AUTHOR=Zhou Jianli , Qin Linyuan TITLE=Associations of urinary caffeine and caffeine metabolites with metabolic syndrome in US adults JOURNAL=Frontiers in Nutrition VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2023.1280215 DOI=10.3389/fnut.2023.1280215 ISSN=2296-861X ABSTRACT=Aims

The relationship between caffeine and metabolic syndrome (MetS) has only been evaluated from the perspective of caffeine consumption. The association between urinary caffeine and MetS is still unclear. This study examined the associations between urinary caffeine and its metabolites and MetS and its components among adults.

Methods

Data from the United States (US) National Health and Nutrition Examination Survey (NHANES) 2011–2014 was analyzed. NHANES is a stratified, multi-stage survey of all non-institutionalized persons in the US. A total of 2,394 subjects aged ≥ 18 years without missing data were selected in this study. Urinary caffeine and caffeine metabolite levels were quantified using high-performance liquid chromatography-electrospray ionization-tandem quadrupole mass spectrometry (HPLC-ESI-MS/MS) with stable isotope-labeled internal standards. We performed principal components analysis (PCA) to investigate the underlying correlation structure of 15 features of urinary caffeine and its metabolites and then used these principal components (PCs) as independent variables to conduct logistic regression analysis with or without restricted cubic spline (RCS) terms to explore the associations between caffeine metabolites and MetS.

Results

Two main PCs that were derived from the PCA explained 90.67% of the total variance of caffeine and its metabolites. The first PC (PC1, strongly correlated with 1-MU, 1,3-DMU, 1,7-DMU, 1,3,7-TMU, 1-MX, 1,3-DMX, 1,7-DMX, 1,3,7-TMX, and AAMU) was positively correlated with risk of MetS (OR = 1.27, p < 0.001) and all its components (all ORs > 1, all p-values < 0.001) in the unadjusted models, while in the adjusted models, it was positively correlated with MetS (OR = 1.16, p = 0.042) and central obesity (OR = 1.22, p < 0.001). In the unadjusted model, there were significant associations between the second PC (PC2, correlated with 3-MU, 7-MU, 3,7-DMU, 3-MX, 7-MX, and 3,7-DMX) and MetS (OR = 1.11, P = 0.030) and central obesity (OR = 1.16, P < 0.001), while in the adjusted models (adjustment variables include gender, age, race/ethnicity, education level and income-poverty ratio, smoking status, drinking, and physical activity), PC2 was positively associated with MetS (OR = 1.15, p = 0.035) and central obesity (OR = 1.15, p = 0.005) and negatively associated with raised triglycerides (TG) (OR = 0.84, p = 0.008). Moreover, we observed U-shaped associations between PC1 and the risk of raised TG both in unadjusted (Pnon–linear = 0.017) and adjusted (Pnon–linear = 0.014) models.

Conclusion

Urinary caffeine metabolites were positively associated with the risk of MetS and its components through different linear or non-linear patterns.