Evidence map›Paper›PMID 38584550›Full record

ArticleCurrent pharmaceutical design2024

Association of Parity with the Risks of Gestational Diabetes and Macrosomia: A Retrospective Cohort Study in Nanjing, China.

Shijie Geng, Junya Wang, Zhi Geng, Juan Wen

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Article in Current pharmaceutical design, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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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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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shijie GengNanjing Maternity and Child Health Care Institute, Nanjing Women and Children's Healthcare Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China.
Junya WangNanjing Maternity and Child Health Care Institute, Nanjing Women and Children's Healthcare Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China.
Zhi GengDepartment of Cardiovascular Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Juan WenNanjing Maternity and Child Health Care Institute, Nanjing Women and Children's Healthcare Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China.

Funding

Jiangsu Provincial Key Research and Development Program, China BE2020626
6 · The paper itself

Abstract

backgroundAfter implementing the two-child policy, more Chinese women who had a previous delivery had their second child. Nevertheless, the impacts of parity on Gestational Diabetes (GDM) and macrosomia have not been fully confirmed. Therefore, we aimed to analyse the characteristics of pregnancy by parity and evaluate the association of parity with risks of GDM/macrosomia in a Chinese population.

methodsA total of 193,410 pregnant women (including 148,293 primiparae and 45,117 multiparae) with complete information were included. Univariate and multivariate logistic regression analyses were used to examine the association between parity and risks of GDM/macrosomia.

resultsWith the gradual implementation of the two-child policy, the proportion of multiparae increased rapidly and then decreased slightly. Multiparae were more likely to be older and have higher intrapartum BMI, as compared to primiparae (P < 0.001). Univariate regression analyses suggested that parity could increase the risks of GDM and macrosomia; while after adjustment, the association between parity and GDM risk disappeared, and the effects of parity on macrosomia risk and birth weight of babies were also weakened. Further, stratified analysis showed that parity only increased the risk of GDM in women over 30 years, and the effects of parity on macrosomia risk and birth weight were more pronounced among women over 30 years compared to women under 30 years.

conclusionParity was not associated with GDM risk, but mildly associated with macrosomia risk. Particular attention should be paid to multiparae with advanced age to reduce the risks of GDM and macrosomia.

Indexed as

Diabetes, GestationalFetal MacrosomiaParityAdultChinaCohort StudiesFemaleHumansPregnancyRetrospective StudiesRisk FactorsChinese populationgestational diabetesintrapartum BMImacrosomiamultiparae.Parity

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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.