Non-linear Relationship of Maternal Age With Risk of Spontaneous Abortion: A Case-Control Study in the China Birth Cohort.

Background: Spontaneous abortion is one of the prevalent adverse reproductive outcomes, which seriously threatens maternal health around the world. Objective: The current study is aimed to evaluate the association between maternal age and risk for spontaneous abortion among pregnant women in China. Methods: This was a case-control study based on the China Birth Cohort, we compared 338 cases ending in spontaneous abortion with 1,352 controls resulting in normal live births. The main exposure indicator and outcome indicator were maternal age and spontaneous abortion, respectively. We used both a generalized additive model and a two-piece-wise linear model to determine the association. We further performed stratified analyses to test the robustness of the association between maternal age and spontaneous abortion in different subgroups. Results: We observed a J-shaped relationship between maternal age and spontaneous abortion risk, after adjusting for multiple covariates. Further, we found that the optimal threshold age was 29.68 years old. The adjusted odds ratio (95% confidence interval) of spontaneous abortion per 1 year increase in maternal age were 0.97 (0.90-1.06) on the left side of the turning point and 1.25 (1.28-1.31) on the right side. Additionally, none of the covariates studied modified the association between maternal age and spontaneous abortion (P > 0.05). Conclusions: Advanced maternal age (>30 years old) was significantly associated with increased prevalence of spontaneous abortion, supporting a J-shaped association between maternal age and spontaneous abortion.

How Parental Predictors Jointly Affect the Risk of Offspring Congenital Heart Disease: A Nationwide Multicenter Study Based on the China Birth Cohort.

Objective: Congenital heart disease (CHD) is complex in its etiology. Its genetic causes have been investigated, whereas the non-genetic factor related studies are still limited. We aimed to identify dominant parental predictors and develop a predictive model and nomogram for the risk of offspring CHD. Methods: This was a retrospective study from November 2017 to December 2021 covering 44,578 participants, of which those from 4 hospitals in eastern China were assigned to the development cohort and those from 5 hospitals in central and western China were used as the external validation cohort. Univariable and multivariable analyses were used to select the dominant predictors of CHD among demographic characteristics, lifestyle behaviors, environmental pollution, maternal disease history, and the current pregnancy information. Multivariable logistic regression analysis was used to construct the model and nomogram using the selected predictors. The predictive model and the nomogram were both validated internally and externally. A web-based nomogram was developed to predict patient-specific probability for CHD. Results: Dominant risk factors for offspring CHD included increased maternal age [odds ratio (OR): 1.14, 95% CI: 1.10-1.19], increased paternal age (1.05, 95% CI: 1.02-1.09), maternal secondhand smoke exposure (2.89, 95% CI: 2.22-3.76), paternal drinking (1.41, 95% CI: 1.08-1.84), maternal pre-pregnancy diabetes (3.39, 95% CI: 1.95-5.87), maternal fever (3.35, 95% CI: 2.49-4.50), assisted reproductive technology (2.89, 95% CI: 2.13-3.94), and environmental pollution (1.61, 95% CI: 1.18-2.20). A higher household annual income (100,000-400,000 CNY: 0.47, 95% CI: 0.34-0.63; > 400,000 CNY: 0.23, 95% CI: 0.15-0.36), higher maternal education level (13-16 years: 0.68, 95% CI: 0.50-0.93; ≥ 17 years: 0.87, 95% CI: 0.55-1.37), maternal folic acid (0.21, 95% CI: 0.16-0.27), and multivitamin supplementation (0.33, 95% CI: 0.26-0.42) were protective factors. The nomogram showed good discrimination in both internal [area under the receiver-operating-characteristic curve (AUC): 0.843] and external validations (development cohort AUC: 0.849, external validation cohort AUC: 0.837). The calibration curves showed good agreement between the nomogram-predicted probability and actual presence of CHD. Conclusion: We revealed dominant parental predictors and presented a web-based nomogram for the risk of offspring CHD, which could be utilized as an effective tool for quantifying the individual risk of CHD and promptly identifying high-risk population.