Risk prevention of different forms of gestational diabetes mellitus based on energy metabolism prior to diagnosis

Author:

Lu Jiayu11,Jiang Hongqing21,Zhang Song1,Yang Yimin1,Hao Dongmei1,Li Xuwen1,Chen Aiqing3,Shao Jing3,Liu Xiaohong3,Yang Lin1

Affiliation:

1. Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China

2. Haidian Maternal and Children Health Hospital, Beijing, China

3. Beijing Yes Medical Devices Co. Ltd., Beijing, China

Abstract

BACKGROUND: Gestational diabetes mellitus (GDM) is a metabolic disease that seriously endangers the health of mothers and children. It is important to monitor GDM in real-time before diagnosis and to prevent it effectively. OBJECTIVE: GDM was divided into the second trimester diagnosed diabetes mellitus (GDM_24) and the third trimester diagnosed diabetes mellitus (GDM_30). The risk prediction of two types of GDM was performed in normal pregnant women at 11–13 and 16–19 weeks of pregnancy, respectively. METHODS: By stages, the K-W test was used to analyze the differences between basic information and energy metabolism factors, and multiple logistic regression was used to analyze the risk of energy metabolism factors and to correct the confounders with significant differences. RESULTS: For the GDM_24 group, each additional unit of oxygen consumption (VO2), carbon dioxide production, and resting energy expenditure (REE) increased the risk by 2.4%, 3.5%, 0.4%, and 2.1%, 2.6%, and 0.3%, respectively, at 11–13 and 16–19 weeks of pregnancy. For the GDM_30 group, each additional unit of VO2 and REE was associated with an increased risk of 2.3% and 0.3%, respectively, at 16–19 weeks of pregnancy. CONCLUSION: The risk of GDM_30 only appeared in pregnant women during 16–19 weeks of pregnancy, which may indicate that GDM_24 and GDM_30 have different pathogenesis.

Publisher

IOS Press

Subject

Health Informatics,Biomedical Engineering,Information Systems,Biomaterials,Bioengineering,Biophysics

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