Y Combinator Startup Podcast - #91 - David Zeevi

Episode Date: August 22, 2018

David Zeevi is a James S. McDonnell independent fellow at the Rockefeller University Center for Studies in Physics and Biology. He focuses on developing computational methods for studying microbial ecology in the human gut and in the marine environment, and its contribution to human and environmental health.He was one of the authors on the paper Personalized Nutrition by Prediction of Glycemic Responses.The YC podcast is hosted by Craig Cannon.Apply for $120K in funding from YC.***Topics01:15 - Why did David start working on personalized nutrition?4:45 - How did the measure the effects of food in their study?11:55 - How was the study standardized across people?15:55 - How they measured an individual’s gut microbiome.17:30 - What is the gut microbiome?22:05 - Is there an ideal gut microbiome?23:20 - How do you manipulate your gut microbiome?24:50 - Fecal transplants.26:55 - Elizabeth Iorns asks - Does post prandial glucose response regulation track with weight regulation? I.e. can they use their test to determine what individual people should eat or not eat to lose weight?28:35 - Has this research been turned into a product?29:35 - Who else worked on this research?30:35 - How was their predictive algorithm made?35:15 - Did they end up with any dietary suggestions?36:15 - David’s bread study.38:55 - Has David changed his own diet?39:25 - Why fat was vilified.43:15 - David’s ocean microbiome and other research.51:05 - Traveling and your microbiome.56:35 - Trying this out yourself.

Hey, how's it going? This is Craig Cannon, and you're listening to Y Combinators podcast. Today's episode is with David Z. V. David is a James S. McDonald Fellow at the Rockefeller University Center for Studies in Physics and Biology. He focuses on developing computational methods for studying microbial ecology in the human gut and in the marine environment, and its contributions to human and environmental health. He was also one of the authors on the paper personalized nutrition by prediction of glycemic responses. You can find David on Twitter at Dave Zeevy. All right, here we go. So today we have David Zeevi on the podcast and you are an author on many papers, but the paper that I initially contacted

you about is called personalized nutrition by prediction of glycemic responses. And this is a quick summary. People eating identical meals present high variability in postmeal blood glucose response. Personalized diets created with the help of an accurate predictor of blood glucose response that integrates parameters such as dietary habits, physical activity, and gut microbiota may successfully lower post-meal blood glucose in its long-term metabolic consequences. Why did you start working on this? So we got to see the amazing statistics on metabolic disease in the world. So right now, four out of ten U.S. adults are obese.

Just to clarify, obese means what? It means a BMI, a body mass index. Mass index of over 30, which is actually not that bad, but it's still considered obese by the CDC. And that's four of ten U.S. adults. Now, it was about one out of ten in the 1980s. So it progressed massively. And this is based both on the World Health Organization and the CDC's. standards for disease control. One of the 10 Americans that are diabetic, and this is an awful

disease. It's a lot of suffering. It's a lot of, you know, related complications. And it's a huge burden not only on people who have the disease, but also in health care systems. And I told you before, it's like $250 billion spent on diabetes and its related costs in 2012. Yeah, annually. So, yeah. So it's, a huge deal. And it's widely accepted that nutrition is a major source of these diseases. Because diabetes was not nearly as prevalent in, for example, 1990. Right. It was not nearly as prevalent in the 1990, 1980s, 70s. It was not as prevalent, and neither was obesity. And when we, you know, came to look at it, we just tried to figure out what are the changes. What are the major changes that were done to our nutrition of the last 30, 40, 50 years or so?

And we came up with, you know, four, five main changes. So first of all, we started consuming much less fat. It was reduced from about 20% of our calories to about 15%. We started, so having fat in your food is tasty. And it's also very, very, very. fulfilling and everything. And if you want to, you know, give food a taste without fat, you usually add sugar. So sugar mainly took the place of fat in our diet.

So there's a graph I sometimes show in lectures where you see the sugar consumption per capita per year since I don't know, I think 1700 till today. and the crazy fact is that the annual consumption in 1700 is the daily consumption today. So we couldn't have evolved to undertake to treat this amount of sugar that's going into our system. The other couple of things that have changed is that we consume much more additives with our food. It's much less food and much more industrialized. And last thing is that meal times changed. We work in shifts.

We have electric light. And that changes when we eat and our daily routine in general. Gotcha. And so then this study, how are you actually measuring the effects of food intake? So this is also an interesting thing because so. We're thinking that if nutrition did cause this epidemic, what can restore healthy nutrition? And when you try to ask, what's healthy nutrition? You can look at, you know, popular Time magazine covers, for example, and we looked at that.

And you can see that some of them say that saturated fats are bad for you. Some they say that fats are good for you, and some say that you should be vegetarian. Some say that you should eat an Atkins diet. And there's a famous one which I really like from 1972 that says, eating may not be good for you. Eating? But we thought as scientists that what you should eat is not a question of trend or fashion or whatever. It's a scientific question.

