Parental health at preconception and gestational age at birth: evidence from a population-based cohort using double machine learning.

IF 1.3 4区 医学 Q4 PEDIATRICS
World Journal of Pediatric Surgery Pub Date : 2025-09-29 eCollection Date: 2025-01-01 DOI:10.1136/wjps-2025-001078
Yuehong Ding, Lanqing Qu, Jinbiao Zhang, Tiezheng Li, Yunyan Wang, Weize Xu, Die Li
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引用次数: 0

Abstract

Background: Gestational age at birth is shaped by complex maternal and paternal factors prior to conception, yet few studies have integrated multidimensional clinical indicators from both parents to estimate their quantitative effects.

Methods: Using a large retrospective cohort with routinely collected preconception health data from both mothers and fathers, we applied a double machine learning (DML) framework combining Lasso, Random Forest and XGBoost regressors to estimate associations between parental biomarkers and gestational age at birth. Model interpretability was enhanced through Shapley Additive Explanations (SHAP) analysis, stratified interaction testing and estimation of individualized treatment effects (ITE).

Results: Elevated maternal fasting glucose, alanine aminotransferase, platelet count and anti-hepatitis B core seropositivity were consistently associated with shortened gestational age at birth across all DML models. For instance, a 10 mmol/L increase in fasting glucose corresponded to 2.8-4.9 days shorter gestation. Paternal Treponema pallidum seropositivity and increased monocyte proportion, defined as the fraction of monocytes within total circulating white blood cells, also demonstrated significant associations, with the former linked to gestational shortening and the latter to modest extension. Stratified and interaction analyses revealed that paternal immune markers modified the associations of maternal metabolic indicators with gestational age at birth. SHAP-based interpretation confirmed model consistency and ITE analysis indicated marked heterogeneity, particularly for fasting glucose.

Conclusions: This study demonstrates the utility of interpretable DML methods for quantifying the effects of multidimensional preconception health indicators from both parents on gestational age at birth. Our findings support integrating maternal and paternal screening into preconception risk assessments to enable early, targeted prevention of preterm birth. Given the established links between preterm birth and neonatal surgical conditions, early risk identification via parental screening may help inform perinatal care strategies and optimize resource allocation in pediatric surgery.

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孕前和出生时胎龄的父母健康:使用双机器学习的基于人群的队列证据
背景:出生胎龄是由母体和父亲在受孕前的复杂因素决定的,但很少有研究综合了父母双方的多维临床指标来估计其定量效应。方法:采用常规收集的父亲和母亲孕前健康数据的大型回顾性队列,我们应用双机器学习(DML)框架,结合Lasso、Random Forest和XGBoost回归因子来估计父母生物标志物与出生时胎龄之间的关联。通过Shapley加性解释(SHAP)分析、分层相互作用检验和个性化治疗效果估计(ITE),增强了模型的可解释性。结果:在所有DML模型中,母亲空腹血糖、丙氨酸转氨酶、血小板计数和抗乙型肝炎核心血清阳性升高与出生时胎龄缩短一致相关。例如,空腹血糖增加10 mmol/L,妊娠期缩短2.8-4.9天。父亲梅毒螺旋体血清阳性和单核细胞比例增加(定义为循环白细胞中单核细胞的比例)也显示出显著关联,前者与妊娠缩短有关,后者与妊娠适度延长有关。分层和相互作用分析显示,父亲的免疫标记改变了母亲的代谢指标与出生时胎龄的关系。基于shap的解释证实了模型的一致性,而ITE分析显示了显著的异质性,尤其是空腹血糖。结论:本研究证明了可解释的DML方法用于量化父母双方多维孕前健康指标对出生胎龄的影响。我们的研究结果支持将母亲和父亲的筛查纳入孕前风险评估,以便及早、有针对性地预防早产。鉴于早产与新生儿手术条件之间的联系,通过父母筛查进行早期风险识别可能有助于告知围产期护理策略并优化儿科外科的资源分配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.40
自引率
12.50%
发文量
38
审稿时长
13 weeks
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