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Mapping postprandial responses sets the scene for targeted dietary advice

Nature Medicine volume 26, pages 828–830 (2020)Cite this article

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A machine-learning model can predict differences between people in how they respond to meals.

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Fig. 1: The PREDICT 1 study used a machine-learning model to predict a person’s unique postprandial response to meals of varying composition.

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Authors and Affiliations

  1. University of Sydney, Sydney, Australia

    Jennie Brand-Miller

  2. Paderborn University, Paderborn, Germany

    Anette Buyken

Authors
  1. Jennie Brand-Miller
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  2. Anette Buyken
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Correspondence to Jennie Brand-Miller or Anette Buyken.

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Competing interests

J.B.-M. is the co-author of books about nutrition and the glycemic index of foods; is the president of the Glycemic Index Foundation, a not-for-profit food endorsement program; and oversees a glycemic index testing service at the University of Sydney. J.B.-M. and A.B. are a members of the International Carbohydrate Quality Consortium, and A.B. is a member of the Carbohydrate Task Force, ILSI Europe.

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Brand-Miller, J., Buyken, A. Mapping postprandial responses sets the scene for targeted dietary advice. Nat Med 26, 828–830 (2020). https://doi.org/10.1038/s41591-020-0909-1

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