David Zeevi on Personalized Nutrition Based on Your Gut Microbiome

So today, we have David CV on the podcasts, 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 successively lower post-meal blood glucose and 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. Hmm. So, right now, 4 out of 10 US adults are obese. You know, and just to clarify, obese means what? It means a BMI, a body mass index over 30, which is actually not that bad, but it's still considered obese by the CDC, and that's for 10 years adults now. It was about 1 out of 10 in the 1980s, so it progressed massively. This is based both on the World Health Organization and the CDC. One in 10 Americans 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 on healthcare systems. I told you before it's like 250 billion dollars spent on diabetes and its related costs in 2012 annual. So, yeah, 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 or 70s. It was not as prevalent.

When we, you know, came to look at that, we just tried to figure out what are the changes, what are the major changes that were done to our nutrition over the last 30, 40, 50 years or so, and we came up with, you know, four or 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, you know, give food a taste without fat, you usually add sugar. So sugar mainly took the place of fat in our diet.

There's a graph I sometimes show in lectures where you see the sugar consumption per capita per year since the mass, since, I don't know, I think 1700 till today. Mm-hmm. And the crazy fact is that the annual consumption in 1700 is the daily consumption today. So we couldn't have evolved to, you know, undertake to treat this amount of sugar that's going into our system. Yeah. 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, you know, industrialized. And the last thing is that mealtimes changed. We work in shifts, we have electric light, and that changes when we eat and our daily routine.

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 were thinking that if nutrition causes this epidemic, what can restore health? 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 say that fats are good for you, and some say that you should be vegetarian; some say that you should even follow the Atkins diet. And there's a famous one which I really like from 1972 that says, "Eating may not be good for you."

But we thought as scientists that, you know, 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, you know, 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, you know, 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 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. Mm-hmm. And it leads to weight gain because it is transferred to fat, right? It turns into fat. Yeah, instant effect. And it generally not just turns into fat, it also, you know, contributes to the natural mechanism of storage. It causes you to store more.

Mmm. 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 conveniently has a tiny needle or a tiny, you know, sensor that goes into your body. I think it's like probably a quarter inch, something like that. It goes into your body, and that measures the glucose levels in your interstitial fluid—that's the fluid between your cells. Mm-hmm. It’s highly correlated with the glucose in your blood, so you get a very accurate measurement of the glucose in your blood or a 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 affected by what you drink, what you eat that morning, the time of day that you exercise, whatever. You can only measure it once in every long period of time because, you know, it's very noisy, and because it changes very slowly. 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 step on the scales every day, but it's usually, you know, recommended to look at it every week or so.

Yeah. If you look at blood glucose, you can measure it every for every meal, so you can just see, get a fast feedback on each and every meal that you ate. Mm-hmm. 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 that healthy phenotype, what you need to do is to probably reduce the glucose responses. Mmm.

That sounded easy, you know, okay? We just collect 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 know, that's it, and we're done.

Yeah. But biology and, you know, the world are 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 their responses to glucose. Right? So, we 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 studies.

So, right, we said, okay, so let's think of what can affect these glucose measures. And we came up with three main, you know, 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 were wrong with for now. I mean, CRISPR is gonna change all that. The second is lifestyle, which we all agree should be, you know, 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.

Mm-hmm. Which we found to be associated with many diseases, many disorders. If we have time, I can tell you a little bit about that.

Yeah. And so we wanted to create a study that combines all these factors: nutrition, nutrition as you know, target genetics, or a proxy for genetics, lifestyle, the microbiome, to predict what's good for people. And that's how we came up with the study.

And so, you standardized the study, so it was something like 800 people. Mm-hmm. And the study was standardized by giving them the same breakfast over the course of a week, right?

Well, there are a couple different breakfasts 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 meals that contained either 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 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, and sorry, one person eating the same meal on two different days was very similar to themselves. Mm-hmm. So we had a correlation of about 0.7 to 0.77, which is very good, you know, considering the noisiness of people. But across people, 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 to vary no flat responses to glucose; the glucose doesn’t go down rapidly after the meal; it doesn’t go rapidly down after that. Yeah. And other people, who were not pre-diabetics or anything, had huge spikes from 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 worse than other foods. Some people responded; they had the highest response to glucose, while some people had the highest response to bread, and a minority had the highest response to bread and butter.

Mm-hmm. Actually, fewer people had a high response to bread and butter than to bread alone, so the butter is sort of fat neutralizing it.

Yeah. Yeah, we think of this, right? And then, interestingly, it's not like 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, and 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.

