Current developments in nutrition · 2025
Principal Components Analysis of Spot Urine Caffeine and Caffeine Metabolites Identifies Promising Exposure Biomarkers of Caffeine and Theobromine Intake in a Cross-Sectional Survey of the United States Population (National Health and Nutrition Examination Survey 2009-2014).
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Pao Ching-I, Rybak Michael E, Sternberg Maya R
Аннотация
BACKGROUND: Spot urine concentrations of caffeine and certain caffeine metabolites correlate well with 24-h caffeine intake and have potential use as caffeine exposure biomarkers. However, a high degree of intercorrelation exists among these urine compounds, and their correlations with intake are very similar, making the selection of a single intake biomarker challenging based on bivariate analyses alone.
OBJECTIVES: To use principal components (PCs) analysis (PCA) as a dimensionality reduction tool to identify underlying correlation structures in the relationship of caffeine intake with spot urine caffeine and caffeine metabolite concentrations.
METHODS: We performed weighted bivariate analyses and a weighted PCA of spot urine concentrations of caffeine and 14 caffeine metabolites, and 24-h intakes of caffeine (foods, beverages, and supplements) and theobromine (foods and beverages) from 7732 participants ≥6 y in the National Health and Nutrition Examination Survey (NHANES) 2009-2014.
RESULTS: Bivariate analyses revealed intercorrelation patterns that effectively divided the urine analytes into 2 groups based on their association with either caffeine or theobromine intake. PCA yielded 2 components (PC1 and PC2) that accounted for 83.0% of the total variance. PC1 (70.9%) showed a positive correlation with all original variables and approximated a weighted sum related to the urine analyte concentrations in the original data. PC2 (12.1%) yielded 2 clusters of correlated variables resembling a weighted contrast of the caffeine and theobromine intake associations observed in the original data. Urine 1-methylxanthine showed the strongest PCA correlation with caffeine intake, followed by 5-acetylamino-6-amino-3-methyluracil, 1-methyluric acid, and 1,7-dimethyluric acid. Urine theobromine and its downstream metabolites showed PCA correlation primarily with theobromine intake, though correlation with caffeine intake was also evident.
CONCLUSIONS: With PCA, we were able to identify an underlying correlation structure in the NHANES 2009-2014 data that revealed promising urine exposure biomarkers of caffeine and theobromine intake.
OBJECTIVES: To use principal components (PCs) analysis (PCA) as a dimensionality reduction tool to identify underlying correlation structures in the relationship of caffeine intake with spot urine caffeine and caffeine metabolite concentrations.
METHODS: We performed weighted bivariate analyses and a weighted PCA of spot urine concentrations of caffeine and 14 caffeine metabolites, and 24-h intakes of caffeine (foods, beverages, and supplements) and theobromine (foods and beverages) from 7732 participants ≥6 y in the National Health and Nutrition Examination Survey (NHANES) 2009-2014.
RESULTS: Bivariate analyses revealed intercorrelation patterns that effectively divided the urine analytes into 2 groups based on their association with either caffeine or theobromine intake. PCA yielded 2 components (PC1 and PC2) that accounted for 83.0% of the total variance. PC1 (70.9%) showed a positive correlation with all original variables and approximated a weighted sum related to the urine analyte concentrations in the original data. PC2 (12.1%) yielded 2 clusters of correlated variables resembling a weighted contrast of the caffeine and theobromine intake associations observed in the original data. Urine 1-methylxanthine showed the strongest PCA correlation with caffeine intake, followed by 5-acetylamino-6-amino-3-methyluracil, 1-methyluric acid, and 1,7-dimethyluric acid. Urine theobromine and its downstream metabolites showed PCA correlation primarily with theobromine intake, though correlation with caffeine intake was also evident.
CONCLUSIONS: With PCA, we were able to identify an underlying correlation structure in the NHANES 2009-2014 data that revealed promising urine exposure biomarkers of caffeine and theobromine intake.