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Continuous glucose monitoring-derived glycemic metrics and adverse pregnancy outcomes among women with gestational diabetes: a prospective cohort study.
Liang, Xinxiu; Fu, Yuanqing; Lu, Sha; Shuai, Menglei; Miao, Zelei; Gou, Wanglong; Shen, Luqi; Liang, Yuhui; Xu, Fengzhe; Tian, Yunyi; Wang, Jiali; Zhang, Ke; Xiao, Congmei; Jiang, Zengliang; Shi, Mei-Qi; Wu, Ying-Ying; Wang, Xu-Hong; Hu, Wen-Sheng; Zheng, Ju-Sheng.
Affiliation
  • Liang X; Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Fu Y; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Lu S; Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Shuai M; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Miao Z; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
  • Gou W; Department of Obstetrics and Gynecology, Hangzhou Women's Hospital (Hangzhou Maternity and Child Health Care Hospital), Hangzhou, China.
  • Shen L; Department of Obstetrics and Gynecology, The Affiliated Hangzhou Women's Hospital of Hangzhou Normal University, Hangzhou, China.
  • Liang Y; Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Xu F; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Tian Y; Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Wang J; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Zhang K; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
  • Xiao C; Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Jiang Z; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Shi MQ; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
  • Wu YY; Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Wang XH; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Hu WS; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
  • Zheng JS; Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Lancet Reg Health West Pac ; 39: 100823, 2023 Oct.
Article in En | MEDLINE | ID: mdl-37927990
ABSTRACT

Background:

Continuous glucose monitoring (CGM) has shown potential in improving maternal and neonatal outcomes in individuals with type 1/2 diabetes, but data in gestational diabetes mellitus (GDM) is limited. We aimed to explore the relationship between CGM-derived metrics during pregnancy and pregnancy outcomes among women with GDM.

Methods:

We recruited 1302 pregnant women with GDM at a mean gestational age of 26.0 weeks and followed them until delivery. Participants underwent a 14-day CGM measurement upon recruitment. The primary outcome was any adverse pregnancy outcome, defined as having at least one of the

outcomes:

preterm birth, large-for-gestational-age (LGA) birth, fetal distress, premature rupture of membranes, and neonatal intensive care unit (NICU) admission. The individual outcomes included in the primary outcome were considered as secondary outcomes. We conducted multivariable logistic regression to evaluate the association of CGM-derived metrics with these outcomes.

Findings:

Per 1-SD difference in time above range (TAR), glucose area under the curve (AUC), nighttime mean blood glucose (MBG), daytime MBG, and daily MBG was associated with higher risk of any adverse pregnancy outcome, with odds ratio 1.22 (95% CI 1.08-1.36), 1.22 (95% CI 1.09-1.37), 1.18 (95% CI 1.05-1.32), 1.21 (95% CI 1.07-1.35), and 1.22 (95% CI 1.09-1.37), respectively. Time in range, TAR, AUC, nighttime MBG, daytime MBG, daily MBG, and mean amplitude of glucose excursions were positively associated, while time blow range was inversely associated with the risk of LGA. Additionally, higher value for TAR was associated with higher risk of NICU admission. We further summarized the potential thresholds of TAR (2.5%) and daily MBG (4.8 mmol/L) to distinguish individuals with and without any adverse pregnancy outcome.

Interpretation:

The CGM-derived metrics may help identify individuals at higher risk of adverse pregnancy outcomes. These CGM biomarkers could serve as potential new intervention targets to maintain a healthy pregnancy status among women with GDM.

Funding:

National Key R&D Program of China, National Natural Science Foundation of China, and Westlake Laboratory of Life Sciences and Biomedicine.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Lancet Reg Health West Pac Year: 2023 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Lancet Reg Health West Pac Year: 2023 Document type: Article Affiliation country: