Exploration between caffeine intake, physical activity, and prostate cancer using data from the large-scale NHANES survey: A cross-sectional study.

ABSTRACT: This study aimed to explore the association between caffeine intake, physical activity (PA), and prostate cancer, machine learning algorithms to build predictive models of prostate cancer. A total of 1789 subjects from the National Health and Nutrition Examination Survey 2009 to 2018 waves were enrolled in this study. Multivariable-adjusted logistic regression was applied to evaluate the association. Then, we conducted 4 machine learning models, including extreme gradient boosting, AdaBoost, Catboos, and Boost tree to predict the occurrence of prostate cancer. In the fully adjusted model, compared to those reporting little caffeine consumption, those who reported large intake had a multivariate adjusted odd ratio (OR) with 95% confidence interval (CI) of 1.25 (2.21-15.52). Participants wit

SUBMITTER: Liu Q 

PROVIDER: S-EPMC12688939 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature