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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Suzuki, K. ; Thangam, M. LU ; Lyssenko, V. LU ; Melander, O. LU orcid ; Tuomi, T. LU orcid ; Ahlqvist, E. LU and Zeggini, E. (2024) In Nature 627(8003). p.347-357
Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10−8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are... (More)
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10−8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care. © The Author(s) 2024. (Less)
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Diabetes Mellitus, Type 2, Endothelial Cells, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Islets of Langerhans, Polymorphism, Single Nucleotide, glucose, proinsulin, cell, diabetes, disease severity, etiology, genetic engineering, genome, heterogeneity, molecular analysis, physiology, adipocyte, ancestry group, Article, atrial fibrillation, body mass, chromatin, clinical outcome, coronary artery disease, diabetic retinopathy, disease exacerbation, end stage renal disease, endothelium cell, enteroendocrine cell, epigenetics, follow up, gene cluster, gene frequency, gene mapping, gene set enrichment analysis, genetic analysis, genetic heterogeneity, genetic risk score, genetic variation, genome-wide association study, genotype, genotyping, glucose intake, haplotype, heart death, heart failure, heart infarction, homeostasis model assessment, hospitalization, human, insulin sensitivity, ischemic stroke, linear regression analysis, lipid metabolism, lipodystrophy, metabolic syndrome X, non insulin dependent diabetes mellitus, obesity, outcome assessment, pancreas islet, pathophysiology, peripheral arterial disease, phenotype, principal component analysis, proportional hazards model, quality control, regression analysis, risk factor, single nucleotide polymorphism, survival, genetic predisposition, genetics
in
Nature
volume
627
issue
8003
pages
11 pages
publisher
Nature Publishing Group
external identifiers
  • scopus:85186615049
  • pmid:38374256
ISSN
0028-0836
DOI
10.1038/s41586-024-07019-6
language
English
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yes
id
9da5e6b0-e6bf-4bcc-8678-7ebee61b7eee
date added to LUP
2025-12-12 11:57:44
date last changed
2025-12-13 03:07:30
@article{9da5e6b0-e6bf-4bcc-8678-7ebee61b7eee,
  abstract     = {{Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10−8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care. © The Author(s) 2024.}},
  author       = {{Suzuki, K. and Thangam, M. and Lyssenko, V. and Melander, O. and Tuomi, T. and Ahlqvist, E. and Zeggini, E.}},
  issn         = {{0028-0836}},
  keywords     = {{Diabetes Mellitus, Type 2; Endothelial Cells; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Islets of Langerhans; Polymorphism, Single Nucleotide; glucose; proinsulin; cell; diabetes; disease severity; etiology; genetic engineering; genome; heterogeneity; molecular analysis; physiology; adipocyte; ancestry group; Article; atrial fibrillation; body mass; chromatin; clinical outcome; coronary artery disease; diabetic retinopathy; disease exacerbation; end stage renal disease; endothelium cell; enteroendocrine cell; epigenetics; follow up; gene cluster; gene frequency; gene mapping; gene set enrichment analysis; genetic analysis; genetic heterogeneity; genetic risk score; genetic variation; genome-wide association study; genotype; genotyping; glucose intake; haplotype; heart death; heart failure; heart infarction; homeostasis model assessment; hospitalization; human; insulin sensitivity; ischemic stroke; linear regression analysis; lipid metabolism; lipodystrophy; metabolic syndrome X; non insulin dependent diabetes mellitus; obesity; outcome assessment; pancreas islet; pathophysiology; peripheral arterial disease; phenotype; principal component analysis; proportional hazards model; quality control; regression analysis; risk factor; single nucleotide polymorphism; survival; genetic predisposition; genetics}},
  language     = {{eng}},
  number       = {{8003}},
  pages        = {{347--357}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature}},
  title        = {{Genetic drivers of heterogeneity in type 2 diabetes pathophysiology}},
  url          = {{http://dx.doi.org/10.1038/s41586-024-07019-6}},
  doi          = {{10.1038/s41586-024-07019-6}},
  volume       = {{627}},
  year         = {{2024}},
}