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Nutrition

A personal forecast

Nature volume 528, pages 484–486 (2015)Cite this article

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Machine learning, applied to complex multidimensional data, is shown to provide personalized dietary recommendations to control blood glucose levels. This is a step towards integrating the gut microbiome into personalized medicine.

Weather forecasters were once the unfortunate subjects of countless jokes. Predicting the weather from multiple interacting meteorological factors that are greatly influenced by underlying geography seemed no better than extracting a forecast from a cloudy crystal ball. The complexity and individuality of the human body pose similar hurdles to making accurate predictions in personalized medicine. But access to huge amounts of data, refinement of mathematical models and enhanced computing power have transformed predictive meteorology1, and the same is beginning to apply to human health. Writing in Cell, Zeevi et al.2 approach the complex problem of how an individual's blood glucose concentration will be affected by particular foods, the microorganisms in their gut and other aspects of their physiology, and present a predictive model that enables personalized food recommendations.

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Figure 1: Machine learning for nutrition advice.

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References

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Authors and Affiliations

  1. Erica D. Sonnenburg and Justin L. Sonnenburg are in the Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305, USA.,

    Erica D. Sonnenburg & Justin L. Sonnenburg

Authors
  1. Erica D. Sonnenburg
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  2. Justin L. Sonnenburg
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Corresponding author

Correspondence to Justin L. Sonnenburg.

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Microbiology: Wealth management in the gut

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Sonnenburg, E., Sonnenburg, J. A personal forecast. Nature 528, 484–486 (2015). https://doi.org/10.1038/528484a

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