#91 - David Zeevi

Hey, how's it going? This is Greg Cannon, and you're listening to Y Combinators podcast. Today's episode is with David Zeevey. David is a James S.

McDonald independent fellow at the Rockefeller University Center for Studies and 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 Zeevey.

Alright, here we go. So today we have David Zeevey 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 post-meal 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 4 out of 10 US adults are obese.

And just to clarify, obese means what? It means a BMI, a body mass index of over 30, which is actually not that bad, but it's still considered obese by the CDC. And that's 4 out of 10 US adults. Now, it was about 1 out of 10 in the 1980s.

It progressed massively. And this is based both on the World Health Organization and the CDC's Centers for Disease Control. One of the 10 Americans are diabetic, and this is an awful disease. It's a lot of suffering.

It's a lot of related complications. And it's a huge burden not only on people who have the disease, but also in healthcare systems. And I told you before, it's like $250 billion spent on diabetes and its related costs in 2012. 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 1980s, 1970s.

It was not as prevalent. And neither was obesity. And when we 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 four, five main changes.

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 fulfilling and everything.

And if you want to 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 lights. 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 we're thinking that if nutrition did cause this epidemic, what can restore health and nutrition? And when you try to ask what's health and nutrition, you can look at popular time magazine covers, for example, and we'll look at that.

And you can see that some of them say that saturated fats are bad for you. Some 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 passion 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, we're 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 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. It turns into fat.

And generally not just turns into fat, it also turns into it gives a boost to the natural mechanism of storage. It causes you to store more. 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 sensor that goes into your body.

I think it's probably quarter inch, something like that goes into your body. And that measures the glucose level in your interstatal fluid, that's the fluid 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. Weight is very noisy.

It's affected by what you drink, what you eat that morning. The time of day that you exercise whatever. And you can only measure it once in every long period of time because it's very noisy and it changes very slowly. So you can see 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 recommended to look at 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 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. We just collected a few people and we looked at their glucose responses and we find the foods that are good for everyone.

And you find the best diet in the world that we reduce glucose responses and that's it and we're done. But biology and the world is more complicated than 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.

So even foods are not categorically good or bad, it also depends on the person. And that was shown in very small scale. 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 going with, yeah, for now I mean, Chris, Chris 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, nutrition as, you know, 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 the study. And so you standardize 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 were a couple different breakfasts that you get them. Yeah, so the first thing we wanted to do in the study is to try to recapitulate the variability that we saw in the small scale studies. And so it's 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 fructose, which had 50 grams of available carbohydrates each.

And that was to be taken in the morning after the night's fast without exercising, without eating before that, only drinking, no exercising in the 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 in two different days was very similar to themselves. So we had a correlation there of 0.7 to 0.7, which is very good considering the noiseiness of people. And 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. Glucose doesn't go up after the meal, doesn't go rapidly down after that.

And other people who were not diabetics, who were not pedabatics or anything had huge spikes to the exact same loaf of white bread. And these people, you know, you couldn't tell the difference otherwise. And again, it's not just that one food is categorically worse 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 fat is somehow neutralizing it. Yeah.

We think it does. And then interestingly, it's not in the pursuit of the optimal diet. 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 who will respond in one way to bread and then differently to ice cream. We started exactly the exact same thing we saw with naturally occurring foods.

Some people have 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. Yeah.

And so then, 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 one when they ate.

Maybe I should say a few things about what we collected in the study. So we recruited people, about 800 people. We had them go through a process in which they gave us blood. They filled in both frequency questionnaires and medical questionnaires.

We had them connected to a continuous glucose monitor, as I told you before. That's 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, slapsticks and so on. And the exact amounts of every food in there.

So we also gave them weights to weigh their escape. The way their food when they eat it. Oh, man, you know, we have some leeway to eat the restaurant. Did the stool sample element with that in the original spec?

And we also collected stool samples which we analyzed to see the microbiome in various levels, both which microbes are in there. Which genes of the microbes are in there. And if I talk about the microbiome, it's an amazing ecosystem. We know with thousands of species about as many cells as in the human body.