And we want to address it with scientific metrics. So we had to choose a metric that was specifically good for this question. And we ended up choosing the blood glucose response. And the reason we chose this is that, well, when we eat the carbohydrates in our food are broken down to sugars, which are then absorbed by our gut into our blood streams, and that causes spikes in our blood glucose levels. These spikes cause insulin secretion from the pancreas,

which signals the body to store these, this glucose as fat or as other storage components. And this leads to weight gain. Now, spikes in blood glucose are also associated with many other metabolic diseases and, of course, with diabetes and obesity. And it leads to weight gain because it is transferred to fat, right? It turns into fat. Yeah, it turns into fat.

And it generally not just turns into fat. It also, you know, turns into it. gives a boost to the natural mechanism of storage. It causes you to store more. Okay. And the last thing that was good about glucose responses was that it was very easy to measure. So you just connect a small device, a continuous glucose monitor, has a tiny needle or a tiny, you know, sensor that goes into your body. I think it's like probably quarter inch, something like that, it goes into your body.

and that measures the glucose levels in your interstated fluid. That's the fluid within between your cells. It's highly correlated with the glucose in your blood, so you get a very accurate measurement of the glucose in your blood or proxy for the glucose in your blood every five minutes. So you have a very high resolution of this metric. So try to think of it. If you now conduct a nutrition study, you can measure weight.

for example. But weight is very noisy. You know, it's, it's affected by what you drink, what you eat that morning, the time of day that you that you exercise, exercise, whatever. And you can only measure it once in every long period of time, because, you know, just because it's very noisy and because it changes very slowly. So you can see the effect or the average effect of a diet over a week. or two or a month or so, even though I know some people who don't get a step on the scale

every day. But it's usually, you know, recommended to look at it every week or so. If you look at blood glucose, you can measure it for every meal. So you can just see, get a fast feedback on each and every meal that you ate. And that's what made this blood glucose such a great metric. for us. I know since it was correlated with so many, so many diseases such as cardiovascular disease, obesity, diabetes, and so on, we quickly realized that in order to maintain health or to restore the healthy phenotype, what you need to do is to probably reduce the glucose responses.

And that sounded easy, you know, okay, we just collected a few people and we look at their glucose responses and we find the foods that are good for everyone. And you find the best diet in the world. And you find the best diet in the world that would reduce glucose responses and that's it and we're done. Yeah. But biology and the world is more complicated. And what we found is that there were several, usually very small-scale studies that

showed that people's glucose response can be very different from one person to another. So two people eating the same loaf of white bread, one would really spike their glucose and one would really stay flat. And that's true even if you normalize their responses to the responses to glucose. Right. So you have just to see if so even foods are not categorically good or bad. Right. It also depends on the person. And that was shown in very small scale.

So we said, okay, so let's think of what can affect these glucose measures. And we came up with three main causes that can affect people's glucose responses or personal responses. One is genetics, which unfortunately we can't really change. We are what we're born with. Yeah, for now. I mean, CRISPR is going to change all that. The second is lifestyle, which we all agree should be healthy, active, and so on. So there's not a lot to do there.

We already know the answer. And the third, one that was when we started sort of flying under the radar, was the human microbiome, which we found to be associated with many diseases, many disorders. And if we have time, I can tell you a little bit about that. And so we wanted to create a study that combines all these factors. Nutrition as a target. Genetics or a proxy for genetics lifestyle, the microbiome, to predict what's good for people to eat. And that's how we came up with this study.

And so you standardized the study. So it was something like 800 people. And the study was standardized by. giving them the same breakfast over the course of a week, right? Well, there are a couple different breakfast that you give them. Yeah. So the first thing we wanted to do in this study is to see, to try to recapitulate the variability that we saw in the small scale studies. Yeah. And so as a controlled way to study variability in people's responses to food, we gave them, we replaced their breakfast with standardized meal that contained either bread, bread and butter, glucose or fruit

which had 50 grams of available carbohydrates each, and that was to be taken in the morning after the nights fast, without exercising, without eating before that, only drinking, no exercising in two hours after eating the meal because we wanted to get a clean response to the food. And what we found is that two people eating the same meal in, sorry, one person eating the same meal on two different days, was very similar to themselves.

So we had a correlation there of 0.7 to 0.77, which is very good, you know, considering the noisiness of people. But across people, across the population, the variability was huge. So people, for any given food, covered the entire range of responses. And they were very, you know, reproducible within themselves. You can see a person eating the same loaf of white, bread, having two very, you know, flat responses to glucose. The glucose doesn't go up after the meal. It doesn't go rapidly down after that. Yeah. And other people who were not diabetics, who were not pre-diabetics or anything, had

huge spikes to the same, the exact same loaf of white bread. And these people, you know, you couldn't tell the difference otherwise. Right. And again, it's not just that one food is categorically worth. than other foods, some people responded, had the highest response to glucose, some people had the highest response to bread, and minority had the highest response to bread and butter. Actually, fewer people had a high response to bread and butter than to bread alone. So the butter, so the fat is somehow neutralizing it?

