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Alharbi AE, Ahmad MS, Damanhouri ZA, Mosli H, Yaghmour KA, Refai F, Issa NM, Alkreathy HM. The Effect of Genetic Variants of SLC22A2 (rs662301 and rs315978) on the response to Metformin in type 2 Saudi diabetic patients. Gene 2024; 927:148648. [PMID: 38852696 DOI: 10.1016/j.gene.2024.148648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 05/16/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE To investigate the allelic and genotypic frequencies of the two genetic variations, NC_000006.12: g.160275887C > T (rs662301) and NC_000006.12:g.160231826 T > C (rs315978), in the SLC22A2 gene among the Saudi population. The primary goal is to elucidate potential associations with these genetic variations and the response to metformin therapy over 6 months to enhance our knowledge of the genetic basis of Type 2 Diabetes Mellitus (T2DM) and its clinical management in the Saudi population. MATERIALS/METHODS 76 newly diagnosed T2DM patients, aged 30 to 60, of both sexes and Saudi origin, were treated with metformin monotherapy. Blood samples were collected before and after 6 months of therapy,80 healthy individuals were included as controls. Genomic DNA was extracted. Genotyping of the SLC22A2 genetic variations was performed using TaqMan® SNP Genotyping Assays. Binary logistic regression was utilized to evaluate how certain clinical parameters influence T2DM concerning the presence of SLC22A2 gene variants. RESULTS Among these patients, 73.3 % were responders, and 26.7 % were non-responders. For these variants, no statistically significant differences in genotype or allele frequencies were observed between responders and non-responders (p = 0.375 and p = 0.384 for rs662301; p = 0.473 and p = 0.481 for rs315978, respectively). For the SLC22A2 variant rs662301, the C/C genotype was significantly associated with increased T2DM risk with age and elevated HbA1c levels. Similarly, rs315978 revealed higher T2DM susceptibility and HbA1c elevation in C/C genotype carriers, specifically with advancing age compared to individuals with C/T and T/T genotypes. CONCLUSION The study offers insights into the genetic landscape of T2DM in Saudi Arabia. Despite the absence of significant associations with treatment response, the study suggests potential age-specific associations, this highlights the complexity of the disease. This research underscores the necessity for expanded research, considering diverse populations and genetic factors, to develop personalized treatment approaches. This study serves as a foundation for future investigations into the Saudi population, recognizing the need for a larger sample size.
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Affiliation(s)
- Amani E Alharbi
- Department of Clinical Pharmacology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Pharmacology and Toxicology, College of Pharmacy, Taibah University, Madinah, Saudi Arabia.
| | - Muhammad S Ahmad
- Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Zoheir A Damanhouri
- Department of Clinical Pharmacology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hala Mosli
- Department of Internal Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Khaled A Yaghmour
- Family Medicine Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Fahd Refai
- Department of Pathology, King Abdulaziz University and King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Noha M Issa
- Department of Medical Genetics, Faculty of Medicine, King Abdul-Aziz University, Saudi Arabia; Department of Human Genetics, Medical Research Institute, Alexandria University, Egypt
| | - Huda M Alkreathy
- Department of Clinical Pharmacology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
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Qian F, Guo Y, Li C, Liu Y, Luttmann-Gibson H, Gomelskaya N, Demler OV, Cook NR, Lee IM, Buring JE, Larsen J, Boring J, McPhaul MJ, Manson JE, Pradhan AD, Mora S. Biomarkers of glucose-insulin homeostasis and incident type 2 diabetes and cardiovascular disease: results from the Vitamin D and Omega-3 trial. Cardiovasc Diabetol 2024; 23:393. [PMID: 39488682 PMCID: PMC11531120 DOI: 10.1186/s12933-024-02470-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 10/13/2024] [Indexed: 11/04/2024] Open
Abstract
BACKGROUND Dysglycemia and insulin resistance increase type 2 diabetes (T2D) and cardiovascular disease (CVD) risk, yet associations with specific glucose-insulin homeostatic biomarkers have been inconsistent. Vitamin D and marine omega-3 fatty acids (n-3 FA) may improve insulin resistance. We sought to examine the association between baseline levels of insulin, C-peptide, HbA1c, and a novel insulin resistance score (IRS) with incident cardiometabolic diseases, and whether randomized vitamin D or n-3 FA modify these associations. METHODS VITamin D and OmegA-3 TriaL (NCT01169259) was a randomized clinical trial testing vitamin D and n-3 FA for the prevention of CVD and cancer over a median of 5.3 years. Incident cases of T2D and CVD (including cardiovascular death, myocardial infarction, stroke, and coronary revascularization) were matched 1:1 on age, sex, and fasting status to controls. Conditional logistic regressions adjusted for demographic, clinical, and adiposity-related factors were used to assess the adjusted odds ratio (aOR) per-standard deviation (SD) and 95%CI of baseline insulin, C-peptide, HbA1c, and IRS (Insulin×0.0295 + C-peptide×0.00372) with risk of T2D, CVD, and coronary heart disease (CHD). RESULTS We identified 218 T2D case-control pairs and 715 CVD case-control pairs including 423 with incident CHD. Each of the four biomarkers at baseline was separately associated with incident T2D, aOR (95%CI) per SD increment: insulin 1.46 (1.03, 2.06), C-peptide 2.04 (1.35, 3.09), IRS 1.72 (1.28, 2.31) and HbA1c 7.00 (3.76, 13.02), though only HbA1c remained statistically significant with mutual adjustments. For cardiovascular diseases, we only observed significant associations of HbA1c with CVD (1.19 [1.02, 1.39]), and IRS with CHD (1.25 [1.04, 1.50]), which persisted after mutual adjustment. Randomization to vitamin D and/or n-3 FA did not modify the association of these biomarkers with the endpoints. CONCLUSIONS Each of insulin, C-peptide, IRS, and HbA1c were associated with incident T2D with the strongest association noted for HbA1c. While HbA1c was significantly associated with CVD risk, a novel IRS appears to be associated with CHD risk. Neither vitamin D nor n-3 FA modified the associations between these biomarkers and cardiometabolic outcomes.
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Affiliation(s)
- Frank Qian
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Section of Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Yanjun Guo
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Chunying Li
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yanyan Liu
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Heike Luttmann-Gibson
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Natalya Gomelskaya
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Olga V Demler
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nancy R Cook
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - I-Min Lee
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Julie E Buring
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Julia Larsen
- Quest Diagnostics Nichols Institute, San Juan Capistrano, CA, USA
| | - Jennifer Boring
- Quest Diagnostics Nichols Institute, San Juan Capistrano, CA, USA
| | | | - JoAnn E Manson
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Aruna D Pradhan
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Bristol Myers Squibb, Cambridge, MA, USA
| | - Samia Mora
- Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Preventive and Cardiovascular Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, 900 Commonwealth Avenue, Boston, MA, 02215, USA.
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Anwar MY, Highland H, Buchanan VL, Graff M, Young K, Taylor KD, Tracy RP, Durda P, Liu Y, Johnson CW, Aguet F, Ardlie KG, Gerszten RE, Clish CB, Lange LA, Ding J, Goodarzi MO, Chen YDI, Peloso GM, Guo X, Stanislawski MA, Rotter JI, Rich SS, Justice AE, Liu CT, North K. Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns. Obesity (Silver Spring) 2024; 32:2024-2034. [PMID: 39497627 PMCID: PMC11540333 DOI: 10.1002/oby.24137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 06/18/2024] [Accepted: 07/22/2024] [Indexed: 11/08/2024]
Abstract
OBJECTIVE Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct cardiometabolic disease patterns. METHODS We performed machine learning-based, integrative unsupervised clustering to identify proteomics- and metabolomics-defined subpopulations of individuals living with obesity (BMI ≥ 30 kg/m2), leveraging data from 243 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Omics that contributed to the observed clusters were functionally characterized. We performed multivariate regression to assess whether the individuals in each cluster demonstrated differential patterns of cardiometabolic traits. RESULTS We identified two distinct clusters (iCluster1 and 2). iCluster2 had significantly higher average BMI values, fasting blood glucose, and inflammation. iCluster1 was associated with higher levels of total cholesterol and high-density lipoprotein cholesterol. Pathways mediating cell growth, lipogenesis, and energy expenditures were positively associated with iCluster1. Inflammatory response and insulin resistance pathways were positively associated with iCluster2. CONCLUSIONS Although the two identified clusters may represent progressive obesity-related pathologic processes measured at different stages, other mechanisms in combination could also underpin the identified clusters given no significant age difference between the comparative groups. For instance, clusters may reflect differences in dietary/behavioral patterns or differential rates of metabolic damage.
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Affiliation(s)
- Mohammad Y Anwar
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Heather Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Victoria Lynn Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kristin Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Russell P Tracy
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Peter Durda
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Yongmei Liu
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Craig W Johnson
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Francois Aguet
- Program of Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Kristin G Ardlie
- Program of Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Robert E Gerszten
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Clary B Clish
- Metabolite Profiling Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Leslie A Lange
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Jingzhong Ding
- Section of Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health System, Danville, Pennsylvania, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston University, Boston, Massachusetts, USA
| | - Kari North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Li J, Li Z, Yang Q. Association between the triglyceride-glucose index and the severity of coronary artery disease in patients with type 2 diabetes mellitus and coronary artery disease: a retrospective study. Acta Cardiol 2024:1-7. [PMID: 39450571 DOI: 10.1080/00015385.2024.2413737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 09/08/2024] [Accepted: 10/02/2024] [Indexed: 10/26/2024]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a primary contributor to coronary artery disease (CAD). Insulin resistance (IR) is a hallmark of T2DM and a significant risk factor for the progression of CAD. The triglyceride-glucose (TyG) index is a new alternative indicator to identify IR. We aimed to explore the association between the TyG index and severity of CAD in patients with T2DM. METHODS 280 inpatients with T2DM were enrolled from November 2019 to November 2022, classified into the CAD group (n = 175) and non-CAD group (n = 105). The TyG index and SYNTAX score were calculated. According to SYNTAX score, patients were further classified into the mid-CAD group (n = 97) and moderate to severe CAD group (n = 78). RESULTS A significant positive correlation between the TyG index and SYNTAX score was found in the CAD group (r = 0.70, p < 0.01). The TyG index predicted the presence of moderate to severe CAD significantly, and the area under the ROC curve was 0.79 (95% CI: 0.71-0.85, p < 0.01). The higher LDL-C and TyG index, the higher risk of developing moderate to severe CAD (OR = 4.40, 95% CI 1.28 - 15.16, p = 0.02; OR = 9.00, 95% CI 3.69 - 21.96, p < 0.01). CONCLUSIONS There was a significantly positive correlation between the TyG index and SYNTAX score in T2DM patients who developed CAD; the TyG index could predict a mid/high SYNTAX score (≥ 23) and increase the risk of moderate to severe CAD.
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Affiliation(s)
- Jing Li
- Department of Cardiovascular Internal Medicine, NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, P.R. China
| | - Zhu Li
- Department of Cardiovascular Internal Medicine, NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, P.R. China
| | - Qin Yang
- Department of Cardiovascular Internal Medicine, NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, P.R. China
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Duenas S, McGee Z, Mhatre I, Mayilvahanan K, Patel KK, Abdelhalim H, Jayprakash A, Wasif U, Nwankwo O, Degroat W, Yanamala N, Sengupta PP, Fine D, Ahmed Z. Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine. Hum Genomics 2024; 18:116. [PMID: 39427205 PMCID: PMC11491019 DOI: 10.1186/s40246-024-00685-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 10/14/2024] [Indexed: 10/21/2024] Open
Abstract
Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one's environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases.
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Affiliation(s)
- Sophia Duenas
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Zachary McGee
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Ishani Mhatre
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Karthikeyan Mayilvahanan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Kush Ketan Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Atharv Jayprakash
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Uzayr Wasif
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Oluchi Nwankwo
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - William Degroat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Naveena Yanamala
- Division of Cardiovascular Diseases and Hypertension, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA
| | - Partho P Sengupta
- Division of Cardiovascular Diseases and Hypertension, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA
| | - Daniel Fine
- Department of Oral Biology, Rutgers School of Dental Medicine, 110 Bergen Street, Newark, NJ, US
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA.
- Division of Cardiovascular Diseases and Hypertension, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA.
- Department of Medicine, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA.
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Capalbo A, de Wert G, Mertes H, Klausner L, Coonen E, Spinella F, Van de Velde H, Viville S, Sermon K, Vermeulen N, Lencz T, Carmi S. Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations. Hum Reprod Update 2024; 30:529-557. [PMID: 38805697 PMCID: PMC11369226 DOI: 10.1093/humupd/dmae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.
