Evidence map›Paper›PMID 32518355›Full record

SynthesisInternational journal of obesity (2005)2021

Association of maternal caffeine intake during pregnancy with low birth weight, childhood overweight, and obesity: a meta-analysis of cohort studies.

Feng Jin, Chong Qiao

Abstract readMeta-AnalysisReview
PubMed Publisher
In one paragraph

Synthesis in International journal of obesity (2005), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
2.8field-weighted citation impact, top 10% 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.

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  14. Maternal caffeine intake and DNA methylation in newborn cord blood.The American journal of clinical nutrition · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Feng JinDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
Chong QiaoDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China. qiaochong2002@hotmail.com.
China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEpidemiological studies reported inconsistent results on the associations between maternal caffeine intake during pregnancy and risk of low birth weight (LBW) and childhood overweight and obesity in their offspring.

methodsWe conducted a meta-analysis of cohort studies to quantitatively assess these associations. Pertinent studies were identified by searching PubMed and Embase through June 2019. Study-specifics risk estimates were combined using fixed effects models, or random-effects models when significant heterogeneity was detected. Dose-response analysis was modeled by using restricted cubic splines.

resultsA total of 15 cohort studies, with 102,347 pregnancy women, was included in the meta-analysis. The pooled relative risk (RR) for LBW was 1.33 (95% CI: 1.12, 1.57) for mothers with the highest compared with the lowest level of caffeine intake during pregnancy, with significant heterogeneity across studies (I

conclusionsMaternal caffeine intake during pregnancy is associated with higher risk of LBW and childhood overweight and obesity. Further studies may focus on investigating the potential mechanisms before the recommendation of complete avoidance of caffeine intake during pregnancy.

Indexed as

Infant, Low Birth WeightAdolescentAdultBody Mass IndexCaffeineChildChild DevelopmentChild, PreschoolCohort StudiesFemaleHumansInfant, NewbornMaternal ExposureMothersPediatric ObesityPregnancyCaffeine

Identifiers

PMID32518355
OpenAlexW3033169501

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.