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. They have about 3 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 delterious effect on our health, we can maybe improve people's health altogether. So you should both explain like 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 a immunocelular creature, fungi, viruses, and small worms or whatever that we have in and around our body that are not of human origin. That's the microbiome, definitely. And all of it's associated genes and genetic material and so on and so forth. So that's what usually people mean when they say microbiome.

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 and liberated from 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, they're caused atherosclerosis, it causes your arteries to 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 show 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 monitoring what people are eating and their stool samples, you can recompose what their gut microbiome is.

Yeah. Right. Well, you have to measure the gut microbiome as well. But 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, 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 liked the most from 2013.

They took pairs of twins that were discordant for obesity. One twin obese and one twin was obese. These are mice? No, these are people.

And they transplanted their microbiome into germ-free mice. Drum-free mice are mice that are born and raised in sterile conditions and they don't have a microbiome on their own. And these mice were transplanted. These microbombs of twins, one will be some one mean, many pairs of twins.

And interestingly, the mice that received the microbes of the obese twin became obese and the mice received the microbes of the lean twin remained lean after eating the same food and doing the same things. And that also showed us that 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 rather than a 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 gut microbiome and 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 the microbiome.

So, once you get to know the exact 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. So, you want to let your kid eat dirt, I guess, or have a dog?

And 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 affect or to exert an effect on the microbiome. You can take antibiotics or very specific antibiotics. You can try and replace this micro by ingesting some sort of probiotic or some sort of microbe that, you know, will occupy the same niche as this microbe just to push it out and, you know, 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.

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 studies before this podcast and I read the book, I Contain Multitudes, but, you know, there's 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 my own supplement.

In large part, have you found that stuff to be effective or is it just kind of bogus? I'm not sure I can answer that with confidence. Yeah, right. Okay.

Because yeah, I guess specifically that the fecal transplant stuff, I think, is the most eye-catching. Yeah, that's the dark side of microbiracines. Yeah, yeah, exactly. But it has been proven effective for some percentage of people, right?

So, so fecal transplants have been used. Their claim to fame is by treating cool-freedom difficillin infections. That's a type of infection that takes over your gut. It's a certain material that takes over your gut.

It pushes everything else out. Now, when you try to treat it with antibiotics, it usually spores. It's great 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-diff 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 stool 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. But people are making big money out of it. So I heard that companies collecting stool out of professional athletes and I felt theirs and they had 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. Exactly. So we have a couple questions people submitted because they're very curious about this.

So Elizabeth Irons from Science Exchange had a couple questions. One of which was, does post-prandial 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-prandial glucose response is associated with changes in weight just because of mechanism I told you about that.

When we eat things that spike our blood glucose, we cause insulin secretion, which 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 that later. But we didn't do anything that's longer term and I think that in order to see differences in weight you need to follow people for months, if not years. 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 prediabetic. Exactly. Which is another huge concern.

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 Two. 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 whether you're doing a number or not in touch with them or anything. But I think 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 your 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. Well, I think it's a good time as I need to say that it's not just me.

Yeah. 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 Dalcarim 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 contact him.

He's a very good scientist and then the supervision of a runsegal. And with the fabulous Idina Weinberger who handled the wet lab and all the samples and everything and made protocols out of where there were none. And so it was an amazing group of tens of people and obviously if I try to thank everyone else for that. They're on paper.

But yeah, please download the paper and see yourself. And there's a cool video you guys mean, but yeah, keep going. Yeah. So we, yeah, you're 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 VMI and to glycated immunogloban, 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 problems. And just to give you an idea of what people used before we can run to do that. They usually, so usually when you think of 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 manual, you get a correlation and 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's 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, you know, try and do something better. So we built an algorithm on these 800 people we collected. We used boosted decision trees on about, we didn't predict people. We predict the meals.

We predict about 40, more than 45,000 meals. We trained on a subset of the 800 people and tested a prediction on the left out court. And we made sure that persons' 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 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 times, sleep times, and so on. And blood parameters, questionnaires.

Meaning blood type? No, not blood type, but for example cholesterol. Okay. Or like in the Moogloban and these sort of things.