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Affiliation(s)
- Antonio Capalbo
- Juno Genetics, Department of Reproductive Genetics, Rome, Italy
- Center for Advanced Studies and Technology (CAST), Department of Medical Genetics, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Guido de Wert
- Department of Health, Ethics & Society, CAPHRI-School for Public Health and Primary Care and GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Heidi Mertes
- Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Liraz Klausner
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Edith Coonen
- Departments of Clinical Genetics and Reproductive Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School for Oncology and Developmental Biology, GROW, Maastricht University, Maastricht, The Netherlands
| | - Francesca Spinella
- Eurofins GENOMA Group Srl, Molecular Genetics Laboratories, Department of Scientific Communication, Rome, Italy
| | - Hilde Van de Velde
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
- Brussels IVF, UZ Brussel, Brussel, Belgium
| | - Stephane Viville
- Laboratoire de Génétique Médicale LGM, Institut de Génétique Médicale d’Alsace IGMA, INSERM UMR 1112, Université de Strasbourg, France
- Laboratoire de Diagnostic Génétique, Unité de Génétique de l’infertilité (UF3472), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Karen Sermon
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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Lv Y, Luo WJ. Dapagliflozin and sacubitril on myocardial microperfusion in patients with post-acute myocardial infarction heart failure and type 2 diabetes. World J Clin Cases 2024; 12:5008-5015. [DOI: 10.12998/wjcc.v12.i22.5008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/09/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND Coronary heart disease and type 2 diabetes mellitus (T2DM) frequently coexist, creating a complex and challenging clinical scenario, particularly when complicated with acute myocardial infarction (AMI).
AIM To examine the effects of dapagliflozin combined with sakubactrovalsartan sodium tablets on myocardial microperfusion.
METHODS In total, 98 patients were categorized into control (n = 47) and observation (n = 51) groups. The control group received noxital, while the observation group was treated with dapagliflozin combined with noxital for 6 months. Changes in myocardial microperfusion, blood glucose level, cardiac function, N-terminal prohormone of brain natriuretic peptide (NT-proBNP) level, growth differentiation factor-15 (GDF-15) level, and other related factors were compared between the two groups. Additionally, the incidence of major adverse cardiovascular events (MACE) and adverse reactions were calculated.
RESULTS After treatment, in the observation and control groups, the corrected thrombolysis in myocardial infarction frame counts were 37.12 ± 5.02 and 48.23 ± 4.66, respectively. The NT-proBNP levels were 1502.65 ± 255.87 and 2015.23 ± 286.31 pg/mL, the N-terminal pro-atrial natriuretic peptide (NT-proANP) levels were 1415.69 ± 213.05 and 1875.52 ± 241.02 ng/mL, the GDF-15 levels were 0.87 ± 0.43 and 1.21 ± 0.56 g/L, and the high-sensitivity C-reactive protein (hs-CRP) levels were 6.54 ± 1.56 and 8.77 ± 1.94 mg/L, respectively, with statistically significant differences (P < 0.05). The cumulative incidence of MACEs in the observation group was significantly lower than that in the control group (P < 0.05). The incidence of adverse reactions was 13.73% (7/51) in the observation group and 10.64% (5/47) in the control group, with no statistically significant difference (P > 0.05).
CONCLUSION Dapagliflozin combined with nocinto can improve myocardial microperfusion and left ventricular remodeling and reduce MACE incidence in patients with post-AMI heart failure and T2DM. The underlying mechanism may be related to the reduction in the expression levels of NT-proANP, GDF-15, and hs-CRP.
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Affiliation(s)
- Yuan Lv
- Department of Cardiology, Lishui People's Hospital, Lishui 323000, Zhejiang Province, China
| | - Wei-Jun Luo
- Department of Cardiology, Lishui People's Hospital, Lishui 323000, Zhejiang Province, China
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8
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Huang Z, Liu N, Chen S, Chen Z, Wang P. Factors influencing accelerated aging in patients with type 2 diabetes mellitus and coronary heart disease. Front Endocrinol (Lausanne) 2024; 15:1416234. [PMID: 39145313 PMCID: PMC11322350 DOI: 10.3389/fendo.2024.1416234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 07/09/2024] [Indexed: 08/16/2024] Open
Abstract
Objective To investigate the factors influencing accelerated aging in patients with type 2 diabetes mellitus (T2DM) and coronary heart disease (CHD). Methods A total of 216 patients diagnosed with T2DM and CHD between August 2019 and August 2023 at Xuzhou Central Hospital were selected. Patients were divided into an aging group and a non-aging group, based on the positive or negative values of phenotypic age acceleration (PhenoAgeAccel). Logistic regression analysis was conducted. Variables that had a univariate analysis P< 0.05 were included in the multivariate analysis to identify factors influencing aging in patients with T2DM and CHD, and the area under the curve of the model was reported. Results This study included 216 patients, with 89 in the accelerated aging group, and 127 in the non-accelerated aging group. The average age of patients was 70.40 (95% CI: 69.10-71.69) years, with 137 males (63.4%). Compared with the non-accelerated aging group, patients in the accelerated aging group were older, with a higher proportion of males, and a higher prevalence of hypertension, stable angina pectoris, and unstable angina pectoris. Multivariate Logistic regression analysis indicated that the absolute value of neutrophils (NEUT#), urea (UREA), adenosine deaminase (ADA), and the triglyceride-glucose index (TyG) were risk factors for accelerated aging, while cholinesterase (CHE) was a protective factor. For each unit increase in NEUT#, UREA, ADA, and TyG, the risk of aging increased by 64%, 48%, 10%, and 789%, respectively. The overall area under the receiver operating characteristic (ROC) curve of the model in the training set was 0.894, with a 95% confidence interval (CI) of 0.851-0.938. Conclusion NEUT#, CHE, UREA, ADA, and TyG are predictors of accelerated aging in patients with T2DM and CHD, with the model showing favorable overall predictive performance.
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Affiliation(s)
| | | | | | | | - Peian Wang
- Xuzhou Central Hospital, Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, Jiangsu, China
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9
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Lin H, Xi Y, Yang Z, Tong Z, Jiang G, Gao J, Kang B, Ma Y, Zhang W, Wang Z. Optimizing Prediction of In-Hospital Mortality in Elderly Patients With Acute Myocardial Infarction: A Nomogram Approach Using the Age-Adjusted Charlson Comorbidity Index Score. J Am Heart Assoc 2024; 13:e032589. [PMID: 38979832 PMCID: PMC11292757 DOI: 10.1161/jaha.123.032589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 06/14/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND To study the age-adjusted Charlson comorbidity index (ACCI) scale, which is a comprehensive quantification of multimorbidity coexistence, for the assessment of the risk of acute myocardial infarction death in elderly people. METHODS AND RESULTS A total of 502 older patients with acute myocardial infarction were studied at Qilu Hospital from September 2017 to March 2022. They were categorized on the basis of ACCI into low (≤5), intermediate (6, 7), and high (≥8) risk groups. Hospitalization duration was observed, with death as the end point. least absolute shrinkage and selection operator regression was used to screen variables, 10-fold cross-validation was performed to validate the screened variables, a Cox regression nomogram predicting the risk of patient death was prepared, hazard ratio with 95% CI was calculated, a nomogram calibration curve was constructed, and a receiver operating characteristic curve, decision curve analysis, and a clinical impact curve were established. From 62 potential factors in a least absolute shrinkage and selection operator regression, 12 were selected via 10-fold cross-validation. Retain variables with significant statistical differences in the Cox regression. A nomogram of the risk of death from acute infarction was constructed, and risk factors included ventricular tachycardia/fibrillation, atrial fibrillation, nicorandil, angiotensin-converting enzyme inhibitors/angiotensin-converting enzyme inhibitors, β blockers, and ACCI score, carbon dioxide combining power, and blood calcium concentration. CONCLUSIONS The ACCI score effectively assesses multimorbidity in the older patients. As ACCI rises, the death risk from acute myocardial infarction grows. The study's nomogram is valid and clinically applicable.
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Affiliation(s)
- He Lin
- Department of Geriatric MedicineQilu Hospital, Shandong UniversityJinanShandongChina
- Key Laboratory of Cardiovascular Proteomics of Shandong ProvinceQilu Hospital, Shandong UniversityJinanShandongChina
| | - Ying‐Bin Xi
- Department of Geriatric MedicineQilu Hospital, Shandong UniversityJinanShandongChina
- Key Laboratory of Cardiovascular Proteomics of Shandong ProvinceQilu Hospital, Shandong UniversityJinanShandongChina
- The Affiliated Weihai Second Municipal Hospital of Qingdao UniversityWeihaiShandongChina
| | - Zhi‐Cheng Yang
- School of Nursing and RehabilitationShandong UniversityJinanShandongChina
| | - Zhou‐Jie Tong
- Department of CardiologyQilu Hospital, Shandong UniversityJinanShandongChina
| | - Guihua Jiang
- Department of CardiologyQilu Hospital, Shandong UniversityJinanShandongChina
| | - Jihong Gao
- Department of Geriatric MedicineQilu Hospital, Shandong UniversityJinanShandongChina
- Key Laboratory of Cardiovascular Proteomics of Shandong ProvinceQilu Hospital, Shandong UniversityJinanShandongChina
| | - Baoxu Kang
- Department of Geriatric MedicineQilu Hospital, Shandong UniversityJinanShandongChina
- Key Laboratory of Cardiovascular Proteomics of Shandong ProvinceQilu Hospital, Shandong UniversityJinanShandongChina
| | - Ying Ma
- Department of Geriatrics, Qilu Hospital (Qingdao)Cheeloo College of Medicine, Shandong UniversityQingdaoChina
| | - Wei Zhang
- Department of Geriatric MedicineQilu Hospital, Shandong UniversityJinanShandongChina
- Key Laboratory of Cardiovascular Proteomics of Shandong ProvinceQilu Hospital, Shandong UniversityJinanShandongChina
| | - Zhi‐Hao Wang
- Department of Geriatric MedicineQilu Hospital, Shandong UniversityJinanShandongChina
- Key Laboratory of Cardiovascular Proteomics of Shandong ProvinceQilu Hospital, Shandong UniversityJinanShandongChina
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10
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Armstrong ND, Patki A, Srinivasasainagendra V, Ge T, Lange LA, Kottyan L, Namjou B, Shah AS, Rasmussen-Torvik LJ, Jarvik GP, Meigs JB, Karlson EW, Limdi NA, Irvin MR, Tiwari HK. Variant level heritability estimates of type 2 diabetes in African Americans. Sci Rep 2024; 14:14009. [PMID: 38890458 PMCID: PMC11189523 DOI: 10.1038/s41598-024-64711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.
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Affiliation(s)
- Nicole D Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Amit Patki
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Tian Ge
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Amy S Shah
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center &, The University of Cincinnati, Cincinnati, OH, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | - James B Meigs
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Elizabeth W Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Nita A Limdi
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
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11
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Yang J, Zhou J, Liu H, Hao J, Hu S, Zhang P, Wu H, Gao Y, Tang W. Blood lipid levels mediating the effects of sex hormone-binding globulin on coronary heart disease: Mendelian randomization and mediation analysis. Sci Rep 2024; 14:11993. [PMID: 38796576 PMCID: PMC11127952 DOI: 10.1038/s41598-024-62695-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 05/20/2024] [Indexed: 05/28/2024] Open
Abstract
Observational studies indicate that serum sex hormone-binding globulin (SHBG) levels are inversely correlated with blood lipid levels and coronary heart disease (CHD) risk. Given that dyslipidemia is an established risk factor for CHD, we aim to employ Mendelian randomization (MR) in conjunction with mediation analysis to confirm the mediating role of blood lipid levels in the association between SHBG and CHD. First, we assessed the causality between serum SHBG levels and five cardiovascular diseases using univariable MR. The results revealed causality between SHBG levels and reduced risk of CHD, myocardial infarction, as well as hypertension. Specifically, the most significant reduction was observed in CHD risk, with an odds ratio of 0.73 (95% CI 0.63-0.86) for each one-standard-deviation increase in SHBG. The summary-level data of serum SHBG levels and CHD are derived from a sex-specific genome-wide association study (GWAS) conducted by UK Biobank (sample size = 368,929) and a large-scale GWAS meta-analysis (60,801 cases and 123,504 controls), respectively. Subsequently, we further investigated the mediating role of blood lipid level in the association between SHBG and CHD. Mediation analysis clarified the mediation proportions for four mediators: high cholesterol (48%), very low-density lipoprotein cholesterol (25.1%), low-density lipoprotein cholesterol (18.5%), and triglycerides (44.3%). Summary-level data for each mediator were sourced from the UK Biobank and publicly available GWAS. The above results confirm negative causality between serum SHBG levels and the risk of CHD, myocardial infarction, and hypertension, with the causal effect on reducing CHD risk largely mediated by the improvement of blood lipid profiles.
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Affiliation(s)
- Juntao Yang
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
- Department of Cardiology, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, Zhejiang, China
| | - Jiedong Zhou
- Department of Cardiology, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, Zhejiang, China
| | - Hanxuan Liu
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
| | - Jinjin Hao
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Songqing Hu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peipei Zhang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Haowei Wu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yefei Gao
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
| | - Weiliang Tang
- Department of Cardiology, Shaoxing People's Hospital, 568 Zhongxing North Road, Shaoxing, 312000, Zhejiang, China.
