Machine learning prediction of the degree of food processing.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
21 04 2023
21 04 2023
Historique:
received:
07
05
2022
accepted:
16
03
2023
medline:
25
4
2023
pubmed:
22
4
2023
entrez:
21
04
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37085506
doi: 10.1038/s41467-023-37457-1
pii: 10.1038/s41467-023-37457-1
pmc: PMC10121643
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2312Subventions
Organisme : NHLBI NIH HHS
ID : P01 HL132825
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL115189
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002541
Pays : United States
Informations de copyright
© 2023. The Author(s).
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