Exploration between caffeine intake, physical activity, and prostate cancer using data from the large-scale NHANES...

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 with large PA were more likely to develop prostate cancer (OR = 1.68, 95% CI: 1.47-3.80), whereas a significant inverse association between medium PA and prostate cancer was observed (OR = 0.66, 95% CI: 0.48-0.81). Extreme gradient boosting, Catboost, and Boost tree all have good prediction effects, with an AUC of up to 0.90 (95% CI: 0.87-0.93). No significant association was observed between small to medium caffeine intake and prostate cancer, large caffeine intake and PA was associated with increased prostate cancer. Moderate PA has the potential to favorably influence prostate cancer.