TY - JOUR

T1 - Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

AU - Suzuki, Ken

AU - Hatzikotoulas, Konstantinos

AU - Southam, Lorraine

AU - Taylor, Henry J

AU - Yin, Xianyong

AU - Lorenz, Kim M

AU - Mandla, Ravi

AU - Huerta-Chagoya, Alicia

AU - Melloni, Giorgio E M

AU - Kanoni, Stavroula

AU - Rayner, Nigel W

AU - Bocher, Ozvan

AU - Arruda, Ana Luiza

AU - Sonehara, Kyuto

AU - Namba, Shinichi

AU - Lee, Simon S K

AU - Preuss, Michael H

AU - Petty, Lauren E

AU - Schroeder, Philip

AU - Vanderwerff, Brett

AU - Kals, Mart

AU - Bragg, Fiona

AU - Lin, Kuang

AU - Guo, Xiuqing

AU - Zhang, Weihua

AU - Yao, Jie

AU - Kim, Young Jin

AU - Graff, Mariaelisa

AU - Takeuchi, Fumihiko

AU - Nano, Jana

AU - Lamri, Amel

AU - Nakatochi, Masahiro

AU - Moon, Sanghoon

AU - Scott, Robert A

AU - Cook, James P

AU - Lee, Jung-Jin

AU - Pan, Ian

AU - Taliun, Daniel

AU - Parra, Esteban J

AU - Grarup, Niels

AU - Chen, Wei-Min

AU - Bork-Jensen, Jette

AU - Islam, Md Tariqul

AU - Jørgensen, Torben

AU - Linneberg, Allan

AU - Witte, Daniel R

AU - Lind, Lars

AU - Hansen, Torben

AU - Pedersen, Oluf

AU - Loos, Ruth J F

AU - VA Million Veteran Program

N1 - © 2024. The Author(s).

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Humans

KW - Adipocytes/metabolism

KW - Chromatin/genetics

KW - Coronary Artery Disease/complications

KW - Diabetes Mellitus, Type 2/classification

KW - Diabetic Nephropathies/complications

KW - Disease Progression

KW - Endothelial Cells/metabolism

KW - Enteroendocrine Cells

KW - Epigenomics

KW - Genetic Predisposition to Disease/genetics

KW - Genome-Wide Association Study

KW - Islets of Langerhans/metabolism

KW - Multifactorial Inheritance/genetics

KW - Peripheral Arterial Disease/complications

KW - Single-Cell Analysis

U2 - 10.1038/s41586-024-07019-6

DO - 10.1038/s41586-024-07019-6

M3 - Journal article

C2 - 38374256

VL - 627

SP - 347

EP - 357

JO - Nature

JF - Nature

SN - 0028-0836

IS - 8003

ER -