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  • Translator: Rhonda Jacobs Reviewer: Leonardo Silva

  • This is me ten years ago.

  • I weighed 40 pounds more than today,

  • and like many people, I wanted to lose weight.

  • Like many people, I wanted to know what is the best diet for humans.

  • Many of us actually have an opinion about this question.

  • Some believe that a low fat, plant-based diet is the best.

  • Others, that a low-carb diet,

  • rich in protein and animal fat, is the best.

  • Others have opinions on how much sugar we should eat,

  • or how much salt, cholesterol, saturated fat, eggs or dairy products

  • we should have in our diet.

  • But the question of what the best diet is,

  • is a scientific one,

  • so there should be no room for opinions or beliefs.

  • If Diet A is really better than Diet B,

  • then a study that compares the two on enough people

  • should show that definitively.

  • No opinions, no beliefs, just hard data, right?

  • What is also clear is that if the best diet does exist,

  • then we haven't yet found it

  • because the incidence of diet-related disease

  • has increased dramatically in the past several decades.

  • Now, you might think it's because people don't listen to what we tell them.

  • But in fact, that's not true,

  • people actually generally do follow dietary guidelines.

  • But according to the Center for Disease Control,

  • if you live in the United States,

  • there's over a 70 percent chance that you're either overweight, diabetic

  • or have non-alcoholic fatty-liver disease.

  • And there's overwhelming evidence that diet and lifestyle

  • are major drivers of these conditions.

  • So why is it that after so much research,

  • we still don't have an answer to the seemingly simple question

  • of what is the best diet for humans?

  • What I'd like to propose to you today is that the reason we don't have an answer

  • is because we've been asking the wrong question.

  • And it's the wrong question because it assumes

  • that the best diet depends only on the food

  • and not on the person eating it.

  • But what if differences in our genetics, lifestyle, our gut bacteria

  • cause us to respond differently to food?

  • What if these differences explain why some diets work for some people

  • but not for others?

  • What if our nutrition needs to be personally tailored to our unique make-up?

  • This is exactly the question we set out to ask in our own research,

  • which I did with my colleague Eran Elinav

  • and several graduate students from the Weizmann Institute of Science.

  • To take a scientific approach,

  • we first searched for a metric of healthy nutrition that we should study.

  • Most studies examine weight loss or risk of heart disease after some diet.

  • But the problem is that these are affected by many factors unrelated to diet,

  • they take many weeks to change,

  • and in the end, you get a single measure of success.

  • And if it didn't work, well then it's very hard to understand why.

  • And so instead, we searched for a metric

  • that would still be relevant for weight management

  • and diet-related disease,

  • but one that we could also easily and accurately measure across many people.

  • And this led us to focus on blood glucose levels,

  • and more precisely, changes in blood glucose levels after a meal.

  • We call this a "meal glucose response."

  • Why is it important?

  • Well, because high glucose levels after a meal

  • promote both hunger and weight gain.

  • After we eat,

  • our body digests the carbohydrates in the food into simple sugars

  • and releases them into the bloodstream.

  • From there, with the help of insulin,

  • cells throughout our body remove the glucose from the blood

  • so that they can use it as a source of energy.

  • But insulin also signals our body to convert excess sugar into fat

  • and store it,

  • and that's a primary way by which we gain weight.

  • In addition, fast flow of glucose into the blood

  • often causes our body to release too much insulin,

  • which could lower our glucose levels to below baseline,

  • making us feel hungry and eat more.

  • Meal glucose responses are also very relevant for our health

  • because they've been shown to be risk factors for obesity,

  • diabetes, cardiovascular disease and other metabolic disorders.

  • A recent study that followed 2,000 people for over 30 years

  • found that higher meal glucose levels after meals

  • predict overall higher mortality.

  • Finally, and not least important, with recent technological advances,

  • we can now follow a person's glucose levels continuously

  • for an entire week.

  • And since the average person eats around 50 meals a week,

  • it allows us to measure glucose responses to 50 meals in just a single week.

  • Meal glucose responses also provide us

  • with a way to directly measure the effect of every single meal,

  • as opposed to common approaches

  • that only evaluate the effect of an overall diet.

  • Now, of course, there are many factors beyond glucose levels

  • that influence a healthy diet.

  • But this is a very important one,

  • and solving it can be a major step forward.

  • Luckily for us, we managed to convince 1,000 healthy people of this idea,

  • and we connected them to one of these small glucose sensors

  • and tracked their glucose levels continuously for an entire week.

  • And during that week, participants logged everything that they ate

  • on a mobile app that we developed.

  • And so that allowed us to measure glucose responses

  • to 50 different meals for each person

  • and around 50,000 different meals across all 1,000 participants,

  • making our study the largest one

  • that was ever done on this problem until today.

  • So what did we find?

  • Well, when we looked at averages, we saw trends.

  • For example, more carbohydrates in the meal

  • generally increase the response.

  • This is not so surprising.

  • Another, perhaps more surprising, trend

  • is that more fat in the meal generally decreased the response.

  • But - and this is the key finding of our study -

  • for every trend we found,

  • there were many people who were very different from it.

  • Basically, when the same person ate the same meal on different days,

  • the response was very similar.

  • But when different people ate the same meal,

  • the response was very different.

  • For example, white bread induced almost no effect

  • on the blood sugar levels of some people,

  • but in others, it induced huge spikes.

