A Genetic Risk Prediction Model for Coronary Artery Disease Integrating CYP17A1 Polymorphisms and Clinical Variables in a Chinese Population.

PURPOSE: This study aimed to investigate the association between CYP17A1 gene polymorphisms and coronary artery disease (CAD) risk in a Chinese population.

PATIENTS AND

METHODS

A total of 2221 subjects (1363 CAD patients and 858 controls) were enrolled. Five single-nucleotide polymorphisms (SNPs) (rs11191548, rs17115100, rs4409766, rs6162, and rs6163) were genotyped. To account for multiple testing across genetic models, the false discovery rate (FDR) correction was applied. A nomogram incorporating CYP17A1 genetic variants and clinical characteristics was developed to predict CAD risk.

RESULTS

In the overall population, preliminary association analyses identified three SNPs (rs11191548, rs17115100, rs4409766) with genotype distributions significantly different between cases and controls under recessive models. These associations remained significant after FDR correction for multiple testing across all 5 SNPs and 3 genetic models tested (all q = 0.035). Subsequent multivariate logistic regression, adjusted for clinical confounders, confirmed independent protective effects: the CC genotype of rs11191548 (OR = 0.507, 95% CI: 0.352-0.730, P < 0.001), the CC genotype of rs4409766 (OR = 0.557, 95% CI: 0.394-0.786, P = 0.001), and the TT genotype of rs17115100 (OR = 0.632, 95% CI: 0.473-0.844, P = 0.002). The integrated nomogram achieved an area under the curve (AUC) of 0.727-0.728 for CAD risk prediction. Moreover, exploratory sex-stratified analyses indicated potential sex-specific associations, with some variants linked to CAD risk in either males or females at a nominal significance level.

CONCLUSION

The rs11191548, rs17115100, and rs4409766 variants of the CYP17A1 gene were found to be associated with CAD in a Chinese population.