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12
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Li J, Liu J, Shi W, Guo J. Role and molecular mechanism of Salvia miltiorrhiza associated with chemical compounds in the treatment of diabetes mellitus and its complications: A review. Medicine (Baltimore) 2024; 103:e37844. [PMID: 38640337 PMCID: PMC11029945 DOI: 10.1097/md.0000000000037844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/08/2024] [Accepted: 03/19/2024] [Indexed: 04/21/2024] Open
Abstract
Diabetes mellitus (DM) is one of the most prevalent diseases worldwide, greatly impacting patients' quality of life. This article reviews the progress in Salvia miltiorrhiza, an ancient Chinese plant, for the treatment of DM and its associated complications. Extensive studies have been conducted on the chemical composition and pharmacological effects of S miltiorrhiza, including its anti-inflammatory and antioxidant activities. It has demonstrated potential in preventing and treating diabetes and its consequences by improving peripheral nerve function and increasing retinal thickness in diabetic individuals. Moreover, S miltiorrhiza has shown effectiveness when used in conjunction with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers (ARBs), and statins. The safety and tolerability of S miltiorrhiza have also been thoroughly investigated. Despite the established benefits of managing DM and its complications, further research is needed to determine appropriate usage, dosage, long-term health benefits, and safety.
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Affiliation(s)
- Jiajie Li
- School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, Anhui, PR China
| | - Jinxing Liu
- School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, Anhui, PR China
| | - Weibing Shi
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, PR China
| | - Jinchen Guo
- School of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, Anhui, PR China
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13
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Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Huerta-Chagoya A, Mandla R, Schroeder PH, Westerman KE, Szczerbinski L, Majarian TD, Kaur V, Williamson A, Zaitlen N, Claussnitzer M, Florez JC, Manning AK, Mercader JM, Gaulton KJ, Udler MS. Multi-ancestry polygenic mechanisms of type 2 diabetes. Nat Med 2024; 30:1065-1074. [PMID: 38443691 PMCID: PMC11175990 DOI: 10.1038/s41591-024-02865-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 02/06/2024] [Indexed: 03/07/2024]
Abstract
Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.
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Affiliation(s)
- Kirk Smith
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron J Deutsch
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carolyn McGrail
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Hyunkyung Kim
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Sarah Hsu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia Huerta-Chagoya
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ravi Mandla
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Philip H Schroeder
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth E Westerman
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Lukasz Szczerbinski
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Timothy D Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Vertex Pharmaceuticals, Boston, MA, USA
| | - Varinderpal Kaur
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alice Williamson
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Melina Claussnitzer
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jose C Florez
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alisa K Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M Mercader
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kyle J Gaulton
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA
| | - Miriam S Udler
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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14
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Norland K, Schaid DJ, Kullo IJ. A linear weighted combination of polygenic scores for a broad range of traits improves prediction of coronary heart disease. Eur J Hum Genet 2024; 32:209-214. [PMID: 37752310 PMCID: PMC10853172 DOI: 10.1038/s41431-023-01463-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 08/07/2023] [Accepted: 09/13/2023] [Indexed: 09/28/2023] Open
Abstract
Polygenic scores (PGS) for coronary heart disease (CHD) are constructed using GWAS summary statistics for CHD. However, pleiotropy is pervasive in biology and disease-associated variants often share etiologic pathways with multiple traits. Therefore, incorporating GWAS summary statistics of additional traits could improve the performance of PGS for CHD. Using lasso regression models, we developed two multi-PGS for CHD: 1) multiPGSCHD, utilizing GWAS summary statistics for CHD, its risk factors, and other ASCVD as training data and the UK Biobank for tuning, and 2) extendedPGSCHD, using existing PGS for a broader range of traits in the PGS Catalog as training data and the Atherosclerosis Risk in Communities Study (ARIC) cohort for tuning. We evaluated the performance of multiPGSCHD and extendedPGSCHD in the Mayo Clinic Biobank, an independent cohort of 43,578 adults of European ancestry which included 4,479 CHD cases and 39,099 controls. In the Mayo Clinic Biobank, a 1 SD increase in multiPGSCHD and extendedPGSCHD was associated with a 1.66-fold (95% CI: 1.60-1.71) and 1.70-fold (95% CI: 1.64-1.76) increased odds of CHD, respectively, in models that included age, sex, and 10 PCs, whereas an already published PGS for CHD (CHD_PRSCS) increased the odds by 1.50 (95% CI: 1.45-1.56). In the highest deciles of extendedPGSCHD, multiPGSCHD, and CHD_PRSCS, 18.4%, 17.5%, and 16.3% of patients had CHD, respectively.
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Affiliation(s)
- Kristjan Norland
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.
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15
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Liu Y, Liu JE, He H, Qin M, Lei H, Meng J, Liu C, Chen X, Luo W, Zhong S. Characterizing the metabolic divide: distinctive metabolites differentiating CAD-T2DM from CAD patients. Cardiovasc Diabetol 2024; 23:14. [PMID: 38184583 PMCID: PMC10771670 DOI: 10.1186/s12933-023-02102-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/25/2023] [Indexed: 01/08/2024] Open
Abstract
OBJECTIVE To delineate the metabolomic differences in plasma samples between patients with coronary artery disease (CAD) and those with concomitant CAD and type 2 diabetes mellitus (T2DM), and to pinpoint distinctive metabolites indicative of T2DM risk. METHOD Plasma samples from CAD and CAD-T2DM patients across three centers underwent comprehensive metabolomic and lipidomic analyses. Multivariate logistic regression was employed to discern the relationship between the identified metabolites and T2DM risk. Characteristic metabolites' metabolic impacts were further probed through hepatocyte cellular experiments. Subsequent transcriptomic analyses elucidated the potential target sites explaining the metabolic actions of these metabolites. RESULTS Metabolomic analysis revealed 192 and 95 significantly altered profiles in the discovery (FDR < 0.05) and validation (P < 0.05) cohorts, respectively, that were associated with T2DM risk in univariate logistic regression. Further multivariate regression analyses identified 22 characteristic metabolites consistently associated with T2DM risk in both cohorts. Notably, pipecolinic acid and L-pipecolic acid, lysine derivatives, exhibited negative association with CAD-T2DM and influenced cellular glucose metabolism in hepatocytes. Transcriptomic insights shed light on potential metabolic action sites of these metabolites. CONCLUSIONS This research underscores the metabolic disparities between CAD and CAD-T2DM patients, spotlighting the protective attributes of pipecolinic acid and L-pipecolic acid. The comprehensive metabolomic and transcriptomic findings provide novel insights into the mechanism research, prophylaxis and treatment of comorbidity of CAD and T2DM.
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Affiliation(s)
- Yingjian Liu
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Ju-E Liu
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Huafeng He
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Min Qin
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Heping Lei
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Jinxiu Meng
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Chen Liu
- Department of Cardiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
| | - Wenwei Luo
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
| | - Shilong Zhong
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
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16
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Hasbani NR, Westerman KE, Kwak SH, Chen H, Li X, Di Corpo D, Wessel J, Bis JC, Sarnowski C, Wu P, Bielak LF, Guo X, Heard-Costa N, Kinney GL, Mahaney MC, Montasser ME, Palmer ND, Raffield LM, Terry JG, Yanek LR, Bon J, Bowden DW, Brody JA, Duggirala R, Jacobs DR, Kalyani RR, Lange LA, Mitchell BD, Smith JA, Taylor KD, Carson AP, Curran JE, Fornage M, Freedman BI, Gabriel S, Gibbs RA, Gupta N, Kardia SLR, Kral BG, Momin Z, Newman AB, Post WS, Viaud-Martinez KA, Young KA, Becker LC, Bertoni AG, Blangero J, Carr JJ, Pratte K, Psaty BM, Rich SS, Wu JC, Malhotra R, Peyser PA, Morrison AC, Vasan RS, Lin X, Rotter JI, Meigs JB, Manning AK, de Vries PS. Type 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e004176. [PMID: 38014529 PMCID: PMC10843644 DOI: 10.1161/circgen.123.004176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/29/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D. METHODS We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program. We included first-order T2D interaction terms in each model to determine whether CAD loci were modified by T2D. The genetic main and interaction effects were assessed using a joint test to determine whether a CAD variant, or gene-based rare variant set, was associated with the respective subclinical atherosclerosis measures and then further determined whether these loci had a significant interaction test. RESULTS Using a Bonferroni-corrected significance threshold of P<1.6×10-4, we identified 3 genes (ATP1B1, ARVCF, and LIPG) associated with CAC and 2 genes (ABCG8 and EIF2B2) associated with carotid intima-media thickness and carotid plaque, respectively, through gene-based rare variant set analysis. Both ATP1B1 and ARVCF also had significantly different associations for CAC in T2D cases versus controls. No significant interaction tests were identified through the candidate single-variant analysis. CONCLUSIONS These results highlight T2D as an important modifier of rare variant associations in CAD loci with CAC.
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Affiliation(s)
- Natalie R Hasbani
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Kenneth E Westerman
- Department of Medicine, Clinical and Translation Epidemiology Unit (K.E.W., A.K.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, South Korea (S.H.K.)
| | - Han Chen
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
- School of Biomedical Informatics, Center for Precision Health (H.C.), The University of Texas Health Science Center at Houston
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health (X. Li, X. Lin), Boston University School of Public Health, MA
| | - Daniel Di Corpo
- Department of Biostatistics (D.D., P.W.), Boston University School of Public Health, MA
| | - Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public Health, Indianapolis, IN (J.W.)
| | - Joshua C Bis
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
| | - Chloè Sarnowski
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Peitao Wu
- Department of Biostatistics (D.D., P.W.), Boston University School of Public Health, MA
| | - Lawrence F Bielak
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-University of California Los Angeles Medical Center, Torrance (X.G., K.D.T.)
| | | | - Gregory L Kinney
- Department of Epidemiology, University of Colorado School of Public Health, Aurora (G.L.K., K.A.Y.)
| | - Michael C Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - May E Montasser
- Department of Medicine, Division of Endocrinology Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore (M.E.M., B.D.M.)
| | - Nicholette D Palmer
- Department of Biochemistry (N.D.P., D.W.B.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill (L.M.R.)
| | - James G Terry
- Department of Radiology, Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN (J.G.T., J.J.C.)
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Jessica Bon
- Department of Medicine, Division of Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh Medical Center, PA (J. Bon)
| | - Donald W Bowden
- Department of Biochemistry (N.D.P., D.W.B.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Jennifer A Brody
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, McAllen (R.D.)
| | | | - Rita R Kalyani
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado, Aurora (L.A.L.)
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore (M.E.M., B.D.M.)
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, MD (B.D.M.)
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor (J.A.S.)
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-University of California Los Angeles Medical Center, Torrance (X.G., K.D.T.)
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson (A.P.C.)
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - Myriam Fornage
- Institute of Molecular Medicine (M.F.), The University of Texas Health Science Center at Houston
| | - Barry I Freedman
- Department of Internal Medicine, Section on Nephrology (B.I.F.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Stacey Gabriel
- Genomics Platform (S.G., N.G.), Broad Institute, Cambridge
| | - Richard A Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX (R.A.G., Z.M.)
| | - Namrata Gupta
- Genomics Platform (S.G., N.G.), Broad Institute, Cambridge
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
| | - Brian G Kral
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Zeineen Momin
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX (R.A.G., Z.M.)
| | - Anne B Newman
- Department of Epidemiology, University of Pittsburgh School of Public Health, PA (A.B.N.)
| | - Wendy S Post
- Division of Cardiology, Johns Hopkins Medicine, Baltimore, MD (W.S.P.)
| | | | - Kendra A Young
- Department of Epidemiology, University of Colorado School of Public Health, Aurora (G.L.K., K.A.Y.)
| | - Lewis C Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Alain G Bertoni
- Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.)
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - John J Carr
- Department of Radiology, Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN (J.G.T., J.J.C.)
| | - Katherine Pratte
- Department of Biostatistics, National Jewish Health, Denver, CO (K.P.)
| | - Bruce M Psaty
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
- Department of Epidemiology (B.M.P.), University of Washington, Seattle
- Department of Health Systems and Population Health (B.M.P.), University of Washington, Seattle
| | | | - Joseph C Wu
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville (J.C.W.)
- Department of Medicine, Division of Cardiovascular Medicine, Stanford Cardiovascular Institute, Stanford University School of Medicine (J.C.W.), Stanford University, CA
| | - Rajeev Malhotra
- Division of Cardiology (R.M.), Massachusetts General Hospital, Boston
- Department of Radiology Molecular Imaging Program at Stanford (R.M.), Stanford University, CA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
| | - Alanna C Morrison
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Ramachandran S Vasan
- Framingham Heart Study, MA (N.H.-C., R.S.V.)
- Department of Quantitative and Qualitative Health Sciences, University of Texas Health San Antonio School of Public Health (R.S.V.)
