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Hu F, Xiong L, Li Z, Li L, Wang L, Wang X, Zhou X, Zheng Y. Deciphering the shared mechanisms of Gegen Qinlian Decoction in treating type 2 diabetes and ulcerative colitis via bioinformatics and machine learning. Front Med (Lausanne) 2024; 11:1406149. [PMID: 38962743 PMCID: PMC11220276 DOI: 10.3389/fmed.2024.1406149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Accepted: 06/07/2024] [Indexed: 07/05/2024] Open
Abstract
Background Although previous clinical studies and animal experiments have demonstrated the efficacy of Gegen Qinlian Decoction (GQD) in treating Type 2 Diabetes Mellitus (T2DM) and Ulcerative Colitis (UC), the underlying mechanisms of its therapeutic effects remain elusive. Purpose This study aims to investigate the shared pathogenic mechanisms between T2DM and UC and elucidate the mechanisms through which GQD modulates these diseases using bioinformatics approaches. Methods Data for this study were sourced from the Gene Expression Omnibus (GEO) database. Targets of GQD were identified using PharmMapper and SwissTargetPrediction, while targets associated with T2DM and UC were compiled from the DrugBank, GeneCards, Therapeutic Target Database (TTD), DisGeNET databases, and differentially expressed genes (DEGs). Our analysis encompassed six approaches: weighted gene co-expression network analysis (WGCNA), immune infiltration analysis, single-cell sequencing analysis, machine learning, DEG analysis, and network pharmacology. Results Through GO and KEGG analysis of weighted gene co-expression network analysis (WGCNA) modular genes and DEGs intersection, we found that the co-morbidity between T2DM and UC is primarily associated with immune-inflammatory pathways, including IL-17, TNF, chemokine, and toll-like receptor signaling pathways. Immune infiltration analysis supported these findings. Three distinct machine learning studies identified IGFBP3 as a biomarker for GQD in treating T2DM, while BACE2, EPHB4, and EPHA2 emerged as biomarkers for GQD in UC treatment. Network pharmacology revealed that GQD treatment for T2DM and UC mainly targets immune-inflammatory pathways like Toll-like receptor, IL-17, TNF, MAPK, and PI3K-Akt signaling pathways. Conclusion This study provides insights into the shared pathogenesis of T2DM and UC and clarifies the regulatory mechanisms of GQD on these conditions. It also proposes novel targets and therapeutic strategies for individuals suffering from T2DM and UC.
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Affiliation(s)
| | | | | | | | | | | | - Xuemei Zhou
- College of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, China
| | - Yujiao Zheng
- College of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, China
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Wang HH, Chong M, Perrot N, Feiner J, Hess S, Yusuf S, Gerstein H, Paré G, Pigeyre M. Vaspin: A Novel Biomarker Linking Gluteofemoral Body Fat and Type 2 Diabetes Risk. Diabetes Care 2024; 47:259-266. [PMID: 38055934 DOI: 10.2337/dc23-1488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/06/2023] [Indexed: 12/08/2023]
Abstract
OBJECTIVE To determine whether adiposity depots modulate vaspin levels and whether vaspin predicts type 2 diabetes (T2D) risk, through epidemiological and genetic analyses. RESEARCH DESIGN AND METHODS We assessed the relationship of plasma vaspin concentration with incident and prevalent T2D and adiposity-related variables in 1) the Prospective Urban and Rural Epidemiology (PURE) biomarker substudy (N = 10,052) and 2) the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial (N = 7,840), using regression models. We then assessed whether vaspin is causally associated with T2D and whether genetic variants associated with MRI-measured adiposity depots modulate vaspin levels, using two-sample Mendelian randomization (MR). RESULTS A 1-SD increase in circulating vaspin levels was associated with a 16% increase in incident T2D in the PURE cohort (hazard ratio 1.16; 95% CI 1.09-1.23; P = 4.26 × 10-7) and prevalent T2D in the ORIGIN cohort (odds ratio [OR] 1.16; 95% CI 1.07-1.25; P = 2.17 × 10-4). A 1-unit increase in BMI and triglyceride levels was associated with a 0.08-SD (95% CI 0.06-0.10; P = 2.04 × 10-15) and 0.06-SD (95% CI 0.04-0.08; P = 4.08 × 10-13) increase, respectively, in vaspin in the PURE group. Consistent associations were observed in the ORIGIN cohort. MR results reinforced the association between vaspin and BMI-adjusted T2D risk (OR 1.01 per 1-SD increase in vaspin level; 95% CI 1.00-1.02; P = 2.86 × 10-2) and showed that vaspin was increased by 0.10 SD per 1-SD decrease in genetically determined gluteofemoral adiposity (95% CI 0.02-0.18; P = 2.01 × 10-2). No relationships were found between subcutaneous or visceral adiposity and vaspin. CONCLUSIONS These findings support that higher vaspin levels are related to increased T2D risk and reduced gluteofemoral adiposity, positioning vaspin as a promising clinical predictor for T2D.
