Machine learning prediction of the degree of food processing

作者：Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian, Albert-Ĺaszló Barabási · 发表于：Nature Communications · 年份：2023 · DOI：10.1038/s41467-023-37457-1 · 被引用次数：147 · 研究领域：Consumer Attitudes and Food Labeling、Nutritional Studies and Diet、Obesity, Physical Activity, Diet

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.