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health (X. Li, X. Lin), Boston University School of Public Health, MA
| | | | - James B Meigs
- Division of General Internal Medicine (J.B.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Alisa K Manning
- Department of Medicine, Clinical and Translation Epidemiology Unit (K.E.W., A.K.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Paul S de Vries
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
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Antar SA, Ashour NA, Sharaky M, Khattab M, Ashour NA, Zaid RT, Roh EJ, Elkamhawy A, Al-Karmalawy AA. Diabetes mellitus: Classification, mediators, and complications; A gate to identify potential targets for the development of new effective treatments. Biomed Pharmacother 2023; 168:115734. [PMID: 37857245 DOI: 10.1016/j.biopha.2023.115734] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 10/21/2023] Open
Abstract
Nowadays, diabetes mellitus has emerged as a significant global public health concern with a remarkable increase in its prevalence. This review article focuses on the definition of diabetes mellitus and its classification into different types, including type 1 diabetes (idiopathic and fulminant), type 2 diabetes, gestational diabetes, hybrid forms, slowly evolving immune-mediated diabetes, ketosis-prone type 2 diabetes, and other special types. Diagnostic criteria for diabetes mellitus are also discussed. The role of inflammation in both type 1 and type 2 diabetes is explored, along with the mediators and potential anti-inflammatory treatments. Furthermore, the involvement of various organs in diabetes mellitus is highlighted, such as the role of adipose tissue and obesity, gut microbiota, and pancreatic β-cells. The manifestation of pancreatic Langerhans β-cell islet inflammation, oxidative stress, and impaired insulin production and secretion are addressed. Additionally, the impact of diabetes mellitus on liver cirrhosis, acute kidney injury, immune system complications, and other diabetic complications like retinopathy and neuropathy is examined. Therefore, further research is required to enhance diagnosis, prevent chronic complications, and identify potential therapeutic targets for the management of diabetes mellitus and its associated dysfunctions.
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Affiliation(s)
- Samar A Antar
- Center for Vascular and Heart Research, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA 24016, USA; Department of Pharmacology and Biochemistry, Faculty of Pharmacy, Horus University, New Damietta 34518, Egypt
| | - Nada A Ashour
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Tanta University, Tanta 31527, Egypt
| | - Marwa Sharaky
- Cancer Biology Department, Pharmacology Unit, National Cancer Institute (NCI), Cairo University, Cairo, Egypt
| | - Muhammad Khattab
- Department of Chemistry of Natural and Microbial Products, Division of Pharmaceutical and Drug Industries, National Research Centre, Cairo, Egypt
| | - Naira A Ashour
- Department of Neurology, Faculty of Physical Therapy, Horus University, New Damietta 34518, Egypt
| | - Roaa T Zaid
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Ahram Canadian University, 6th of October City, Giza 12566, Egypt
| | - Eun Joo Roh
- Chemical and Biological Integrative Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Bio-Medical Science & Technology, University of Science and Technology, Daejeon 34113, Republic of Korea
| | - Ahmed Elkamhawy
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang 10326, Republic of Korea; Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt.
| | - Ahmed A Al-Karmalawy
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Ahram Canadian University, 6th of October City, Giza 12566, Egypt; Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Horus University-Egypt, New Damietta 34518, Egypt
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18
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Lima do Vale MR, Buckner L, Mitrofan CG, Tramontt CR, Kargbo SK, Khalid A, Ashraf S, Mouti S, Dai X, Unwin D, Bohn J, Goldberg L, Golubic R, Ray S. A synthesis of pathways linking diet, metabolic risk and cardiovascular disease: a framework to guide further research and approaches to evidence-based practice. Nutr Res Rev 2023; 36:232-258. [PMID: 34839838 DOI: 10.1017/s0954422421000378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Cardiovascular disease (CVD) is the most common non-communicable disease occurring globally. Although previous literature has provided useful insights into the important role that diet plays in CVD prevention and treatment, understanding the causal role of diets is a difficult task considering inherent and introduced weaknesses of observational (e.g. not properly addressing confounders and mediators) and experimental research designs (e.g. not appropriate or well designed). In this narrative review, we organised current evidence linking diet, as well as conventional and emerging physiological risk factors, with CVD risk, incidence and mortality in a series of diagrams. The diagrams presented can aid causal inference studies as they provide a visual representation of the types of studies underlying the associations between potential risk markers/factors for CVD. This may facilitate the selection of variables to be considered and the creation of analytical models. Evidence depicted in the diagrams was systematically collected from studies included in the British Nutrition Task Force report on diet and CVD and database searches, including Medline and Embase. Although several markers and disorders linked to conventional and emerging risk factors for CVD were identified, the causal link between many remains unknown. There is a need to address the multifactorial nature of CVD and the complex interplay between conventional and emerging risk factors with natural and built environments, while bringing the life course into the spotlight.
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Affiliation(s)
| | - Luke Buckner
- NNEdPro Global Centre for Nutrition and Health, Cambridge, UK
| | | | | | | | - Ali Khalid
- NNEdPro Global Centre for Nutrition and Health, Cambridge, UK
| | - Sammyia Ashraf
- NNEdPro Global Centre for Nutrition and Health, Cambridge, UK
| | - Saad Mouti
- University of California Berkeley, Consortium for Data Analytics in Risk, Berkeley, CA, USA
| | - Xiaowu Dai
- University of California Berkeley, Consortium for Data Analytics in Risk, Berkeley, CA, USA
| | | | - Jeffrey Bohn
- University of California Berkeley, Consortium for Data Analytics in Risk, Berkeley, CA, USA
- Swiss Re Institute, Zürich, Switzerland
| | - Lisa Goldberg
- University of California Berkeley, Consortium for Data Analytics in Risk, Berkeley, CA, USA
| | - Rajna Golubic
- NNEdPro Global Centre for Nutrition and Health, Cambridge, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Diabetes Trials Unit, University of Oxford, Oxford, UK
| | - Sumantra Ray
- NNEdPro Global Centre for Nutrition and Health, Cambridge, UK
- University of Ulster, School of Biomedical Sciences, Coleraine, UK
- University of Cambridge, School of the Humanities and Social Sciences, Cambridge, UK
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19
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Ruan R, Liu Y, Zhang X. Circulating mir-199-3p screens the onset of type 2 diabetes mellitus and the complication of coronary heart disease and predicts the occurrence of major adverse cardiovascular events. BMC Cardiovasc Disord 2023; 23:563. [PMID: 37974073 PMCID: PMC10655316 DOI: 10.1186/s12872-023-03601-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Coronary heart disease (CHD) is a major complication of type 2 diabetes mellitus (T2DM), which causes an adverse prognosis. There is an urgent need to explore effective biomarkers to evaluate the patients' adverse outcomes. OBJECTIVE This study aimed to identify a novel indicator for screening T2DM and T2DM-CHD and predicting adverse prognosis. MATERIALS AND METHODS The study enrolled 52 healthy individuals, 85 T2DM patients, and 97 T2DM patients combined with CHD. Serum miR-199-3p levels in all study subjects were detected with PCR, and its diagnostic significance was evaluated by receiver operating curve (ROC) analysis. The involvement of miR-199-3p in disease development was assessed by the Chi-square test, and the logistic regression analysis was performed to estimate the risk factor for major adverse cardiovascular events (MACE) in T2DM-CHD patients. RESULTS Significant downregulation of miR-199-3p was observed in the serum of both T2DM and T2DM-CHD patients, which discriminated patients from healthy individuals and distinguished T2DM and T2DM-CHD patients. Reduced serum miR-199-3p was associated with the increasing blood glucose, glycated hemoglobin (HbA1c), and homeostasis model assessment-insulin resistance index (HOMA-IR) of T2DM patients and the increasing triglycerides (TG), low-density lipoprotein (LDL), fibrinogen, and total cholesterol (TC) and decreasing high-density lipoprotein (HDL) of T2DM-CHD patients. miR-199-3p was also identified as a biomarker predicting the occurrence of MACE. CONCLUSION Downregulated miR-199-3p could screen the onset of T2DM and its complication with CHD. Reduced serum miR-199-3p was associated with the severe development of T2DM and T2DM-CHD and predicted the adverse outcomes of T2DM-CHD patients.
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Affiliation(s)
- Renjie Ruan
- Department of Cardiology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Yanwei Liu
- Department of Emergency, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - Xiang Zhang
- Department of Cardiology, People's Hospital of Rizhao, No.126 Taian Road, Donggang District, Rizhao, 276827, China.
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20
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Yari A, Karam ZM, Meybodi SME, Sargazi ML, Saeidi K. CDKN2B-AS (rs2891168), SOD2 (rs4880), and PON1 (rs662) polymorphisms and susceptibility to coronary artery disease and type 2 diabetes mellitus in Iranian patients: A case-control study. Health Sci Rep 2023; 6:e1717. [PMID: 38028681 PMCID: PMC10665643 DOI: 10.1002/hsr2.1717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/28/2023] [Accepted: 11/02/2023] [Indexed: 12/01/2023] Open
Abstract
Background and Aims Coronary artery disease (CAD) is a devastating illness and primary cause of death worldwide that arises from a combination of genetic and environmental factors. Several large-scale studies found that 9p21.3, superoxide dismutase 2 (SOD2), and paraoxonase 1 (PON1) polymorphisms increase type 2 diabetes mellitus (T2DM) and/or coronary artery disease (CAD) risk. Our research aimed to investigate whether the SNPs of the 9p21.3 locus (rs28911698), SOD2 (rs4880), and PON1 (rs662) genes were associated with the risk of T2DM and/or CAD in the Iranian population. Methods In this case-control study four group subjects including patients with CAD non-T2DM, with CAD and T2DM, non-CAD with T2DM, and non-CAD non-T2DM were recruited to the study from 2019 to 2020. Molecular analysis was carried out by allele specific-polymerase chain reaction (AS-PCR) technique for rs4880, Taqman genotyping assay for rs2891168, and PCR followed by restriction fragment length polymorphism (PCR-RFLP) technique for rs662. Results The rs2891168 polymorphism presented an elevated risk of CAD in non-T2DM with CAD and with T2DM CAD groups compared to the non-T2DM non-CAD group with GG genotype and dominant model after adjustment (p < 0.05). G-allele in PON1 rs662 polymorphism associated with increased risk of T2DM in T2DM non-CAD, and T2DM CAD groups compared to non-T2DM non-CAD group with dominant model, GG and AG genotypes (p < 0.05). However, SOD2 rs4880 polymorphism presented no significant association with the development of diabetes or CAD. Conclusion These results provide a prime witness that rs2891168 and rs662 gene variants might have a possible increased risk of CAD and T2DM occurrence, respectively. To obtain more definitive and accurate results in this area, further research is required.
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Affiliation(s)
- Abolfazl Yari
- Cellular and Molecular Research CenterBirjand University of Medical SciencesBirjandIran
- Department of Medical Genetics, Afzalipour Faculty of MedicineKerman University of Medical SciencesKermanIran
| | - Zahra M. Karam
- Department of Medical Genetics, Afzalipour Faculty of MedicineKerman University of Medical SciencesKermanIran
- Physiology Research Center, Institute of NeuropharmacologyKerman University of Medical SciencesKermanIran
| | - Seyed M. E. Meybodi
- Yazd Cardiovascular Research Center, Non‐communicable Disease Research InstituteShahid Sadoughi University of Medical SciencesYazdIran
| | - Marzieh L. Sargazi
- Physiology Research Center, Institute of NeuropharmacologyKerman University of Medical SciencesKermanIran
| | - Kolsoum Saeidi
- Physiology Research Center, Institute of NeuropharmacologyKerman University of Medical SciencesKermanIran
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Fajardo CM, Cerda A, Bortolin RH, de Oliveira R, Stefani TIM, Dos Santos MA, Braga AA, Dorea EL, Bernik MMS, Bastos GM, Sampaio MF, Damasceno NRT, Verlengia R, de Oliveira MRM, Hirata MH, Hirata RDC. Influence of polymorphisms in IRS1, IRS2, MC3R, and MC4R on metabolic and inflammatory status and food intake in Brazilian adults: An exploratory pilot study. Nutr Res 2023; 119:21-32. [PMID: 37716291 DOI: 10.1016/j.nutres.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/18/2023]
Abstract
Polymorphisms in genes of leptin-melanocortin and insulin pathways have been associated with obesity and type 2 diabetes. We hypothesized that polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory markers and food intake composition in Brazilian subjects. This exploratory pilot study included 358 adult subjects. Clinical, anthropometric, and laboratory data were obtained through interview and access to medical records. The variants IRS1 rs2943634 A˃C, IRS2 rs1865434 C>T, MC3R rs3746619 C>A, and MC4R rs17782313 T>C were analyzed by real-time polymerase chain reaction. Food intake composition was assessed in a group of subjects with obesity (n = 84) before and after a short-term nutritional counseling program (9 weeks). MC4R rs17782313 was associated with increased risk of obesity (P = .034). Multivariate linear regression analysis adjusted by covariates indicated associations of IRS2 rs1865434 with reduced low-density lipoprotein cholesterol and resistin, MC3R rs3746619 with high glycated hemoglobin, and IRS1 rs2943634 and MC4R rs17782313 with increased high-sensitivity C-reactive protein (P < .05). Energy intake and carbohydrate and total fat intakes were reduced after the diet-oriented program (P < .05). Multivariate linear regression analysis showed associations of IRS2 rs1865434 with high basal fiber intake, IRS1 rs2943634 with low postprogram carbohydrate intake, and MC4R rs17782313 with low postprogram total fat and saturated fatty acid intakes (P < .05). Although significant associations did not survive correction for multiple comparisons using the Benjamini-Hochberg method in this exploratory study, polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory status in Brazilian adults. IRS1 and MC4R variants may influence carbohydrate, total fat, and saturated fatty acid intakes in response to a diet-oriented program in subjects with obesity.
