Department of Gynecology and Obstetrics, The First Affiliated Hospital of Xiamen University, No. 55, Zhenhai Road, Siming District, Xiamen, 361003, Fujian, China.
Computer Management Center, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
Sci Rep. 2021 Apr 1;11(1):7335. doi: 10.1038/s41598-021-86818-7.
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/m 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.
妊娠期糖尿病（GDM）受到广泛关注，因为它影响了全球约 1.8-25.1%的妊娠。本研究旨在探讨孕前人口统计学参数和孕早期实验室生物标志物与晚期 GDM 风险的关系，并进一步建立一个列线图预测模型。本研究基于厦门 10 家“AAA”医院的大型产科数据。GDM 的诊断依据国际糖尿病与妊娠研究协会（IADPSG）标准。数据采用 Stata（v14.1）和 R（v3.5.2）进行分析。共有 187432 例无孕前糖尿病的妊娠妇女符合分析条件，其中 49611 例患有 GDM，137821 例未患有 GDM。无论是否进行混杂因素调整，有 8 个独立因素始终与 GDM 显著相关，包括孕前体重指数（BMI）、孕前叶酸摄入量、白细胞计数、血小板计数、丙氨酸氨基转移酶、白蛋白、直接胆红素和肌酐（p<0.001）。值得注意的是，孕前 BMI 每增加 3kg/m2，患 GDM 的风险增加 22%[调整后的比值比（OR）1.22，95%置信区间（CI）1.21-1.24，p<0.001]，而孕前叶酸摄入可降低 27%的 GDM 风险（调整后的 OR 0.73，95%CI 0.69-0.79，p<0.001）。8 个显著因素的校准和判别统计以及决策曲线分析显示出较好的预测性能。为了增强临床应用，通过纳入年龄和以上 8 个因素建立了一个列线图模型，重要的是，该模型在早期检测随后发生 GDM 的孕妇方面具有 87%的准确率。总之，8 个独立的孕前/孕早期预测因素与晚期 GDM 风险显著相关，重要的是，一个包含这些预测因素的列线图模型在早期检测随后发生 GDM 的孕妇方面具有超过 85%的准确率。
