EVALUATING IMAGING AND TAP2 GENE POLYMORPHISM INTERACTIONS WITH SMOKING: IMPLICATIONS FOR ATHLETE HEALTH AND PERFORMANCE

Abstract

Objective: This study aims to investigate the interactions between TAP2 gene polymorphism and passive smoking on renal tumor incidence, using advanced imaging and deep learning models to enhance diagnostic accuracy and understand genetic-environmental interplay. Methods: A case-control study was conducted involving 128 patients with confirmed renal tumors (case group) and 145 healthy individuals undergoing routine physical examinations (control group). Deep learning techniques were employed to refine imaging-based quantification, acquisition, and differential diagnosis. The associations between TAP2 gene polymorphisms, passive smoking, and renal tumor incidence were analyzed using multivariate logistic regression and cross-over analysis methods to discern both multiplicative and additive interactions. Results: Univariate analysis indicated significant differences in the distribution of passive smoking, TAP2 rs1135216 allele, and genotype frequencies between the cases and controls (P<0.05). Multivariate logistic regression revealed that individuals with the AG genotype at rs1135216 and exposure to passive smoking had significantly increased odds of developing renal tumors (OR=7.320, 95%CI: 2.394-22.381 for AG genotype; OR=1.978, 95%CI=1.193-3.281 for passive smoking). Conclusion: The interaction between TAP2 gene polymorphism and passive smoking significantly correlates with the incidence of renal tumors. This study highlights the potential of integrating deep learning with imaging techniques to enhance the understanding of genetic and environmental influences on health outcomes. For the sports medicine community, these findings underscore the importance of considering genetic predispositions and lifestyle factors in athlete health management, potentially influencing screening and preventive strategies in athletic populations.