Adverse impact of paternal age on embryo euploidy: insights from retrospective analysis and interpretable Machine learning.

Abstract

The trend of delayed childbearing has increased the average age of parents, with the impact of paternal age on embryo euploidy remaining controversial. Therefore, this study aimed to investigate the impact of paternal age on embryo euploidy using retrospective clinical data and interpretable machine learning. This retrospective study included 960 couples and 4,718 embryos undergoing preimplantation genetic testing for aneuploidy (PGT-A). Couples were divided into two groups based on paternal age (Group 1 ≥ 40 years and Group 2 < 40 years). Statistical methods, including generalized estimating equation (GEE) and restricted cubic spline, were used to evaluate the relationship between paternal age and embryo euploidy. Interpretable machine learning models were employed to predict the likelihood of having at least one euploid embryo, validating the impact of paternal age on embryo euploidy. 867 and 3,851 blastocysts were selected as Group 1 and control Group 2, respectively. Couples with higher paternal age showed a significantly higher rate of embryo aneuploidy (60.21% vs. 41.03%, P < 0.001). Logistic regression using GEE confirmed the association between paternal age and aneuploidy rate (OR: 1.396, 95% CI: 1.150-1.695, P < 0.01). Combining clinical data analysis and interpretable machine learning models, the study provides evidence that paternal age negatively impacts embryo euploidy, emphasizing the need to consider paternal age in reproductive planning.