Yeah. Yeah, we think it does. Right. And then interestingly, it's not, again, like in the pursuit of the optimal diet. it. It's not just that, oh, white bread has a lot of sugar, ice cream also has a lot of sugar. This isn't good for you. You can't eat it. So you'll have someone will respond in one way to bread and then differently to ice cream. Yeah, we saw that exactly the exact same thing we saw with naturally occurring foods. Some people have a high response to rice, for example, and low response to ice cream. And other people would be the other way around. And that's with the exact same amount of carbohydrates in the food. and yeah. Yeah. And so then, okay, so, oh yeah, we should clarify. So the breakfast was standardized, then they could eat whatever they wanted afterward.

But they had to log it. We also gave them an app in which they recorded what and when they ate. Maybe I should say a few things about what we collected in this study. So we recruited people, about 800 people. We had them go through a process in which they gave us blood. They filled in questionnaires, both food frequency questionnaires and general medical questionnaires. We had them

connected to a continuous glucose monitor, as I told you before. That measured their blood glucose every five minutes for the duration of a week. And then this week, we also gave them an app which we developed in which they recorded one when they ate, slept exercise, and so on,

and the exact amounts of every food in their diet. So we also gave them weights to, you know, weigh their... Oh, you gave them a scale. They're a scale. Oh, yeah. The way their food when they go to, when they eat at home, and, you know, we give them some leeway to eat at restaurants. And did the stool sample element, was that in the original spec?

And we also, yeah, we also collected stool samples, which we analyzed to see the microbeam in various levels, both which microbes are in there. Yeah. Which genes of the microbes are in there. and if I talk later about the microbiome, it's an amazing ecosystem we know with thousands of species about as many cells as in the human body. Just all in your stomach. All in your gut. It weighs as much as your brain or a little bit more than your brain.

It's like people call it like a forgotten organ, not so forgotten now. And these microbes have 150 times more genes than are in the human genome. three million genes. So they have huge metabolic potential. And this metabolic potential can be harnessed or can be accounted for when we're looking at what people are eating. And this is very interesting because unlike genetics, the microbes can be changed. So if we figure out a way to change the microbes that are affecting or have a deleterious effect on our health, we can maybe improve people's health altogether. So you should both explain what this gut microbiome is actually for people, because I think

like this word gets thrown around a lot, and then you're talking about changing it, and how would you even go about doing that? So for context, let's give like a proper definition for folks. So the gut microbiome is the ecosystem of bacteria, archaea, which is also a type of unicellular creature, fungi, viruses, and small, I don't know, worms or whatever that we have in and around our body that are not of human origin. Right. That's the microbiome generally.

And all of its associated genes and genetic material and so on and so forth. So that's what usually people mean when they say microbound. And as I said before, it's huge. There's a lot of cells, there's a lot of diversity there. There are a lot of genes, and there are more and more related, more and more relations are found between this gut microbiome and many disorders and different outcomes. So I can name a few examples. So one of my favorite microbiome studies was done in Stanley Hazen's group. in the Cleveland Clinic.

They looked at carnitin, which is a compound that is found in red meat. This carnitin is metabolized by the microbiome to form TMA. It's a compound. TMA is then oxidized in the liver to form TMAO. And TMAO causes a reduce in reverse cholesterol transport and bile acid synthesis. And these are long words,

but what it eventually means is that it causes atherosclerosis. These two processes, if they're reduced, it causes therosclerosis. It causes your arteries the clot. And interestingly, if you remove these specific microbes that metabolize carnitin from the equation, the downstream effects are attenuated as well. And this was a major thing for us because this is the first time we saw that their microbiome can affect how each and every one of us responds to nutrition. So it was beautiful. Another study, I think it was by Nan Chin and colleagues in 2014. I'm not sure.

Maybe it was published in nature, but I'm not sure. They showed that you can accurately detect cirrhosis liver disease by only looking at your gut microbes. And that showed us that the gut microbes can reflect our health status. So in then, yeah. Sorry. No, in monitoring what people are eating in their stool samples, you can kind of recompose what their gut microbiome is, right? Yeah.