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MESH Headings
- Adult
- Humans
- Pilot Projects
- Diabetes Mellitus, Type 2/genetics
- Polymorphism, Single Nucleotide
- Brazil
- Obesity/genetics
- Obesity/metabolism
- Eating
- Carbohydrates
- Fatty Acids
- Receptor, Melanocortin, Type 4/genetics
- Receptor, Melanocortin, Type 4/metabolism
- Insulin Receptor Substrate Proteins/genetics
- Insulin Receptor Substrate Proteins/metabolism
- Receptor, Melanocortin, Type 3/genetics
- Receptor, Melanocortin, Type 3/metabolism
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Affiliation(s)
- Cristina Moreno Fajardo
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Alvaro Cerda
- Department of Basic Sciences, Center of Excellence in Translational Medicine, CEMT-BIOREN, Universidad de La Frontera, Temuco 4810296, Chile
| | - Raul Hernandes Bortolin
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil; Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, United States
| | - Raquel de Oliveira
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Tamires Invencioni Moraes Stefani
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Marina Aparecida Dos Santos
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Aécio Assunção Braga
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Egídio Lima Dorea
- Medical Clinic Division, University Hospital, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | | | - Gisele Medeiros Bastos
- Laboratory of Molecular Research in Cardiology, Institute of Cardiology Dante Pazzanese, Sao Paulo 04012-909, Brazil; Hospital Beneficiencia Portuguesa de Sao Paulo, Sao Paulo 01323-001, Brazil
| | - Marcelo Ferraz Sampaio
- Hospital Beneficiencia Portuguesa de Sao Paulo, Sao Paulo 01323-001, Brazil; Medical Clinic Division, Institute of Cardiology Dante Pazzanese, Sao Paulo 04012-909, Brazil
| | | | - Rozangela Verlengia
- Research Laboratory in Human Performance, Methodist University of Piracicaba, Piracicaba 13400-901, Brazil
| | | | - Mario Hiroyuki Hirata
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Rosario Dominguez Crespo Hirata
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil.
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Han S, Fang J, Yu L, Li B, Hu Y, Chen R, Li C, Zhao C, Li J, Wang Y, Gao Y, Tan H, Jin Q. Serum‑derived exosomal hsa‑let‑7b‑5p as a biomarker for predicting the severity of coronary stenosis in patients with coronary heart disease and hyperglycemia. Mol Med Rep 2023; 28:203. [PMID: 37711034 PMCID: PMC10539999 DOI: 10.3892/mmr.2023.13090] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Exosomal microRNAs (miRNAs/miRs) are potential biomarkers for the diagnosis and treatment of cardiovascular disease, and hyperglycemia serves an important role in the development of atherosclerosis. The present study aimed to investigate the expression profile of serum‑derived exosomal miRNAs in coronary heart disease (CHD) with hyperglycemia, and to identify effective biomarkers for predicting coronary artery lesions. Serum samples were collected from eight patients with CHD and hyperglycemia and eight patients with CHD and normoglycemia, exosomes were isolated and differentially expressed miRNAs (DEMIs) were filtered using a human miRNA microarray. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using standard enrichment computational methods for the target genes of DEMIs. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the values of the selected DEMIs in predicting the severity of coronary stenosis. A total of 10 DEMIs, including four upregulated miRNAs (hsa‑let‑7b‑5p, hsa‑miR‑4313, hsa‑miR‑4665‑3p and hsa‑miR‑940) and six downregulated miRNAs (hsa‑miR‑4459, hsa‑miR‑4687‑3p, hsa‑miR‑6087, hsa‑miR‑6089, hsa‑miR‑6740‑5p and hsa‑miR‑6800‑5p), were screened in patients with CHD and hyperglycemia. GO analysis showed that the 'cellular process', 'single‑organism process' and 'biological regulation' were significantly enriched. KEGG pathway analysis revealed that the 'mTOR signaling pathway', 'FoxO signaling pathway' and 'neurotrophin signaling pathway' were significantly enriched. Among these DEMIs, only hsa‑let‑7b‑5p expression was positively correlated with both hemoglobin A1C levels and Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery score. ROC curves showed that hsa‑let‑7b‑5p could serve as an effective biomarker for differentiating the severity of coronary stenosis. In conclusion, the present study demonstrated that serum‑derived exosomal hsa‑let‑7b‑5p is upregulated in patients with CHD and hyperglycemia, and may serve as a noninvasive biomarker for the severity of coronary stenosis.
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Affiliation(s)
- Shufang Han
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Jie Fang
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Lili Yu
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Bin Li
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Yuhong Hu
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Ruimin Chen
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Changyong Li
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Chuanxu Zhao
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Jiaying Li
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Yinan Wang
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Yuqi Gao
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Hong Tan
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
| | - Qun Jin
- Department of Cardiology, The 960th Hospital of The Joint Service Support Force of The People's Liberation Army, Jinan, Shandong 250031, P.R. China
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23
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Wu H, Liu X, Jiang W, Hu C, Wang X, Tian Z, Gu W, Sun C, Han T, Wei W. The rest-activity rhythm, genetic susceptibility and risk of type 2 diabetes: A prospective study in UK Biobank. Diabetes Obes Metab 2023; 25:3366-3376. [PMID: 37654212 DOI: 10.1111/dom.15236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 09/02/2023]
Abstract
AIMS This study aims to examine the association between the rest-activity rhythm (RAR) and the incidence of type 2 diabetes (T2D). MATERIALS AND METHODS In total, 97 503 participants without diabetes in the UK Biobank cohort were recruited. Wearable accelerometry was used to monitor circadian behaviour. The parameters of RAR including inter-daily stability, intra-daily variability, relative amplitude (RA), most active continuous 10 h period (M10), and least active continuous 5 h period (L5) were calculated to evaluate the robustness and regularity of the RAR. The weighted polygenic risk score for T2D (T2D-PRS) was calculated. Cox proportion hazards models were used to evaluate the survival relationship and the joint and interaction effects of RAR parameters and T2D-PRS on the occurrence of T2D. RESULTS During 692 257 person-years follow-ups, a total of 2434 participants were documented. After adjustment for potential confounders, compared with participants in the highest quartile of RA and M10, the participants in the lowest quartile had a greater risk of T2D (HRRA = 2.06, 95% CI: 1.76-2.41; HRM10 = 1.33, 95% CI: 1.19-1.49). Meanwhile, the highest quartile of L5 was related to a higher risk of T2D (HR = 1.78, 95% CI: 1.55-2.24). The joint analysis showed that the high T2D-PRS with the lowest quartile of RA and M10, or highest quartile of L5 jointly increased the risk of T2D (HRRA = 4.46, 95% CI: 3.36-6.42; HRM10 = 3.15, 95% CI: 2.29-4.32; HRL5 = 3.09, 95% CI: 2.40-3.99). No modification effects of T2D-PRS on the association between the RAR parameters and risk of T2D were observed (p > .05). CONCLUSION The unbalanced RAR are associated with a greater risk of T2D, which are independent of known risk factors of T2D.
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Affiliation(s)
- Huanyu Wu
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Xin Liu
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Wenbo Jiang
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
- Department of Cardiology, the First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Cong Hu
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Xuanyang Wang
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Zhen Tian
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Wenbo Gu
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Changhao Sun
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Tianshu Han
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Wei Wei
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
- College of Pharmacy Key Laboratory of Cardiovascular Research, Department of Pharmacology, Ministry of Education, Harbin Medical University, Harbin, China
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24
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Feng W, Guo L, Liu Y, Ren M. Unraveling the role of VLDL in the relationship between type 2 diabetes and coronary atherosclerosis: a Mendelian randomization analysis. Front Cardiovasc Med 2023; 10:1234271. [PMID: 37965087 PMCID: PMC10642525 DOI: 10.3389/fcvm.2023.1234271] [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: 06/05/2023] [Accepted: 10/04/2023] [Indexed: 11/16/2023] Open
Abstract
Background The causal link between Type 2 diabetes (T2D) and coronary atherosclerosis has been established through wet lab experiments; however, its analysis with Genome-wide association studies (GWAS) data remains unexplored. This study aims to validate this relationship using Mendelian randomization analysis and explore the potential mediation of VLDL in this mechanism. Methods Employing Mendelian randomization analysis, we investigated the causal connection between T2D and coronary atherosclerosis. We utilized GWAS summary statistics from European ancestry cohorts, comprising 23,363 coronary atherosclerosis patients and 195,429 controls, along with 32,469 T2D patients and 183,185 controls. VLDL levels, linked to SNPs, were considered as a potential mediating causal factor that might contribute to coronary atherosclerosis in the presence of T2D. We employed the inverse variance weighted (IVW), Egger regression (MR-Egger), weighted median, and weighted model methods for causal effect estimation. A leave-one-out sensitivity analysis was conducted to ensure robustness. Results Our Mendelian randomization analysis demonstrated a genetic association between T2D and an increased coronary atherosclerosis risk, with the IVW estimate at 1.13 [95% confidence interval (CI): 1.07-1.20]. Additionally, we observed a suggestive causal link between T2D and VLDL levels, as evidenced by the IVW estimate of 1.02 (95% CI: 0.98-1.07). Further supporting lipid involvement in coronary atherosclerosis pathogenesis, the IVW-Egger estimate was 1.30 (95% CI: 1.06-1.58). Conclusion In conclusion, this study highlights the autonomous contributions of T2D and VLDL levels to coronary atherosclerosis development. T2D is linked to a 13.35% elevated risk of coronary atherosclerosis, and within T2D patients, VLDL concentration rises by 2.49%. Notably, each standard deviation increase in VLDL raises the likelihood of heart disease by 29.6%. This underscores the significant role of lipid regulation, particularly VLDL, as a mediating pathway in coronary atherosclerosis progression.
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Affiliation(s)
- Wenshuai Feng
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Liuli Guo
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yiman Liu
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Ming Ren
- Baokang Hospital Affiliated to Tianjin University of Traditional Chinese Medicine, Tianjin, China
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25
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Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Mandla R, Schroeder PH, Westerman KE, Szczerbinski L, Majarian TD, Kaur V, Williamson A, Claussnitzer M, Florez JC, Manning AK, Mercader JM, Gaulton KJ, Udler MS. Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity. RESEARCH SQUARE 2023:rs.3.rs-3399145. [PMID: 37886436 PMCID: PMC10602111 DOI: 10.21203/rs.3.rs-3399145/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.
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Affiliation(s)
- Kirk Smith
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron J. Deutsch
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carolyn McGrail
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Hyunkyung Kim
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Sarah Hsu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ravi Mandla
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Philip H. Schroeder
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth E. Westerman
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Lukasz Szczerbinski
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Timothy D. Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Varinderpal Kaur
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alice Williamson
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Melina Claussnitzer
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jose C. Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alisa K. Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M. Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Miriam S. Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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26
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Lin Y, Zhou F, Wang X, Guo Y, Chen W. Effect of the index of cardiac electrophysiological balance on major adverse cardiovascular events in patients with diabetes complicated with coronary heart disease. PeerJ 2023; 11:e15969. [PMID: 37818331 PMCID: PMC10561639 DOI: 10.7717/peerj.15969] [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] [Received: 06/30/2023] [Accepted: 08/06/2023] [Indexed: 10/12/2023] Open
Abstract
Purpose To investigate the prognostic value of the index of cardio-electrophysiological balance (ICEB) and its association with major adverse cardiac events (MACE) and cardiovascular death in diabetic patients complicated with coronary heart disease. Methods A total of 920 diabetic patients were enrolled in this longitudinal study. Participants were categorized into three groups based on their ICEB levels: normal ICEB, low ICEB, and high ICEB. The primary outcome was the occurrence of MACE, and secondary outcomes included cardiovascular death, coronary heart disease (CHD), heart failure (HF), and sudden cardiac arrest (SCA). Patients were followed for a median period of 3.26 years, and the associations between ICEB levels and various outcomes were evaluated. Results Over the follow-up period, 46 (5.0%) MACE were observed in the normal ICEB group, 57 (6.2%) in the low ICEB group, and 62 (6.8%) in the high ICEB group. Elevated ICEB levels were found to be associated with a higher risk of MACE and cardiovascular death. A significant relationship between ICEB levels and the risk of MACE was observed for both genders. The risk of MACE increased with each unit increment in the ICEB index. However, the two-stage linear regression model did not outperform the single-line linear regression models in determining the threshold effect. Conclusion This study demonstrates the potential utility of ICEB, derived from a standard non-invasive ECG, as a prognostic tool for predicting MACE and cardiovascular death in diabetic patients complicated with CVD. The associations between ICEB levels and the risk of MACE highlight the importance of understanding cardiac electrophysiological imbalances and their implications in CVD.
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Affiliation(s)
- Yuan Lin
- Department of Endocrinology, The Second Affiliated Hospital of Xi'an Medical University, Xi'an, China
| | - Fang Zhou
- Department of Cardiology, The Second Affiliated Hospital of Xi'an Medical University, Xi'an, China
| | - Xihui Wang
- Department of Cardiology, The Second Affiliated Hospital of Xi'an Medical University, Xi'an, China
| | - Yaju Guo
- Department of Endocrinology, The Second Affiliated Hospital of Xi'an Medical University, Xi'an, China
| | - Weiguo Chen
- Department of Cardiology, The Second Affiliated Hospital of Xi'an Medical University, Xi'an, China
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Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Mandla R, Schroeder PH, Westerman KE, Szczerbinski L, Majarian TD, Kaur V, Williamson A, Claussnitzer M, Florez JC, Manning AK, Mercader JM, Gaulton KJ, Udler MS. Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.28.23296294. [PMID: 37808749 PMCID: PMC10557820 DOI: 10.1101/2023.09.28.23296294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.
