Actually maybe the opposite. So you might get some acute issue, but then long term a benefit. But I do think it's something to take into account like if you're trying to, if you're trying to control a blood sugar issue like myself, I shifted to, to espresso because there's less caffeine per shot than drip coffee. And I did that so I could tune the amount of caffeine I'm drinking because I, I do have such an aggressive response.

Ben Greenfield [00:07:01]: Yeah, I still run into some people who think espresso, cuz it's more concentrated, has higher amounts of caffeine, but not the case. It is interesting though. You know what you said about the CYP genetics, You know, the enzyme that will dictate how quickly you can kind of metabolize caffeine. I would suspect that the people who are slow caffeine metabolizers, which is like a metric shown on most decent genetic tests nowadays, would have a more pronounced and elongated blood glucose response. I have pretty fast CYP genetics. I see a little blip if I have some coffee, especially in the morning, but it's not that significant and it's interesting. I would love to hear your take on this. You can find some decent research on coffee's effects on, on type 2 diabetes and long term blood sugar management.

Ben Greenfield [00:07:51]: You don't see that with caffeine pills or just slamming some no dose tablets or whatever. Probably because of the antioxidants or polyphenols, flavanols, maybe some of the cholesterol precursors in coffee, et cetera. So it seems like it's one of those things that gives you a short term transient rise in blood sugar. But I think if consumed in moderation and responsibly could result in long term stabilization of something like blood sugar or glycemic variability.

Josh C [00:08:23]: Yeah, absolutely. This is something I think needs to be studied more deeply. There are some really interesting rabbit holes we can go down, but this is one of them. Like these, these acute dynamic changes that would look negative but tend to have positive benefits. Exercise, like high intensity exercise is another one, but where you just see, you know, if you're just wearing a blood sugar monitor and, and judging the, the, the healthiness of an activity based on glucose elevation, you might say Never do like CrossFit again or never do a high intensity workout again. And, and caffeine might, might say the same thing.

Ben Greenfield [00:08:52]: Makes you diabetic. You heard it here first.

Josh C [00:08:54]: You heard it here first. Yeah. You know, for Myself when, when I was really into CrossFit, I would hit 200 plus milligrams per deciliter like regularly in these workouts. And I was really confused by it, trying to figure out, you know, is there a trade off here? And, and maybe there is, maybe there's some point at which like there's a diminishing return of intensity. But these are the questions that we, we just don't know super well how to answer. But we can say that there's in the, in the epidemiology and sort of the long term population studies, coffee is related to health improvement on almost every axis. And, and I think specifically that the point there is important coffee, not necessarily caffeine, like slamming monster energy drinks as a replacement probably doesn't have the same benefits. Right.

Josh C [00:09:35]: To your point, the polyphenols, the antioxidants.

Ben Greenfield [00:09:37]: Yeah, to that point, to interject real quick, you see similar effects with decaf. So there's obviously more going on in the coffee than just the caffeine.

Josh C [00:09:44]: Yeah. And so if you're, if you love coffee like me, I have a separate grinder on the table for my decaf. And so we've always got like a good decaf bean as well because I just love drinking coffee. And so I'll, I'll, you know, brew a couple shots during the day of decaf, get some of those extra antioxidants, get some of the polyphenols. It seems to, it seems to have, you know, benefits independent of whatever's happening with the gluten or with the cortisol axis.

Ben Greenfield [00:10:08]: Yeah. I'd be curious to hear your take on this or how you explain this to somebody who say like wearing a CGM and seeing these spikes during what would generally considered be considered to be healthy activities. You know, the way I think about this is very similar to like blood pressure or heart rate.

Josh C [00:10:23]: Right.

Ben Greenfield [00:10:24]: You work out hard or maybe you hit the sauna for a while and you will see if you were wearing some kind of a monitor, a rise in heart rate and a rise in blood pressure. But the long term effect of that is that over the course of the day there's a decrease in heart rate and decrease in blood pressure that results in, once you get that 24 hour average, a favorable response in heart rate and blood pressure with something like running from the line of CrossFit or High Intensity interval training or a cup of coffee or and I'm sure you've probably seen this too, like a sauna jacks up blood glucose. In my case, higher than exercise or a cup of coffee. Cold therapy seems to transiently do it as well, accompanied by a pretty significant drop afterwards for me and a lot of other people. But how do you explain what's going on there? Like, why the big spike that you see doing some of the things that a lot of people trying to be healthy do is, is not something to be that concerned about.

Josh C [00:11:27]: So firstly, my sort of general thesis right now that I feel very strongly about is that we are approaching data with a very backwards kind of framework where right now health data is treated as either good or bad and the reason for, or like necessary or unnecessary. And I think that's, that's the wrong framework. Like, health data is strictly better to have than not. But what we do with it is what matters. And today we in, in our, like, I think the system that we've all grown up in, we are at a, a disadvantage because health data is very scarce. And my premise, which I'll get to, I'll kind of like walk through this real quick, but my premise is that we need to shift to an abundance mindset in which we will make entirely different decisions about the quality or, or usefulness or, or, you know, goodness or badness of any sort of tool that measures, you know, a metric. So, okay, so the, the point I, I'm trying to make is that right now, the. Here's an analogy.

Josh C [00:12:34]: Every modern car produces insane amounts of data. And it's, it's what's considered health data for that vehicle. So you have all these sensors on the car. The average modern car produces something like 25 gigabytes per hour of sensor data. And these sensors are all monitoring how is this vehicle performing. And they're not actually measuring a disease or a symptom of bad performance. They're actually measuring healthy for the car in order that as soon as something deviates from healthy, you can intervene. And there are all sorts of RPMs will climb in the car and power and torque demands will climb.