Right. Well, I mean, you have to measure the gut microbiome as well. But you can, maybe you can get some idea on their health status and what they're eating from the gut microbiome. And it's not only that microbes can affect your health or reflect your health, they can also, sorry, it's not only that microbes can reflect your health, they can also actively affect your health. And there are a few very nice studies by Jeff Gordon's group at Washington University of St. Louis. Especially one that I like the most from 2013,

they took pairs of twins that were discordant for obesity. one twin obese and one twin was... These are mice? No, no. These are people? Okay. And they transplanted their microbiome into germ-free mice. Germ-free mice are mice that are born and raised in sterile conditions,

and they don't have a gut microbiome of their own. And these mice were transplanted. These microbiomes of twins, one obese and one lean, many pairs of twins. And interestingly, the mice that received the microbes of the obese twin became obese, and the microbes of the lean to remain lean after eating the same food and, you know, doing the same things. And that also showed us that, you know, it's pretty... Yeah. And so, like, kind of the, maybe the logical extension in the sense that every human wants things to be black or white,

where you often ask, like, okay, is there an ideal gut microbiome? Because, like, rather than the diet, maybe we just do the gut microbiome, and then we do the transfer and everyone has the same one. So I'm not sure if there's an answer or there's a clear answer. I think people are trying to study the guy of microbiome in health and disease. The thing is that it's, and this is maybe just my opinion, it's so diverse that you need a huge sample to study what's good and what's bad in a microbiome. So once you get to know the exact effect,

size of the microbiome on human health and whatever, maybe then you can start asking the question of what is healthy and what is not healthy. We know right now that we know of some species that are healthier than others or are associated with better health. Generally, a microbiome diversity, a high diversity of the microbiome, is associated with a healthy host. Yeah. So you want to let your kid eat dirt, I guess, or have a dog. That's usually contributing to a healthy microbiome. Okay. And so then in the context of, you know, Jeff Gordon's group where they identify maybe a certain bacteria that's not ideal, what is the process of trying to eliminate it?

So I don't know if I have a good answer for that. there are a lot of ways to to exert an effect on the gut microbiome. You can take antibiotics or very specific antibiotics. You can try and replace this microbe by ingesting some sort of probiotic

or some sort of a microbe that will occupy the same niche as this microbe just to push it out and take over. You can take prebiotics, which is some sort of fiber, but I'm not sure that, you know, people have an idea of the full effects of each and every of these things. Each and every one of these things.

So there's still a lot of study to be done in this field. Yeah. I've always wondered. I mean, like, I read a couple of studies before this podcast and I read the book. I contained multitudes. But, you know, there are so many things out there between, like, fecal transplants and, like, the pills that you can digest where, you know, companies say we have found, like, the optimal. probiotic or gut biome supplement in large part do you have you found that stuff to be effective or is it just kind of bogus no i'm not sure i can answer yeah with uh with confidence yeah right okay

because yeah i guess specifically the the the fecal transplant stuff i think is the most eye-catching yeah definitely that's the dark side of michael biome science yeah yeah exactly but but it has been proven effective for some percentage of people right so um So fecal transplants have been used. Their claim to fame is by treating chlostridium difficile infections. That's a type of infection that takes over your gut. It's a certain bacterium that takes over your gut. It pushes everything else out.

Now, when you try to treat it with antibiotics, it usually sporulates. It creates spores, and it resists the antibiotic. The antibiotic kills everything else. And this thing just takes over, you know, all the gut spaces that were left by other bacteria. So usually when you have a C-DIF infection, it's predominantly the most abundant microbe in your gut. And it causes extreme diarrhea and these sort of things. Now, when you treat these patients with antibiotics, it's not working, so you want to treat them with something else. You want to replace their healthy microbiome, and you indeed transplant's tool into these

people. And that transplant works mainly because their microbiome is so depleted and it's like, you know, cultivating an ecosystem in a place where there was none. So if you take this ecosystem and you try to transplant it to a person with a healthy ecosystem, that's not necessarily going to work. Right. Okay. But people are making big money out of it. So I heard that companies collecting stool out of professional athletes, NFL players, NBA players, and so on to transplant to other people. And, you know, I support that. Yeah, I mean, however you want to get paid, go for it. Yeah, exactly.

So we have a couple of questions people submitted because they were very curious about this. So Elizabeth Irons from a science exchange had a couple questions, one of which was, does post-pranthial glucose response, which is the response that you're measuring with the glucose monitor, does it track with weight regulation, i.e., can they use, can you guys use their test to determine what individual people should eat or not eat to lose weight? So, theoretically, post-pranning glucose response is associated with changes in weight, just because of the mechanism I told you about that when we eat things that. spike our blood glucose, we cause insulin secretion, which, you know, signals the body to

store things as fat, among other things. We haven't tried and tested it specifically. Our study was a short-term study. Even the intervention that we did was a two-week intervention, a good week and a bad week. We can get to that later. Yeah. But we didn't do anything that's longer term. And I think that, you know, in order to see differences in weight, you need to follow people for months, if not years. Okay. But choosing the foods that are right for you out of your own diet gives you an advantage if indeed it does improve your blood glucose and therefore your weight because you don't

have to change your diet drastically. You only have to eat out of your own diet the foods that are good for you. Right. And it could, at the very least, steer you away from becoming pre-diabetic. Exactly. Which is, yeah, another huge concern. Yeah. And so, yeah, we should talk about the follow-on stuff, but I think another very common question is who is turning this into a product?

