Evidence mapPaperPMID 38530928Full record

ArticleDiabetes2024

Genetic Evidence for Distinct Biological Mechanisms That Link Adiposity to Type 2 Diabetes: Toward Precision Medicine.

Angela Abraham, Madeleine Cule, Marjola Thanaj, Nicolas Basty, M Amin Hashemloo, Elena P Sorokin, Brandon Whitcher, Stephen Burgess, Jimmy D Bell, Naveed Sattar and 2 more

Open access · bronzeAbstract read
In one paragraph

Article in Diabetes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.6field-weighted citation impact, top 5% 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

14 citing papers in PubMed, 12 citations in OpenAlex.

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  14. Use of Cross-Sectional Imaging Body Composition Assessment to Predict Pancreas Transplant Outcomes.Transplant international : official journal of the European Society for Organ Transplantation · 2025
    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

12 authors at 7 institutions in 2 countries.

Angela AbrahamJoseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, U.K.ORCID 0000-0002-9010-8909
Madeleine CuleCalico Life Sciences LLC, South San Francisco, CA.
Marjola ThanajResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K.
Nicolas BastyResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K.
M Amin HashemlooDepartment of Life Sciences, Brunel University London, Uxbridge, U.K.
Elena P SorokinCalico Life Sciences LLC, South San Francisco, CA.
Brandon WhitcherResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K.
Stephen BurgessMedical Research Council Biostatistics Unit, University of Cambridge, Cambridge, U.K.
Jimmy D BellResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K.
Naveed SattarSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, U.K.ORCID 0000-0002-1604-2593
E Louise ThomasResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K.
Hanieh YaghootkarJoseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, U.K.ORCID 0000-0001-9672-9477
University of Westminster · GBEnzo Life Sciences (United States) · USUniversity of Lincoln · GBBrunel University of London · GBMRC Biostatistics Unit · GBRoyal Marsden NHS Foundation Trust · GBUniversity of Glasgow · GB

Funding

British Heart Foundation RE/18/6/34217Diabetes UK 17/0005594Wellcome Trust 225790
6 · The paper itself

Abstract

We aimed to unravel the mechanisms connecting adiposity to type 2 diabetes. We used MR-Clust to cluster independent genetic variants associated with body fat percentage (388 variants) and BMI (540 variants) based on their impact on type 2 diabetes. We identified five clusters of adiposity-increasing alleles associated with higher type 2 diabetes risk (unfavorable adiposity) and three clusters associated with lower risk (favorable adiposity). We then characterized each cluster based on various biomarkers, metabolites, and MRI-based measures of fat distribution and muscle quality. Analyzing the metabolic signatures of these clusters revealed two primary mechanisms connecting higher adiposity to reduced type 2 diabetes risk. The first involves higher adiposity in subcutaneous tissues (abdomen and thigh), lower liver fat, improved insulin sensitivity, and decreased risk of cardiometabolic diseases and diabetes complications. The second mechanism is characterized by increased body size and enhanced muscle quality, with no impact on cardiometabolic outcomes. Furthermore, our findings unveil diverse mechanisms linking higher adiposity to higher disease risk, such as cholesterol pathways or inflammation. These results reinforce the existence of adiposity-related mechanisms that may act as protective factors against type 2 diabetes and its complications, especially when accompanied by reduced ectopic liver fat. ARTICLE HIGHLIGHTS:

Indexed as

AdiposityDiabetes Mellitus, Type 2Precision MedicineAllelesCluster AnalysisFemaleGenetic VariationGenome-Wide Association StudyHumansMaleQuantitative Trait Loci

Identifiers

PMID38530928
PMCPMC11109787
OpenAlexW4393187007

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.