Individual Responses to the Same Food

When you do this study not on ten but on 1,000 people – like we have done – you will see that individual differences in the glycaemic responses can be huge. For example, when we gave an identical amount of glucose or bread to 1,000 people, the average was exactly the well-known glycaemic response of glucose or bread. However, while some people eat sugar and their blood sugar levels do not spike at all, they rise to diabetic levels in others.

Therefore, the average response to certain kinds of food does not predict the individual response. This variability, which we have seen in several different kinds of food, really limits the applicability of the glycaemic index as an average. This tells us that the concept of fol-lowing a one-size-fits-all diet cannot be effective. So, our goal is to develop a personalised approach to dieting.

In contrast to classic diets, such as those mentioned above, our approach could lead to tailor-made diets that actually work. The basis is a large-scale study which we performed as a strategic collaboration with my friend and colleague Eran Segal, a mathematician heading a large group at the Weizmann Institute, which we started around four years ago. For this study, we recruited participants online. Even though we never publicised it, by the end of the study close to 20,000 people were on the waiting list. Once a candidate was admitted to participate, we asked them to let us perform tests on them for a week. First, they filled out a large body of questionnaires on their medical backgrounds, their family history, their dietary preferences and so forth. We analysed their host genetics and performed an array of blood tests. And, of course, we took stool samples that were deeply analysed (in-action sequencing) for composi-tion and funccomposi-tion of the participants’ gut microbiomes. Finally, we connected each participant to a glucose monitor that sits on the skin and takes samples of blood sugar levels every five minutes.

During this test week and the following week, the ‘follow-up week’, the monitor would collect a total of 2,000 blood sugar measurements. In addition, we gave each participant a smartphone app specifically designed for this project. They used it to tell us everything they did during the follow-up week: what they were eating, how much of it, when they were wak-ing up, when they were gowak-ing to sleep – as much information as possible.

After the conclusion of the follow-up week, we created a ‘mirror image’ for each partici-pant, an overview of their habits based on their smartphone diary entries. We also gave people a very detailed analysis of their gut microbial frames. Most participants were really surprised by their own mirror image, a testament to the fact that we experience our own bodies very dif-ferently compared to external observers. And we were pleasantly surprised to see how quickly people got emotionally attached to their gut microbes.

Most importantly, a couple of weeks after the completion of the blood sugar measure-ments and app reports, a large, talented, and smart group of computational biologists – stu-dents and post-docs in both of our groups – took this unprecedented amount of big data and devised a machine learning algorithm that trained itself to predict the participants’ individual physiological responses to any of the foods they had been exposed to. So far, we have pro-filed over 1,000 people, studied individual responses to over 50,000 meals and analysed over

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two million blood glucose samples. In addition, we sequenced over ten billion meta-genome reads. This is the largest study of its kind to ever have been performed.

If we look at the statistics of this one cohort – the Israeli cohort – we see how their profile resembles what we find in populations of developed countries all over world: 50 % of the people are overweight, 20 % are obese, and 25 % are pre-diabetic. Pre-diabetic individuals form a very important sub-set of society: they already feature disturbances in their blood sugar maintenance but they are not yet diabetic. However, they have a 70 % chance of devel-oping frank diabetes within ten years of the diagnosis. We really have no way of controlling the progression of this condition. From my previous experience as a physician, I can tell you that when these people come to our practice, we basically have nothing to offer them. We tell them to exercise and to lose weight, but they never do it.

The microbiome was a central part of the analysis of the Israeli cohort. Figure 2 shows the results of a comparison of the microbiome in people from different parts of the developed word.

Fig. 2 Principal components analyses of microbiome distribution. Each dot here represents one person. Colours and symbols represent different cohorts. Dots that are very close to each other represent individuals with a very similar microbiome. Dots that are very far away from each other represent individuals with very distinct microbiomes. The result of the analysis represents each person’s microbiome (one dimension per component) in a diagramme with only two dimensions. It shows that the composition of the microbiomes collected in the Israeli cohort overlaps greatly with microbiomes collected in studies in the US or in Europe.

We tested whether the other information that we collected from our cohort, i.e. the interac-tion of glycaemic response and body mass index, corresponds to well-known results from other studies.

We expected from those studies that the more obese people were, the higher their overall glycaemic response to foods would be. And that is exactly what we found in our cohort. The

Eran Elinav

same direct correlation was also found with the haemoglobin A1c, wake up glucose and age – the older you are the higher your average glycaemic response. We also found an inverse cor-relation with the ‘good’ cholesterol (HDL). All these were checks aimed at ensuring that our participants were not altogether different from participants in previous studies.

In this study, we performed only a single intervention: we asked each individual to eat a breakfast that we gave them after a night-time fast each morning of the seven days of follow-up period. This allowed us to give an identical breakfast that would allow us to directly compare the entire 1,000-person cohort. The breakfast included identical pieces of white bread in two of the days, bread plus butter on two other days, sugar on two other days, and fructose on one day.

The average glycaemic response to any of these test foods was exactly the glycaemic index of that food, but the variability in the response across participants was huge. Figure 3 shows on its left that the average response is the glycaemic index but the variability is massive.

A B

Fig. 3 Testing the cohort response for standardised meals. (A): glycaemic responses and indices; (B): individual responses.

If we look at how a certain person responds to a certain food on two different days, we can see that the response is very uniform (Fig. 3B). The variability between people is high, but low for any individual from day to day. And it looks like there is a certain rule for everyone that is deep-ly rooted within their physiology. No one knows yet what that rule is and how it comes about.

We collected a huge amount of data that will keep us busy for the next ten or 15 years. But we are already beginning to see clues about factors that may be part of the individual response rules. Of course, the more diabetic you are and the more obese you are, the more radical your response would be to whatever food you eat. So this was well expected. But we started to see novel things, for instance that responses to fructose are associated with the emergence of certain bacteria.

Sure, these were responses to test foods, but participants also reported their real-life be-haviour. So we also have a huge amount of food-related data and the variability in the re-sponse to them is huge, just like for the test foods. There are very interesting counterintuitive examples: people who eat a bowl of rice versus people who eat a bowl of ice cream. You may expect – I did – that all people have a huge glycaemic response to ice cream and a smaller one to rice. But there are individuals who showed exactly the opposite: they did not spike on

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ice cream but did greatly spike on rice. Actually, when we tested the entire cohort, we found that 70 % of the individuals do not spike on ice cream – which may be a good inspiration for many of us ice cream lovers.