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Affiliation(s)
- Kirk Smith
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron J. Deutsch
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carolyn McGrail
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Hyunkyung Kim
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Sarah Hsu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ravi Mandla
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Philip H. Schroeder
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth E. Westerman
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Lukasz Szczerbinski
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Timothy D. Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Varinderpal Kaur
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alice Williamson
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Melina Claussnitzer
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jose C. Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alisa K. Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M. Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Miriam S. Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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28
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Peng W, Yang B, Qiao H, Liu Y, Lin Y. Metformin use is associated with reduced acute kidney injury following coronary artery bypass grafting in patients with type 2 diabetes: An inverse probability of treatment weighting analysis. Pharmacotherapy 2023; 43:778-786. [PMID: 37199291 DOI: 10.1002/phar.2827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 03/26/2023] [Accepted: 03/30/2023] [Indexed: 05/19/2023]
Abstract
STUDY OBJECTIVE Acute kidney injury (AKI) is a common and serious complication after coronary artery bypass grafting (CABG) surgery. Patients with diabetes are commonly associated with renal microvascular complications and have a greater risk of AKI after CABG surgery. This study aimed to explore whether preoperative metformin administration could reduce the incidence of postoperative AKI following CABG in patients with type 2 diabetes. DESIGN Patients with diabetes who underwent CABG were retrospectively included in this study. AKI after CABG was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. The effects of metformin on postoperative AKI following CABG in patients were compared and analyzed. DATA SOURCE Patients were enrolled in this study between January 2019 and December 2020 in Beijing Anzhen Hospital. PATIENTS A total of 812 patients were enrolled. The patients were divided into the metformin group (203 cases) and the control group (609 cases) according to whether metformin was used preoperatively. INTERVENTION Inverse probability of treatment weighting (IPTW) was applied to minimize baseline differences between the two groups. IPT-weighted p values were analyzed to evaluate the postoperative outcomes between the two groups. MEASUREMENTS AND MAIN RESULTS The incidence of AKI in the metformin group and the control group was compared. After IPTW adjustment, the incidence of AKI in the metformin group was lower than the control group (IPTW-adjusted p < 0.001). In the subgroup analysis, metformin showed significant protective effects in the estimated glomerular filtration rate (eGFR) < 60 mL/min per 1.73 m2 and eGFR 60-90 mL/min per 1.73 m2 subgroups, which was not observed in the eGFR ≥90 mL/min per 1.73 m2 subgroup. No significant differences in the incidence of renal replacement therapy, reoperation due to bleeding, in-hospital mortality, or red blood cell transfusion volume were observed between the two groups. CONCLUSIONS In this study, we provided evidence that preoperative metformin was associated with a significant reduction of postoperative AKI following CABG in patients with diabetes. Metformin showed significant protective effects in patients with mild-to-moderate renal insufficiency.
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Affiliation(s)
- Wenxing Peng
- Department of Pharmacy, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Bo Yang
- Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Huanyu Qiao
- Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yongmin Liu
- Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yang Lin
- Department of Pharmacy, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Kwak SH, Hernandez-Cancela RB, DiCorpo DA, Condon DE, Merino J, Wu P, Brody JA, Yao J, Guo X, Ahmadizar F, Meyer M, Sincan M, Mercader JM, Lee S, Haessler J, Vy HMT, Lin Z, Armstrong ND, Gu S, Tsao NL, Lange LA, Wang N, Wiggins KL, Trompet S, Liu S, Loos RJ, Judy R, Schroeder PH, Hasbani NR, Bos MM, Morrison AC, Jackson RD, Reiner AP, Manson JE, Chaudhary NS, Carmichael LK, Chen YDI, Taylor KD, Ghanbari M, van Meurs J, Pitsillides AN, Psaty BM, Noordam R, Do R, Park KS, Jukema JW, Kavousi M, Correa A, Rich SS, Damrauer SM, Hajek C, Cho NH, Irvin MR, Pankow JS, Nadkarni GN, Sladek R, Goodarzi MO, Florez JC, Chasman DI, Heckbert SR, Kooperberg C, Dupuis J, Malhotra R, de Vries PS, Liu CT, Rotter JI, Meigs JB. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.25.23293180. [PMID: 37546893 PMCID: PMC10402212 DOI: 10.1101/2023.07.25.23293180] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD. METHODS From 16 studies of the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D. RESULTS A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance (P<5.0×10-8): rs147138607 (intergenic variant between CACNA1E and ZNF648) with a hazard ratio (HR) 1.23, 95% confidence interval (CI) 1.15 - 1.32, P=3.6×10-9, rs11444867 (intergenic variant near HS3ST1) with HR 1.89, 95% CI 1.52 - 2.35, P=9.9×10-9, and rs335407 (intergenic variant between TFB1M and NOX3) HR 1.25, 95% CI 1.16 - 1.35, P=1.5×10-8. Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with P<0.05, and 5 were significant after Bonferroni correction (P<0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95% CI 1.12 - 1.16) per 1 standard deviation increase (P=1.0×10-16). CONCLUSIONS The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.
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Affiliation(s)
- Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | | | - Daniel A DiCorpo
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | | | - Jordi Merino
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Jie Yao
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Fariba Ahmadizar
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Data Science and Biostatistics, Julius Global Health, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mariah Meyer
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Murat Sincan
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Josep M. Mercader
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Sujin Lee
- Division of Vascular Surgery and Endovascular Therapy, Massachusetts General Hospital, Boston, MA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
| | - Ha My T. Vy
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Zhaotong Lin
- Department of Biostatistics, University of Minnesota, Minneapolis, MN
| | - Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - Shaopeng Gu
- Department of Internal Medicine, Sanford Health, Sioux Falls, SD
| | - Noah L. Tsao
- Corporal Michael Crescenz VA Medical Center, and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
| | - Leslie A. Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Ningyuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Kerri L. Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Simin Liu
- Department of Epidemiology, Brown University, Providence, RI
| | - Ruth J.F. Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Renae Judy
- Corporal Michael Crescenz VA Medical Center, and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
| | - Philip H. Schroeder
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Natalie R. Hasbani
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Maxime M. Bos
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Rebecca D. Jackson
- Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Ohio State University, Columbus, OH
| | - Alexander P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - JoAnn E. Manson
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Ninad S. Chaudhary
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | | | - Yii-Der Ida Chen
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Kent D. Taylor
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Joyce van Meurs
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Ron Do
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kyong Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- The Netherlands Heart Institute, Utrecht, the Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | - Scott M. Damrauer
- Corporal Michael Crescenz VA Medical Center, and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, Philadelphia, PA
| | - Catherine Hajek
- Department of Internal Medicine, Sanford Health, Sioux Falls, SD
| | - Nam H. Cho
- Department of Preventive Medicine, Ajou University School of Medicine, Suwon, Korea
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - James S. Pankow
- Department of Biostatistics, University of Minnesota, Minneapolis, MN
| | - Girish N. Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Robert Sladek
- Department of Medicine and Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center
| | - Jose C. Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Daniel I. Chasman
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Rajeev Malhotra
- Cardiovascular Research Center, Cardiology Division of the Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jerome I. Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - James B. Meigs
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Department of General Internal Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, MA
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Škrlec I, Biloglav Z, Talapko J, Džijan S, Daus-Šebeđak D, Cesar V. Myocardial Infarction Susceptibility and the MTNR1B Polymorphisms. Int J Mol Sci 2023; 24:11444. [PMID: 37511203 PMCID: PMC10380655 DOI: 10.3390/ijms241411444] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Melatonin is a circadian hormone with antioxidant properties that protects against myocardial ischemia-reperfusion injury. Genetic variations of the melatonin receptor 1B gene (MTNR1B) play an important role in the development of type 2 diabetes, a risk factor for cardiovascular diseases. Accordingly, MTNR1B polymorphisms are crucial in numerous disorders of the cardiovascular system. Therefore, the aim of the present study was to investigate a possible association of MTNR1B polymorphisms with chronotype and susceptibility to myocardial infarction. The present case-control study included 199 patients with myocardial infarction (MI) (57% men) and 198 control participants (52% men) without previous cardiovascular diseases who underwent genotyping for the MTNR1B polymorphisms rs10830963, rs1387153, and rs4753426 from peripheral blood samples. Chronotype was determined using the Morningness-Eveningness Questionnaire (MEQ). As estimated by the chi-square test, no significant association was found in the distribution of alleles and genotypes between myocardial infarction patients and controls. In addition, there was no association between MTNR1B polymorphisms and chronotype in MI patients. As some previous studies have shown, the present negative results do not exclude the role of the MTNR1B polymorphisms studied in the development of myocardial infarction. Rather, they may indicate that MTNR1B polymorphisms are a minor risk factor for myocardial infarction.
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Affiliation(s)
- Ivana Škrlec
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Zrinka Biloglav
- Department of Medical Statistics, Epidemiology and Medical Informatics, School of Public Health Andrija Štampar, 10000 Zagreb, Croatia
- School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
| | - Jasminka Talapko
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Snježana Džijan
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- DNA Laboratory, Genos Ltd., 10000 Zagreb, Croatia
| | | | - Vera Cesar
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Department of Biology, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
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31
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Wu K, Wang Y, Liu R, Wang H, Rui T. The role of mammalian Sirtuin 6 in cardiovascular diseases and diabetes mellitus. Front Physiol 2023; 14:1207133. [PMID: 37497437 PMCID: PMC10366693 DOI: 10.3389/fphys.2023.1207133] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023] Open
Abstract
Cardiovascular diseases are severe diseases posing threat to human health because of their high morbidity and mortality worldwide. The incidence of diabetes mellitus is also increasing rapidly. Various signaling molecules are involved in the pathogenesis of cardiovascular diseases and diabetes. Sirtuin 6 (Sirt6), which is a class III histone deacetylase, has attracted numerous attentions since its discovery. Sirt6 enjoys a unique structure, important biological functions, and is involved in multiple cellular processes such as stress response, mitochondrial biogenesis, transcription, insulin resistance, inflammatory response, chromatin silencing, and apoptosis. Sirt6 also plays significant roles in regulating several cardiovascular diseases including atherosclerosis, coronary heart disease, as well as cardiac remodeling, bringing Sirt6 into the focus of clinical interests. In this review, we examine the recent advances in understanding the mechanistic working through which Sirt6 alters the course of lethal cardiovascular diseases and diabetes mellitus.
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32
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Lee H, Choi J, Kim NY, Kim JI, Moon MK, Lee S, Park KS, Kwak SH. Earlier Age at Type 2 Diabetes Diagnosis Is Associated With Increased Genetic Risk of Cardiovascular Disease. Diabetes Care 2023; 46:1085-1090. [PMID: 36939558 PMCID: PMC10154664 DOI: 10.2337/dc22-2144] [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: 11/04/2022] [Accepted: 02/24/2023] [Indexed: 03/21/2023]
Abstract
OBJECTIVE We investigated genetic risk of cardiovascular disease (CVD) by age at type 2 diabetes (T2D) diagnosis. RESEARCH DESIGN AND METHODS We compared incident CVD events by age at T2D diagnosis using UK Biobank (N = 12,321) and the Seoul National University Hospital (SNUH) cohort (N = 1,165). Genetic risk was quantified using polygenic risk score (PRS). RESULTS Individuals with earlier T2D diagnosis had higher CVD risk. In UK Biobank, the effect size of coronary artery disease (CAD) PRS on incident CAD was largest in individuals diagnosed with T2D at ages 30-39 years (hazard ratio 2.25; 95% CI 1.56-3.26) and decreased as age at diagnosis increased: ages 40-49 (1.51; 1.30-1.75), 50-59 (1.36; 1.24-1.50), and 60-69 years (1.30; 1.14-1.48) (Pinteraction = 0.0031). A similar trend was observed in the SNUH cohort. This increased genetic risk associated with earlier T2D diagnosis was largely mitigated by a healthy lifestyle. CONCLUSIONS Individuals with an earlier T2D diagnosis have a higher genetic risk of CAD, and this information could be used to tailor lifestyle interventions.
