Evidence map›Paper›PMID 41593238›Full record

ArticleNature metabolism2026

Unravelling the molecular mechanisms causal to type 2 diabetes across global populations and disease-relevant tissues.

Ozvan Bocher, Ana Luiza Arruda, Satoshi Yoshiji, Chi Zhao, Alicia Huerta-Chagoya, Chen-Yang Su, Xianyong Yin, Davis Cammann, Henry J Taylor, Jingchun Chen and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

24 authors.

Ozvan Bocher *Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. ozvan.bocher@univ-brest.fr.ORCID http://orcid.org/0000-0002-2467-9236
Ana Luiza Arruda *Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Satoshi Yoshiji *Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-8863-2413
Chi Zhao *Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.
Alicia Huerta-ChagoyaPrograms in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Chen-Yang SuMcGill Genome Centre, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0001-6071-4660
Xianyong YinDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China.
Davis CammannNevada Institute of Personalized Medicine, University of Nevada, Las Vegas, Las Vegas, NV, USA.
Henry J TaylorCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0003-2088-5240
Jingchun ChenNevada Institute of Personalized Medicine, University of Nevada, Las Vegas, Las Vegas, NV, USA.
Ken SuzukiDepartment of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Ravi MandlaPrograms in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Ta-Yu YangCenter for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.ORCID http://orcid.org/0000-0002-5379-5340
Fumihiko MatsudaCenter for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Josep M MercaderPrograms in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-8494-3660
Jason FlannickPrograms in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-3618-795X
James B MeigsPrograms in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Alexis C WoodUSDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA.
Marijana VujkovicCorporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0003-4924-5714
Benjamin F VoightCorporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-6205-9994
Cassandra N SpracklenDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.ORCID http://orcid.org/0000-0003-3590-7182
Jerome I RotterInstitute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.ORCID http://orcid.org/0000-0001-7191-1723
Andrew P MorrisCentre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0002-6805-6014
Eleftheria ZegginiInstitute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. eleftheria.zeggini@helmholtz-munich.de.ORCID http://orcid.org/0000-0003-4238-659X

Funding

Mapping the gene regulatory architecture of pancreatic islet-specific cell types to diabetesR01DK140340 · UNIVERSITY OF PENNSYLVANIA · 2025 to 2025
$787k
American Diabetes Association (ADA) 11-22-JDFPM-06American Diabetes Association (ADA) 11-23-PDF-35Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada) AD6-200177Japan Society for the Promotion of Science London (JSPS London) 202460267NIA NIH HHS R15 AG083618NIDDK NIH HHS R01 DK140340NIDDK NIH HHS UM1 DK126194RCUK | MRC | Medical Research Foundation MR/W029626/1
6 · The paper itself

Abstract

Type 2 diabetes (T2D) is a prevalent disease arising from complex molecular mechanisms. Here we leverage T2D genetic associations to identify causal molecular mechanisms in an ancestry-aware and tissue-aware manner. Using two-sample Mendelian randomization corroborated by colocalization across four global ancestries, we analyse 20,307 gene and 1,630 protein expression levels using blood-derived cis-quantitative trait loci (QTLs). We detect causal effects of genetically predicted levels of 335 genes and 46 proteins on T2D risk, with 16.4% and 50% replication in independent cohorts, respectively. Using gene expression cis-QTLs derived from seven T2D-relevant tissues, we identify causal links between the expression of 676 genes and T2D risk, refining known associations such as BAK1 and describing additional ones like CPXM1. Causal effects are mostly shared across ancestries but are highly heterogeneous across tissues. Our findings provide insights into cross-ancestry and tissue-informed multi-omics causal inference approaches and demonstrate their power in uncovering molecular processes driving T2D.

Indexed as

Diabetes Mellitus, Type 2Genetic Predisposition to DiseaseGenome-Wide Association StudyHumansMendelian Randomization AnalysisPolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID41593238
PMCPMC12945685

What Socratic holds

Textmetadata
LicenceCC BY
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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.