Yeah, we saw 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.

Mm-hmm. 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—maturity. So we recruited 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'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 when they ate, slept, exercised, and so on, and the exact amounts of every food.

That's what we also gave them—weights, too, you know.

Oh, you give them a scale?

Their scale?

Yeah, the weight of their food when they go to eat at home. And, you know, we gave him some leeway to eat out at the restaurant.

Did the stool sample element—was that in the original?

Yeah, and also, yeah, 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. If I can talk later about the microbiome—it’s an amazing ecosystem.

We know it's with thousands of species, about as many cells as in the human body—all in your gut—weights 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, but—and these microbes have 150 times more genes than are in the human genome. There are about 3 million genes, so they have huge metabolic potential.

And this bit about potential can be harnessed—or can be accounted for when we're looking at what people are eating. 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, 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 unicellular creature—fungi, viruses, and small worms, or whatever, that we have in and around our body that are not of human origin, right? That's the microbiome.

Absolutely. 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 microbiome. And as I said before, it's huge. There are a lot of cells, there's a lot of diversity there, there are a lot of genes, and more and more relations are being found between this gut microbiome and many disorders and different outcomes.

So, I can name a few examples. One of my favorite microbiome studies was done in Stanley Hazen's group in the Cleveland Clinic. They looked at carnitine, which is a compound that is found in red meat. This carnitine 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 reduction in reverse cholesterol transport and bile acid synthesis.

Mm-hmm. And these are long words. But what it eventually means is that it causes atherosclerosis—these two processes, if they're reduced, cause atherosclerosis. It causes your arteries to clog. And interestingly, if you remove these specific microbes that metabolize carnitine 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 the 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 Chen 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.

Mm-hmm. And that showed us that, you know, the microbes can reflect our health status.

So, and 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 yeah, you can get some idea on their health status and what they're eating from the chemical bond.

And it's not only that microbes can affect your health or reflect your health, they can also—sorry, it's not only the 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 was obese and one twin was lean.

These are mice?

No, no, 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 their own gut microbiome. Their own, and these mice were transplanted their 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 mice that received the microbes of the lean twin remained lean after eating the same food and, you know, doing the same things. And that also showed us that, you know, it's pretty clear.

And so, like the maybe the logical extension in the sense that every human wants things to be black or white, were you often asked, like, "Okay, is there an ideal gut microbiome?" Because, like, rather than a diet, maybe we just do the gut microbiome, and then we do the transfer, and everyone has the same one.

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 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.

Mm-hmm. 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.

Okay, 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 effect or to do, you know, exert an effect on the gut microbiome. You take antibiotics or very specific antibiotics, you can try and replace this microbe 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 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 Contain 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 microbiome supplement.” In large part, do you have—have you found that stuff to be effective, or is it just kind of bogus?

Man, that's very convincing.

Yeah, was that with confidence?

Yeah, right?

Okay.

Because, yeah, I guess specifically that the fecal transplant stuff I think is the most eye-catching.

Yeah, definitely. It's the dark side of it.

Yeah, exactly. But it has been proven effective for some percentage of people, right?

So, fecal transplants have been used—their claim to fame is by treating Clostridium 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. Yeah, and this thing just takes over, you know, all the gut spaces that were left by the other microbes. So, usually, when you have a C. diff infection, it's predominantly the most abundant microbe in your gut.

Mmm.

And it causes extreme diarrhea and these sort of things. Now, when you treat these patients with antibiotics, they say 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.

Mm-hmm. 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 gonna work, right?

Okay, but people are making big money out of it. I heard that companies are collecting stool out of professional athletes and I felt there's NBA players and so on to transplant it for 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 of questions people submitted because they're very curious about this. So, Elizabeth Irons from a Science exchange had a couple questions, one of which was, does postprandial glucose response, which is the response that you're measuring with the glucose monitor, does it track with weight regulation? I.e., can you use—you guys use their tests to determine what individual people should eat or not eat to lose weight?

So theoretically, postprandial 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.

Mm-hmm.

Even the intervention that we did—that 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 was 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, you know, 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 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 their 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.

Mm-hmm.

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 not 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 for me 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—and this is Doug Graham, who’s gonna start a faculty position in Columbia in the fall.

So if you're a potential PhD candidate or a postdoc listening to this podcast, you can contact him. Yeah, he’s a very good scientist, and under the supervision of Ron Siegel 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 none. So it was an amazing group of tens of people and a lot of, you know—and obviously, if I try to thank everyone else, I forget there are. But yeah, please download the paper and see for yourself, and there’s a cool video I made, but yeah, keep going.

