Machine learning prediction of the degree of food processing

Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian and Albert-László Barabási ()
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Giulia Menichetti: Harvard Medical School
Babak Ravandi: Northeastern University
Dariush Mozaffarian: Tufts Friedman School of Nutrition Science and Policy
Albert-László Barabási: Northeastern University

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37457-1

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DOI: 10.1038/s41467-023-37457-1

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