ArticleFrontiers in nutrition2025
Association between serum carotenoid levels and gallstones in adults: a cross-sectional study using the National Health and Nutrition Examination Survey.
Article in Frontiers in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Carotenoids, known for their antioxidant properties and potential health benefits, have attracted considerable attention. Nonetheless, the association between carotenoid levels and gallstone prevalence has not been adequately explored. This study aimed to investigate the association between serum carotenoid levels and gallstone risk in an adult population. Methods: This cross-sectional study utilized the 2017-2018 NHANES data. Multivariate logistic regression analyses were performed to assess the association between serum carotenoid levels and gallstone prevalence. Subgroup and interaction analyses were conducted to confirm these findings. Additionally, generalized additive model (GAM) regression combined with smooth curve fitting techniques was utilized to clarify potential non-linear associations, and a mediation analysis was conducted to identify possible mediators in the association between serum carotenoid levels and gallstones. Results: This study included 3,809 participants aged ≥20 years, among whom 412 had gallstones. After adjusting for confounders (Model 3), serum carotenoid levels were inversely associated with gallstone prevalence. The participants in the highest quartile of total carotenoid levels had a 48% lower gallstone risk than those in the lowest quartile (Q4: odds ratio [OR] = 0.52, Conclusion: Observational data demonstrated inverse associations between serum carotenoid levels and gallstone prevalence, with BMI mediating 16.7% of the total effect. These findings suggest that maintaining high serum carotenoid levels may reduce the gallstone risk. Future studies should explore the protective mechanisms of carotenoids and validate their causal relationships using longitudinal studies.
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