ArticleNature communications2023
Machine learning prediction of the degree of food processing.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ultra-Processed Food Consumption and Cancer Risk: An Umbrella Review of Systematic Reviews and Meta-Analyses.Journal of food science · 2026Pooled it
- Ultra-Processed Food and Chronic Kidney Disease Risk: A Systematic Review, Meta-Analysis, and Recommendations.Nutrients · 2025Pooled it
- Short-term effects of high-protein, lower-carbohydrate ultra-processed foods on human energy balance.Nature metabolism · 2025Trial
- Review
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- Food inflammation index and risks of all-cause and cardiovascular mortality among individuals with cardiovascular-kidney-metabolic syndrome: a nationally representative cohort study from NHANES 1999-2018.Scientific reports · 2026Article
- Dietary Oxysterols Reprogram Hepatic Lipid Metabolism and Reshape the Gut Metabolome-Microbiome Interface.bioRxiv : the preprint server for biology · 2026Article
- Interpretable machine learning for the grade prediction of strong flavorFood chemistry: X · 2026Article
- Exploring approaches to computational representation and classification of user-generated meal logs.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Artificial intelligence applications for assessing ultra-processed food consumption: a scoping review.The British journal of nutrition · 2026Article
- Antioxidant vitamin index and risk of age-related macular degeneration: multicenter validation and clinical translation.npj aging · 2026Article
- Mapping regional disparities in discounted grocery products.NPJ science of food · 2026Article
- Improving the SSIR Method: Implementation of an Exhaustive Multilevel Scan for Categorical Variables.International journal of molecular sciences · 2026Article
- A metabolite index of ultra-processed food intake is associated with stroke, cancer mortality, and all-cause mortality.medRxiv : the preprint server for health sciences · 2026Article
- Robustness of nutrient-based NOVA food classification under profile-separated nested validation.Frontiers in nutrition · 2026Article
- A Multinutrient Clustering Framework for Personalized Food Recommendations.International journal of food science · 2026Article
- Ultra-processed food exposure and health outcomes: Current evidence, controversies, and future perspectives.EXCLI journal · 2026Review
- Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight.Nutrients · 2025Article
- Development and Validation of a New Tool for Assessing the Intake of Ultra-Processed Foods in Adult Population: GR-UPFAST.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2025Article
- The networks of ingredient combinations as culinary fingerprints of world cuisines.NPJ science of food · 2025Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.