So overall, we had 137 features after feature selection on 40 something, 1000 meals, and we ran this prediction. 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 in two different days. 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 them.

Meaning you took a stool sample. We took a stool sample. We hadn't go through a week of glucose monitoring. Yep.

We ignored the 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 R of 0.7 again, which was great. So that means that this predictor is generalizable at least for the Israeli public.

I was wondering that having not been to Israel, is there a large difference in the types of foods? I don't know. You really get a track on the commas and that kind of stuff. So yeah, people ate hummus.

But people also ate, I think in Israel people ate that Western diet. Maybe fortified with more vegetables. One thing I can tell about New York is that it's harder to find fresh vegetables here, even though there's the fruit cards that are really nice. Still.

Yeah, not quite so much. Were there dietary suggestions that you took away from this? Or did you kind of just step back? For instance, you mentioned fat, right?

I know this is now a thing that's much more common people doing. 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 kind of beats the purpose of what we found that people are very different. And anything universal, any universal dietary recommendation would be sub-optimal at best. So there were no foods where you consistently were good?

No. Not for people. 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 increased the amount of bread someone consumed over them. I think you said it from 15 to 30%. 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 Abra and 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, over the big cohort that we did over this 20 people cohort, 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 microbombs didn't change. So you can see that their microbombs remained mostly similar to their own microbombs and still this similar to other people, even though they changed their diets drastically.

And how long were the effects of increasing the bread consumption on the microbiome? So we didn't see any effect that was that we can consider that. 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, depends mostly on your initial microbiome composition. And we still need to study how certain things affect your microbiome, given your initial microbiome configuration. So are there any long-term studies being done now on microbiome and changes in microbiome? So around cycles group, the group in which I conducted these studies is doing a long-term study on, I think, 200 or 1,300 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 beautiful data. Spoken like a true nerd. So what have you changed your diet? Because you said you were part of the beta test before the full-on 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. It's reading the history that convinced me.

So I can say in a few words why fat got vilified. So it all started in the 1950s where a guy named Ansel Keys, who in, I think he had a notion that something is clogging the artery, this thing is fat. And fat is probably the cause of 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 US and Australia. And he correlated the fat percentage out of the total calories consumed by a 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... That's making it into the study? And that didn't make it into that study. I don't know if it's a study or just 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 the McGovern Committee that was the Committee on Nutrition and Human Needs, or something like that.

And it recommended reduction of fat. And what came out of this committee was what's known today as the Food Pyramid. Have you seen the Food Pyramid? So it usually has, it's lined up with a lot of bread.

The bread is a foundation. And it'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 the NHANES 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?

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 down on a billion people and just by reading the history, I, I still being afraid of dietary fats. Right. And you're fine. And I'm fine, yeah.

So I, you were mentioning the research that you're, you're working up to right now. And I found it very interesting because you're, you're thinking about the ocean. You're thinking about the bacteria in the ocean. And I found this interesting trend in that, like you're just seemingly just trying to help people.

You're studying 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 than you study? So, in a word, I'm trying to move from, you know, more human oriented view. Instead of looking at the human microbiome and trying to see how it affects 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 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, you know, maybe seeing which material, which conditions can sequester more CO2 from the atmosphere to see how we can treat pollution in the ocean. Acidification of the ocean causes all the corals that that's the sort of things that 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 interesting one to me, because when we were finished with this big study of 800 and 900 people, we next start on our next thoughts.

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 each for each piece which bacteria came from or which material 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 micro, some of the maps to two microbes because they're very promiscuous about sharing DNA. Yeah, I didn't realize that until I read the book. And that was crazy. They transfer a lot of stuff.

Yeah, that's really crazy. And so we wrote an algorithm that would help delineate a little bit. And then we wrote another algorithm that would find regions 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 and something people that we looked at. We just compiled a lot of people from all the studies. And these regions were prevalent across all microbes. They were all there.