Or how is that being done? So there's a company called Day 2. You can go to the website. They're working on that. And what they're doing is they did a study similar to ours in which they, collected participants and they had them go through this sort of analysis. And I think, I'm not sure what they're doing. I'm not in touch with them or anything.

But I think that what they do now is they have you fill in a questionnaire and they take a sample of their microbiome and they give you a prediction for each food that you eat if it's good for you or bad for you. Right. Because you guys, I mean, you're doing some computer science stuff. as well, right? You built essentially an algorithm from the 800 people. Well, I think it's a good time, as I need to say, that it's not just me. Yeah, of course. We're a huge group of people you can see on the paper, and mostly the person that I've worked with closest on this is Talcoram, who's going to

start faculty position in Colombia in the fall. So if you're a potential PhD candidate or postdoc listening to this podcast, then you can kind of. team. Yeah. He's a very good scientist. And under the supervision of Iran Segal. And with the fabulous Adina Weinberger, who handled the wet lab and all the samples and everything, and, you know, made protocols out of where there were a nun.

And so it was an amazing group, an amazing group of tens of people and a lot of, you know, and obviously, if I try to thank everyone, I'll forget some. They're on the paper. But yeah, please download the paper and see for yourself. And there's a cool video you guys made, but yeah, keep going. Yeah. So we, yeah, you were asking about the algorithm. So we developed an algorithm that was based on people's metrics on their.

So what we first did was to see if these responses to food were associated with any of the other metrics that we found. And we found many associations between, you know, the response to standardized meals, for example, to BMI and to glycated demoglobin, which is a metric for diabetes. And we found many, many associations with gut microbes. And we said, okay, why not, you know, try to combine all these signals into something that would predict people's responses to any given meal? And just to give you an idea of what people used before we, came around to do that, they usually, so usually when you think of blood glucose responses, you think of counting carbs. So you just take the correlation between the carbs and the meal, and if you take the correlation

between the carbs and the meal and the post-branding glucose response of the meal, you get a correlation an R of 0.38, which is not a very good correlation. It's significant because, you know, it's a lot of points. But for example, there's meals in which there is a huge amount of carbs, but not a high response to glucose and the other way around also happens. So we were set out to fix that, to try and do something better. So we build an algorithm on these 800 people we collected. We used boosted decision trees on about, we didn't predict people, we predicted meals,

we predicted about 40, more than 45,000 meals. We trained on a subset of the 800 people and we tested our prediction on the left-out cohort and we made sure that a person's meals were not both in training and tests, so this thing would be more generalizable. In terms of features, we took the microbiome composition of people, including the microbiome genes and microbiome growth rates, which is from a different very nice study. We looked at the nutrients in every meal.

fat, carbohydrates, and so on, but also sodium and other nutrients, other recorded features, meal time, sleep times, and so on. And blood parameters, questionnaires. Meaning blood type, that sort of thing? No, not blood type, but for example, cholesterol in the blood. Or glycateaumoglobin and these sort of things. So overall we had 137 features after feature selection on 40-something thousand meals. And we ran this prediction. And this prediction got us to an R of 0.68 compared to the previous 0.38.

And this R of 0.68 is pretty close to the 0.7 that we get when we look at the same person eating two different meals, the same person eating the same meals on two different days. Right. So this is a theoretical upper bound that we almost reached. We then collected 100 additional people that were not used to create the algorithm or anything, and we tested this prediction on dim. Meaning you took a stool sample. We took a stool sample.

We had them go through a week of glucose monitoring. Yep. We ignored their glucometer, and we tried to use all the data that we collected on them to predict how their spikes would look. Yeah. And we got an hour of point seven again, which was great. So that means that this predictor is generalizable, at least for the Israeli public. I was wondering that, like, having not been to Israel, like, is there a large difference in

types of foods? Like, I don't know, are you really good at tracking like hummus and that kind of stuff? So, yeah, people ate hummus. But people also ate, you know, in Israel, I think in Israel, people eat. a Western diet, maybe fortified with more vegetables. Okay. Yeah. One thing I, one thing I, I can tell about New York is that it's harder to find

fresh vegetables here. Yeah. Even though there's the fruit carts that are really nice, but, you know, still. Yeah. Not quite so much. Were there, were there dietary suggestions that you took away from this? or did you kind of just step back? For instance, you mentioned fat, right?

