Effect of air pollution on clinical pregnancy outcomes following intrauterine insemination: a machine learning-based analysis.

Objectives: Numerous studies have explored the impact of air pollution on Methods: Clinical data from 2,328 IUI cycles performed at the Reproductive Centre of Zhongshan City People's Hospital between January 2014 and December 2025 were retrospectively analysed together with air pollution data from the central urban area of Zhongshan during the same period. Data analysis in this study was conducted using machine learning algorithms and bioinformatics approaches. Results: Generalized estimating equation analysis showed that carbon monoxide (CO) exposure during the transition from the antral follicle stage to the mature follicle stage was significantly and inversely associated with clinical pregnancy. Least Absolute Shrinkage and Selection Operator regression analysis revealed a non-zero regression coefficient for CO. Random forest model analysis identified CO as one of the two most important predictors of clinical pregnancy during this stage. Furthermore, incorporation of air pollutant variables improved model performance, increasing precision, recall, F1-score, and the area under the precision-recall curve. Restricted cubic spline analysis demonstrated that the clinical pregnancy rate decreased from 16.09 to 8.50% with increasing CO concentrations during the same period. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that inflammation- and immune-related pathways may represent potential biological mechanisms underlying the observed association between CO exposure and reduced clinical pregnancy rates. Conclusion: Overall, these findings indicate that CO exposure during the transition from the antral follicle stage to the mature follicle stage is adversely associated with clinical pregnancy outcomes following IUI.