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NIH Scientists Create Biomarker Score to Predict Intake of Ultra-Processed Foods
- NIH scientists have identified metabolite patterns in blood and urine that reflect consumption of energy from ultra-processed foods for the first time.
- Metabolites are byproducts of metabolic reactions converting food to energy, leading to the creation of a poly-metabolite score for dietary assessment.
- This innovative tool aims to improve accuracy in quantifying ultra-processed food intake compared to traditional self-reported dietary surveys.
- Ultra-processed foods are linked to health issues like obesity, diabetes, and cardiovascular disease due to their high calorie content and nutrient deficiencies.
- The study involved older adults in observational cohorts and a clinical trial feeding two different diets to assess metabolic responses.
- Machine learning algorithms were used to identify metabolites correlating with ultra-processed food intake, generating robust poly-metabolite scores.
- Poly-metabolite scores offer a scalable means to objectively quantify ultra-processed food consumption, potentially revealing new diet-disease associations.
- The study acknowledges limitations in diverse demographic validation and longitudinal disease studies, suggesting the need for further research.
- Metabolomics provides insights into how dietary processing impacts health risks, offering potential for preventive health strategies based on mechanistic pathways.
- This research combines observational data with controlled trials to strengthen causal inference, paving the way for refined dietary recommendations and cancer prevention strategies.
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