You know, I know this is now I think that's much more common people doing, you know, ketogenic diets or just adding more fat, fewer carbs. Did you guys walk away with suggestions or did you kind of not choose to make any? So we chose not to make suggestions. Yeah. Because I think this is, this kind of beats the purpose of what we found that, um, you know, people are very different. And anything universal, any universal dietary recommendation would be suboptimal at best. So there were no foods where consistently they were good?

No, not for our people. Wow. No. I didn't expect that. So we should talk about your bread study, because I found that a little bit. That's interesting and related, where you basically increase the amount of bread. someone consumed over, I think, would you say, from 15 to 30

percent? So people, so this study spiked another study that was about bread. We collected 20 individuals, we gave them just white bread for a week, we gave them two weeks

of washout, and then whole wheat bread made in traditional methods and that sort of thing. It was randomized and some people started with that brand, some people started with this bread. And we measured the microbiome along the way. And one take-home message from this study is that people's microbiome changed from this huge consumption of bread.

So usual bread consumption over this cohort, of the big cohort that we did in over this 20-people cohort, was about 10% of daily calories came from bread. In this study, we upped their dose to 25, 30% of their calories. And despite this change, this significant change in diet, their microbiolums didn't change. So you can see that their microbiomes remained mostly similar to their own microbeams and still dissimilar to other people, even though they changed their diet drastically. And how long were the effects of increasing the bread consumption? on the microbiome.

So we didn't see any, so we didn't see any effect that was, that we can consider consistent across the population. So there were some effects on some people and other effects on other people, but there was not a consistent change across people. And I think that depends on mostly the effect, It depends mostly on your initial microbiome composition, and we still need to study how certain things affect your microbiome, given your initial microbiome configuration.

Yeah. So are there any long-term studies being done now on microbiome and changes in microbiome? So Iran Segel's group, the group in which I conducted these studies, is doing a long-term study on, I think, 200 or 300 people. They follow them for six months or a year. Doing the same stuff. Doing similar stuff. And I think it's going to be a very exciting study with very exciting data. Yeah.

Because it's going to be, yeah, it's going to be beautiful data. Spoken like a true nerd. Yeah. So what have you changed your diet? Because you said you were part of like the beta test basically before the full-odd study happened. I did participate in the bread. study. Oh, you did? Okay. What have you changed about your diet or have you? Well, I'm not afraid of dietary fats anymore. Okay. That's one thing that, but it's not just this study that

convinced me, you know, it's reading the history that convinced me. So I can say in a few words, why fat got vilified. And so it all started in the 1950s where a guy named Ansel Keys, who in, I think he had a notion that, okay, something is clogging the artery, this thing is fat, and fat is probably the cause, dietary fat can probably cause this thing. And he supported his claim by looking at six countries. I have it somewhere in my notes. It was Japan, Italy, the UK, Canada, the U.K., Canada, the U.S.

in Australia. And he correlated the fat percentage out of the total calories consumed by person with cardiovascular disease. And he saw an almost perfect correlation

and that led him to get funding for studying other stuff. Now, there was data on 22 countries at that time. Including, for example, France, that had a huge amount of fat from calories, but not a huge amount of courage. That didn't make it into the study.

And that didn't make it into that. Into the, I mean, I don't know if it's a study or just, you know, something that prompted the study. But anyway, he got very famous. He was on the cover of Time magazine. And in 1961, the American Heart Association had a recommendation to decrease fat consumption. And, you know, this kept going. And in 1970s, there was a committee of the Senate, called them a Governing Committee,

that was a committee on nutrition and human needs or something like that. And it recommended reduction in fat. And what came out of this committee was what's known today as the food pyramid. Have you seen a food pyramid? Of course, yeah. So it usually has, it's like lined up with a lot of bread. The bread is a foundation. Yeah, and there's like a small portion of fat.

at the top. And this indeed caused Americans to, Americans in the world over, to stop consuming fat and start consuming more carbs. And you can see it. If you look at, and there's something called in the Hain study, it's the National Health and Nutritional Examination Survey. They publish something every few years. And if you look at their stats, you can see that people did consume more.

carbs and less fat. And just when they started consuming more carbs and less fat did this epidemic of obesity and diabetes begin. Yeah. Now, is this related? Maybe not just

this. Maybe there are probably other effects, including the rise in sugar and high fructose corn syrup and all that and additives to the diet. But that's probably one of the one of the effects. So

you know, just by looking at this experiment done on a billion people, and just by reading the history, I stop being afraid of dietary fats. Right. And you're fine now. And I'm fine, yeah. So you were mentioning the research that you're working up to right now, and I found it very interesting because you're thinking about the ocean, you're thinking about bacteria in the ocean.

