Evidence map›Paper›PMID 41986815›Full record

ArticleDiabetologia2026

Multi-tissue multi-omics integration reveals tissue-specific pathways, gene networks and drug candidates for type 1 diabetes.

Montgomery Blencowe, Zara Saleem, Ruoshui Liu, Margaret Wang, I-Hsin Tseng, Julian Wier, Stefan Mutter, Florin Vaida, Yi Guo, Niina Sandholm and 3 more

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In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

13 authors.

Montgomery BlencoweDepartment of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA. montyblencowe@ucla.edu.
Zara SaleemDepartment of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA.
Ruoshui LiuDepartment of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA.
Margaret WangDepartment of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA.
I-Hsin TsengDepartment of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA.
Julian WierDepartment of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA.
Stefan MutterResearch Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Florin VaidaDivision of Biostatistics and Bioinformatics, School of Public Health, University of California San Diego, La Jolla, CA, USA.
Yi GuoCollege of Medicine, University of Florida, Gainesville, FL, USA.
Niina SandholmResearch Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Courtney AckeifiBreakthrough T1D, New York, NY, USA.
Daniel L KaufmanDepartment of Molecular and Medical Pharmacology, UCLA School of Medicine, University of California, Los Angeles, CA, USA.
Xia YangDepartment of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA. xyang123@ucla.edu.

Funding

Juvenile Diabetes Research Foundation International JDRF 2-SRA-2021-1028-S-B
6 · The paper itself

Abstract

aims/hypothesisAlthough genome-wide association studies (GWAS) have identified loci associated with type 1 diabetes, the specific pathways and regulatory networks linking these loci to disease pathology remain largely unknown. We hypothesised that type 1 diabetes genetic risk factors disrupt tissue-specific biological pathways and gene networks that ultimately lead to beta cell loss.

methodsWe conducted a multi-tissue multi-omics analysis that integrates human GWAS data for type 1 diabetes with tissue-specific regulatory data for gene expression and gene network models across relevant tissues to highlight key pathways and key driver (KD) genes contributing to type 1 diabetes pathogenesis. KD genes were validated using islet-specific gene expression and protein data from non-obese diabetic (NOD) mice compared with type 2 diabetic and non-diabetic mouse models. Drug repositioning predictions were generated using the INCS L1000 and PharmOmics platforms, and candidate drugs were tested using electronic medical records (EMRs) of individuals with type 1 diabetes from the OneFlorida+ Clinical Data Network.

resultsOur integrative genomics approach identified known immune pathways across multiple tissues, such as adaptive immune responses, cytokine-mediated inflammation, primary immunodeficiency, and interactions between lymphoid and non-lymphoid cells. Tissue-specific signals included genes related to type 2 diabetes in lymphocytes, viral response pathways in macrophages and monocytes, and Notch signalling in adipose tissue and immune cells. In pancreatic islet analysis, we observed significant enrichment for type 1 diabetes and type 2 diabetes gene sets alongside immune-related pathways, including antigen processing, systemic lupus erythematosus and IFN signalling. Removing HLA genes from the analysis revealed additional immune pathways, such as retinoic acid-inducible gene I (RIG-I)/melanoma differentiation-associated protein 5 (MDA5) induction of interferons, together with melanogenesis, steroid hormone synthesis and iron transport. Network modelling highlighted the autoimmune basis of disease, with KDs such as FYN, TAP1, WAS and HLA-B/C/G, as well as additional immunomodulatory proteins such as LCK, LCP2 and genes such as EMR1 and GC. These KDs were further supported by gene and protein expression data from NOD mice. We additionally highlight various drug classes that target the type 1 diabetes genetic networks and may be useful to delay type 1 diabetes development; some of these were supported by our EMR screen. CONCLUSIONS/

interpretationOur multi-tissue multi-omics approach provides a detailed landscape of the tissue-specific genetic networks and regulators underlying type 1 diabetes. This analysis confirms the roles of known immune pathways while uncovering additional regulatory elements and disease-associated networks, thus expanding our understanding of type 1 diabetes pathogenesis. The identification of potential drug candidates through network analysis, with supporting evidence from EMRs, offers potential therapeutic strategies for targeting disease pathways and holds promise for delaying or preventing type 1 diabetes progression.

Indexed as

Diabetes Mellitus, Type 1Gene Regulatory NetworksAnimalsGenetic Predisposition to DiseaseGenome-Wide Association StudyGenomicsHumansMiceMice, Inbred NODMultiomicsDrug repositioningElectronic medical recordsGene networksGenome-wide association studiesKey driversMulti-omicsType 1 diabetes

Identifiers

PMID41986815
PMCPMC13236778

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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.