For the first time, researchers at the National Institutes of Health (NIH) have successfully identified distinct patterns of metabolites present in blood and urine that objectively reflect an individual’s consumption of energy derived from ultra-processed foods. These metabolites emerge as byproducts of complex biochemical reactions that occur during metabolism, the body’s process of converting food into usable energy. Leveraging this breakthrough, scientists have developed a composite poly-metabolite score, encompassing multiple metabolite signals. This new tool promises to revolutionize how dietary intake of ultra-processed foods is quantified, reducing the pervasive reliance on often inaccurate self-reported dietary surveys commonly used in epidemiological studies.
Traditional dietary questionnaires, while widely implemented, suffer from well-documented limitations including recall bias, underreporting, and variations in portion size estimations. These factors have long hampered the precision of nutritional epidemiology, particularly in evaluating diets high in ultra-processed foods. Ultra-processed foods, defined as ready-to-eat or ready-to-heat industrially manufactured products, are typically high in calories yet deficient in essential nutrients. Their consumption has been epidemiologically linked to an array of adverse health outcomes including obesity, type 2 diabetes, cardiovascular disease, and certain forms of cancer. The advent of metabolomics-based measurement presents an unprecedented opportunity to obtain objective biomarkers that can enhance the accuracy of dietary exposure assessment.
The research team integrated data from an extensive observational cohort comprising 718 older adults who provided comprehensive biospecimens and dietary information over a period of 12 months. Complementing this, a controlled clinical trial involving 20 healthy adults at the NIH Clinical Center implemented a randomized crossover feeding design. Participants consumed two distinct diets: one characterized by a high proportion of ultra-processed foods accounting for 80% of caloric intake, and another devoid of ultra-processed foods, representing 0% of caloric intake. Each dietary phase lasted two weeks, allowing precise monitoring of metabolic responses attributable solely to the dietary intervention.
Advanced machine learning algorithms were utilized to sift through large datasets of metabolomic profiles derived from blood plasma and urine samples. This computational approach enabled the identification of hundreds of metabolites whose concentrations correlated strongly with the proportion of energy intake from ultra-processed foods. By integrating these metabolites, the scientists generated poly-metabolite scores separately for blood and urine matrices. These composite scores demonstrated robust discriminatory power in distinguishing between dietary phases within the controlled trial, confirming their validity as objective markers of ultra-processed food consumption.
The application of poly-metabolite scores marks a pivotal development for nutritional science, offering a scalable and replicable means to precisely quantify ultra-processed food intake across diverse populations. Such objective markers have the potential to unmask previously obscured associations between diet and health outcomes by bypassing the inaccuracies of self-reported dietary assessments. Importantly, these findings lay foundational groundwork for future investigations into metabolic pathways influenced by dietary processing and how these pathways may mediate disease risk.
Despite these promising advances, the study acknowledges important limitations. The primary study population consisted of older U.S. adults, whose dietary patterns and metabolic responses may differ substantially from younger individuals or populations in other geographic regions. Replication of these results across heterogeneous demographic groups with varying dietary habits will be essential to validate and possibly refine these metabolite-based scores. Additionally, the poly-metabolite scores warrant further evaluation in longitudinal studies to elucidate their predictive capacity for chronic diseases.
Furthermore, this metabolomics-driven approach opens new frontiers for exploring the biological mechanisms underpinning the health risks associated with ultra-processed foods. Metabolites detected may serve as intermediates or effectors in pathways that contribute to inflammation, carcinogenesis, insulin resistance, or lipid dysregulation. Elucidating these mechanistic links could yield novel targets for therapeutic intervention or preventive strategies, thereby enhancing public health initiatives aiming to mitigate diet-related disease burdens.
The rigor of combining observational data with tightly controlled feeding trials enhances the overall robustness of this research. Controlled dietary interventions remain the gold standard for studying nutrient metabolism but are limited in scale and duration. Conversely, large-scale observational cohorts provide epidemiological breadth but rely heavily on subjective measures. By leveraging both methodologies, the research team has created a powerful hybrid model that strengthens causal inference and facilitates translation of findings into public health practice.
NIH’s National Cancer Institute spearheaded this comprehensive effort, highlighting the intersection of diet, metabolomics, and cancer epidemiology. Given the well-established links between diet quality and cancer incidence, development of objective biomarkers for ultra-processed food consumption holds significant promise to refine dietary recommendations and inform cancer prevention strategies. Ongoing research will seek to correlate poly-metabolite scores with incidence rates of cancer, type 2 diabetes, and other chronic conditions to quantify the magnitude of health risk attributable to ultra-processed diets.
In summary, this pioneering study demonstrates the feasibility of metabolomics as a powerful tool to objectively quantify dietary intake of ultra-processed foods. The poly-metabolite scores derived from blood and urine represent the first validated biochemical indices reflecting adherence to diets high in processed industrial food products. Continued research and refinement of these biomarkers will enhance nutrition research pipelines, reduce measurement error in diet-disease association studies, and ultimately help tailor precision nutrition strategies aimed at improving population health outcomes.
This groundbreaking advancement provides a new lens through which the scientific and medical communities can study the ever-expanding role of ultra-processed foods in global health. As dietary environments continue to evolve, the integration of metabolomics-based biomarkers into clinical and public health research is poised to substantially improve the understanding and prevention of diet-related diseases in the years to come.
Subject of Research: Identification and validation of metabolomic biomarkers for ultra-processed food intake
Article Title: Identification and validation of poly-metabolite scores for diets high in ultra-processed food: An observational study and post-hoc randomized controlled crossover-feeding trial.
News Publication Date: 20-May-2025
Web References: http://dx.doi.org/10.1371/journal.pmed.1004560
References:
Abar L, Steele EM, Lee SK, Kahle L, Moore SC, Watts E, et al. (2025) Identification and validation of poly-metabolite scores for diets high in ultra-processed food: An observational study and post-hoc randomized controlled crossover-feeding trial. PLoS Med 22(5). doi:10.1371/journal.pmed.1004560
Keywords: Public health, Dietetics, Diets, Personalized medicine, Biomarkers, Obesity, Childhood obesity
Tags: biochemical reactions in metabolismdietary intake measurementhealth outcomes of ultra-processed dietsinnovative dietary assessment methodsmetabolic byproducts of foodmetabolite patterns in bloodNIH biomarker scorenutritional epidemiology improvementsobesity and processed foodsprecision nutrition researchself-reported dietary surveysultra-processed food consumption
