Evidence map›Paper›PMID 27435863›Full record

ArticleDiabetologia2016

Can genetic evidence help us to understand the fetal origins of type 2 diabetes?

Rachel M Freathy

Open access · bronzeAbstract readComment
In one paragraph

Article in Diabetologia, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
2.6field-weighted citation impact, top 9% 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, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Rachel M FreathyInstitute of Biomedical and Clinical Science, University of Exeter Medical School, The Research, Innovation, Learning and Development (RILD) building, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK. r.freathy@ex.ac.uk.
University of Exeter · GB

Funding

Wellcome Trust 104150
6 · The paper itself

Abstract

Lower birthweight is consistently associated with a higher risk of type 2 diabetes in observational studies, but the mechanisms underlying this association are not fully understood. Animal models and studies of famine-exposed populations have provided support for the developmental origins hypothesis, under which exposure to poor intrauterine nutrition results in reduced fetal growth and also contributes to the developmental programming of later type 2 diabetes risk. However, testing this hypothesis is difficult in human studies and studies aiming to do so are mostly observational and have limited scope for causal inference due to the presence of confounding factors. In this issue of Diabetologia, Wang et al (doi: 10.1007/s00125-016-4019-z ) have used genetic variation associated with birthweight in a Mendelian randomisation analysis to assess evidence of a causal link between fetal growth and type 2 diabetes. Mendelian randomisation offers the potential to examine associations between exposures and outcomes in the absence of factors that would normally confound observational studies. This commentary discusses the results of the Mendelian randomisation study carried out by Wang et al, in relation to the study design and its limitations. Challenges and opportunities for future studies are also outlined.

Indexed as

Birth WeightDiabetes Mellitus, Type 2Fetal DevelopmentGenome-Wide Association StudyHumansInfant, Low Birth WeightMendelian Randomization AnalysisBirthweightFetal growthGeneticsGenome-wide association studyInstrumental variableMendelian randomisationPleiotropyType 2 diabetes

Identifiers

PMID27435863
PMCPMC4973887
OpenAlexW2491592310

What OpenQuestion holds

Textmetadata
LicenceTDM
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.