Abstract
BACKGROUND: Some studies have shown that dietary antioxidants may prevent the occurrence of Human Papillomavirus (HPV) infection. However, the relationship between the composite dietary antioxidant index (CDAI) and HPV infection among adult women in the United States remains unknown. METHODS: Participants from the National Health and Nutrition Examination Survey (NHANES) during 2003-2016 were included. Multivariable logistic regression, restricted cubic spline (RCS) regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were used to analyze the associations between CDAI and its sub-components and HPV infection. In addition, nine machine learning (ML) methods were employed to construct predictive models, and SHapley Additive exPlanations (SHAP) was used to further interpret the optimal model. RESULTS: This study enrolled 9,224 adult female participants. After adjusting for multiple confounding variables, CDAI was independently negatively associated with HPV infection (OR: 0.98, 95%CI: 0.97-0.99, p = 0.01). RCS indicated an L-shaped association between CDAI and HPV infection. In the WQS model, the WQS index of CDAI was still robustly negatively associated with HPV infection (OR: 0.78, 95%CI: 0.71-0.86, p < 0.0001). In the mixture effect, BKMR analysis confirmed the negative association between six antioxidants and HPV infection. Both WQS and BKMR confirmed that vitamin E had the strongest negative association with HPV infection. Additionally, among the nine machine-learning models, the Gradient Boosting Machine (GBM) showed the best predictive performance [area under curve (AUC) = 0.685]. SHAP analysis indicated that marital status, smoking, drinking, race, age, and CDAI had a significant impact on the model's prediction. CONCLUSION: Antioxidant-rich diets, especially increased intake of vitamin E, are significantly negatively associated with HPV infection. A GBM model with 12 features can effectively predict the occurrence of HPV infection, among which CDAI is an important factor in the model.