And I found this interesting trend in that, like, you're just seemingly just trying to help people with your studies, your research. You know, the first one being like, help people lose weight, maintain health. The second one being possibly across the entire environment of carbon dioxide. But could you explain what you're interested in what you're working on in a new study? So, in a word, I'm trying to move from, you know, a more human-oriented view. Instead of looking at the human microbiome and trying to see how to feel. human health. I'm trying to look at the ocean or soil microbiome and see how it affects

global health. Microbes in the ocean, for example, are responsible for about 50% of the oxygen that you breathe. They recycle a lot of metabolites. They do a lot of these things. And what I'm trying to do is to apply, you know, my know-how both in microbiome analysis and in data science and to combine data that's publicly available on the ocean or samples that I will collect. with other data that's publicly available on a bunch of other things that you can collect from the ocean. And see where it gets me in maybe seeing which bacteria or which conditions can sequester more CO2 from the atmosphere to see how we can treat pollution in the ocean, acidification of the ocean that causes all the corals to die.

that's the sort of things that's the sort of questions I'm after right now but actually before that we're in the process of publishing a different study that still looks into the human microbiome

and this is a really this is a really interesting one to me because when we were finished with this big study of 800 900 people we next thought on our next thoughts were let's see if we can, you know, try to clarify what role the microbiome has in this.

Now, usually what studies do predominantly is that they either look at a whole bacterium to see if it's there. They just count the number of microbes that are in your gut. They do that by taking your stool. they produce DNA out of the microbes and they sequence it. They use a sequencing machine that breaks it down to small pieces and tells you each, and then you can map it and say for each piece, which bacteria it came from. Yeah.

Or which bacterial gene it came from. And what we thought is that this is interesting, but what we really want to see is something that's bigger than genes but smaller than microbes. smaller than a genome. So we want to see regions in the microbiome and how they change within people. So we produce an algorithm. I won't get into it right now,

but that accurately maps each of these small, tiny DNA fragments into a microbe. Some of them map to two microbes because bacteria are very promiscuous about sharing DNA. Yeah, I didn't realize that until I read the book. Man, that was crazy.

They transfer a lot of stuff. Yeah, it's really crazy. Yeah, yeah. And so we wrote a sort of algorithm that would, you know, help delineate it a little bit. And then we wrote another algorithm that would find regions in people's, in the genomes of people's microbes that were either deleted completely or that are present in a higher copy number. And we looked at these regions. We found about 5,000, 6,000 of these regions across the 900-something people that we looked at. We just compiled all the people from all the studies.

And these regions were prevalent across all microbes. They were all there. And we correlated these regions with metrics of health that we also collected in these studies, like BMI, weight, glycated hemoglobin, and these sort of things. And what we found is that we found many, many correlations, about 100 or more correlations, and one specific correlation that we dived into just, you know, to see what we can get from this region showed us a maybe or, you know, a proposed mechanistic connection between the microbound, and human health.

So this is like a, well, it's a tiny region in the microbe. It's probably 1% of the microbiome genome, of the microbes genome. And for people who have this region, people who have this region in the genome of their microbiome are about 15 pounds thinner than people who don't have this region. And this is, yeah, we were baffled. Yeah, wow. And now this is, and now the reason on why we thought that, you know, the interesting thing was non-microbes, not genes, but something in the middle,

is that we could look at this region and see what genes are there and try to compile them into some sort of, you know, a pathway, a metabolic pathway. So apparently what this region does is it takes up sugar or sugar alcohols from the gut. And in an energy favorable process for the bacteria, it turns it into be. buterate. Now, buterate is a compound that was shown to be very advantageous for the host because it reduces inflammation and it helps treat in mice, I think, supplementing their diet with buterate or adding buterate to their gut directly, really improved their metabolism, the glucose metabolism, and so on. So this is, of course, not proof. This is not causality or

anything and we're still set out to prove it or to show it some way. But it could be that these bacteria are enjoying a compound that's just lying there. They're producing buterate. And then the host is enjoying this buterate. And if this region doesn't exist, then the host is not enjoying this great buterate. So could you just take supplemental buterate? Maybe. I don't know if it will help you.

And it would probably taste awful. But for, I mean, for that extra 15 pounds, people will. I don't think so. I think that you would gain more from, you know, having a bacterium that metabolizes things that you eat and, you know, fiber that you eat into buterate than eating buterate directly. Okay. So another question could be, could you supplement people with this specific region and maybe? Maybe. Or some kind of crisper situation where you're at it. Yeah. So yeah, so let's go back to the ocean studies. What's what's coming up next for you? So coming up next, I'm going to look at

microbiota in the ocean and I'm going to look at many layers of data including oil refineries, oil wells and that sort of thing that are situated in the ocean. I'm going to try, for example, to look for genes that metabolize these things, these compounds or metabolize plastic in the plastic islands in the Pacific, for example. Yeah. I'm going to also add many other data layers that you can get from NASA, just to, you know, ask very basic and interesting questions in the ocean microbiome that, you know, that I'm interested in. And I remember just a random question.

