Evidence map›Paper›PMID 33568359›Full record

ArticleBMJ open diabetes research & care2021

Associations of pre-pregnancy impaired fasting glucose and body mass index among pregnant women without pre-existing diabetes with offspring being large for gestational age and preterm birth: a cohort study in China.

Jie Tang, Ruoling Chen, Yizhen Yu, Wei Bao, Henning Tiemeier, Amanda Rodney, Xinhong Zhu, Mingzhen Li, Dongming Huang, Qingguo Zhao

Open access · goldAbstract read
In one paragraph

Article in BMJ open diabetes research & care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

6 citing papers in PubMed, 9 citations in OpenAlex.

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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

10 authors at 6 institutions in 3 countries.

Jie TangDepartment of Preventive Medicine, School of Public Health, Guangzhou Medical University, Guangzhou, China gytanjie@163.com.ORCID 0000-0002-0568-8444
Ruoling ChenFaculty of Education, Health and Wellbeing, University of Wolverhampton, Wolverhampton, UK.
Yizhen YuDepartment of Maternal and Child Health Care, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Wei BaoDepartment of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USA.
Henning TiemeierDepartment of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.
Amanda RodneyFaculty of Education, Health and Wellbeing, University of Wolverhampton, Wolverhampton, UK.ORCID 0000-0001-5261-8150
Xinhong ZhuDepartment of Children Health Care, Guangdong Women and Children Hospital, Guangzhou, China.
Mingzhen LiDepartment of Epidemiology, Family Planning Special Hospital of Guangdong, Guangzhou, China.
Dongming HuangDepartment of Epidemiology, Family Planning Special Hospital of Guangdong, Guangzhou, China.
Qingguo ZhaoDepartment of Epidemiology, Family Planning Special Hospital of Guangdong, Guangzhou, China.
Guangdong Provincial Family Planning Hospital · CNUniversity of Wolverhampton · GBGuangdong Province Women and Children Hospital · CNHarvard University · USHuazhong University of Science and Technology · CNUniversity of Iowa · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAssociations of pre-pregnancy impaired fasting glucose (IFG) and body mass index (BMI) with large for gestational age (LGA) and preterm birth (PTB) have been poorly understood. We aimed to investigate the associations of maternal BMI, separately and together with pre-pregnancy IFG, with LGA and PTB in Chinese population. We also aimed to quantify these associations by maternal age. RESEARCH DESIGN AND

methodsThis was a retrospective cohort study of women from the National Free Preconception Health Examination Project with singleton birth from 121 counties/districts in 21 cities of Guangdong Province, China, from January 1, 2013 to December 31, 2017. Women were included if they did not have pre-existing chronic diseases (diabetes, hypertension, etc). Participants were divided into eight groups according to their BMI (underweight (BMI <18.5 kg/m

resultsWe included 634 030 women. The incidences of LGA, severe LGA, PTB and early PTB for the study population were 7.1%, 2.5%, 5.1% and 1.1%, respectively. Compared with normal weight mothers with normoglycemia, overweight and obese mothers irrespective of IFG had a higher risk of LGA (eg, obesity with IFG aIRR 1.85 (1.60-2.14)) and severe LGA (eg, obesity with IFG 2.19 (1.73-2.79)). The associations of BMI and pre-pregnancy fasting glucose status with LGA were similar found among women of all age groups. Underweight with normoglycemia had 6.0% higher risk of PTB (1.06 (1.03-1.09)) and 8.0% higher risk of early PTB (1.08 (1.02-1.17)), underweight with IFG had 14.0% higher risk of PTB (1.14 (1.02-1.27)), and obese mothers with IFG had 45.0% higher risk of PTB (1.45 (1.18-1.78)). The associations of BMI and pre-pregnancy fasting glucose status with PTB differed by maternal age.

conclusionOverweight and obesity regardless of IFG were associated with an increased risk of LGA, and these associations were similarly observed among mothers of all age groups. Underweight regardless of IFG, and obesity with IFG were associated with an increased risk of PTB, but the associations differed by maternal age. Findings from this study may have implications for risk assessment and counselling before pregnancy.

Indexed as

Prediabetic StatePremature BirthBody Mass IndexChinaCohort StudiesFastingFemaleGestational AgeGlucoseHumansInfant, NewbornPregnancyPregnant PeopleRetrospective StudiesGlucosebody mass indexcohort studieshigh-riskprediabetic statepregnancy

Identifiers

PMID33568359
PMCPMC7878137
OpenAlexW3128064538

What OpenQuestion holds

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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.