Lixia Shen1, Daljit S Sahota2, Piya Chaemsaithong3, Wing Ting Tse2, Man Yan Chung2, Jeffery Ka Him Ip2, Tak Yeung Leung2, Liona Chiu Yee Poon2. 1. Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, shenlx06@163.com. 2. Department of Obstetrics and Gynaecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong, China. 3. Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Abstract
INTRODUCTION: This study aimed to identify risk factors among maternal characteristics, obstetric history, and first trimester preeclampsia-specific biomarkers that were associated with subsequent development of gestational diabetes mellitus (GDM) and evaluate the performance of the prediction models. METHODS: This study was a secondary analysis of a prospective cohort study. The performance of the prediction models was assessed by area under the receiver operating characteristic curve (AUROC). RESULTS: A total of 837 (8.9%) cases of GDM and 8,535 (91.1%) unaffected cases were included. The AUROC of the prediction model combining maternal characteristics and obstetric history (0.735) was better than that of the model utilizing maternal characteristics (AUROC 0.708) and preeclampsia-specific biomarkers (AUROC 0.566). Among the preeclampsia-specific biomarkers, the mean arterial pressure (MAP) contributed to the increasing risk of GDM; however, its addition did not improve the AUROC of the model combining maternal characteristics and obstetric history (0.738). CONCLUSION: The first trimester prediction model for GDM with maternal characteristics and obstetric history achieves moderate predictability. The inclusion of MAP in the model combining maternal characteristics and obstetric history does not improve the screening performance for GDM. Future studies are needed to explore the effect of blood pressure control from early pregnancy on preventing GDM.
INTRODUCTION: This study aimed to identify risk factors among maternal characteristics, obstetric history, and first trimester preeclampsia-specific biomarkers that were associated with subsequent development of gestational diabetes mellitus (GDM) and evaluate the performance of the prediction models. METHODS: This study was a secondary analysis of a prospective cohort study. The performance of the prediction models was assessed by area under the receiver operating characteristic curve (AUROC). RESULTS: A total of 837 (8.9%) cases of GDM and 8,535 (91.1%) unaffected cases were included. The AUROC of the prediction model combining maternal characteristics and obstetric history (0.735) was better than that of the model utilizing maternal characteristics (AUROC 0.708) and preeclampsia-specific biomarkers (AUROC 0.566). Among the preeclampsia-specific biomarkers, the mean arterial pressure (MAP) contributed to the increasing risk of GDM; however, its addition did not improve the AUROC of the model combining maternal characteristics and obstetric history (0.738). CONCLUSION: The first trimester prediction model for GDM with maternal characteristics and obstetric history achieves moderate predictability. The inclusion of MAP in the model combining maternal characteristics and obstetric history does not improve the screening performance for GDM. Future studies are needed to explore the effect of blood pressure control from early pregnancy on preventing GDM.