College of Mechanical Engineering, Sichuan University, Chengdu, China.
Department of Applied Mechanics, Sichuan University, Chengdu, China.
J Matern Fetal Neonatal Med. 2022 Jul;35(13):2457-2463. doi: 10.1080/14767058.2020.1786517. Epub 2020 Aug 6.
Our objective was to develop a first 19 weeks risk prediction model with several potential gestational diabetes mellitus (GDM) predictors including hepatic and renal and coagulation function measures.
A total of 490 pregnant women, 215 with GDM and 275 controls, participated in this case-control study. Forty-three blood examination indexes including blood routine, hepatic and renal function, and coagulation function were obtained. Support vector machine (SVM) and light gradient boosting machine (lightGBM) were applied to estimate possible associations with GDM and build the predict model. Cutoff points were estimated using receiver operating characteristic curve analysis.
It was observed that a cutoff of Prothrombin time (PAT-PT) and Activated partial thromboplastin time (PAT-APTT) could reliably predict GDM with sensitivity of 88.3% and specificity of 99.47% (AUC of 94.2%). If we only use hepatic and renal function examination, a cutoff of DBIL and FPG with sensitivity of 82.6% and specificity of 90.0% (AUC of 91.0%) was obvious and a negative correlation with PAT-PT (=-0.430549) and patient activated partial thromboplastin time (PAT-APTT) (=-0.725638). A negative correlation with direct bilirubin (DBIL) (=-0.379882) and positive correlation with fasting plasma glucose (FPG) ( = 0.458332) neglect coagulation function examination.
The results of this study point out the possible roles of PAT-PT and PAT-APTT as potential novel biomarkers for the prediction and earlier diagnosis of GDM. A first 19 weeks risk prediction model, which incorporates novel biomarkers, accurately identifies women at high risk of GDM, and relevant measures can be applied early to achieve the prevention and control effects.
本研究旨在建立一个包含多种潜在妊娠期糖尿病（GDM）预测指标的首个 19 周风险预测模型，这些指标包括肝肾功能和凝血功能的测量值。
本病例对照研究共纳入了 490 名孕妇，其中 215 名为 GDM 患者，275 名为对照组。我们获得了包括血常规、肝肾功能和凝血功能在内的 43 项血液检查指标。我们应用支持向量机（SVM）和轻梯度提升机（lightGBM）来评估与 GDM 相关的可能关联，并构建预测模型。使用受试者工作特征曲线分析来估计截断值。
我们发现，凝血酶原时间（PAT-PT）和活化部分凝血活酶时间（PAT-APTT）的截断值可以可靠地预测 GDM，其敏感性为 88.3%，特异性为 99.47%（AUC 为 94.2%）。如果仅使用肝肾功能检查，DBIL 和 FPG 的截断值具有 82.6%的敏感性和 90.0%的特异性（AUC 为 91.0%），且与 PAT-PT（=-0.430549）和患者活化部分凝血活酶时间（PAT-APTT）（=-0.725638）呈负相关。与直接胆红素（DBIL）（=-0.379882）呈负相关，与空腹血糖（FPG）（ = 0.458332）呈正相关，可忽略凝血功能检查。
本研究结果指出，PAT-PT 和 PAT-APTT 可能作为 GDM 预测和早期诊断的潜在新型生物标志物。该模型纳入了新型生物标志物，可以准确识别出患有 GDM 的高风险女性，并可早期采取相关措施，实现预防和控制效果。
