Subtitles section Play video Print subtitles 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.