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Researchers at the National Institutes of Health have developed a novel scoring method using biomarkers in blood and urine to estimate an individual’s consumption of ultra-processed foods. The new approach, known as a poly-metabolite score, offers a more objective alternative to traditional self-reported dietary data, potentially transforming how nutrition-related health outcomes are studied in large populations.

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The poly-metabolite score is based on the presence and patterns of metabolites—molecules produced during the body’s metabolism of food. In the study, hundreds of metabolites were found to correlate with ultra-processed food intake, allowing researchers to build a scoring system using machine learning techniques. The method was tested in two separate groups: a clinical trial of 20 adults who consumed highly controlled diets, and an observational study involving 718 older U.S. adults over a year-long period. The scores reliably differentiated between diets high in ultra-processed foods and those without.

Diets dominated by ultra-processed foods, typically high in calories and low in nutrients, have been linked to increased risks of obesity and chronic diseases such as cancer and type 2 diabetes. However, accurately tracking these diets has been difficult due to reliance on self-reporting. The biomarker-based scoring system could reduce reporting biases and enhance the quality of research into the health consequences of dietary patterns.

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While promising, researchers emphasized the need to replicate findings in younger and more diverse populations. The scoring model may require further refinement to ensure accuracy across different dietary habits and food environments. Ongoing studies are expected to explore how these biomarker scores may predict disease risk and contribute to preventive health strategies.

Article by multiple RFHC contributors, based upon information from the National Institutes of Health and the National Cancer Institute press release.


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