Personalized Nutrition by Prediction of Glycemic Responses.

Cell 2015 Nov;163(5):1079-1094

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:

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.

Abstract Video

Personalized Nutrition/ Cell November 19, 2015 (Vol. 163, Issue 5)


Source: Cell Press

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http://www.wisdom.weizmann.ac.il/~/eran/zeevi_cell_2015.pdf
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http://linkinghub.elsevier.com/retrieve/pii/S009286741501481
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http://dx.doi.org/10.1016/j.cell.2015.11.001DOI Listing
November 2015
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