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Affiliation(s)
- Hyunsuk Lee
- 1Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- 2Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea
- 3Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea
| | - Jaewon Choi
- 4Division of Data Science Research, Innovative Biomedical Technology Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Na Yeon Kim
- 5Graduate School of Data Science, Seoul National University, Seoul, Korea
| | - Jong-Il Kim
- 3Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea
- 6Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Min Kyong Moon
- 7Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- 8Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
| | - Seunggeun Lee
- 5Graduate School of Data Science, Seoul National University, Seoul, Korea
| | - Kyong Soo Park
- 1Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- 3Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea
- 7Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- 9Department of Genomic Medicine, Seoul National University Hospital, Seoul, Korea
| | - Soo Heon Kwak
- 1Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- 7Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
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Liu X, Collister JA, Clifton L, Hunter DJ, Littlejohns TJ. Polygenic Risk of Prediabetes, Undiagnosed Diabetes, and Incident Type 2 Diabetes Stratified by Diabetes Risk Factors. J Endocr Soc 2023; 7:bvad020. [PMID: 36819459 PMCID: PMC9933896 DOI: 10.1210/jendso/bvad020] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Indexed: 02/03/2023] Open
Abstract
Context Early diagnosis of type 2 diabetes is crucial to reduce severe comorbidities and complications. Current screening recommendations for type 2 diabetes include traditional risk factors, primarily body mass index (BMI) and family history, however genetics also plays a key role in type 2 diabetes risk. It is important to understand whether genetic predisposition to type 2 diabetes modifies the effect of these traditional factors on type 2 diabetes risk. Objective This work aimed to investigate whether genetic risk of type 2 diabetes modifies associations between BMI and first-degree family history of diabetes with 1) prevalent prediabetes or undiagnosed diabetes; and 2) incident confirmed type 2 diabetes. Methods We included 431 658 individuals aged 40 to 69 years at baseline of multiethnic ancestry from the UK Biobank. We used a multiethnic polygenic risk score for type 2 diabetes (PRST2D) developed by Genomics PLC. Prediabetes or undiagnosed diabetes was defined as baseline glycated hemoglobin greater than or equal to 42 mmol/mol (6.0%), and incident type 2 diabetes was derived from medical records. Results At baseline, 43 472 participants had prediabetes or undiagnosed diabetes, and 17 259 developed type 2 diabetes over 15 years follow-up. Dose-response associations were observed for PRST2D with each outcome in each category of BMI or first-degree family history of diabetes. Those in the highest quintile of PRST2D with a normal BMI were at a similar risk as those in the middle quintile who were overweight. Participants who were in the highest quintile of PRST2D and did not have a first-degree family history of diabetes were at a similar risk as those with a family history who were in the middle category of PRST2D. Conclusion Genetic risk of type 2 diabetes remains strongly associated with risk of prediabetes, undiagnosed diabetes, and future type 2 diabetes within categories of nongenetic risk factors. This could have important implications for identifying individuals at risk of type 2 diabetes for prevention and early diagnosis programs.
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Affiliation(s)
- Xiaonan Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
| | - Jennifer A Collister
- Nuffield Department of Population Health, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
| | - Lei Clifton
- Nuffield Department of Population Health, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
| | - David J Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Thomas J Littlejohns
- Nuffield Department of Population Health, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
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Ding X, Zhao L, Cui X, Qi L, Chen Y. Mendelian randomization reveals no associations of genetically-predicted obstructive sleep apnea with the risk of type 2 diabetes, nonalcoholic fatty liver disease, and coronary heart disease. Front Psychiatry 2023; 14:1068756. [PMID: 36846222 PMCID: PMC9949721 DOI: 10.3389/fpsyt.2023.1068756] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Obstructive sleep apnea (OSA) has been reported to affect cardiometabolic diseases. However, whether such association is causal is still unknown. Here, we attempt to explore the effect of OSA on type 2 diabetes (T2D), nonalcoholic fatty liver disease (NAFLD) and coronary heart disease (CHD). METHODS Genetic variants associated with OSA were requested from a published genome-wide association study (GWAS) and those qualified ones were selected as instrumental variables (IV). Then, the IV-outcome associations were acquired from T2D, NAFLD and CHD GWAS consortia separately. The Mendelian randomization (MR) was designed to estimate the associations of genetically-predicted OSA on T2D, NAFLD and CHD respectively, using the inverse-variance weighted (IVW) method. We applied the Bonferroni method to adjust the p-value. Besides, MR-Egger regression and weighted median methods were adopted as a supplement to IVW. The Cochran's Q value was used to evaluate heterogeneity and the MR-Egger intercept was utilized to assess horizontal pleiotropy, together with MR-PRESSO. The leave-one-out sensitivity analysis was carried out as well. RESULTS No MR estimate reached the Bonferroni threshold (p < 0.017). Although the odds ratio of T2D was 3.58 (95% confidence interval (CI) [1.06, 12.11], IVW-p-value = 0.040) using 4 SNPs, such causal association turned insignificant after the removal of SNP rs9937053 located in FTO [OR = 1.30 [0.68, 2.50], IVW p = 0.432]. Besides, we did not find that the predisposition to OSA was associated with CHD [OR = 1.16 [0.70, 1.91], IVW p = 0.560] using 4 SNPs. CONCLUSION This MR study reveals that genetic liability to OSA might not be associated with the risk of T2D after the removal of obesity-related instruments. Besides, no causal association was observed between NAFLD and CHD. Further studies should be carried out to verify our findings.
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Affiliation(s)
- Xiaoxu Ding
- Department of Otorhinolaryngology, Shengjing Hospital Affiliated With China Medical University, Shenyang, Liaoning, China
| | - Lanqing Zhao
- Department of Otorhinolaryngology, Shengjing Hospital Affiliated With China Medical University, Shenyang, Liaoning, China
| | - Xiangguo Cui
- Department of Otorhinolaryngology, Shengjing Hospital Affiliated With China Medical University, Shenyang, Liaoning, China
| | - Li Qi
- Department of Otorhinolaryngology, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yu Chen
- Department of Otorhinolaryngology, Shengjing Hospital Affiliated With China Medical University, Shenyang, Liaoning, China
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Pang S, Zhang Z, Zhou Y, Zhang J, Yan B. Genetic Variants of SIRT1 Gene Promoter in Type 2 Diabetes. Int J Endocrinol 2023; 2023:6919275. [PMID: 36747995 PMCID: PMC9899147 DOI: 10.1155/2023/6919275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/05/2023] [Accepted: 01/19/2023] [Indexed: 01/29/2023] Open
Abstract
Type 2 diabetes (T2D) is a highly heterogeneous and polygenic disease. To date, genetic causes and underlying mechanisms for T2D remain unclear. SIRT1, one member of highly conserved NAD-dependent class III deacetylases, has been implicated in many human diseases. Accumulating evidence indicates that SIRT1 is involved in insulin resistance and impaired pancreatic β-cell function, the two hallmarks of T2D. Thus, we speculated that altered SIRT1 levels, resulting from the genetic variants within its regulatory region of SIRT1 gene, may contribute to the T2D development. In this study, the SIRT1 gene promoter was genetically analyzed in T2D patients (n = 218) and healthy controls (n = 358). A total of 20 genetic variants, including 7 single-nucleotide polymorphisms (SNPs), were identified. Five heterozygous genetic variants (g.4114-15InsA, g.4801G > A, g.4816G > C, g.4934G > T, and g.4963_64Ins17bp) and one SNP (g.4198A > C (rs35706870)) were identified in T2D patients, but in none of the controls. The frequencies of two SNPs (g.4540A > G (rs3740051) (OR: 1.75, 95% CI: 1.24-2.47, P < 0.001 in dominant genetic model) and g.4821G > T (rs35995735)) (OR: 3.58, 95% CI: 1.94-6.60, P < 0.001 in dominant genetic model) were significantly higher in T2D patients. Further association and haplotype analyses confirmed that these two SNPs were strongly linked, contributing to the T2D (OR: 1.442, 95% CI: 1.080-1.927, P < 0.05). Moreover, most of the genetic variants identified in T2D were disease-specific. Taken together, the genetic variants within SIRT1 gene promoter might contribute to the T2D development by altering SIRT1 levels. Underlying molecular mechanism needs to be further explored.
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Affiliation(s)
- Shuchao Pang
- Shandong Provincial Sino-US Cooperation Research Center for Translational Medicine, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, Shandong 272029, China
| | - Zhengjun Zhang
- Division of Endocrinology, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, Shandong 272029, China
| | - Yu Zhou
- Shandong Provincial Sino-US Cooperation Research Center for Translational Medicine, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, Shandong 272029, China
| | - Jie Zhang
- Cardiovascular Center, Beijing Tongren Hospital, Capital Medical University, Dongcheng, Beijing 100730, China
| | - Bo Yan
- Shandong Provincial Sino-US Cooperation Research Center for Translational Medicine, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, Shandong 272029, China
- Institute of Precision Medicine, Jining Medical University, Jining, Shandong 272067, China
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Domingo-Relloso A, Gribble MO, Riffo-Campos AL, Haack K, Cole SA, Tellez-Plaza M, Umans JG, Fretts AM, Zhang Y, Fallin MD, Navas-Acien A, Everson TM. Epigenetics of type 2 diabetes and diabetes-related outcomes in the Strong Heart Study. Clin Epigenetics 2022; 14:177. [PMID: 36529747 PMCID: PMC9759920 DOI: 10.1186/s13148-022-01392-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The prevalence of type 2 diabetes has dramatically increased in the past years. Increasing evidence supports that blood DNA methylation, the best studied epigenetic mark, is related to diabetes risk. Few prospective studies, however, are available. We studied the association of blood DNA methylation with diabetes in the Strong Heart Study. We used limma, Iterative Sure Independence Screening and Cox regression to study the association of blood DNA methylation with fasting glucose, HOMA-IR and incident type 2 diabetes among 1312 American Indians from the Strong Heart Study. DNA methylation was measured using Illumina's MethylationEPIC beadchip. We also assessed the biological relevance of our findings using bioinformatics analyses. RESULTS Among the 358 differentially methylated positions (DMPs) that were cross-sectionally associated either with fasting glucose or HOMA-IR, 49 were prospectively associated with incident type 2 diabetes, although no DMPs remained significant after multiple comparisons correction. Multiple of the top DMPs were annotated to genes with relevant functions for diabetes including SREBF1, associated with obesity, type 2 diabetes and insulin sensitivity; ABCG1, involved in cholesterol and phospholipids transport; and HDAC1, of the HDAC family. (HDAC inhibitors have been proposed as an emerging treatment for diabetes and its complications.) CONCLUSIONS: Our results suggest that differences in peripheral blood DNA methylation are related to cross-sectional markers of glucose metabolism and insulin activity. While some of these DMPs were modestly associated with prospective incident type 2 diabetes, they did not survive multiple testing. Common DMPs with diabetes epigenome-wide association studies from other populations suggest a partially common epigenomic signature of glucose and insulin activity.
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Affiliation(s)
- Arce Domingo-Relloso
- Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Madrid, Spain.
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
- Department of Statistics and Operations Research, University of Valencia, Valencia, Spain.
| | - Matthew O Gribble
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Angela L Riffo-Campos
- Millennium Nucleus On Sociomedicine (SocioMed) and Vicerrectoría Académica, Universidad de La Frontera, Temuco, Chile
- Department of Computer Science, ETSE, University of Valencia, Valencia, Spain
| | - Karin Haack
- Population Health Program, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Shelley A Cole
- Population Health Program, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Maria Tellez-Plaza
- Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Madrid, Spain
| | - Jason G Umans
- MedStar Health Research Institute, Hyattsville, MD, USA
- Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, USA
| | - Amanda M Fretts
- Department of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Ying Zhang
- Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - M Daniele Fallin
- Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Ana Navas-Acien
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Todd M Everson
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
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Xiao H, Ma Y, Zhou Z, Li X, Ding K, Wu Y, Wu T, Chen D. Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture. Cardiovasc Diabetol 2022; 21:276. [PMID: 36494812 PMCID: PMC9738029 DOI: 10.1186/s12933-022-01715-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/02/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D. METHOD A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D (comorbidity, CHD without T2D, T2D without CHD and all none). We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets. RESULTS We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × [Formula: see text]). Among them, disease patterns involving CHD and T2D were related to distinct SNP sets (Hypergeometric test, P < 2.17 × [Formula: see text]). Accordingly, numerous genes (e.g., KLKs, GRM8, SHANK2) and pathways (e.g., fatty acid metabolism) were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy). CONCLUSION The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways.
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Affiliation(s)
- Han Xiao
- grid.11135.370000 0001 2256 9319Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191 China
| | - Yujia Ma
- grid.11135.370000 0001 2256 9319Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191 China
| | - Zechen Zhou
- grid.11135.370000 0001 2256 9319Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191 China
| | - Xiaoyi Li
- grid.11135.370000 0001 2256 9319Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191 China
| | - Kexin Ding
- grid.11135.370000 0001 2256 9319Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191 China
| | - Yiqun Wu
- grid.11135.370000 0001 2256 9319Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191 China
| | - Tao Wu
- grid.11135.370000 0001 2256 9319Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191 China
| | - Dafang Chen
- grid.11135.370000 0001 2256 9319Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191 China
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Chai X, Jin Y, Wei Y, Yang R. The effect of vitamin D supplementation on glycemic status and C-reactive protein levels in type 2 diabetic patients with ischemic heart disease: A protocol for systematic review and meta-analysis. Medicine (Baltimore) 2022; 101:e32254. [PMID: 36626510 PMCID: PMC9750511 DOI: 10.1097/md.0000000000032254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Vitamin D might be beneficial in diabetic patients with ischemic heart disease through its favorable effect on metabolic profiles and biomarkers of inflammation and oxidative stress. We performed a protocol for systematic review and meta-analysis to assess whether vitamin D supplementation could improve glucose control and inflammation in type 2 diabetic patients with ischemic heart disease. METHODS The proposed systematic review and meta-analysis will conform to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols. Seven electronic databases including Web of Science, Embase, PubMed, Wanfang Data, Scopus, Science Direct, Cochrane Library were searched in October 2022 by 2 independent reviewers. The risk of bias assessment of the included studies was assessed using the tool recommended in the Cochrane Handbook for Systematic Reviews of Interventions (version 5.1.0). Data analysis was performed with Review Manager Software (RevMan Version 5.4, The Cochrane Collaboration, Copenhagen, Denmark). RESULTS This study will provide a high-quality synthesis to assess the effectiveness and safety of vitamin D supplementation on type 2 diabetic patients with ischemic heart disease. CONCLUSION This systematic review may lead to several recommendations, for both patients and researchers, as which is the best therapy for type 2 diabetic patients with ischemic heart disease and how future studies need to be designed, considering what is available now and what is the reality of the patient.
