Evidence map›Paper›PMID 27053236›Full record

ArticleDiabetologia2016

Evaluation of type 2 diabetes genetic risk variants in Chinese adults: findings from 93,000 individuals from the China Kadoorie Biobank.

Wei Gan, Robin G Walters, Michael V Holmes, Fiona Bragg, Iona Y Millwood, Karina Banasik, Yiping Chen, Huaidong Du, Andri Iona, Anubha Mahajan and 8 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
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 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
6.3field-weighted citation impact, top 4% 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

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.

  1. Pooled it
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  4. Polygenic risk scores for prediction of breast cancer risk in Asian populations.Genetics in medicine : official journal of the American College of Medical Genetics · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors at 5 institutions in 3 countries.

Wei GanOxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital Campus, Old Road, Headington, Oxford, OX3 7LJ, UK.
Robin G WaltersClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Michael V HolmesClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Fiona BraggClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Iona Y MillwoodClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Karina BanasikOxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital Campus, Old Road, Headington, Oxford, OX3 7LJ, UK.
Yiping ChenClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Huaidong DuClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Andri IonaClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Anubha MahajanOxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital Campus, Old Road, Headington, Oxford, OX3 7LJ, UK.
Ling YangClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Zheng BianChinese Academy of Medical Sciences, Dong Cheng District, Beijing, People's Republic of China.
Yu GuoChinese Academy of Medical Sciences, Dong Cheng District, Beijing, People's Republic of China.
Robert J ClarkeClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Liming LiChinese Academy of Medical Sciences, Dong Cheng District, Beijing, People's Republic of China.
Mark I McCarthyOxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital Campus, Old Road, Headington, Oxford, OX3 7LJ, UK. mark.mccarthy@drl.ox.ac.uk.
Zhengming ChenClinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK. zhengming.chen@ctsu.ox.ac.uk.
China Kadoorie Biobank Collaborative Group
University of Oxford · GBCentre for Human Genetics · GBChinese Academy of Medical Sciences & Peking Union Medical College · CNChurchill Hospital · GBUniversity of Copenhagen · DK

Funding

Identifying variants causal for Type 2 Diabetes in Major human populationsU01DK085545 · NIDDK · UNIVERSITY OF OXFORD · PI CHAN, JULIANA CN, EBRAHIM, SHAH BRIAN · 2009 to 2013
$2.8M
Medical Research Council G0802432Medical Research Council MC_PC_13049Medical Research Council MC_PC_14135Medical Research Council MC_U137686851Medical Research Council MC_UU_12026/2NIDDK NIH HHS U01 DK085545Wellcome Trust 088158/Z/09/ZWellcome Trust 104085/Z/14/Z
6 · The paper itself

Abstract

aims/hypothesisGenome-wide association studies (GWAS) have discovered many risk variants for type 2 diabetes. However, estimates of the contributions of risk variants to type 2 diabetes predisposition are often based on highly selected case-control samples, and reliable estimates of population-level effect sizes are missing, especially in non-European populations.

methodsThe individual and cumulative effects of 59 established type 2 diabetes risk loci were measured in a population-based China Kadoorie Biobank (CKB) study of 93,000 Chinese adults, including >7,100 diabetes cases.

resultsAssociation signals were directionally consistent between CKB and the original discovery GWAS: of 56 variants passing quality control, 48 showed the same direction of effect (binomial test, p = 2.3 × 10(-8)). We observed a consistent overall trend towards lower risk variant effect sizes in CKB than in case-control samples of GWAS meta-analyses (mean 19-22% decrease in log odds, p ≤ 0.0048), likely to reflect correction of both 'winner's curse' and spectrum bias effects. The association with risk of diabetes of a genetic risk score, based on lead variants at 25 loci considered to act through beta cell function, demonstrated significant interactions with several measures of adiposity (BMI, waist circumference [WC], WHR and percentage body fat [PBF]; all p interaction < 1 × 10(-4)), with a greater effect being observed in leaner adults. CONCLUSIONS/

interpretationOur study provides further evidence of shared genetic architecture for type 2 diabetes between Europeans and East Asians. It also indicates that even very large GWAS meta-analyses may be vulnerable to substantial inflation of effect size estimates, compared with those observed in large-scale population-based cohort studies. ACCESS TO RESEARCH MATERIALS: Details of how to access China Kadoorie Biobank data and details of the data release schedule are available from www.ckbiobank.org/site/Data+Access .

Indexed as

Biological Specimen BanksAdultAsian PeopleChinaDiabetes Mellitus, Type 2FemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansMaleMiddle AgedPolymorphism, Single NucleotideWaist CircumferenceBiobankChineseGenetic risk scorePopulation-based cohort studiesType 2 diabetesWinner’s curse

Identifiers

PMID27053236
PMCPMC4901105
OpenAlexW2325832504

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Registered trials

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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.