Yeah, so we—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 hemoglobin, 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 glucose responses, you think of counting carbs.

Mm-hmm.

So you just take the correlation between the carbs in the meal, and if you think the carbs in the meal and the postprandial glucose response of the meal, you get a correlation of about 0.38, which is not a very good correlation.

It's significant because, you know, it’s a lot of points.

Mm-hmm.

But for example, there are meals in which there is a huge amount of carbs but not a high response to glucose, and we also happen to be, yeah, so we were set out to fix that, 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 predicted meals. We predicted about more than 45,000 meals.

We trained on a subset of the 800 people because the prediction on the left-out cohort—and we made sure that a person's meals were not both in training and test, so this thing would be more generalizable.

In terms of features, we took the microbiome composition of people, including the microbiome genes and microbial 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.

We recorded features, meal times, sleep times, and so on, and blood parameters, questionnaires meaning, slide type.

Yeah, that’s certainly—no, not blood type, but for example, cholesterol.

Okay, in the blood.

Yep, or like a new globin and these sort of things. So overall, we had a hundred and thirty-seven features after feature selection on forty-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 point seven 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 them, meaning you took a stool sample.

We took a stool sample; we had them 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, 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, 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, I think in Israel people eat the Western diet, very standard, maybe fortified with more vegetables.

Yeah. One thing I can tell about New York is that it's harder to find fresh vegetables here.

Yeah.

I mean, though there are the fruit carts—that's really nice—but, you know, still, yeah.

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 a thing that's much more common; people are 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, you know, people are very different, and anything universal—a universal dietary recommendation would be suboptimal at best.

So there weren’t—there were no foods where you consistently found they were good?

No, no.

That’s great. 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, what did you say, from 15% to 30%?

So people—so this study expected another study that was, yeah, about bread. We collected twenty 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 the white bread; 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 course of—the big horde that we did over this 20 people cohort was about 10% of daily calories came from bread.

Mm-hmm.

In this study, we up their dose to 25-30% of their calories, and despite this change—this significant change in diet—their microbiomes didn’t change. So you can see that their microbiomes remained mostly similar to their own microbiomes 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—that we can consider consistent across the population.

Mm-hmm.

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 on your initial microbiome composition, and, you know, 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 the microbiome and changes in the microbiome?

So Iran's cycle'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 gonna be a very exciting study with very exciting data.

Yeah, 'cause it's gonna be—it’s gonna be a beautiful data.

I spoken like a true nerd.

Yeah. So, what have you changed in your diet? Because you said you were part of, like, the beta test basically before the full-on study happens.

I did—I did participate in the bread study, so, oh, you did?

Okay.

What have you changed about your diet?

Are you happy?

Well, I'm not afraid of dietary fats anymore.

Okay, that's one thing.

But it's not just this study that convinced me, you know. It's reading in 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 Ancel Keys, who, I think he had a notion that, okay, something is clogging the arteries. This thing is fat, and fat is probably the cause.

Dietary fat can probably cause this thing, and he supported this 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 cardiovascular disease, and that didn’t make it into that—what do you call it? That scary study.

I mean, I don't know if it's a—it’s a study or just, you know, something that prompted this 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 the 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 his recommendation was to reduce fat.

And what came out of this committee is what's known today as the food pyramid. Have you seen a food pyramid?

Yeah, so it usually has—it’s like lined up with a lot of bread, foundationally, and there’s like a small portion of fat at the top.

So, and this indeed caused Americans and 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, and it’s the National Health and Nutrition Examination Survey.

They published 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 additives to diet. But that’s probably 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 stopped being afraid of dietary fats.

Right. And you're fine?

Yeah, 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.

Mm-hmm.

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 to 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 the new study?

So in a word, trying to move from a, 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 percent 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, and you know, maybe seeing which bacteria, which conditions can sequester more CO2 from the atmosphere to see how we can treat pollution in the ocean, the acidification of the ocean that causes all the corals to die.

Mmm. That's the sort of thing—these are 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 interesting one to me, because when we were finished with this big study of 800, 900 people, we next—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 ever, and they sequence it. They use the sequencing machine that breaks it down to small pieces and tells you each and—you can map it and say each for each piece which bacteria came from, 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 produced 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, and that was crazy.

Yeah, they transfer a lot of stuff.

Yeah. That’s really crazy.

