ArticleDiabetologia2023
High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease.
Article in Diabetologia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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59 citing papers in PubMed, 81 citations in OpenAlex.
- Prediabetes Subgroups, Type 2 Diabetes Risk, and Differential Effects of Preventive Interventions.The Journal of clinical endocrinology and metabolism · 2025 · on this mapTrial
- Refining the Genetic Contribution to Type 2 Diabetes Subtypes.Diabetes, obesity & metabolism · 2026Article
- Genetic and molecular signatures highlight diverse pathways linking obesity to type 2 diabetes.Nature communications · 2026Article
- Plasma metabolite association profiles for type 2 diabetes genetic clusters in Finnish men.Diabetologia · 2026Article
- Converging TCF7L2 and CDKAL1 pathways in the pathogenesis of type 2 diabetes mellitus.Acta diabetologica · 2026Review
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- Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning.Cell genomics · 2026Article
- Partitioning genetic pleiotropy within tissue-specific regulatory patterns.Cell genomics · 2026Article
- Functional genomics reveals mediators of beta cell survival in ER stress and type 2 diabetes risk.bioRxiv : the preprint server for biology · 2026Article
- Diverse combination of factors associated with the development of diabetic kidney disease among data-driven diabetes subtypes: analysis of the J-DREAMS registry.Diabetologia · 2026Article
- Five tenets for advancing evidence-based precision medicine.Nature medicine · 2026Review
- Geographical differences in the prevalence of diabetic kidney disease in middle-aged and elderly patients in China: an analysis based on the Bayesian conditional autoregressive model.BMC nephrology · 2026Article
- Bridging the variant-to-function gap in type 2 diabetes: advances and challenges.Diabetologia · 2026Review
- Functional ADAM12 variants modulate proteolytic activity and influence metabolic traits.Scientific reports · 2026Article
- Heterogeneity of diabetes and disease progression with a tree-like representation: findings from the China Cardiometabolic Disease and Cancer Cohort (4C) study.Diabetologia · 2026Article
- Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records.Scientific reports · 2025Article
- Aetiological clustering of newly diagnosed type 2 diabetes using machine learning: a retrospective cross-sectional study in Dubai, UAE.BMJ open · 2025Article
- Polygenic risk score and cluster-based analysis suggests links between type 2 diabetes and vascular dementia in the KARE study.Nature communications · 2025Article
- From omics to AI-mapping the pathogenic pathways in type 2 diabetes.FEBS letters · 2025Review
- Ethnic diversity in precision medicine: a reality or an aspiration?Diabetologia · 2025Review
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Authors and funding
14 authors at 3 institutions in 2 countries.
Funding
Abstract
aims/hypothesisType 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways.
methodsWe developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance.
resultsWe identified ten clusters of genetic loci, recapturing the five from our prior analysis as well as novel clusters related to beta cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein A and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple overlapping clusters with type 2 diabetes. CONCLUSIONS/
interpretationOur approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.
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