The relationship between number of pregnancies and serum 25-hydroxyvitamin D levels in women with a prior pregnancy: a cross - sectional analysis, machine learning - based prediction model, and SHAP - assisted feature importance evaluation

作者：Ziyi Wu, Wěi Li, Haichuan Zhang · 发表于：Frontiers in Endocrinology · 年份：2025 · DOI：10.3389/fendo.2025.1589002 · 被引用次数：2 · 研究领域：Vitamin D Research Studies、Gestational Diabetes Research and Management、Pregnancy and Medication Impact

Background: The primary aim of this study is to explore the association between gravidity and serum 25-hydroxyvitamin D [25(OH)D] levels in women, as existing research rarely addresses gravidity's cumulative impact on maternal vitamin D status. Secondarily, it seeks to develop and evaluate a machine learning model for predicting vitamin D insufficiency (serum 25(OH)D < 50 nmol/L) using reproductive data (including gravidity) and biochemical indicators, with contribution analysis in the model further validating this relationship, thereby translating the findings into a clinically useful tool. Methods: The study included 8,003 parous women from the NHANES survey conducted between 2011 and 2018, excluding those with missing data on vitamin D or gravidity. For the primary objective, we employed covariate-adjusted linear regression analyses to examine the relationship between gravidity and serum 25(OH)D levels. Three hierarchical models were constructed: Model 1 (unadjusted); Model 2, adjusted for age and race/ethnicity; and Model 3, adjusted for all potential confounders (including body mass index, blood urea nitrogen, glycated hemoglobin, and diabetes status). For the secondary objective of model development, multiple regression analysis and six machine learning algorithms (including XGBoost and Random Forest) were employed. These algorithms are well-suited to handle mixed-type biomedical data (e.g., continuous biochemical indices and categorical reproductive factors), aligning ...