Josh C [00:13:08]: And that's not a bad thing. If pressure in the combustion chamber goes up, you're monitoring that you know that the car is being accelerated. And this is a good situation or a normal situation. And so the vast majority of the data from a, for a car that's being monitored is actually mundane. It's like not super interesting. You kind of like just keep it for logs, but you're not making some sort of intervention on every data point. Now, humans, remember, that's 25 gigabytes per hour of data we collect on our cars. Humans will get on average 80 gigabytes of data in their entire lives.

Josh C [00:13:43]: And so you're getting something like four times more data, I'm sorry, 80 megabytes per data in their entire lives. Okay, so, so this sort of healthcare paradigm is dramatically backwards, where you have many times more information coming out of our machines which we can use to maintain them and observe the like, health trends than we will get in our entire lives. And what that means is that for the average person, you've got to take action on most of the health measurements you get in your life. If you get an MRI and there's like some finding, your doctor's going to be like, I don't know, we might have to biopsy this, we might have to like cut you open to figure out what this is. And I think that's a problem because in the paradigm I want to get to where we're in an abundance of health data, you just wait a little while and get another mri and it's like readily accessible and cheap and you have multiple measurements in order to make these difficult decisions. So, okay, so that's a long winded way of getting to my point, which is that right now when people put a CGM on, they kind of feel like this is either going to be good data or bad data. And I need to react immediately to the information I'm seeing. And this can generate some anxiety.

Josh C [00:14:51]: But what I want people to sort of think about is like, this is a measurement of what's happening under the hood, whether you're measuring it or not. And what we can do with that is enrich our understanding of how our bodies are responding to the behaviors that we're, that we're taking on, like the lifestyle we're building. And right now it's kind of a mindset flip. But you know, if you think about a pilot flying a twin engine aircraft with 200 people across the Atlantic Ocean, is that pilot experiencing anxiety, knowing that there are 10,000 sensors measuring the performance of that jet engine at every second? I think it's actually quite the opposite. They feel peace of mind knowing that the system is being monitored. And even if there's a blip in one direction or another, they'll be able to contextualize that because we have, you know, reams and reams of history, health history data for these sorts of jet engines to know whether that's good or bad. Right. We need to get to that similar paradigm for the human body.

Josh C [00:15:40]: And today, you know, we were just talking about caffeine. Like we don't even Know the effects of caffeine or blood pressure or transient acute responses because we've never measured it. And in order to do that, we got to kind of flip the frame.

Ben Greenfield [00:15:50]: I agree with you on almost everything you just said, except like, I think that you have to kind of categorize some of the health or self quantification metrics that might create excess burden on the medical system. I don't think a CGM creates excess burden on the medical system. I think if we get to the point where everybody's doing a monthly MRI or something like that, then we get into potentially unnecessary diagnostic imaging procedures. So I think in general the theory makes sense, but it would also need to be gated in a somewhat sane manner so that we're not signing millions of Americans up for monthly or sometimes even annual CT and geographies and MRIs. At least that's the way I see it.

Josh C [00:16:39]: Yeah, for sure. And I think I use MRI as an example because there's no radiation dose. It's like a very, very. It's a really incredible technology and I think the health system at large would benefit tremendously from. Actually, if everyone did get an annual full body scan. I think that would generate such a trove of valuable information for radiology for like the understanding of MRI and soft tissue development, all that stuff. It would actually be a huge net benefit. But yeah, CTA is anything with radiation like we should be very, very conscious of.

Josh C [00:17:06]: And so there's for sure a trade. I think I'm just pushing in the direction of right now, it's like 2500 bucks and like a six month lead time to get an MRI for like a damaged shoulder. And in an ideal world, I think that sort of thing would be readily available and abundant. And right now I think the system is actually gating too aggressively. Most health data, right. It's typically you have to be manifesting symptoms of some major issue before you can even get a measurement. And in the paradigm I think we'll all benefit from, everybody would sort of own the data that defines their health. And then you would use that to make decisions when something starts to go sideways.

Ben Greenfield [00:17:42]: Yeah, yeah. I think an MRI based on symptomatic issues or acute injuries, you know, for example, bum knee, bum shoulder, whatever, is a great diagnostic tool and a radiation free diagnostic tool. I think that we still have an issue that could use solving in that a lot of the, whatever, the Silicon Valley tech billionaire crowd, biohacker crowd, self quantification crowd, doing a lot of mri, like diagnostic procedures are often coming up with a laundry list of false positives leading to fret, worry, anxiety, benign issues that they normally wouldn't worry about. And now they're getting the mri. I hate to use this analogy because it's gross and upsetting to a lot of people, but it's kind of like the Angelina Jolie phenomenon of, oh, I have the BRCA mutation, so I'm just going to cut my breasts off. I think that the medical community and I think AI could probably play a role here, needs to get better at interpretation and doing a really good job explaining to people. Yes, this is their comma. You don't need to worry about it.

Josh C [00:18:55]: Yeah, I totally agree with you. My hope for the trend that we're heading down or the direction we're heading is that with improvements in cost of hardware, we should be able to increase the volume of data without needing to increase the number of interventions and actually do the exact opposite. Where unlike what Angela and Jolie did, you can take that genomic information and sort of marry it with other multimodal information like consistent soft tissue scans, like consistent metabolic or molecular, like continuous monitoring and blood work, and actually monitor very, very precisely that risk factor turning into a manifestation of cancer, which, know, as far as I understand it, like, was not the case for her. And so she did an early intervention that was maybe life altering in a negative way. So. So the point is, you know, my hope is that by making these tools much more accessible and, and merging them together through, yes, better AI synthesis of, of all the different models, you can get a very, very good health state prediction for an individual that can go way further in the direction of saying, this is not a concern for you in a way that feels convincing that because today if you walk into the doctor, and I've done this many times, you know, I'm physically fit, I go into the doctor, I say, you know, this, something is bothering me, or even just say my circulating blood sugar is like really out of control in the mornings. Like, I'm trying to get this under, under control. And most doctors will just kind of say, hey, look, look, you're fine, you're healthy, don't worry about it.

Josh C [00:20:19]: And it doesn't feel convincing. It doesn't feel grounded in, in evidence. Right. And, and so I think a lot of people are reacting negatively to that because they feel the system needs to understand them better before saying something like, don't worry about it.

Ben Greenfield [00:20:30]: Yeah, yeah. And at the same time, relevant to the topic at hand, you know, with something like a continuous glucose monitor, I think there's a lot fewer issues with continuous monitoring and accessibility for a wide number of people because we're in many cases talking about low cost, acute DIY interventions that produce a pretty observable response, often in a short period of time. And that kind of leads to like, you know, something I wanted to ask you that goes beyond some of the things we've mentioned already. Like, yeah, we see coffee rises blood sugar or raises blood sugar, anything acutely stressful, high intensity exercise, CrossFit, sauna, cold plunge seems to do it. But I'm curious beyond that stuff, you know, because I'm sure you've had a chance just like, look at some big data, see some trends. What are some things you guys are seeing that are just surprising or interesting about daily habits people have in any area of nutrition, supplements, fitness, whatever, that are affecting their blood sugar in ways that either surprised you or might surprise people to hear.

Josh C [00:21:41]: The scope of this is so wildly large that I feel woefully incapable of speaking to all of the points that, that we hope that's okay because.

Ben Greenfield [00:21:52]: Warning. This is the biggest question I wanted to ask you, so you can spend the most time on this one.

Josh C [00:21:56]: Yeah, yeah. So, okay. One of the things we did early on with levels, because we wanted to be able to answer this sort of question when, when, when I started levels, we were at a point where CGMs were essentially not accessible. So people with diabetes even had a hard time getting them, let alone someone without diabetes. They're prescription controlled, very expensive, and there wasn't really a mechanism by which somebody who didn't already have insulin dependent diabetes could get their hands on this tool. And that felt like a big challenge and a problem in again, in my framework, where each person who is trying to live a healthier life on a daily basis actually needs to make really good decisions that compound in kind of hard to measure ways over decades into a different health outcome, right? And right now, many people are compounding in a negative direction and not because they want to, but because they don't know the effects of the choices they're making every day. So I felt very strongly that CGM would be a really powerful tool to pull back the curtain and help people, you know, kind of see the effects on a continuous basis and make better ones. So the first thing we had to do is like, make this tool more accessible.

Josh C [00:22:59]: And then we started to get data, right? People were using the CGMs in their daily lives and we were starting to see all manner of, you know, both intuitive and counterintuitive results in Various people. And so one of the first things we did thereafter is set up a very large study to be able to get people to consent if they wanted to participate into sharing their data into this study, pop into the study database so that one day we would be able to get really, really publishable and large scale results to try to answer these questions for people that don't have diabetes because that population is very understudied. So we understand what happens with diabetes. We don't for non diabetics. Okay. So we set up an IRB approved study. It ran for over three years. We're actually winding that down this month and week.

Josh C [00:23:47]: So we've been sort of tapering out, but we've, we've got 75,000 participants, many of whom have been using CGM for years inside this, this data set. And it's the largest by far of its kind ever. And with that data set we'll be able to be working with outside parties, you know, at, at, at prestigious institutions who have, you know, close ties to metabolic health to start to dig deep on a lot of these vectors. So I just wanted to set that context. Like a lot of this stuff, we're very preliminary on the, this, the study's winding down and we're going to be able to answer these and hopefully publish on them. Um, okay, so the things that I think are really interesting to talk about, you know, kind of the first one that comes to mind because it, it's just so fascinating, so understudied is alcohol. There's been a lot of chatter over the past few years. You know, like Andrew Huberman kind of kicked the hornets nest and got a lot of people upset by saying there, there's like, there's no alcohol intake level that is not negative for your health.

Ben Greenfield [00:24:38]: All poison. All poison.

Josh C [00:24:39]: Right. And a lot of people were not happy about that. Um, so I, I won't say whether or not I, you know, how, how I feel about this, but I will say that alcohol has a really, really aggressive effect on blood sugar control and it's not necessarily the way you might think. So for most people, they think, okay, I, I drink, I drink alcohol. That's, I think that's carbohydrates, that's going to raise my blood sugar. And the answer is for most people, for a higher alcohol concentration drink, your blood sugar will crash and the response to a carbohydrate laden meal while you have alcohol circulating will be in many cases totally deleted. So at levels we, we call alcohol the, the invisibility cloak because it, it essentially just totally changes the blood sugar dynamics for somebody. If you drink a, you know, if I drink a glass of wine and then I eat a bucket of french fries, my blood sugar stays rock solid and 20 points lower than my average.

Josh C [00:25:30]: Now, is that good? My theory, and I've talked to some, some researchers like Dom d' Agostino on this. He's a ketogenic researcher and just brilliant guy. And, and we couldn't find any evidence really that was convincing. In the literature, it's basically not studied, but it seems like your liver kicks over into essentially metabolizing the alcohol out of your system as soon as possible. And the way it does that is by producing triglycerides, right? Basically shifting the alcohol, alcohol into a fat that you can then store. And it seems the, the theory would be that it's doing the same thing to the carbohydrates you eat during that timeframe. So rather than just letting that glucose circulate, it's like grabbing it and packaging it into triglycerides, just like the ethanol. And so you, you would expect to see higher fat gain and triglycerides in the blood during this timeframe, even though glucose goes down.

Josh C [00:26:21]: Um, bottom line, this is like a fascinating thing and the effect is so huge, it's like hard to ignore when people have, we're, we're constantly getting people in the data set saying, like, it's amazing I found the secret weapon. It's like, I just need to have red wine if I want to have a bunch of carbs at night.

