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ORIGINAL RESEARCH article

Front. Nutr.
Sec. Nutritional Epidemiology
Volume 11 - 2024 | doi: 10.3389/fnut.2024.1497483

Associations of urinary caffeine metabolites with sex hormones: comparison of three statistical models

Provisionally accepted
Jianli Zhou Jianli Zhou 1*Linyuan Qin Linyuan Qin 2
  • 1 Guilin People’s hospital, Guilin, China
  • 2 Guilin Medical University, Guilin, Guangxi Zhuang Region, China

The final, formatted version of the article will be published soon.

    The association between urinary caffeine and caffeine metabolites with sex hormones remains unclear. This study used three statistical models to explore the associations between urinary caffeine and its metabolites and sex hormones among adults.We selected the participants aged ≥18 years in the National Health and Nutrition Examination Survey (NHANES) data 2013-2014 as our study subjects. We performed principal components analysis (PCA) to investigate the underlying correlation structure of urinary caffeine and its metabolites. Then we used these principal components (PCs) as independent variables to conduct multiple linear regression analysis to explore the associations between caffeine metabolites and sex hormones (E2, TT, SHBG). We also fitted weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) methods to further assess these relationships.Results: In the PCA-multivariable linear regression, PC2 negatively correlates with E2: β=-0.01, P-value=0.049 (male population). In the WQS regression model, the WQS indices were associated with SHBG and TT both in male (SHBG: WQS index=-0.11, P <0.001; TT: WQS index=-0.10, P <0.001) and female (SHBG: WQS index=-0.10, P <0.001; TT: WQS index=-0.04, P <0.001) groups (All P-values<0.05). Besides, the WQS index was significantly associated with E2 in females (P <0.05). In the BKMR model, despite no significant difference in the overall association between caffeine metabolites and the sex hormones (E2, TT, SHBG), there was nonetheless a declining trend in the male population E2 group, in the male and female population SHBG groups also observed a downward trend.Conclusion: When considering the results of these three models, the whole-body burden of caffeine metabolites, especially the caffeine metabolites in the PC2 metabolic pathway was significantly negatively associated with E2 in males, SHBG, and TT.Considering the advantages and disadvantages of the three statistical models, we recommend applying diverse statistical methods and interpreting their results together.

    Keywords: Bayesian Kernel Machine Regression, Caffeine metabolites, multiple linear regression, sex hormone, Weighted quantile sum regression

    Received: 17 Sep 2024; Accepted: 17 Dec 2024.

    Copyright: © 2024 Zhou and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jianli Zhou, Guilin People’s hospital, Guilin, China

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