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Affiliation(s)
- Harry Hezhou Wang
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Nicolas Perrot
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - James Feiner
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sibylle Hess
- Global Medical Diabetes, Sanofi, Frankfurt, Germany
| | - Salim Yusuf
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Department of Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Hertzel Gerstein
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Department of Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Marie Pigeyre
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Department of Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
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Xourafa G, Korbmacher M, Roden M. Inter-organ crosstalk during development and progression of type 2 diabetes mellitus. Nat Rev Endocrinol 2024; 20:27-49. [PMID: 37845351 DOI: 10.1038/s41574-023-00898-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/29/2023] [Indexed: 10/18/2023]
Abstract
Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.
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Affiliation(s)
- Georgia Xourafa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Melis Korbmacher
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany.
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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Franks PW, Cefalu WT, Dennis J, Florez JC, Mathieu C, Morton RW, Ridderstråle M, Sillesen HH, Stehouwer CDA. Precision medicine for cardiometabolic disease: a framework for clinical translation. Lancet Diabetes Endocrinol 2023; 11:822-835. [PMID: 37804856 DOI: 10.1016/s2213-8587(23)00165-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.
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Affiliation(s)
- Paul W Franks
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
| | - William T Cefalu
- Division of Diabetes, Endocrinology and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - John Dennis
- Institute of Biomedical and Clinical Science, Royal Devon and Exeter Hospital, University of Exeter, Exeter, UK
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Chantal Mathieu
- Clinical and Experimental Endocrinology, UZ Gasthuisberg, KU Leuven, Leuven, Belgium
| | - Robert W Morton
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | | | - Henrik H Sillesen
- Department of Clinical Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | - Coen D A Stehouwer
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands; Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, Netherlands
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Khan I, Chong M, Le A, Mohammadi-Shemirani P, Morton R, Brinza C, Kiflen M, Narula S, Akhabir L, Mao S, Morrison K, Pigeyre M, Paré G. Surrogate Adiposity Markers and Mortality. JAMA Netw Open 2023; 6:e2334836. [PMID: 37728925 PMCID: PMC10512100 DOI: 10.1001/jamanetworkopen.2023.34836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/15/2023] [Indexed: 09/22/2023] Open
Abstract
Importance Body mass index (BMI) is an easily obtained adiposity surrogate. However, there is variability in body composition and adipose tissue distribution between individuals with the same BMI, and there is controversy regarding the BMI associated with the lowest mortality risk. Objective To evaluate which of BMI, fat mass index (FMI), and waist-to-hip (WHR) has the strongest and most consistent association with mortality. Design, Setting, and Participant This cohort study used incident deaths from the UK Biobank (UKB; 2006-2022), which includes data from 22 clinical assessment centers across the United Kingdom. UKB British participants of British White ancestry (N = 387 672) were partitioned into a discovery cohort (n = 337 078) and validation cohort (n = 50 594), with the latter consisting of 25 297 deaths and 25 297 controls. The discovery cohort was used to derive genetically determined adiposity measures while the