Ben Greenfield [00:26:37]: I think taken in the larger context of the systemic overview of the body as a holistic unit, if you look at ethanol and the breakdown byproducts of it, indeed shifting the body towards triglyceride formation as well as acetaldehyde accumulation, there is kind of like this extra source of fuel for the cell that could provide you with a little bit of a drop in blood glucose. Very similar to a ketone, like 1,3 butane dial. That's basically what ketone IQ is, very similar to what that could do. The toxic byproducts, though, affect on the liver, affect on non alcoholic fatty liver disease potential, et cetera, would far, I think, outweigh any benefits from it causing blood sugar to become lower. And then I think there's also, you know, and this is not to say, you know, go out and drink a bunch and use NAD as an excuse, but it also does exhaust a lot of NAD precursors by affecting that enzyme that, that helps to produce nad. So it's kind of like a triple whammy type of effect and you get lower blood sugar. I actually didn't realize you get lower blood sugar until you just said it. But I understand what the mechanism of action could be.

Ben Greenfield [00:27:57]: And the long story short is yes, lower blood sugar, but also nad, exhaustion, messed up liver, et cetera.

Josh C [00:28:05]: Yeah, I love it. Right there. You just generated five really interesting hypothesis study endpoints that we won't be able to answer entirely with our research data set because it was descriptive, it was just observational. Um, but it will queue up a bunch of these hypotheses. We'll be able to look at nutrition, like basically intake of meals and their, their structure and start to form some really strong hypotheses and observational descriptions. So this is one that we just gotta get into further.

Ben Greenfield [00:28:33]: Yeah, and, and by the way, you know, of course, you know, dose response is important here because you could, you could make a decent case from an epidemiological standpoint. The very small amounts of alcohol can lower risk for things like cardiovascular disease, dementia, Alzheimer's and even diabetes. And some of those mechanisms of action. Right. Like some amount of a reduction in glycemic variability with a small glass of, let's say organic biodynamic wine with dinner may infer a protective effect. So this could also make a case for, yes, really small doses timed strategically may actually be a health strategy. But that's all in the context of understanding that the liver gets overloaded pretty darn fast.

Josh C [00:29:15]: I love it. That's exactly. I would love to be able to, I mean, be prescriptive for people in the future about how to use these things to their advantage if it's possible. Right now I think there's kind of an all or nothing people are like, oh, avoid it or use it. And you know, we just, it's just really interesting to see the degree of effect that people have. And that tees up some really important questions for the future. Other really interesting stuff. And I mean we can get through the, maybe the more intuitive and obvious ones.

Ben Greenfield [00:29:43]: Sexy ones. Pick the sexy ones.

Josh C [00:29:44]: Okay. Sexy ones. You know, one, one that I'm really interested in right now. Really interested in. And I wasn't for a while. I was, you know, I have to say I was a bit hesitant about this. But red light specific wavelengths, you know, in the 450 to 620 nanometer wavelengths, there's like a number of, of red light wavelengths that when exposed for 15 to 30 minutes have a direct, seemingly direct response and dose dependent response on Circulating blood sugar. So there are small end studies out there and then there are people who log red light in the data set.

Josh C [00:30:21]: And I've done this now and gone to red light sessions and observed my blood sugar. So the studies I'm referring to, and there was one from late last year that showed about a 28% decrease in certain. It's not a randomized control trial. People know if they've been exposed to red light, but in any case, it's about a 28% decrease in blood sugar in the two hours I think after exposure for 15 to 20 minutes to red light. I went and did a red light session, a full body like, you know, kind of in this bathtub of red lights. And I saw, surprise, surprise, about a 30% decrease over the course of a 45 minute session in my circulating blood sugar. And it was really interesting that it's not a, it's not an infrared sauna. Like it didn't, There was no body temperature elevation or decrease.

Josh C [00:31:06]: And I'm just fascinated by this. I, I have no, There are a ton of reasonable explanations and some of these studies propose them, but it's just, it's just this like incredibly deep rabbit hole of the, the, basically the, the photonic responses of our cells to different wavelengths. And you know, you're activating some pathway through these specific wavelengths that we don't fully understand as far as I can tell.

Ben Greenfield [00:31:30]: Obviously you mentioned. I'm sure the researchers have plenty of other proposed mechanisms of action, but it makes me wonder, is it just a little bit of an increase in parasympathetic drive and a drop in cortisol shift into fatty acid oxidation? And I think a lot of people may not realize that when you're in the red light, one of the main ways that it's increasing mitochondrial health is those photons are basically kicking something called nitric oxide synthase off of a complex called cytochrome C oxidase in your mitochondria? And basically in layperson speak, what that means is increased efficiency in ATP production. So theoretically, if you were increasing the efficiency of any particular machine, you would reduce the necessary fuel being circulated through the bloodstream to fuel that machine. So, so yeah, that's super interesting. Um, that's one to play around with for sure. I'm gonna, I'm gonna test that myself.

Josh C [00:32:29]: Yeah, and, and your immediate reaction there of parasympathetic changes was my assumption. So like I said, I, I have my cortisol, if I measure it in the first, say four hours of the morning is Basically near the top of the charts all the time, you know, is what it is. I've got a, you know, a bunch going on in life, but I also have of a very elevated fasting glucose. My dawn effect is really aggressive. My cortisol response is aggressive, as we talked about. So I am one of these people that is like running hot most of the time. And my first response is like, okay. You know, laying in this comfortable bathtub of red light is actually a very, very calming experience.

Josh C [00:33:06]: And my first reaction is this is probably just parasympathetic and a change in my cortisol levels. I don't yet know whether that's true or if it's this. You know, the authors of the study say that it's a mitochondrial stimulation, though.

Ben Greenfield [00:33:20]: Best, best N equals. One way to figure that out is just lay in the red light bed and play death metal through your bed. See if your blood glucose still goes down.

Josh C [00:33:28]: There you go. There you go. Yeah, I love it. So what are some other, other good ones? You know, there's a, there's a, there's an interesting. I, I find this really fascinating. There's this Hawthorne effect, which it's named for, I think the Hawthorne Electronics Company somewhere in the Chicago area of Illinois back in the, you know, in the industrial revolution era where this. There was. The effect is known because it's essentially the behavioral modification that happens just through being, through knowing that a certain system or behavior is being measured.

