Biomarker for Diets High in Ultra-processed Foods
, by Jennifer K. Loukissas, M.P.P.
Patterns of metabolites in blood and urine can be used as an objective measure of an individual’s consumption of energy from ultra-processed foods, according to a new study published May 20, 2025, in the journal PLOS Medicine. They used these data to develop a poly-metabolite score that could one day reduce the reliance on self-reported dietary intake.
Diets high in ultra-processed foods, which are typically energy-dense and low in essential nutrients, have been linked to increased risk of obesity and related chronic diseases including some types of cancer. Large population studies quantifying the health effects of ultra-processed foods typically rely on self-reported data from dietary questionnaires. Such measures may be subject to differences in reporting and insensitive to changes in the food supply over time. The researchers wanted a more objective measure of ultra-processed food intake for studying associations between diets high in energy from ultra-processed foods and health outcomes.
In the new study, the researchers, led by Erikka Loftfield, Ph.D., M.P.H., Earl Stadtman Investigator in the Metabolic Epidemiology Branch, used data from complementary observational and experimental human studies to identify metabolites in blood and urine that were associated with UPF intake and then develop and test a metabolite signature that was predictive of diets high in energy from ultra-processed foods. Observational data came from 718 participants in the Interactive Diet and Activity Tracking in AARP (IDATA) Study who provided biospecimens and detailed dietary intake information. Experimental data came from a domiciled feeding study consisting of 20 subjects who were admitted to the NIH Clinical Center and randomized to one of two conditions: diet high in UPF (80% of calories) or diet with zero UPF (0% energy) for two weeks immediately followed by the alternate diet for two weeks.
They found that hundreds of metabolites were correlated with the percentage of energy from ultra-processed foods in the diet. Using machine learning, they identified patterns of metabolites in blood and urine that were predictive of high intake of ultra-processed foods and calculated poly-metabolite scores based on these signatures. Additionally, these blood and urine scores could accurately differentiate within trial subjects between the highly processed and the unprocessed diet condition. In addition to improving exposure assessment, poly-metabolite scores could provide novel insight into the role of ultra-processed foods in human health.
Reference
Abar L et al. Identification and Validation of Poly-Metabolite Scores for Diets High in Ultra-Processed Food: An Observational Study and Post-Hoca Randomized Controlled Crossover-Feeding Trial. PLOS Medicine. 2025.