validation cohort was used for analyses. Exposure-outcome associations were analyzed through observational and mendelian randomization (MR) analyses. Exposures BMI, FMI, and WHR. Main Outcomes and Measures All-cause and cause-specific (cancer, cardiovascular disease [CVD], respiratory disease, or other causes) mortality. Results There were 387 672 and 50 594 participants in our observational (mean [SD] age, 56.9 [8.0] years; 177 340 [45.9%] male, 210 332 [54.2%], female), and MR (mean [SD] age, 61.6 [6.2] years; 30 031 [59.3%] male, 20 563 [40.6%], female) analyses, respectively. Associations between measured BMI and FMI with all-cause mortality were J-shaped, whereas the association of WHR with all-cause mortality was linear using the hazard ratio (HR) scale (HR per SD increase of WHR, 1.41 [95% CI, 1.38-1.43]). Genetically determined WHR had a stronger association with all-cause mortality than BMI (odds ratio [OR] per SD increase of WHR, 1.51 [95% CI, 1.32-1.72]; OR per SD increase of BMI, 1.29 [95% CI, 1.20-1.38]; P for heterogeneity = .02). This association was stronger in male than female participants (OR, 1.89 [95% CI, 1.54-2.32]; P for heterogeneity = .01). Unlike BMI or FMI, the genetically determined WHR-all-cause mortality association was consistent irrespective of observed BMI. Conclusions and Relevance In this cohort study, WHR had the strongest and most consistent association with mortality irrespective of BMI. Clinical recommendations should consider focusing on adiposity distribution compared with mass.
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Affiliation(s)
- Irfan Khan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- College of Medicine and Health, University College Cork, Cork, Ireland
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Ann Le
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Pedrum Mohammadi-Shemirani
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Robert Morton
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Christina Brinza
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
- School of Medicine, Queen’s University, Kingston, Ontario, Canada
| | - Michel Kiflen
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
- Temerty Faculty of Medicine, University of Toronto, Medical Sciences Building, Toronto, Ontario, Canada
| | - Sukrit Narula
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Loubna Akhabir
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Shihong Mao
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Katherine Morrison
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
| | - Marie Pigeyre
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Barley Cardiac, Vascular and Stroke Research, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
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Mohammadi-Shemirani P, Sood T, Paré G. From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators. Curr Atheroscler Rep 2023; 25:55-65. [PMID: 36595202 PMCID: PMC9807989 DOI: 10.1007/s11883-022-01078-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE OF REVIEW 'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed. RECENT FINDINGS Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.
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Affiliation(s)
- Pedrum Mohammadi-Shemirani
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
| | - Tushar Sood
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON Canada
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7
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Coral DE, Fernandez-Tajes J, Tsereteli N, Pomares-Millan H, Fitipaldi H, Mutie PM, Atabaki-Pasdar N, Kalamajski S, Poveda A, Miller-Fleming TW, Zhong X, Giordano GN, Pearson ER, Cox NJ, Franks PW. A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes. Nat Metab 2023; 5:237-247. [PMID: 36703017 PMCID: PMC9970876 DOI: 10.1038/s42255-022-00731-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/20/2022] [Indexed: 01/27/2023]
Abstract
Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.