And we correlate these regions with metrics of health that we also collected in these studies like BMI, weight, like immunoglobine, and these sort of things. And what we found is that we found many many correlations, about hundreds of more correlations. And one specific correlation that we dive into just to see what we can get from this region showed us a maybe or a proposed mechanistic connection between the microbiome and human health. So this is like a, well, it's a tiny region.

The microbe is probably 1% of the microbiome genome of the microbes genome. And for 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. And now the reason on why we thought that the interesting thing was not microbes and 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, 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 into butyrate. Now butyrate is a compound that was shown to be very advantageous for the host because reduces inflammation and it helps treat, in mice, I think, supplementing their diet with butyrate or adding butyrate to their gut directly really improve their metabolism, the glucose metabolism and so on. So this was 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 butyrate. And then the host is enjoying this butyrate. And if this region doesn't exist, then the host is not enjoying this great butyrate.

So could you just take supplemental butyrate? 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, probably do anything. I don't think so. I think that you would gain more from having a bacterium that metabolizes things that you eat and fiber that you eat into butyrate than eating butyrate directly. So another question could be could you supplement people with this specific region and maybe?

Maybe. Or some kind of CRISPR situation where you had it. So yeah, so let's go back to the ocean studies. What's coming up next for you?

So coming up next, I'm going to look at microbola 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 compounds or metabolize plastic in the plastic islands in the Pacific, for example. I'm going to also add many other data layers that you can get from NASA just to ask very basic and interesting questions in the ocean microbiome that I'm interested in.

I have just a random question. So now you've been in New York, you said for like a year after were you in Israel your whole life? Most of the most part. Have you noticed any changes in your personal microbiome since moving to a new country, a new food, any weirdness?

I haven't tested the microbiome. I'm vegetarian so I don't see why it would change so much. I'm not eating any food that is due processed. I've just heard these explanations of like going to whatever pick a country.

So going to Israel is in America and you're like your stomach is a little off, you're not in your own way, but it's been fine. 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 microbiome. They traveled a lot and you can really see changes, differences in the microbiome when traveling.

I'm not sure I'm trying to, I'm probably not doing good to this. It's okay. This is what I do. But I think that it bounced back when it got back.

You get this distribution of a theory in your gut that even when you go someplace else, it changes in abundance, but it doesn't change in presence perhaps. So it bounces back when you get to a different place. What about food poisoning? That could cause your microbiome to change a little bit.

But also I think it reinoculates in its stabilizers. So we have a lot of things that stabilize our microbiome. Some people think that maybe the appendix is related to that. And maybe it stores microbiome for times of distress.

The net event, yeah, interesting. Yeah, food poisoning your microbiome is swept out and then the appendix reinoculates. 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 looking for cheap answers for now. That was London. Yeah.

So earlier you mentioned doing an intervention in the 800 person study. The one published in Cell. What does that actually mean? So what we wanted to do is to get a proof of concept just to show that this predicted diets can actually work.

And we wanted to see for ourselves. So we collected 26 participants, most of them were pre-diabetics. 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.

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 tonch and so on and so forth. And people actually didn't know if they were on the bad week on the good week.

They were based on meals that they usually eat. And half of the people, about half of the people were predicted that 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, Orlean Dafna, who looked at people's 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. 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 on the bad diet and for two people, it was on the good diet. So you want to hope that pizza is unlucky. Based on this very small sample, you have a 33% chance of it like you were a pizza. In the bad week, we saw huge glucose peaks for most people.

Some that if you were a physician, you would look at them and say this person's a pedabatic. And these peaks completely normalized during the good week. And for some people, the difference between the good and 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 it worked the same.

So we were very happy about that. And since we followed the microbombs of people every day, you could see consistent changes to the microbombs following a good diet or bad diet. And these changes were consistent both within people and consistent with literature showing that, you know, with your diet, we're considered beneficial. And with your decrease during the good diet or increase during the bad diet, we're considered delterious or harmful.

That's great. So if I wanted to do the 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. 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 a thousand people or try, you know, like open your ears to see if there's any upcoming studies. Or, you know, go to day two, but I'm not trying to give them a promotion or anything. You haven't tried it yet. I haven't.

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.Y 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. See you next time.