When we eat, the carbohydrates from the food
are broken down to simple sugars which are then absorbed from our intestine
into the bloodstream. Blood sugar levels are a key factor that affects the pathogenesis of diseases such as diabetes and obesity. Diets aimed at controlling blood glucose levels
are often similar, even for different people. But what if we told you that these very common diets,
aimed at maintaining stable blood sugar levels may, in some people, achieve the exact opposite? People are different in many ways for example, in their genetic makeup, in their lifestyle, and also in their microbiomes. The microbiome is a huge ecosystem
of trillions of bacteria living inside our body with more than 100 times the number of genes contained in the human genome. The microbiome is influenced by what we eat,
and in turn affects our response to food. And as the microbiome differs greatly
from one person to another, it can also affect the blood sugar response to food. For the past few years,
scientists at the Weizmann Institute have studied the factors underlying variations in
post-meal blood sugar responses They collected health and lifestyle data
from 800 volunteers, who were connected to a device that monitored their blood sugar level every 5 minutes for an entire week. The participants also used a mobile App to document what and when they ate exercised, slept and… so on Stool samples were collected in order to analyze the composition and activity of their microbiomes The scientists discovered that when different people ate identical foods,
they often reacted in a very different way. For example, the blood sugar level of some people rose more significantly after eating sushi
than after eating ice cream. The scientists were able to integrate all of the data they collected into an algorithm that successfully predicted the blood sugar response to the meals of the 800 participants. The same algorithm achieved similar accuracy when predicting the sugar responses of 100 new participants. The scientists also showed how the algorithm
could be used to prescribe personalized diets – a “good” diet that lowers post-meal sugar response,
and a “bad” diet that raises sugar responses. Interestingly, some foods that appeared
on the “good” diet of one person appeared on the “bad” diet of another. Let’s hear what the researchers themselves have to say. If I highlight the key contributions of our work I would say that the first is in highlighting the need for personalized nutrition which we demonstrate by showing that the blood sugar response of different people to identical meals can be hugely different and as soon as we saw this data we realized that general dietary recommendations, given to the entire population, may have limited efficacy The second, is in then measuring for every individual in our nearly 1000 people cohort a very comprehensive profile that includes their medical background, questionnaires, physical activity, blood tests and gut microbiome function and composition and then integrating this data
into a computational algorithm that could successfully predict the personalized blood glucose response of people to arbitrary meals and then finally, in showing that applying this algorithm to design personally tailored
dietary interventions in individuals could significantly lower their
blood sugar response to food and that was accompanied by
consistent alterations to the gut microbiome We know that nutrition is a very important risk factor for human metabolic disease, and especially to the obesity and diabetes epidemics that are affecting the lives of
close to half of the world’s population In this work, we link nutrition, in a personalized manner, to human risk to develop elevated blood sugar levels and their many complications As scientists, we often deal with very basic questions but in this work we are very happy to also introduce a potential, that if further developed, would allow to benefit the health of
millions across the world. This research marks an important step towards personalized nutrition by predicting
post-meal blood glucose responses. The scientists hope that this approach will help
to achieve a healthier lifestyle and prevent metabolic disease worldwide.