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Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.

Personalized Nutrition by Prediction of Glycemic Responses.

Author information

1
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
2
Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel; Internal Medicine Department, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; Research Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel.
3
Day Care Unit and the Laboratory of Imaging and Brain Stimulation, Kfar Shaul Hospital, Jerusalem Center for Mental Health, Jerusalem 9106000, Israel.
4
Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel.
5
Research Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel; Digestive Center, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel.
6
Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel. Electronic address: eran.elinav@weizmann.ac.il.
7
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel. Electronic address: eran.segal@weizmann.ac.il.

Abstract

Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.

TRIAL REGISTRATION:

ClinicalTrials.gov NCT01892956.

PMID:
26590418
DOI:
10.1016/j.cell.2015.11.001
[Indexed for MEDLINE]
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