Yeah, yeah. And so we wrote a sort of algorithm that would, you know, help delineate a little bit, and then we ran 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 five thousand, six thousand of these regions across the nine hundred and something people that we looked at. We just, you know, compiled all the people from all the studies, and these regions were, you know, prevalent across all microbes.

They were very—very, very—they were all there, and we correlated these regions with, you know, metrics of health that we also collected in these studies, like BMI, weights, glycated hemoglobin, and these sort of things.

And what we found is that we found many, many correlations—about a hundred 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 maybe—or, you know, proposed a mechanistic connection between the microbiome and human health.

So this is like—well, it's a tiny region; the microbe is probably one percent of the microbiome genome of the microbe's 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 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 tried to compile them into some sort of, you know, a pathway—a metabolic pathway.

So apparently what this region does is it takes—it takes up sugar or sugar alcohols from the gut and in an energy-favorable process for the bacteria, it turns it into butyrate.

Now butyrate 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 butyrate or adding butyrate 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 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'll help you.

Uh, and it would probably taste awful.

But for—I mean, if you're that extra 15 pounds, people do it.

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 butyrate.

Okay.

I'm eating butyrate directly.

Mmm.

So another question could be, could you supplement people with this specific region and maybe, or some kind of CRISPR situation where you added it?

Yeah.

So, let’s go back to the ocean side. What’s coming up next for you?

So coming up next, I'm gonna look at microbiota in the ocean, and I'm gonna look at many layers of data, including oil refineries, oil wells, and that sort of thing that are situated in the ocean.

I'm gonna try, for example, to look for genes that metabolize these compounds—or metabolize plastic—and the plastic islands in the Pacific, for example.

Yeah, I'm gonna 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 just a random question: so you’ve been in New York, you said, for like a year. After you were in Israel your whole life?

Most of them.

You’re smart.

Yeah.

Um, have you noticed any changes in your personal microbiome since moving to a new country, a new food? Any weirdness?

I haven't. I haven't tested the microbiome.

Vegetarians?

I don’t see where it would change so much; I’m not eating any, you know, food that is too processed, so...

Yeah, I mean, I’ve just heard these explanations of, like, you know, going to whatever, pick-up countries, so going to Israel as an American, and you're like your stomach is a little off, you know? You’re a little weird.

But yeah, it’s been fine.

That happens a lot. I think Eric Almond, at MIT if I'm not mistaken, had a study in which he followed his—all his food, the postdoc of his, diet for a year, and they traveled a lot, and you can really see changes and differences in the microbiome when traveling.

Yeah, but I think—I'm not sure. I’m trying to—I’m probably doing—I’m not doing good.

This is a good call, yeah.

But I think that it bounced back when they got back. You get this—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, and so it bounces back when you get to a different place.

What about food poisoning? That could cause your microbiome to, you know, really change a little bit. But also I think, you know, after a while, 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 sweeps out your microbiome, and then the appendix re-nactuates.

Yeah, yeah. Because I was traveling earlier this year and then got food poisoning like two hours before the flight back from London and, 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, right?

That was London!

Yeah, so earlier you mentioned doing an intervention in the 800-person study, the one published, and so 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-diabetics; we haven’t go through a week of profiling like we did with the 800-plus-100 cohort.

And then we haven’t 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. You see, these weeks were followed in random order, they were double blinded, and they were isocaloric; 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—kind of knew 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 that the good and bad weeks were predicted using a predictor.

Mm-hmm.

And since we didn’t have anything to compare to, we created our own gold standard, which were two researchers—Orly and Daphna—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, you know, 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 in the bad diets of some people that were on the good diets of other people. So for, for example, pizza was on the bad diet for two people; it was on the good diet.

Nice!

So, you know, you wanna hope that because, yeah, you might get lucky, like based on this very small sample, yeah, like a 33% chance of getting lucky with 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, and you’d say this person is a pre-diabetic, and these peaks completely normalized during the good week.

And for some people, the difference between in a bad week were almost two or three folds in the responses to meals.

Mm-hmm.

And this was both for the gold standard and the predictor, and it worked the same.

Wow! So we were very happy about that.

And since we followed the microbiomes of people every day, I could see consistent changes to the microbiome following a good diet or a bad diet, and these changes were consistent, both through 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 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. Well, I guess you need the support of all the other people who participated in the study, you know, for the algorithm to work, right now.

The best option is either collect a thousand people or try to, you know, like, open your ears to see if there are any upcoming studies, or, you know, go to Day 2, but I’m not trying to give them a promotion or anything.

You haven't tried it yet?

I haven't, no.

Okay, cool!

All right, well, thanks so much for your time.

Thank you!