NIH Researchers Develop New Biomarker Score for Diets High in Ultra-Processed Foods
On May 20, 2025, a team from the National Institutes of Health (NIH) announced a breakthrough in identifying metabolic patterns in blood and urine that provide an objective measure of how much ultra-processed food individuals consume. This analysis revolves around metabolites, which are the byproducts of our metabolism—the process where food gets converted into energy. The researchers created a poly-metabolite score that could complement or reduce the reliance on self-reported dietary data in large population studies.
Lead investigator Erikka Loftfield, Ph.D., M.P.H., at NIH’s National Cancer Institute, acknowledged the known issues with self-reported dietary habits. She expressed optimism that metabolomics could refine how we objectively assess complex factors, including diet and ultra-processed food consumption, while also shedding light on how these diets impact health.
Ultra-processed foods—essentially products that are ready to eat or heat and typically loaded with calories but low in nutrients—are associated with a higher risk for obesity and chronic ailments, including certain cancers. Historically, studies examining the health impacts of these foods have heavily relied on dietary questionnaires completed by participants, which can lead to inconsistencies and fail to reflect changes in food availability over time. This new biomarker offers a more reliable measure of ultra-processed food consumption, advancing the investigation into its health effects.
The researchers based their study on several datasets to pinpoint metabolites linked to ultra-processed food intake. They observed data from 718 older adults who shared biospecimens and dietary information over a year. Additionally, a small clinical trial with 20 adults at the NIH Clinical Center had participants alternating between diets high in ultra-processed foods (making up 80% of their energy intake) and completely unprocessed diets (0% of energy) for two weeks each.
The study revealed hundreds of metabolites connected to the energy percentage derived from ultra-processed foods. By employing machine learning, the team spotted metabolic patterns tied to high consumption, successfully calculating separate poly-metabolite scores for blood and urine. Subsequent evaluations demonstrated that these scores could distinctly identify the diet phases among the trial participants.
The participants were older adults from the U.S., which means their dietary patterns might not apply universally. Thus, the researchers highlighted the need for further validation across different age groups and populations with diverse dietary habits. They suggested that additional studies should focus on the correlation between these poly-metabolite scores and disease risks, like cancer and type 2 diabetes.