  • And the same was true for every single food we tested,

  • including rice, pizza, sushi and even chocolate.

  • For every food, there were some people who had low responses,

  • others who had medium responses,

  • and yet others that had very high responses.

  • It wasn't just about the food,

  • it was also about the person eating it.

  • So while averages and trends are informative,

  • for any given individual, they may not mean much.

  • Now, it wasn't just about how good the body was at handling sugar,

  • each person had different foods that spiked his levels.

  • Some people even had opposite responses.

  • For example, some people spiked for ice cream but not for rice.

  • But then others spiked for rice and not for ice cream.

  • In fact, more people spiked for rice than for ice cream.

  • Now, my wife is a clinical dietician,

  • so when I showed her this data, she was shocked,

  • because as a practitioner, she of course relies on general dietary guidelines,

  • and so one of the first things

  • that she tells her many newly diagnosed pre-diabetics

  • is to stop eating foods such as ice cream

  • and instead eat more complex carbohydrates such as brown rice.

  • So, as soon as she saw our data,

  • she of course realized that for most of her patients

  • not only does her dietary advice not help,

  • but in fact, it pushes them faster to develop the very same disease

  • that her advice was meant to prevent.

  • So these results of ours on such a large data set

  • convinced us that responses to food are personal,

  • and that diets that maintain normal blood glucose levels

  • must therefore be personally tailored to the individual.

  • They also show, in our view, why the current nutritional paradigm

  • that searches for that one best diet is inherently flawed.

  • The best diet for humans does not exist.

  • Our responses to food are personal,

  • so our dietary advice must also be personal.

  • And personalized dietary advice was our next challenge.

  • To tackle it, we measured many parameters across participants

  • that we thought may explain people's variability

  • in glucose response to meals.

  • And these included basic metrics and lifestyle factors like age, weight,

  • height and physical activity,

  • but also blood tests, medical background and food frequency questionnaires,

  • and also DNA sequencing of both the human genome

  • and the gut bacteria composition of each person.

  • Now, of these, the gut bacteria

  • was perhaps the most novel component that we examined.

  • For hundred of years, we know that bacteria live within our body.

  • But only with recent advances in DNA sequencing

  • could we begin to study them extensively.

  • And when we did, we found that this vast collection

  • of hundreds of different species that we each host,

  • collectively termed "our microbiome,"

  • has a major impact on our health and disease.

  • And what makes the microbiome even more exciting

  • is that unlike our genetics, we can also change it

  • even by simple means, such as changing what we eat.

  • Our bacteria help us digest some of the food that we eat,

  • and in turn, produce molecules that are taken by our own cells

  • and affect our physiology.

  • For example, in our own research, we studied artificial sweeteners,

  • which the vast majority of us consume on a daily basis

  • in various diet soda drinks and other products.

  • And we found that consumption of artificial sweeteners

  • alters the composition of the gut bacteria such that when transferred into mice

  • causes the mice to develop symptoms of diabetes.

  • And so this and several other studies

  • led us to ask whether the microbiome would also be important

  • for explaining people's glucose variability in response to meals.

  • And so we took this microbiome and other clinical data that we collected,

  • and we used advanced machine learning algorithms

  • to automatically search for rules

  • that predict personalized glucose responses to meals.

  • For example, one such rule could be

  • that if you're over 50, and you have a certain bacterial species,

  • then your response to a banana will be high.

  • The overall algorithm combined tens of thousands of such rules

  • that it automatically deduced from the data.

  • This approach is actually similar

  • to how websites like Amazon make book recommendations,

  • except that we applied it to how people respond to food.

  • And we could show that this algorithm could then take any person,

  • even people who are not part of our original study,

  • and predict the response to arbitrary meals with high accuracy.

  • So as a final step, we asked whether we can also use this algorithm

  • to design personalized diets that normalize blood glucose levels.

  • So we recruited and profiled new participants,

  • and we asked the algorithm to predict two diets for each person;

  • in one diet, which we called the "bad diet,"

  • we asked the algorithm to predict foods

  • for which that person would have high responses.

  • And in the other - "good diet" -

  • we asked it to predict foods for which that person would have low responses.

  • And each person then followed each diet for one week.

  • Now, by design, the diets had to be identical in calories.

  • In fact, all breakfasts, lunches and dinners

  • had the same calories on different days.

  • And it's also important to note

  • that each person received a different personalized diet,

  • and there were even some foods

  • that were given to some people on their good diet

  • but to others on their bad diet.

  • Now, to show you that these diets

  • are not the obvious ones you might think of,

  • here they are for one of our participants.

  • Now, take a moment and see if you can guess for yourself

  • which one the algorithm predicted to be the good diet

  • and which to be the bad diet for this particular participant.

  • And as you look at these, notice that each diet contains foods

  • that would not typically appear in standard diets.

  • And now for fun, let's play a quick guessing game,

  • and you all have to participate.

  • So, raise your hand if you think the diet on the right is the good one.

  • Okay. Now raise your hand if you think the diet on the left is the good one.

  • Okay, definitely we see nearly a 50/50 split here,

  • showing you that it's definitely not trivial to guess.

  • And I can tell you that for this participant,

  • the algorithm predicted the diet on the right,

  • the one with the ice cream, to be the good one.

  • And so now the only question is how good did these diets work.

  • And what I'll show you next is in our view

  • perhaps the most striking result that came out of our study.

  • So here are the continuous glucose levels

  • of this participant when following the bad diet.