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Machine learning prediction of the degree of food processing

Nature Communications · 4 authors, 6 centres

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Higher individual dietary reliance on ultra-processed food (iFPro) correlates with increased risk of metabolic syndrome, diabetes, angina, elevated blood pressure, and accelerated biological age, while reducing vitamin bio-availability. The study also demonstrates that substituting less processed food alternatives can significantly reduce these health associations. Implications suggest that making processing degree information accessible to consumers could help shift dietary patterns toward less processed foods and improve population health.

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other

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nutrition

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ORIGINAL PAPER

PMC10121643

Machine learning prediction of the degree of food processing

Nature Communications

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ABSTRACT

Despite the accumulating evidence that increased consumption of ultra-processed food has adverse health implications, it remains difficult to decide what constitutes processed food. Indeed, the current processing-based classification of food has limited coverage and does not differentiate between degrees of processing, hindering consumer choices and slowing research on the health implications of processed food. Here we introduce a machine learning algorithm that accurately predicts the degree of processing for any food, indicating that over 73% of the US food supply is ultra-processed. We show that the increased reliance of an individual’s diet on ultra-processed food correlates with higher risk of metabolic syndrome, diabetes, angina, elevated blood pressure and biological age, and reduces the bio-availability of vitamins. Finally, we find that replacing foods with less processed alternatives can significantly reduce the health implications of ultra-processed food, suggesting that access to information on the degree of processing, currently unavailable to consumers, could improve population health.

CITATION EXPORT

Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian et al.. Machine learning prediction of the degree of food processing. Nature Communications. (2023). https://doi.org/10.1038/s41467-023-37457-1

AUTHORS

4 · 6 CENTRES

Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian, Albert-László Barabási

1grid.38142.3c000000041936754XChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
2grid.261112.70000 0001 2173 3359Network Science Institute and Department of Physics, Northeastern University, Boston, MA USA
3Tufts Friedman School of Nutrition Science and Policy, Boston, MA USA
4grid.429997.80000 0004 1936 7531Tufts School of Medicine and Medical Center, Boston, MA USA
5Department of Network and Data Science, Central European University, Budapest, Hungary
6grid.38142.3c000000041936754XDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA