母体循环代谢生物标志物及其对妊娠期糖尿病相关巨大儿的预测性能。
Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia.
机构信息
Department of Pediatrics, The First People's Hospital of Lianyungang, Xuzhou Medical University Affiliated Hospital of Lianyungang (Lianyungang Clinical College of Nanjing Medical University), Lianyungang, China.
Nanjing Maternity and Child Health Care Institute, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China.
出版信息
BMC Pregnancy Childbirth. 2023 Feb 14;23(1):113. doi: 10.1186/s12884-023-05440-9.
INTRODUCTION
Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M.
METHODS
Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model.
RESULTS
We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR < 0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P = 0.897).
CONCLUSION
The Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM.
简介
妊娠糖尿病（GDM）是一种与代谢相关的妊娠并发症，与巨大儿的风险增加显著相关。我们假设 GDM 合并巨大儿（GDM-M）和 GDM 合并新生儿正常体重（GDM-N）的产妇循环代谢生物标志物存在差异，并且对 GDM-M 具有良好的预测性能。
方法
使用 Olink Proseek 多指标代谢分析方法检测了 44 例 GDM-M 和 44 例 GDM-N 的血浆样本，该方法靶向 92 种生物标志物。结合不同的临床特征和 Olink 标志物，采用 LASSO 回归进行变量选择优化，并应用 Logistic 回归构建预测模型。基于选定的变量，通过直观的方法建立了列线图。采用受试者工作特征（ROC）曲线、校准图和临床影响曲线对模型进行验证。
结果
我们发现了 4 种代谢相关的生物标志物在两组之间存在差异[CLU1（聚类素样蛋白 1）、VCAN（软骨素核心蛋白）、FCRL1（Fc 受体样蛋白 1）、RNASE3（嗜酸性粒细胞阳离子蛋白），FDR<0.05]。基于不同的临床特征和 Olink 标志物，通过 LASSO 回归共确定了 9 个预测因子，即孕前体重指数（BMI）、24 周时体重增加、产次、24 周时口服葡萄糖耐量试验（OGTT）2 小时血糖、24 周时高密度脂蛋白（HDL）和低密度脂蛋白（LDL）以及 24 周时 CLUL1、VCAN 和 RNASE3 的血浆表达。使用这 9 个预测因子构建的模型对 GDM-M 具有良好的预测性能，ROC 曲线下面积为 0.970（灵敏度=0.955，特异性=0.886），且校准效果良好（P=0.897）。
结论
该模型包括孕前 BMI、24 周时体重增加、产次、24 周时 OGTT 2 小时血糖、24 周时 HDL 和 LDL 以及 24 周时 CLUL1、VCAN 和 RNASE3 的血浆表达，对 GDM 孕妇发生巨大儿具有良好的预测性能。
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