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Affiliation(s)
- Daniel E Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
| | - Juan Fernandez-Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Neli Tsereteli
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Pomares-Millan
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Fitipaldi
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Pascal M Mutie
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Naeimeh Atabaki-Pasdar
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Alaitz Poveda
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Tyne W Miller-Fleming
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xue Zhong
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Ewan R Pearson
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Population Health and Genomics, University of Dundee, Dundee, UK
| | - Nancy J Cox
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Sarnowski C, Conomos MP, Vasan RS, Meigs JB, Dupuis J, Liu CT, Leong A. Genetic Effect on Body Mass Index and Cardiovascular Disease Across Generations. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e003858. [PMID: 36598822 PMCID: PMC9974769 DOI: 10.1161/circgen.122.003858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 10/05/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Whether genetics contribute to the rising prevalence of obesity or its cardiovascular consequences in today's obesogenic environment remains unclear. We sought to determine whether the effects of a higher aggregate genetic burden of obesity risk on body mass index (BMI) or cardiovascular disease (CVD) differed by birth year. METHODS We split the FHS (Framingham Heart Study) into 4 equally sized birth cohorts (birth year before 1932, 1932 to 1946, 1947 to 1959, and after 1960). We modeled a genetic predisposition to obesity using an additive genetic risk score (GRS) of 941 BMI-associated variants and tested for GRS-birth year interaction on log-BMI (outcome) when participants were around 50 years old (N=7693). We repeated the analysis using a GRS of 109 BMI-associated variants that increased CVD risk factors (type 2 diabetes, blood pressure, total cholesterol, and high-density lipoprotein) in addition to BMI. We then evaluated whether the effects of the BMI GRSs on CVD risk differed by birth cohort when participants were around 60 years old (N=5493). RESULTS Compared with participants born before 1932 (mean age, 50.8 yrs [2.4]), those born after 1960 (mean age, 43.3 years [4.5]) had higher BMI (median, 25.4 [23.3-28.0] kg/m2 versus 26.9 [interquartile range, 23.7-30.6] kg/m2). The effect of the 941-variant BMI GRS on BMI and CVD risk was stronger in people who were born in later years (GRS-birth year interaction: P=0.0007 and P=0.04 respectively). CONCLUSIONS The significant GRS-birth year interactions indicate that common genetic variants have larger effects on middle-age BMI and CVD risk in people born more recently. These findings suggest that the increasingly obesogenic environment may amplify the impact of genetics on the risk of obesity and possibly its cardiovascular consequences.
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Affiliation(s)
- Chloé Sarnowski
- Dept of Biostatistics, Boston Univ School of Public Health, Boston, MA
- Dept of Epidemiology, Human Genetics, & Environmental Sciences, Univ of Texas Health Science Center, School of Public Health, Houston, TX
| | | | - Ramachandran S. Vasan
- National Heart Lung and Blood Institute & Boston Univ’s Framingham Heart Study, Framingham
- Section of Preventive Medicine & Epidemiology, Evans Dept of Medicine, Massachusetts General Hospital
- Whitaker Cardiovascular Institute & Cardiology Section, Evans Dept of Medicine, Boston Univ School of Medicine, Massachusetts General Hospital
| | - James B. Meigs
- Division of General Internal Medicine, Massachusetts General Hospital
- Dept of Medicine, Harvard Medical School, Boston
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT & Harvard, Cambridge, MA
| | - Josée Dupuis
- Dept of Biostatistics, Boston Univ School of Public Health, Boston, MA
| | - Ching-Ti Liu
- Dept of Biostatistics, Boston Univ School of Public Health, Boston, MA
| | - Aaron Leong