Josh C [00:34:01]: So we, we are all prone to this. If we know we're being measured, we change our behavior by default. Right.

Ben Greenfield [00:34:06]: This is kind of like a human version of the observer effect in quantum physics.

Josh C [00:34:10]: Yes, exactly. Exactly right. That's a great analogy. And so the Hawthorne effect, it was named because this plant, they were like, they announced to all the workers, we're going to be increasing productivity. And the first way we're going to do it is we're going to be monitoring, basically studying worker productivity over the next few weeks. And then we're going to implement changes. Well, they never actually launched the study, but productivity went up by like, you know, some massive number. I don't, I don't remember it.

Josh C [00:34:34]: Because the workers thought that they were being exposed to some sort of monitoring. So they modified their behavior and improved productivity. So just simply, you know, feeling that this is happening changes behavior. So there seems to be a direct relationship, and this looks dose dependent in our data set between using a CGM and weight loss. So people who use levels and are specifically logging their food and using cgm, especially those who start with a higher bmi, just continuously and sort of in a monotonic way decrease body weight over time. And I think it's really interesting because there's clearly, you know, there's a number of factors that could be at play. Maybe they're reducing calories, maybe they're shifting to a more, you know, carbohydrate restricted diet. And maybe that's helping with insulin resistance.

Josh C [00:35:21]: Like, we do see that if you selectively restrict carbohydrates, you'll see changes in insulin dynamics, like very quickly. So that could help kick off weight loss. Uh, I don't really know. You know, this probably isn't super surprising, but that's one that's just like super, super rock solid in the data set is just.

Ben Greenfield [00:35:36]: Yeah, it's pretty, pretty intuitive, but it's interesting. And the low carb piece is interesting too. I don't know if you've come across this or even if you guys have had the opportunity to take a look at this in the people who are logging their diet, but there's a little bit of a paradox in nutrition metabolism in that someone who is restricting carbohydrates pretty intensively. Let's say you're eating like 30 to 50 grams of carbs per day, which is pretty dang low carb, you know, shifting into ketosis almost, you tend to see a slightly higher average blood glucose because since there's far less exogenous carbohydrate, exogenous glucose coming in, you see an increase in endogenous glucose production. Now there's even some people that argue that that's a bad thing, that it's due to a cortisolic response to lack of fuel substrate availability that's shifting the body into a, into a stressed out state. I interviewed a guy named Jay Feldman who believes that. I'm not sure that I think that's the case, but it is interesting that when you lower carbohydrates to a certain threshold, you would expect to see even lower blood glucose. And sometimes you see the opposite.

Ben Greenfield [00:36:49]: You see a slight rise in average blood glucose. So the person who's eating, let's say 200 grams of carbohydrates a day might have stable resting blood glucose of 80 to 90. And then the person who's eating like 40 grams of carbohydrates per day might see it at 90 to 100.

Josh C [00:37:06]: Definitely an effect we've seen. You know, what I, what I think is that this is also highly personal. There are people who, who are eating comparable macronutrient breakdown and do not have the same glucose dynamics. And, and this is like maybe, maybe one of the meta factors that, that really needs to be studied more is, you know, whether it's genetic or whether it's body composition, whether it's or exercise level there we need to be working on controlled cohorts to answer these questions better. But right now there's just so much disparity between the same sort of on paper nutritional behaviors and your blood sugar that it's all I can answer right now is it's quite personal. And for me, if I go into a, into a restricted carbohydrate state, I will see an improvement in my fasting glucose, my sort of basal glucose between meals, but I will see a much more aggressive response to any carbohydrates I do introduce. And that, that seems to make sense. Other people don't see that effect they have.

Josh C [00:38:01]: They actually can, they're, they seem to be in like sort of a glycogen diminished state and they can very quickly replenish those stores when they consume carbs without a major elevation, maybe even better than when they're in a, in a heavily, you know, carbohydrate fed state. And, and so I see this weird like dual duality where impaired post meal glucose but improved basal glucose.

Ben Greenfield [00:38:23]: Yeah, yeah, that's interesting. Here's what throws a little monkey wrench into the equation. It appears that either exogenous ketones or a state of ketosis can result in kind of the opposite. The ability of someone on a low carb diet to be able to function. And I've experienced this personally and be just fine at like 50 to 60, which is close to where the alarms are going off on your dexcom. And so someone might see until they're keto adapted, which is honestly like a 6 to 12 plus month process. A higher average blood glucose when they first start to eat a lower amount of carbohydrates and then that eventually becomes a much lower blood glucose than their original values before they shifted their diet. Or the person who is eating low carb, seeing the blood glucose go up a little bit more, but then starts to use something like exogenous ketones, like drinking liquid ketones or whatever, they would then see that drop.

Ben Greenfield [00:39:26]: And if I'm using ketones like especially in the more powerful ones, Josh, like funky names like beta hydroxybutyrate for example, I can go through a day and feel awesome at a blood glucose between 60 and 70 just because I got full on ketones on board for Utilization. So it's kind of like a little bit of a, almost like a hack for going low carb, seeing the glucose do what it's going to do, but because you're kind of masking everything with ketones, it doesn't matter that much anyways.

Josh C [00:39:55]: Yeah, it seems like, you know, our bodies are super sensitive to, in a, in a, I guess in an insulin sensitive state, very sensitive to the wider sort of fuel substrate availability. And if you've got a lot of ketones available that can provide energy to the brain in particular, you know, it makes sense that they, you know, you downregulate the amount of blood sugar circulating in a functional system. And I'd be really curious to look at that, you know, across kind of the, the maybe insulin sensitivity gradient or spectrum. How well does that hold up for people that have some insulin resistance, for example? Would you see that benefit? There's just like so many fascinating questions to, to dig into there.

