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Machine learning prediction of the degree of food processing
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
Diet
Fast Foods
Food Handling
Food, Processed
Nutritive Value
SDG 3 - Good Health and Well-being
Fast Foods
Food Handling
Food, Processed
Nutritive Value
SDG 3 - Good Health and Well-being
URI
https://hdl.handle.net/20.500.14018/27127
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2023-04-21
Language
ISBN
Identifiers
10.1038/s41467-023-37457-1