- Division of General Internal Medicine, Massachusetts General Hospital
- Dept of Medicine, Harvard Medical School, Boston
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT & Harvard, Cambridge, MA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital
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Thompson WD, Beaumont RN, Kuang A, Warrington NM, Ji Y, Tyrrell J, Wood AR, Scholtens DM, Knight BA, Evans DM, Lowe WL, Santorelli G, Azad R, Mason D, Hattersley AT, Frayling TM, Yaghootkar H, Borges MC, Lawlor DA, Freathy RM. Higher maternal adiposity reduces offspring birthweight if associated with a metabolically favourable profile. Diabetologia 2021; 64:2790-2802. [PMID: 34542646 PMCID: PMC8563674 DOI: 10.1007/s00125-021-05570-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/14/2021] [Indexed: 11/30/2022]
Abstract
AIMS/HYPOTHESIS Higher maternal BMI during pregnancy is associated with higher offspring birthweight, but it is not known whether this is solely the result of adverse metabolic consequences of higher maternal adiposity, such as maternal insulin resistance and fetal exposure to higher glucose levels, or whether there is any effect of raised adiposity through non-metabolic (e.g. mechanical) factors. We aimed to use genetic variants known to predispose to higher adiposity, coupled with a favourable metabolic profile, in a Mendelian randomisation (MR) study comparing the effect of maternal 'metabolically favourable adiposity' on offspring birthweight with the effect of maternal general adiposity (as indexed by BMI). METHODS To test the causal effects of maternal metabolically favourable adiposity or general adiposity on offspring birthweight, we performed two-sample MR. We used variants identified in large, published genetic-association studies as being associated with either higher adiposity and a favourable metabolic profile, or higher BMI (n = 442,278 and n = 322,154 for metabolically favourable adiposity and BMI, respectively). We then extracted data on the metabolically favourable adiposity and BMI variants from a large, published genetic-association study of maternal genotype and offspring birthweight controlling for fetal genetic effects (n = 406,063 with maternal and/or fetal genotype effect estimates). We used several sensitivity analyses to test the reliability of the results. As secondary analyses, we used data from four cohorts (total n = 9323 mother-child pairs) to test the effects of maternal metabolically favourable adiposity or BMI on maternal gestational glucose, anthropometric components of birthweight and cord-blood biomarkers. RESULTS Higher maternal adiposity with a favourable metabolic profile was associated with lower offspring birthweight (-94 [95% CI -150, -38] g per 1 SD [6.5%] higher maternal metabolically favourable adiposity, p = 0.001). By contrast, higher maternal BMI was associated with higher offspring birthweight (35 [95% CI 16, 53] g per 1 SD [4 kg/m2] higher maternal BMI, p = 0.0002). Sensitivity analyses were broadly consistent with the main results. There was evidence of outlier SNPs for both exposures; their removal slightly strengthened the metabolically favourable adiposity estimate and made no difference to the BMI estimate. Our secondary analyses found evidence to suggest that a higher maternal metabolically favourable adiposity decreases pregnancy fasting glucose levels while a higher maternal BMI increases them. The effects on neonatal anthropometric traits were consistent with the overall effect on birthweight but the smaller sample sizes for these analyses meant that the effects were imprecisely estimated. We also found evidence to suggest that higher maternal metabolically favourable adiposity decreases cord-blood leptin while higher maternal BMI increases it. CONCLUSIONS/INTERPRETATION Our results show that higher adiposity in mothers does not necessarily lead to higher offspring birthweight. Higher maternal adiposity can lead to lower offspring birthweight if accompanied by a favourable metabolic profile. DATA AVAILABILITY The data for the genome-wide association studies (GWAS) of BMI are available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files . The data for the GWAS of body fat percentage are available at https://walker05.u.hpc.mssm.edu .
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Affiliation(s)
- William D Thompson
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Alan Kuang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yingjie Ji
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Jessica Tyrrell
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Andrew R Wood
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Denise M Scholtens
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Bridget A Knight
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - David M Evans
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia
| | - William L Lowe
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Gillian Santorelli
- Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, UK
| | - Rafaq Azad
- Department of Biochemistry, Bradford Royal Infirmary, Bradford, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, UK
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Timothy M Frayling
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Hanieh Yaghootkar
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
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10
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Martin S, Cule M, Basty N, Tyrrell J, Beaumont RN, Wood AR, Frayling TM, Sorokin E, Whitcher B, Liu Y, Bell JD, Thomas EL, Yaghootkar H. Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease. Diabetes 2021; 70:1843-1856. [PMID: 33980691 DOI: 10.2337/db21-0129] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022]
Abstract
To understand the causal role of adiposity and ectopic fat in type 2 diabetes and cardiometabolic diseases, we aimed to identify two clusters of adiposity genetic variants: one with "adverse" metabolic effects (UFA) and the other with, paradoxically, "favorable" metabolic effects (FA). We performed a multivariate genome-wide association study using body fat percentage and metabolic biomarkers from UK Biobank and identified 38 UFA and 36 FA variants. Adiposity-increasing alleles were associated with an adverse metabolic profile, higher risk of disease, higher CRP, and higher fat in subcutaneous and visceral adipose tissue, liver, and pancreas for UFA and a favorable metabolic profile, lower risk of disease, higher CRP and higher subcutaneous adipose tissue but lower liver fat for FA. We detected no sexual dimorphism. The Mendelian randomization studies provided evidence for a risk-increasing effect of UFA and protective effect of FA for type 2 diabetes, heart disease, hypertension, stroke, nonalcoholic fatty liver disease, and polycystic ovary syndrome. FA is distinct from UFA by its association with lower liver fat and protection from cardiometabolic diseases; it was not associated with visceral or pancreatic fat. Understanding the difference in FA and UFA may lead to new insights in preventing, predicting, and treating cardiometabolic diseases.
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Affiliation(s)
- Susan Martin
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | | | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Robin N Beaumont
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, U.K.
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
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11
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Pigeyre M, Sjaarda J, Chong M, Hess S, Bosch J, Yusuf S, Gerstein H, Paré G. ACE and Type 2 Diabetes Risk: A Mendelian Randomization Study. Diabetes Care 2020; 43:835-842. [PMID: 32019855 DOI: 10.2337/dc19-1973] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/07/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To determine whether ACE inhibitors reduce the risk of type 2 diabetes using a Mendelian randomization (MR) approach. RESEARCH DESIGN AND METHODS A two-sample MR analysis included 17 independent genetic variants associated with ACE serum concentration in 4,147 participants from the Outcome Reduction with Initial Glargine INtervention (ORIGIN) (clinical trial reg. no. NCT00069784) trial, and their effects on type 2 diabetes risk were estimated from 18 studies of the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. A genetic risk score (GRS) underpinning lower ACE concentration was then tested for association with type 2 diabetes prevalence in 341,872 participants, including 16,320 with type 2 diabetes, from the UK Biobank. MR estimates were compared after standardization for blood pressure change, with the estimate obtained from a randomized controlled trial (RCT) meta-analysis of ACE inhibitors versus placebo (n = 31,200). RESULTS Genetically lower ACE concentrations were associated with a lower risk of type 2 diabetes (odds ratio [OR] per SD 0.92 [95% CI 0.89-0.95]; P = 1.79 × 10-7). This result was replicated in the UK Biobank (OR per SD 0.97 [0.96-0.99]; P = 8.73 × 10-4). After standardization, the ACE GRS was associated with a larger decrease in type 2 diabetes risk per 2.4-mmHg lower mean arterial pressure (MAP) compared with that obtained from an RCT meta-analysis (OR per 2.4-mmHg lower MAP 0.19 [0.07-0.51] vs. 0.76 [0.60-0.97], respectively; P = 0.007 for difference). CONCLUSIONS These results support the causal protective effect of ACE inhibitors on type 2 diabetes risk and may guide therapeutic decision making in clinical practice.
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Affiliation(s)
- Marie Pigeyre
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Department of Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jennifer Sjaarda
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sibylle Hess
- R&D, Translational Medicine & Early Development, Biomarkers & Clinical Bioanalyses, Sanofi Aventis Deutschland GmbH, Frankfurt, Germany
| | - Jackie Bosch
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada
| | - Salim Yusuf
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
| | - Hertzel Gerstein
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Department of Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada .,Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada.,Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada.,Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
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