Open Access

Annika Blümlhuber, Dennis Freuer, Nina Wawro, Florian Rohm, Christine Meisinger, Jakob Linseisen

• Background: Chronic non-communicable diseases (NCDs) are a major global health challenge, with unhealthy diets contributing significantly to their burden. Metabolomics data offer new possibilities for identifying nutritional biomarkers, as demonstrated in short-term intervention studies. This study investigated associations between habitual dietary intake and urinary metabolites, a not well-studied area. Methods: Data were available from 496 participants of the population-based MEIA study. Linear and median regression models examined associations between habitual dietary intake and metabolites, adjusted for possible confounders. K-means clustering identified urinary metabolite clusters, and multinomial regression models were applied to analyze associations between food intake and metabolite clusters. Results: Using linear regression models, previously reported associations could be replicated, including citrus intake with proline betaine, protein intake with urea, and fiber intake withBackground: Chronic non-communicable diseases (NCDs) are a major global health challenge, with unhealthy diets contributing significantly to their burden. Metabolomics data offer new possibilities for identifying nutritional biomarkers, as demonstrated in short-term intervention studies. This study investigated associations between habitual dietary intake and urinary metabolites, a not well-studied area. Methods: Data were available from 496 participants of the population-based MEIA study. Linear and median regression models examined associations between habitual dietary intake and metabolites, adjusted for possible confounders. K-means clustering identified urinary metabolite clusters, and multinomial regression models were applied to analyze associations between food intake and metabolite clusters. Results: Using linear regression models, previously reported associations could be replicated, including citrus intake with proline betaine, protein intake with urea, and fiber intake with hippurate. Novel findings include positive associations of poultry intake with taurine, indoxyl sulfate, 1-methylnicotinamide, and trimethylamine-N-oxide. Milk substitutes were positively associated with urinary uracil, pseudouridine, 4-hydroxyhippurate, and 3-hydroxyhippurate, and inversely associated with quinic acid. Dietary fiber intake showed a positive association with 3-(3-hydroxyphenyl)-3-hydroxypropionic acid and a negative association with indoxyl sulfate. We identified sucrose and taurine as key metabolites differentiating metabolite clusters. Multinomial regression analysis confirmed significantly different dietary associations across clusters, particularly for fruits, processed meat, poultry, and alcoholic beverages. Conclusions: This study highlights established and novel food–metabolite associations, demonstrating the potential of urinary metabolomics for use as nutritional biomarkers in individuals from the general population.…