Evidence map›Paper›PMID 37308533›Full record

ArticleScientific reports2023

Parity and incident type 2 diabetes in older Chinese women: Guangzhou Biobank Cohort Study.

Huimin Su, Chaoqiang Jiang, Weisen Zhang, Feng Zhu, Yali Jin, Karkeung Cheng, Taihing Lam, Lin Xu

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

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

8 authors.

Huimin SuSchool of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, China.
Chaoqiang JiangMolecular Epidemiology Research Centre, Guangzhou Twelfth People's Hospital, Guangzhou, 510620, China.
Weisen ZhangMolecular Epidemiology Research Centre, Guangzhou Twelfth People's Hospital, Guangzhou, 510620, China. zwsgzcn@163.com.
Feng ZhuMolecular Epidemiology Research Centre, Guangzhou Twelfth People's Hospital, Guangzhou, 510620, China.
Yali JinMolecular Epidemiology Research Centre, Guangzhou Twelfth People's Hospital, Guangzhou, 510620, China.
Karkeung ChengInstitute of Applied Health Research, University of Birmingham, Birmingham, B15 2TT, UK.
Taihing LamMolecular Epidemiology Research Centre, Guangzhou Twelfth People's Hospital, Guangzhou, 510620, China.
Lin XuSchool of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, China. xulin27@mail.sysu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examined the association between parity and incident type 2 diabetes in older Chinese women and estimated the mediation effect of adiposity indicators. A total of 11,473 women without diabetes at baseline from 2003 to 2008 were followed up until 2012. We used Cox proportional hazards regression to assess the association between parity and incident type 2 diabetes, and mediation analysis to estimate the mediation effect of adiposity indicators. Compared to women with one parity, the hazard ratio (HR) (95% confidence interval (CI)) for incident type 2 diabetes was 0.85 (0.44-1.63), 1.20 (1.11-1.30), 1.28 (1.16-1.41) and 1.27 (1.14-1.42) for women with parity of 0, 2, 3, and ≥ 4, respectively. The proportion of indirect effect (95% CI) mediated by body mass index, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio and body fat percentage was 26.5% (19.2-52.2%), 54.5% (39.4-108.7%), 25.1% (18.2-49.1%), 35.9% (25.6-74.1%), 50.3% (36.5-98.6%) and 15.1% (- 66.4 to 112.3%), respectively. Compared to women with one parity, women with multiparity (≥ 2) had a higher risk of incident type 2 diabetes and up to half of the association was mediated by abdominal obesity.

Indexed as

Diabetes Mellitus, Type 2AgedBiological Specimen BanksCohort StudiesEast Asian PeopleFemaleHumansObesityParityPregnancy

Identifiers

PMID37308533
PMCPMC10261042

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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.