So, no, you've been in New York, you said, for like a year after you were you in Israel your whole life? Most of the most part, yeah. Have you noticed any changes in your personal microbiome since moving to a new? country, new food, any weirdness, any good things? I haven't tested the micro bomb. I'm vegetarians, so I don't see why it would change so much. I'm not eating any, you know, food that is too processed. Yeah.

I've just heard these explanations of like, you know, going to, whatever, pick a country. So going to Israel as an American, you're like, your stomach is a little off. You know, you're on a plane. You're a little weird. But, yeah, it's been fine for you. That happens a lot. I think Eric Alm at MIT. if I'm not mistaken, had a study in which he followed his and the postdoc of his diet for,

a microbiome for a year, and they traveled a lot, and you can really see changes, differences in the microbiome when traveling. Yeah. But I think I'm not sure, you know, I'm trying to, I'm probably doing, I'm not doing good to this. It's okay. This is what I do all the time. But, yeah, but I think that it bounced back when. And they got back.

So you get this distribution of bacteria in your gut that even when you go someplace else, it changes in abundance, but it doesn't change in presence or absence. So it bounces back when you get to a different place. What about food poisoning? That could cause your microbiome to, you know, change a little bit. But also, I think, you know, it re-inoculates and it stabilizes. So we have a lot of things that stabilize our microbiome. You know, some people think that maybe the appendix is related to that. And maybe it stores microbiome for, you know, times of distress.

In that event. Yeah. Interesting. Yeah, food poisoning, your microbeams is swept out. And then the appendix re-inoculates your gut. Yeah, because I was traveling earlier this year and then got food poisoning like two hours before the flight back from London. But it was like a week or two. I just felt off and I couldn't explain it.

So I'm just like looking for cheap answers right now. That was London. Yeah. So earlier you mentioned doing an intervention in the 800-person study, the one published and sell. What does that actually mean? So what we wanted to do is to, you know, get like a proof of concept just to show that this predicted diets can actually work.

Yeah. And we wanted to see for ourselves. So we collected 26 participants. Most of them were pre-dihabetics. We had them go through a week of profiling like we did with the 800 plus 100 cohort. And then we had them go through a good week that was designed to reduce their blood glucose levels and a bad week that was designed to increase their blood glucose levels. These weeks were followed in random order.

They were double-blinded and they were isochloric. They had the same amount of calories for each day, for each breakfast. for each lunch and so on and so forth. And people actually didn't know if they were on the bad week and the good week. They were so, because they were based on people's, you know, meals that they usually eat. Yeah. And half of the people, about half of the people were predicted, the good and bad weeks were predicted using a predictor.

And since we didn't have anything to compare to, we created our own cold standard, which were two researchers, Orly in Daphna, who looked at people. glucose responses during the profiling week for half of the people. And just based on their responses, something that's not available to people usually, they divided their foods into good week and bad week. So this is something that can only be done for foods you've tested. And with a predictor, you can do it for any given food, right?

But we wanted something to compare to. And this worked perfectly. First of all, we had some foods that were on the bad diets of some people were on the good diets of other people. So for four people, for example, pizza was under bad diet and for two people it was on the good diet. Nice. So, you know, you want to hope that pizza is on a good diet. Yeah, you might get lucky. Like, based on this very small sample, you have like a 33% chance of getting lucky with pizza.

In the bad week, we saw huge glucose peaks for most people, some that if you were a physician, you would look at. that and you would say this person is a pre-diabetic. And these peaks completely normalized during the good week. And for some people, the difference between the good and the bad week were almost two or three folds in the responses to meals. And this was both for the gold standard and the predictor and worked the same. Wow. So we were very happy about that.

And since we followed the microbiomes of people every day, we could see consistent changes to the microbiome following a good diet or a bad diet. And these changes were consistent both within people and consistent with the literature showing that, you know, bacteria that increased during the good diet were considered beneficial. And bacteria that decreased during the good diet or increased during the bad diet were considered, you know, deleterious or harmful. That's great. So if I wanted to do this study on myself, basically, could I just buy a continuous glucose monitor?

and go for it. I guess I need some kind of way to measure my gut bio. Well, I guess you need the support of all the other people who participated in the study for the algorithm to work. Right now, the best option

is either collect 1,000 people or try, you know, like open your ears to see if there's any upcoming studies. Okay. Or, you know, go to day two, but I'm not trying to give them a promotion or anything.

You haven't tried it. I haven't, no. Okay, cool. All right, well, thanks so much for your time. Thank you. All right, thanks for listening. So, as always, you can find the transcript and the video at blog.combinator.com. And if you have a second, it would be awesome to give us a rating and review wherever you find your podcast.

See you next time.