Correlation analysis on the association of composite dietary antioxidant index with serum cotinine levels in the general adult population

Objectives: This study utilized data from the National Health and Nutrition Examination Survey (1999-2020) to explore the relationship between dietary antioxidant intake and serum cotinine levels among US adults. Methods: A total of 41 132 participants were included after exclusions. The composite dietary antioxidant index (CDAI), incorporating vitamins A, C, and E, zinc, selenium, and carotenoids, was derived from 24-hour dietary recalls. Associations were assessed using multiple linear regression and weighted quantile sum models. Results: Higher CDAI quartiles were significantly associated with lower serum cotinine levels in the overall population, indicated by a ,B coefficient of-0.42 (95% confidence interval [CI]:-0.51,-0.33). Subgroup analyses revealed that Quartile 4 showed an inverse association with serum cotinine levels compared to Quartile 1 (,B =-0.34, 95% CI:-0.45,-0.23; P < 0.001) among never-smokers, with stronger effects observed among former and current smokers. Specifically, among former smokers, Quartile 4 exhibited a ,B coefficient of-0.53 (95% CI:-0.77,-0.30; P < 0.001), and among current smokers (n = 8280), Quartile 4 had a ,B coefficient of-0.44 (95% CI:-0.58,-0.30; P < 0.001). Importantly, no significant interaction was observed between CDAI and smoking status. Weighted quantile sum analysis demonstrated a negative association between combined antioxidant intake and cotinine levels (,B =-0.073 [-0.087,-0.059], P < 0.001), with vitamin A exerting the most significant influence (36.30%). Conclusions: Higher dietary antioxidant intake, particularly vitamin A, is associated with lower serum cotinine levels, suggesting potential protective effects against tobacco exposure in US adults. Future research should focus on longitudinal studies to examine the potential role of dietary antioxidants in mitigating the effects of tobacco exposure. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.