Evidence map›Paper›PMID 39871173›Full record

ArticleBMC pregnancy and childbirth2025

Gestational weight gain patterns as predictors of cesarean deliveries in women diagnosed with gestational diabetes mellitus.

Tzu-Ling Chen, Su-Fen Cheng, Chien-Lin Kuo, Chu-Yu Huang, Chia-Hsun Wu

Abstract read
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Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Tzu-Ling ChenDepartment of Nurse-Midwifery and Women's Health, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan.ORCID http://orcid.org/0000-0002-0235-8159
Su-Fen ChengDepartment of Allied Health Education and Digital Learning, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan.ORCID http://orcid.org/0000-0003-0478-1046
Chien-Lin KuoDepartment of Allied Health Education and Digital Learning, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan.ORCID http://orcid.org/0000-0002-6375-5502
Chu-Yu HuangSchool of Nursing, Cedarville University, Cedarville, OH, USA.ORCID http://orcid.org/0000-0002-6610-1360
Chia-Hsun WuObstetrician and Gynecologist, Department of Obstetrics and Gynecology, Cheng-Hsin General Hospital, Taipei, Taiwan. ntunhschenghsin@gmail.com.

Funding

Cheng-Hsin General Hospital (Taiwan) CHGH113-(IU)02
6 · The paper itself

Abstract

backgroundObesity and excessive gestational weight gain (GWG) have been linked to an increased risk of cesarean section. However, existing literature primarily focuses on weight gain during individual trimesters, lacking a comprehensive assessment of GWG trajectories across all three trimesters. This study aimed to investigate the impact of pre-pregnancy BMI and changes in GWG trajectories from the first to the third trimester on cesarean section in women with confirmed gestational diabetes mellitus (GDM).

methodsThis retrospective cohort study enrolled 947 women with GDM who delivered between January 2012 and July 2022 in northern Taiwan. GDM was diagnosed by oral glucose tolerance test (OGTT) during 24-28 weeks of gestation. Body mass index cut-offs were based on Asian-specific guidelines. The weekly GWG rate was based on baseline weight and weight at two follow-up measurements. GWG patterns were identified using group-based trajectory modeling analysis (GBTM).

resultsGestational weight gain patterns were classified into two groups during the pregnancy period: group 1 (non-rapid excessive weight gain) and group 2 (rapid excessive weight gain), comprising 70.2% and 29.8% of participants, respectively. Our study found that being pre-pregnancy underweight and experiencing rapid excessive weight gain contributed to cesarean section. Binary logistic regression analysis revealed that pre-pregnancy normal body mass index (OR = 2.06; 95% CI: 1.08-3.92) and being overweight/obese (OR = 4.04; 95%CI: 2.12-7.70) were associated with the incidence of cesarean sections. Multiparous women and women with a trajectory of rapid excessive weight gain were more likely to undergo cesarean sections.

conclusionsHealthcare professionals should provide education on weight management from pre-pregnancy to pregnancy to reduce the incidence of cesarean sections. Particular attention should be paid to women with gestational diabetes mellitus (GDM) who exhibit rapid excessive weight gain to minimize the risk of cesarean delivery.

Indexed as

Cesarean SectionDiabetes, GestationalGestational Weight GainAdultBody Mass IndexFemaleHumansPregnancyRetrospective StudiesRisk FactorsTaiwanWeight GainCesarean sectionGestational diabetes mellitusGestational weight gainGroup-based trajectory modelingRetrospective cohort study

Identifiers

PMID39871173
PMCPMC11770906

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.