Ben Greenfield [00:40:32]: Okay, so alcohol, red light, logging your diet, self awareness in general.

Josh C [00:40:38]: Yep.

Ben Greenfield [00:40:38]: What else?

Josh C [00:40:39]: So overnight, you know, I think it's interesting, a lot of people are tracking sleep these days and heart rate and heart rate variability, you know, using whoops and such and you know, overnight meal timing, AKA the distance between meal and sleep and then the glucose load of late meals. This relationship with heart rate variability and sleep quality is really, really strong as well. Uh, we can predict next day average glucose by, in one hour of sleep increments. So roughly from our data set, an additional hour of, of quality sleep translates to about a 9 milligram per deciliter reduction in average glucose time and range the next day. And, and this seems to be very, very related to the quality of sleep. And the duration of sleep is very related to the amount of carbohydrate or, or net carbohydrate that somebody takes in in their last meal and its timing relative to going to sleep. So if you eat a late high carb meal with low fiber, you are going to sleep worse and that's going to manifest the next day in higher circulating glucose, probably due to some insulin resistance, some stress, some like, you know, advanced sympathetic tone. And that is a, you know, that's a vicious cycle.

Josh C [00:41:53]: If you get into that cycle where you're more stressed during the day, your blood sugar's more elevated, you have, you know, worse insulin dynamics, and then you're like kind of eating late on a consistent schedule that just cascades. Yeah.

Ben Greenfield [00:42:05]: Okay, so late, how late is late?

Josh C [00:42:09]: That number I don't have off right or off the top of my head. But you know, the, I think that what we were mostly looking at was the relationship between meal timing and sleep. And so within about a four hour window. You know, Brian Johnson talks about this a lot. Our data supports what he's saying there. Within about a three to four hour window before bed is where you start.

Ben Greenfield [00:42:26]: To see the impact. I sacrificed that part for family dinners. But he is right. I think you can make a case for the largest amount of research showing two to three hours and you're still going to be okay. But if you're finishing dinner at 8 and going to bed at 10, you're pushing it pretty close. And it's interesting too because you know what I would be curious to see Josh and I don't know if you guys have looked at this is high carb meal four hours prior to bed and high carb meal like one hour prior to bed. Because you know, you, you could theorize that because they are a precursor to serotonin and thus upstream of melatonin, that eating enough or an appreciable amount of carbohydrates with dinner may improve sleep quality if it's done far enough away from bedtime to where your glucose is stabilized and your body temperature has come back down before bed.

Josh C [00:43:17]: Yeah, I think that's absolutely the case. And if you look close enough at the distribution of this effect, you'll see that there are people who are consuming high carb meals before bed. Right. And high net carb meals and are getting great sleep, like long durations and you know, no impact on circulating glucose in the following days. Right. So there's more work for us to kind of tease this apart and there's surely there are some great findings in there, but I think you, you're probably onto something there where you know, putting the body into a well fed, low stress state, leading into sleep, you know, and, and well fed, being, you know, having some sort of balanced macronutrients, not, not being super deficient in any one thing, including probably car carbohydrates or, or you know, glycogen replenishment probably does set you up for better rest than if you're in some sort of fasting paradigm or, or something that's like putting extra stress on you. So I, I tend to agree with you.

Ben Greenfield [00:44:10]: Okay, interesting. By, by the way, if you're listening, you can, you can track all this stuff like kind of goes beyond just the native, hey, this is my blood glucose. It leads you run these little tests on yourself and log everything and take photos. So it Kind of turned turns your blood glucose monitoring a much more powerful tool. And Josh again runs that company. But throw one more at me, Josh. Let's do one more. And I got a few other quick questions to ask you.

Josh C [00:44:36]: Yeah, okay, so I'm going to throw two at you. One of them is, you know, levels. We started blood work a long time ago as well because we want to add extra context to that glucose glucose data. And one of the findings that I think is really interesting is the relationship between HSC reactive protein, so high sensitivity C reactive protein when above a threshold of about 3 milligrams per deciliter. So if you have more than 3 milligrams per decilimeter of HSCRP, those people have a higher fasting insulin and longer duration post meal glucose elevations in their CGM data. It's like pretty clear. So there's this inflection point, it seems like when inflammation is above a certain level that corresponds with insulin and with blood sugar dynamics. So, so that's one, there are some, some eating behaviors that correspond to this, this kind of phenotype.

Ben Greenfield [00:45:21]: By the way, real quick, before we leave that one, did you guys look at or if you thought much about, you know, not popping ibuprofen all day but you know, like could curcumin or bear brain? Well, we know berberine has an effect on blood sugar. I'm trying to think of like a traditional anti inflammatory that's natural. Maybe that would have an indirect blood glucose lowering effect. Yeah, I guess. Something like curcumin for example.

Josh C [00:45:46]: Yeah. You know what I would love is to sort of seed the, you know, the wider ecosystem of nutrition and supplements to start to dig into these things. CGMs are widely available. Like there are big data sets and tons of people using them distributed sort of remote studies on these things are actually really easy to launch. And I don't have the answer to your question right now and we could dig into our, our data set but what I would love to see is brands go about like running sort of straightforward studies using, using data streams like this to be able to answer these questions better.

Ben Greenfield [00:46:20]: Yeah, you're super nerded out. You know, get your bottle of BPC157. That's, that's a pretty potent one. You know, inject that and see what happens to your blood glucose. Okay, what's, what's the, what's, what's the next one?

Josh C [00:46:31]: Okay, so the last one is I think probably going to be very intuitive for lots of your listeners, but that is the effect of of hormones and in particular female hormones on blood sugar control. So everybody has the dawn effect. This is a, you know, this is cortisol. It's a hormone. Everybody has a dawn effect. So that's, that's one thing that in the literature previously, you know, the, the literature would say that the dawn effect, basically when you wake up having an elevation that is not related to eating anything in your blood sugar, that was something that was previously thought to really only be the domain of diabetes. But it's pretty obvious that everybody has this morning cortisol rise and a corresponding roughly 8 milligrams per deciliter.

Ben Greenfield [00:47:11]: I wake up every morning diabetic.

Josh C [00:47:13]: Yeah, me too. I see like a 20 to 30 point spike in the morning. It's like, it's quite aggressive. Probably not ideal, but, you know, who knows. So the dawn effect is, is very common. And then for, for women, that effect and every other element of glucose dynamics directly corresponds with menstrual cycle. So the follicular phase, basically the sort of first 14 days, there's an estrogen dominant state. And estrogen improves insulin sensitivity.

Josh C [00:47:41]: And women tend to see very, very favorable responses to a higher carbohydrate content. Like, you know, even lower fiber with carbs seems to be much better tolerated. And then in the luteal phase, when progesterone goes up, you see basically like an induced insulin resistance and it kind of flips really quickly. And this can be really confusing and frustrating. We've, we've recently added, you know, reproductive cycle tracking in, in levels because it's just so directly related. You can't, you can't really use a cgm, you know, as a woman with menstrual cycle without seeing this effect and being confused by it. If you're not tracking those two things together. So just the degree of, of impact of the, the progesterone estrogen cycle is wild for a lot of women and it can be really frustrating.

Josh C [00:48:25]: But actually you can use it to your advantage and tune basically nutrition decisions to phase of, of cycle. And then you see, you see kind of the capstone here is menopause, where you see a reduction in estrogen and a corresponding reduction in insulin sensitivity. And so you start to see this kind of sustained, chronic and for many women, very frustrating elevation in circulating blood sugar, circulating insulin, and you know, a lot of the effects, weight gain, et cetera. And so I think this is going to be a really powerful, you know, monitoring these things can be a really powerful way to use lifestyle to, to help sort of minimize the Effect, but also, you know, long term, I'd love to see studies that, that roll in glucose dynamics and slim dynamics to hormone replacement therapy.

Ben Greenfield [00:49:07]: Okay, so, so putting your practical application hat on, if I'm a woman and say I want to know, all right, I'm in my luteal phase. Tell me what I should eat or at least tell me how I should adjust my macronutrients. Um, what's the best app app combo tracking kind of stack to do that?

Josh C [00:49:27]: Well, there's a, there, there are a lot of products to, to track cycle. I, you know, I would obviously recommend levels because it's all in one place. We, we, we can do the hormone cycle track link tracking we can do.

Ben Greenfield [00:49:37]: But how are you doing that? Is that like body temperature or so.

Josh C [00:49:40]: So women will, will track that. You can, you can use either body temperature or just timing based on period and so basically manually time it and, and it's obviously works, it works best right now if there's a consistent cycle. But there are, there are apps that can, that can add even more information if you want to do body temperature, et cetera. And so, and we also do blood work to measure hormones. So we can, we sort of calibrate in the, the basal levels of reproductive hormones as well. So that gives like a really good kind of benchmark. And then, you know, how, how do you know, many women in our, in our user base tend to shift their dietary decisions towards higher protein and higher fiber during the luteal phase. And so just kind of like shifting away from the fast acting sort of net carbs and trying to find, you know, maybe even a higher fat content depending on, you know, you know, your sort of heart health goals.

Josh C [00:50:34]: A lot of women are trying to avoid saturated fat these days. And so, so it's basically a protein fiber axis that, that women tend to be shifting towards to get controlled blood sugar through that luteal phase and then the follicular phase just leaning into fruits and vegetables and like kind of eating more of the, the net carbs without really a concern and just basically tuning in portion control and timing to you know, with the cgm, but, but overall just being a little bit less restrictive.

Ben Greenfield [00:50:59]: Okay, so like you know, whoop aura. Those are a couple. I got my wife to finally start wearing a whoop, which was a miracle. And it is starting to tell her about her cycle for tracking, you know, based on body temperature and some of the other metrics that they have. Is there a way to like pull that data into levels? Do you guys have any type of Integration or API.

Josh C [00:51:23]: Most of these, these trackers now connect with Google Health and Apple Health. Right? The two kind of like aggregation apps on the average smartphone. And so we can pull that data straight in so that that basically puts it on autopilot. If you do have one of these devices that's tracking accurately that that will calibrate cycle.

Ben Greenfield [00:51:41]: You know what? I feel like a dummy. I have not even connected my whoop to my levels. I didn't think of this. So I can do that.

Josh C [00:51:47]: Yeah, you turn, turn on your Apple Health integration from whoop to Apple Health and then from levels to Apple Health.

Ben Greenfield [00:51:52]: And then I should have been doing this. Okay, note to self, CTA post podcast. Okay, cool. This is super interesting. I've got a few kind of more like logistical questions for you. There's the Dexcom, there's the Libre, now this new Stello which is actually what I've got now, which I like because again dude, with my use of ketones, a lot of times I am around 50 and I could not get that Dexcarm alarm to just fricking stop going off like 2am or whatever because it thinks I'm going hypoglycemic and I'm fine. So now I use a Stello which seems to do a better job with that. Does it matter like which one do you recommend to people or, or do you recommend a certain one to people?

Josh C [00:52:37]: I try not to to pick favorites here. I will say that right now levels is, is really leaning into Stello because for the reasons that you, you mentioned that the basically the previous Dexcom G series and the Libre from Abbott were both developed for diabetes management and so they had some features that were just not ideal for the non diabetic population, like alarms. You know, it's a very risky situation if somebody who's insulin dependent starts to go super low. Not the case for somebody who, who isn't. And so now we've got these over the counter devices. The cost has improved the performance. So that the Dexcom Stelo is. And if you look at the FDA approval letter for Dexcom when the Celo was approved, they say it right in there.

Josh C [00:53:16]: It's the same hardware, same chemistry as the Dexcom G7. They just have changed the software for this other modulation.

Ben Greenfield [00:53:21]: Yeah, I was going to say the whole thing looks the same except. Except it's not. The app isn't as annoying in my opinion.

Josh C [00:53:26]: Yeah, yeah. So the Stello is super popular right now and I think for good reason. I'm really excited to see, you know, purpose built tools for this user base. Right. I mean 2019, when we started we were using Dexcom G6 and Freestyle Libre. That was really kind of quite annoying for a lot of the population and now they're much, much better sellers. So I would definitely recommend starting with a device like the Dexcom Stello and then I'm just really excited about the trend. I think they have differences that the Abbott devices and the Dexcom devices.

Josh C [00:53:55]: They have subtle differences in how the device measures, but overall quite comparable performance is very comparable. You know, you'll, you'll see people who prefer one or the other due to brand loyalty, but overall I think Dexcom, Dexcom is doing it first and I think they're, they're a really exceptional brand. They really, this is, their entire product stack is cgms and, and so it's a, you know, they care a lot about getting it right. So there are a couple of things that are coming down the sort of pipeline for the Stella. They're, they're constantly listening to you member feedback and improving. And so you know, a couple things like, you know, the range of the. Right now it's like 70 to 200 that it reads out and I think they're going to be improving that range. They're going to be improving the timeliness of the data.

Josh C [00:54:33]: So some exciting things. So it's, it's heading in the right direction and we're getting really good feedback on it.

Ben Greenfield [00:54:38]: Cool. Okay. Your team told me, I don't know if this is like top secret data or whatever, but there's like some kind of a new app feature that lets people measure their blood glucose levels without actually like wearing the monitor. Is that a thing?

Josh C [00:54:53]: Okay, so yes, it's a thing that we've been experimenting with for a long time and I'll, I'll give some more context here and maybe some, some thinking for the future because we, we don't have it widely available at the moment right now. With the size of our data set, you know, we have billions of data points, CGM data points being the vast majority. And we have by far the largest data set that corresponds in a non diabetic population. CGM data with nutrition specifically and exercise. So that is an incredible set to train models that can predict blood sugar and we are actually able to build a model that can quite accurately predict the average glucose response to a specific stimulus. So a meal with a certain composition. The difficulty is that average doesn't apply to the individual. So we had this feature where you could basically like log a meal even if you didn't have CGM on and it would show the predicted blood sugar response for an average person.

Josh C [00:55:52]: The problem is that in our, in our testing, you know, people want absolute values. They want to be very confident, like tell me what my blood sugar is. And showing an absolute value was obviously quite difficult and had some challenges, if you know what I mean, for safety and other things. So we ended up, we experimented quite a bit with this. And at the end of the day, what we need is to first learn about an individual's metabolic responses and then we'll be able to actually enhance that model to be able to potentially take over in between CGM monitoring periods to give you a sense for how you are very likely responding based on our historical data on you. So this is something that, it'll be getting better and better over time and eventually we will respond, release this out to, to our member base. Right now it's a little too premature, but it's, it's such an exciting direction. I think this is the bigger context of that abundance of data concept is like when we get even more molecules in a continuous nature, when we have more blood work, we have just more like health calibration data for individual.

Josh C [00:56:51]: Building a model that is like kind of your digital twin that, that you can trust to say, you know, how you're likely responding is, is totally possible.

Ben Greenfield [00:56:59]: Oh yeah, the digital twin thing. I mean if you guys could acquire a formal relationship where you get access to anonymous genetic information and you're able to tie that genetic information to blood sugar responses and meal logging and maybe pull in bloods too. So you have biomarkers and genetics to back up what you're seeing with the meals. You could get to the point where it's like, yeah, whatever, 500 million people have eaten that sweet potato, chicken, ranch dressing, cucumber meal. You know, a million of the of them have the same genetics and very similar biomarkers as you do. Here's your blood glucose. No needle required.

Josh C [00:57:36]: Something along those lines does not seem unreasonable, especially if we've, if we've calibrated to you the individual. So yeah, it's, it's going to be very, very exciting to see where it goes. And you know, we're kind of in this AI revolution and the models are just blowing people's minds and what they're capable of. We haven't yet really seen regulations that, that basically dictate how this can be done for healthcare yet. So remains to be seen like what, what, what direction this takes. But over time I would expect Much more of this predictive modeling.

Ben Greenfield [00:58:03]: Yeah, super cool folks. This is Estelle. I wear mine in the back of my arm. Where do you wear yours, Josh?

Josh C [00:58:08]: Same thing, back of the arm, right in the same spot.

Ben Greenfield [00:58:10]: Yeah, yours always looks super simple. Tracks it for a couple weeks. So if you don't have one, you know, the past was you got to like whatever, have a medical condition, etc. Etc. Now you can just like wear these things. They're super, super useful. It's like, you know, I've got my cheapo Timex watch which I used to say was $12. Now they're like $30.

Ben Greenfield [00:58:33]: I've got my whoop, which is a measurement tool and then I've got my cgm. Those are like the main, main three things that I use. I got time metrics and sugar. Josh, thanks so much man. This is super interesting. Fascinating actually.

Josh C [00:58:46]: Love catching up. And yeah, I mean, I'm looking forward to getting more of these big picture questions answered through the data over the next few years. So hopefully we can do this again and talk about some published data rather than just kind of the preliminary stuff.

Ben Greenfield [00:58:59]: Oh yeah, for sure. Let's talk again in five years. So BenGreenfieldLife.com glucose surprises because we talk about things you might be surprised about with your blood glucose. This is where the shownotes are at. And then Levels has a discount for my listeners. I'll hunt it down and put it in there too if you guys want to get one of these and get a good deal on it. And I think that's it. So Josh, thanks man.

Josh C [00:59:21]: Thanks again, Ben, and thanks to the listeners.

Ben Greenfield [00:59:23]: All right folks, I'm Ben Greenfield along with Josh Clemente of Levels, signing out from BenGreenfieldLife.com have an amazing week to.

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