AI Article Synopsis
- There is increasing evidence that ultra-processed foods negatively impact health, but defining what qualifies as processed food is challenging and limits research and consumer choices.
- A new machine learning algorithm can determine the degree of processing in foods, revealing that over 73% of the US food supply is ultra-processed.
- Diets high in ultra-processed foods are linked to greater risks of health issues like metabolic syndrome and reduced vitamin absorption, but switching to less processed options can improve health outcomes.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121643 | PMC |
| http://dx.doi.org/10.1038/s41467-023-37457-1 | DOI Listing |
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