| Summary: | Abstract Gestational diabetes mellitus (GDM) has aroused wide public concern, as it affects approximately 1.8–25.1% of pregnancies worldwide. This study aimed to examine the association of pre-pregnancy demographic parameters and early-pregnancy laboratory biomarkers with later GDM risk, and further to establish a nomogram prediction model. This study is based on the big obstetric data from 10 “AAA” hospitals in Xiamen. GDM was diagnosed according to the International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria. Data are analyzed using Stata (v14.1) and R (v3.5.2). Total 187,432 gestational women free of pre-pregnancy diabetes mellitus were eligible for analysis, including 49,611 women with GDM and 137,821 women without GDM. Irrespective of confounding adjustment, eight independent factors were consistently and significantly associated with GDM, including pre-pregnancy body mass index (BMI), pre-pregnancy intake of folic acid, white cell count, platelet count, alanine transaminase, albumin, direct bilirubin, and creatinine (p < 0.001). Notably, per 3 kg/m2 increment in pre-pregnancy BMI was associated with 22% increased risk [adjusted odds ratio (OR) 1.22, 95% confidence interval (CI) 1.21–1.24, p < 0.001], and pre-pregnancy intake of folic acid can reduce GDM risk by 27% (adjusted OR 0.73, 95% CI 0.69–0.79, p < 0.001). The eight significant factors exhibited decent prediction performance as reflected by calibration and discrimination statistics and decision curve analysis. To enhance clinical application, a nomogram model was established by incorporating age and above eight factors, and importantly this model had a prediction accuracy of 87%. Taken together, eight independent pre-/early-pregnancy predictors were identified in significant association with later GDM risk, and importantly a nomogram modeling these predictors has over 85% accuracy in early detecting pregnant women who will progress to GDM later.
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