“We chose to focus on blood sugar because elevated levels are a major risk factor for diabetes, obesity, and metabolic syndrome. The huge differences that we found in the rise of blood sugar levels among different people who consumed identical meals highlights why personalized eating choices are more likely to help people stay healthy than universal dietary advice, said Prof. Eran Segal of the Department of Computer Science and Applied Mathematics and Dr. Eran Elinav of the Department of Immunology. “Our aim in this study was to find factors that underlie personalized blood glucose responses to food,” said Dr. Elinav. “We used that information to develop personal dietary recommendations that can help prevent and treat obesity and diabetes, which are among the most severe epidemics in human history.”
The study monitored 800 research participants and their glucose level and determined that different people responded very differently to both simple and complex meals. Study participants were outfitted with small monitors that continuously measured their blood sugar levels and were asked to record everything they ate, as well as such lifestyle factors as sleep and physical activity. Overall, the researchers assessed the responses of different people to more than 46,000 meals.
The study included an analysis of the gut microbiome and a large number of the participants’ blood sugar levels rose sharply after they consumed a standardized glucose meal, but in many others, blood glucose levels rose sharply after they ate white bread, but not after glucose.
Based on multiple factors the scientists generated an algorithm for individualized response to food. The algorithm bases its information on the person’s lifestyle, medical background, and the composition and function of his or her microbiome. The researchers followed up with another 100 volunteers and the algorithm successfully predicted the rise in blood sugar in response to different foods.
Lifestyle of the research participants affected the algorithm, as the same food affected blood sugar levels differently in the same person, depending, for example, on whether its consumption had been preceded by exercise or sleep.
A dietary intervention method was developed based on the algorithm and was based on prescribed personal dietary recommendations for lowering blood-glucose level responses to food. The diet that had the most impact impacted on the composition of the research participants’ microbiome, suggesting that the microbiome may be influenced by the personalized diets while also playing a role in participants’ blood sugar responses.
1.David Zeevi, Tal Korem, Niv Zmora, David Israeli, Daphna Rothschild, Adina Weinberger, Orly Ben-Yacov, Dar Lador, Tali Avnit-Sagi, Maya Lotan-Pompan, Jotham Suez, Jemal Ali Mahdi, Elad Matot, Gal Malka, Noa Kosower, Michal Rein, Gili Zilberman-Schapira, Lenka Dohnalová, Meirav Pevsner-Fischer, Rony Bikovsky, Zamir Halpern, Eran Elinav, Eran Segal. Personalized Nutrition by Prediction of Glycemic Responses. Cell, 2015; 163 (5): 1079 DOI: 10.1016/j.cell.2015.11.001