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Abstract
Immunity has shown potentials in informing drug development for cardiometabolic diseases, such as type 2 diabetes (T2D) and coronary artery disease (CAD). Here, we performed a transcriptome-wide Mendelian randomization (MR) study to estimate the putative causal effects of 11,021 gene expression profiles during CD4+ T cells activation on the development of T2D and CAD. Robust MR and colocalization evidence was observed for 162 genes altering T2D risk and 80 genes altering CAD risk, with 12% and 16% respectively demonstrating CD4+ T cell specificity. We observed temporal causal patterns during T cell activation in 69 gene-T2D pairs and 34 gene-CAD pairs. These genes were eight times more likely to show robust genetic evidence. We further identified 25 genes that were targets for drugs under clinical investigation, including LIPA and GCK. This study provides evidence to support immune-to-metabolic disease connections, and prioritises immune-mediated drug targets for cardiometabolic diseases.
Here, the authors identify genes and drug targets that show immune cell-specific and activation time point-specific effects on type 2 diabetes and coronary heart disease and find strong immune-to-metabolic disease connections.
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1 Shanghai Jiao Tong University School of Medicine, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293); Shanghai Jiao Tong University School of Medicine, Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293)
2 University of Bristol, MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603); University of Bristol, Population Health Sciences, Bristol Medical School, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603)
3 University of Bristol, MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603); University of Bristol, Population Health Sciences, Bristol Medical School, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603); University of Bristol, School of Cellular and Molecular Medicine, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603)
4 University of Bristol, MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603); University of Bristol, Population Health Sciences, Bristol Medical School, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603); NIHR Bristol Biomedical Research Centre, Bristol, UK (GRID:grid.511076.4)
5 Shanghai Jiao Tong University School of Medicine, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293); Shanghai Jiao Tong University School of Medicine, Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293); University of Bristol, MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603)