Evidence map›Paper›PMID 31821322›Full record

ArticlePLoS medicine2019

Homogeneity in the association of body mass index with type 2 diabetes across the UK Biobank: A Mendelian randomization study.

Michael Wainberg, Anubha Mahajan, Anshul Kundaje, Mark I McCarthy, Erik Ingelsson, Nasa Sinnott-Armstrong, Manuel A Rivas

Abstract read
In one paragraph

Article in PLoS medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

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. Trial
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  15. New Horizons: the value of UK Biobank to research on endocrine and metabolic disorders.The Journal of clinical endocrinology and metabolism · 2022
    Article
  16. Article
  17. Article
  18. The Need for a Human Pangenome Reference Sequence.Annual review of genomics and human genetics · 2021
    Review
  19. Review
  20. Article
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

7 authors.

Michael WainbergDepartment of Computer Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0001-6061-0239
Anubha MahajanWellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-5585-3420
Anshul KundajeDepartment of Computer Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0003-3084-2287
Mark I McCarthyWellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-4393-0510
Erik IngelssonMolecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-2256-6972
Nasa Sinnott-ArmstrongDepartment of Genetics, Stanford University, Stanford, California, United States of America.ORCID 0000-0003-4490-0601
Manuel A RivasDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
NHGRI Genome Sequencing Program Coordinating CenterU24HG008956 · NHGRI · RUTGERS, THE STATE UNIV OF N.J. · PI BUYSKE, STEVEN G, MATISE, TARA C. · 2016 to 2020
$4.9M
Decoding the regulatory architecture of the human genome across cell types, individuals and diseaseU01HG009431 · NHGRI · STANFORD UNIVERSITY · PI PRITCHARD, JONATHAN K · 2017 to 2021
$3.5M
Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to functionR01DK106236 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2016 to 2020
$2.4M
Deep learning frameworks for regulatory genomics.DP2GM123485 · NIGMS · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL · 2016 to 2016
$2.4M
SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASESR01HG010140 · NHGRI · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2018 to 2022
$2.0M
Integrating genome-scale data to reveal causal mechanisms in type 2 diabetesU01DK105535 · NIDDK · UNIVERSITY OF OXFORD · PI GLOYN, ANNA LOUISE · 2015 to 2019
$1.7M
Causal associations of circulating biomarkers with cardiovascular diseaseR01HL135313 · NHLBI · STANFORD UNIVERSITY · PI INGELSSON, ERIK · 2017 to 2019
$1.6M
Department of Health NF-SI-0617-10090Medical Research Council MC_PC_12028Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG010140NHGRI NIH HHS U01 HG009080NHGRI NIH HHS U01 HG009431NHGRI NIH HHS U24 HG008956NHLBI NIH HHS R01 HL135313NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK106236NIDDK NIH HHS U01 DK105535NIGMS NIH HHS DP2 GM123485Wellcome TrustWellcome Trust 090532Wellcome Trust 098381Wellcome Trust 203141
6 · The paper itself

Abstract

backgroundLifestyle interventions to reduce body mass index (BMI) are critical public health strategies for type 2 diabetes prevention. While weight loss interventions have shown demonstrable benefit for high-risk and prediabetic individuals, we aimed to determine whether the same benefits apply to those at lower risk. METHODS AND

findingsWe performed a multi-stratum Mendelian randomization study of the effect size of BMI on diabetes odds in 287,394 unrelated individuals of self-reported white British ancestry in the UK Biobank, who were recruited from across the United Kingdom from 2006 to 2010 when they were between the ages of 40 and 69 years. Individuals were stratified on the following diabetes risk factors: BMI, diabetes family history, and genome-wide diabetes polygenic risk score. The main outcome measure was the odds ratio of diabetes per 1-kg/m2 BMI reduction, in the full cohort and in each stratum. Diabetes prevalence increased sharply with BMI, family history of diabetes, and genetic risk. Conversely, predicted risk reduction from weight loss was strikingly similar across BMI and genetic risk categories. Weight loss was predicted to substantially reduce diabetes odds even among lower-risk individuals: for instance, a 1-kg/m2 BMI reduction was associated with a 1.37-fold reduction (95% CI 1.12-1.68) in diabetes odds among non-overweight individuals (BMI < 25 kg/m2) without a family history of diabetes, similar to that in obese individuals (BMI ≥ 30 kg/m2) with a family history (1.21-fold reduction, 95% CI 1.13-1.29). A key limitation of this analysis is that the BMI-altering DNA sequence polymorphisms it studies represent cumulative predisposition over an individual's entire lifetime, and may consequently incorrectly estimate the risk modification potential of weight loss interventions later in life.

conclusionsIn a population-scale cohort, lower BMI was consistently associated with reduced diabetes risk across BMI, family history, and genetic risk categories, suggesting all individuals can substantially reduce their diabetes risk through weight loss. Our results support the broad deployment of weight loss interventions to individuals at all levels of diabetes risk.

Indexed as

AdultAgedBiological Specimen BanksBody Mass IndexDiabetes Mellitus, Type 2FemaleHumansMaleMendelian Randomization AnalysisMiddle AgedMultifactorial InheritanceObesityRisk FactorsUnited Kingdom

Identifiers

PMID31821322
PMCPMC6903707

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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.