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Autor(en) / Beteiligte
Titel
Personalized Nutrition by Prediction of Glycemic Responses
Ist Teil von
  • Cell, 2015-11, Vol.163 (5), p.1079-1094
Ort / Verlag
United States: Elsevier Inc
Erscheinungsjahr
2015
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
  • 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. [Display omitted] [Display omitted] •High interpersonal variability in post-meal glucose observed in an 800-person cohort•Using personal and microbiome features enables accurate glucose response prediction•Prediction is accurate and superior to common practice in an independent cohort•Short-term personalized dietary interventions successfully lower post-meal glucose People eating identical meals present high variability in post-meal blood glucose response. Personalized diets created with the help of an accurate predictor of blood glucose response that integrates parameters such as dietary habits, physical activity, and gut microbiota may successfully lower post-meal blood glucose and its long-term metabolic consequences.
Sprache
Englisch
Identifikatoren
ISSN: 0092-8674
eISSN: 1097-4172
DOI: 10.1016/j.cell.2015.11.001
Titel-ID: cdi_proquest_miscellaneous_1735903028

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