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Affiliation(s)
- Xuxia Chai
- Department of Clinical Laboratory, Zhangye People’s Hospital Affiliated to Hexi University, Gansu, China
| | - Yonghe Jin
- Department of Clinical Laboratory, Zhangye People’s Hospital Affiliated to Hexi University, Gansu, China
| | - Yongmei Wei
- Department of Clinical Laboratory, Zhangye People’s Hospital Affiliated to Hexi University, Gansu, China
| | - Rong Yang
- Department of Clinical Laboratory, Zhangye People’s Hospital Affiliated to Hexi University, Gansu, China
- * Correspondence: Rong Yang, Department of Clinical Laboratory, Zhangye People’s Hospital Affiliated to Hexi University, Gansu 734000, China (e-mail: )
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Bilal H, Sharif A, Malik MNH, Zubair HM. Aqueous Ethanolic Extract of Adiantum incisum Forssk. Protects against Type 2 Diabetes Mellitus via Attenuation of α-Amylase and Oxidative Stress. ACS OMEGA 2022; 7:37724-37735. [PMID: 36312418 PMCID: PMC9607679 DOI: 10.1021/acsomega.2c04673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Purpose : This study was designed to investigate the antidiabetic effects of the aqueous ethanolic extract of Adiantum incisum Forssk. whole plant (AE-AI) in order to validate the folkloric claim. Methods : Streptozotocin (STZ) was used to induce type 2 diabetes mellitus (TII DM) in male Sprague-Dawley rats. STZ-induced diabetic rats were later treated orally with either AE-AI (125, 250, and 500 mg/kg) or glibenclamide for 35 days. Blood glucose levels were measured weekly and on day 35, animals were sacrificed, and blood samples and tissues were harvested for subsequent antioxidant and histopathological analyses. AE-AI was also analyzed in vitro for phytochemical, antioxidant, and α-amylase inhibitory assays. Results : The phytochemical screening of AE-AI confirmed the presence of essential bioactive compounds like cardiac glycosides, flavonoids, phenolic compounds, saponins, and fixed oils. AE-AI demonstrated abundant amounts of total phenolic and flavonoid contents and displayed prominent antioxidant activity as assessed via DPPH, phosphomolybdate, and nitric oxide scavenging assays. AE-AI treatment also showed α-amylase inhibitory activity comparable to acarbose. In addition, AE-AI treatment exhibited a wide margin of safety in rats and dose-dependently reduced STZ-induced blood glucose levels. Moreover, AE-AI increased the levels of GSH, SOD, catalase, and reduced MDA, and therefore prevented pathological effects of STZ on the kidney, liver, and pancreas. The blood glucose regulatory effect and antioxidant activity of AE-AI also aided in normalizing TII DM-mediated dyslipidemias. GC-MS analysis also demonstrated several potential antidiabetic phytoconstituents in AE-AI. Conclusion : These findings reveal that AE-AI possesses certain pharmacologically active compounds that can effectively treat STZ-induced TII DM owing to its antioxidant and α-amylase inhibitory potentials.
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Affiliation(s)
| | - Ali Sharif
- Faculty
of Pharmacy, University of Lahore, Lahore54000, Pakistan
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Wang Y, Liu Y, Yang R, Li Z, Su J, Yang T, Ma M, Pan G, Wang X, Li L, Yu C. Remnant cholesterol for the detection of glucose metabolic states in patients with coronary heart disease angina pectoris. Acta Diabetol 2022; 59:1339-1347. [PMID: 35871108 DOI: 10.1007/s00592-022-01935-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 06/29/2022] [Indexed: 11/01/2022]
Abstract
BACKGROUND This study aimed to evaluate the relationship between remnant cholesterol (RC) and glucose metabolic states in coronary heart disease (CHD) patients with angina pectoris. METHODS This study collected data from 11,557 CHD patients with angina pectoris aged 35-75 years in Tianjin, China. Participants were divided into normal glucose regulation (NGR), prediabetes (Pre-DM) and diabetes mellitus (DM) groups according to glucose metabolic states. Linear regression analysis was used to explore the relationship between glucose metabolism [fasting blood glucose (FBG) and glycated hemoglobin (HbA1c)] and RC levels. Logistic regression was performed to analyze the relationship between RC levels and glucose metabolic states. RESULTS Among all participants, 5883 (50.9%) had a DM state and 4034 (34.9%) had a Pre-DM state. FBG levels and HbA1c levels were positively related with RC in all patients (P < 0.001). NGR was used as a reference, multi-adjusted model showing that RC level was significantly associated with Pre-DM [Odds ratio (OR): 1.37; 95% confidence interval (CI) 1.19-1.56; P < 0.001] and DM state (OR:1.47; 95% CI 1.29-1.67; P < 0.001). When considering RC as categorical variables (tertiles), using T1 as a reference, T3 had the strongest relationship between RC levels and Pre-DM and DM state in univariate model and multivariate model. In the stratified analyses, the association between RC levels and pre-DM and DM in women was higher than that in men, and the elderly patients was higher than in the middle-aged patients. CONCLUSION The study demonstrated a significant association between RC levels and pre-DM and DM state among CHD patients with angina pectoris, and the relationship was stronger in women and elderly patients.
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Affiliation(s)
- Yang Wang
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yijia Liu
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Rongrong Yang
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Zhu Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jinyu Su
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Tong Yang
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Mei Ma
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Guangwei Pan
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Xianliang Wang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
| | - Lin Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Chunquan Yu
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
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Phenotypic and Genetic Evidence for a More Prominent Role of Blood Glucose than Cholesterol in Atherosclerosis of Hyperlipidemic Mice. Cells 2022; 11:cells11172669. [PMID: 36078077 PMCID: PMC9455034 DOI: 10.3390/cells11172669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/16/2022] [Accepted: 08/25/2022] [Indexed: 11/17/2022] Open
Abstract
Hyperlipidemia and type 2 diabetes (T2D) are major risk factors for atherosclerosis. Apoe-deficient (Apoe−/−) mice on certain genetic backgrounds develop hyperlipidemia, atherosclerosis, and T2D when fed a Western diet. Here, we sought to dissect phenotypic and genetic relationships of blood lipids and glucose with atherosclerotic plaque formation when the vasculature is exposed to high levels of cholesterol and glucose. Male F2 mice were generated from LP/J and BALB/cJ Apoe−/− mice and fed a Western diet for 12 weeks. Three significant QTL Ath51, Ath52 and Ath53 on chromosomes (Chr) 3 and 15 were mapped for atherosclerotic lesions. Ath52 on proximal Chr15 overlapped with QTL for plasma glucose, non-HDL cholesterol, and triglyceride. Atherosclerotic lesion sizes showed significant correlations with fasting, non-fasting glucose, non-fasting triglyceride, and body weight but no correlation with HDL, non-HDL cholesterol, and fasting triglyceride levels. Ath52 for atherosclerosis was down-graded from significant to suggestive level after adjustment for fasting, non-fasting glucose, and non-fasting triglyceride but minimally affected by HDL, non-HDL cholesterol, and fasting triglyceride. Adjustment for body weight suppressed Ath52 but elevated Ath53 on distal Chr15. These results demonstrate phenotypic and genetic connections of blood glucose and triglyceride with atherosclerosis, and suggest a more prominent role for blood glucose than cholesterol in atherosclerotic plaque formation of hyperlipidemic mice.
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Ahmadloo S, Ling KH, Fazli A, Larijani G, Ghodsian N, Mohammadi S, Amini N, Hosseinpour Sarmadi V, Ismail P. Signature pattern of gene expression and signaling pathway in premature diabetic patients uncover their correlation to early age coronary heart disease. Diabetol Metab Syndr 2022; 14:107. [PMID: 35906673 PMCID: PMC9336005 DOI: 10.1186/s13098-022-00878-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Coronary Heart Disease (CHD) is the leading cause of death in industrialized countries. There is currently no direct relation between CHD and type 2 diabetes mellitus (T2D), one of the major modifiable risk factors for CHD. This study was carried out for genes expression profiling of T2D associated genes to identify related biological processes/es and modulated signaling pathway/s of male subjects with CHD. METHOD the subjects were divided into four groups based on their disease, including control, type 2 diabetes mellitus (T2D), CHD, and CHD + T2D groups. The RNA was extracted from their blood, and RT2 Profiler™ PCR Array was utilized to determine gene profiling between groups. Finally, the PCR Array results were validated by using Q-RT-PCR in a more extensive and independent population. RESULT PCR Array results revealed that the T2D and T2D + CHD groups shared 11 genes significantly up-regulated in both groups. Further analysis showed that the mRNA levels of AKT2, IL12B, IL6, IRS1, IRS2, MAPK14, and NFKB1 increased. Consequently, the mRNA levels of AQP2, FOXP3, G6PD, and PIK3R1 declined in the T2D + CHD group compared to the T2D group. Furthermore, in silico analysis indicated 36 Gene Ontology terms and 59 signaling pathways were significantly enriched in both groups, which may be a culprit in susceptibility of diabetic patients to CHD development. CONCLUSION Finally, the results revealed six genes as a hub gene in altering various biological processes and signaling pathways. The expression trend of these identified genes might be used as potential markers and diagnostic tools for the early identification of the vulnerability of T2D patients to develop premature CHD.
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Affiliation(s)
- Salma Ahmadloo
- Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
- Vaccination Department, Pasteur Institute of Iran, Tehran, Iran
| | - King-Hwa Ling
- Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
- Genetics and Regenerative Medicine Research Center, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
| | - Ahmad Fazli
- Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
| | - Ghazaleh Larijani
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Nooshin Ghodsian
- Department of Biomedical Engineering, University of British Columbia, Vancouver, Canada
| | - Sanaz Mohammadi
- Faculty of Biological Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Naser Amini
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran
- Institutes of Regenerative Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Vahid Hosseinpour Sarmadi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran.
- Institutes of Regenerative Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Patimah Ismail
- Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
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Yun JS, Jung SH, Shivakumar M, Xiao B, Khera AV, Won HH, Kim D. Polygenic risk for type 2 diabetes, lifestyle, metabolic health, and cardiovascular disease: a prospective UK Biobank study. Cardiovasc Diabetol 2022; 21:131. [PMID: 35836215 PMCID: PMC9284808 DOI: 10.1186/s12933-022-01560-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Few studies have examined associations between genetic risk for type 2 diabetes (T2D), lifestyle, clinical risk factors, and cardiovascular disease (CVD). We aimed to investigate the association of and potential interactions among genetic risk for T2D, lifestyle behavior, and metabolic risk factors with CVD. METHODS A total of 345,217 unrelated participants of white British descent were included in analyses. Genetic risk for T2D was estimated as a genome-wide polygenic risk score constructed from > 6 million genetic variants. A favorable lifestyle was defined in terms of four modifiable lifestyle components, and metabolic health status was determined according to the presence of metabolic syndrome components. RESULTS During a median follow-up of 8.9 years, 21,865 CVD cases (6.3%) were identified. Compared with the low genetic risk group, participants at high genetic risk for T2D had higher rates of overall CVD events, CVD subtypes (coronary artery disease, peripheral artery disease, heart failure, and atrial fibrillation/flutter), and CVD mortality. Individuals at very high genetic risk for T2D had a 35% higher risk of CVD than those with low genetic risk (HR 1.35 [95% CI 1.19 to 1.53]). A significant gradient of increased CVD risk was observed across genetic risk, lifestyle, and metabolic health status (P for trend > 0.001). Those with favorable lifestyle and metabolically healthy status had significantly reduced risk of CVD events regardless of T2D genetic risk. This risk reduction was more apparent in young participants (≤ 50 years). CONCLUSIONS Genetic risk for T2D was associated with increased risks of overall CVD, various CVD subtypes, and fatal CVD. Engaging in a healthy lifestyle and maintaining metabolic health may reduce subsequent risk of CVD regardless of genetic risk for T2D.
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Affiliation(s)
- Jae-Seung Yun
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA
- Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, St. Vincent's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA
- Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Brenda Xiao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Amit V Khera
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6021, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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Xue H, Pan W. Robust inference of bi-directional causal relationships in presence of correlated pleiotropy with GWAS summary data. PLoS Genet 2022; 18:e1010205. [PMID: 35576237 PMCID: PMC9135345 DOI: 10.1371/journal.pgen.1010205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 05/26/2022] [Accepted: 04/15/2022] [Indexed: 11/25/2022] Open
Abstract
To infer a causal relationship between two traits, several correlation-based causal direction (CD) methods have been proposed with the use of SNPs as instrumental variables (IVs) based on GWAS summary data for the two traits; however, none of the existing CD methods can deal with SNPs with correlated pleiotropy. Alternatively, reciprocal Mendelian randomization (MR) can be applied, which however may perform poorly in the presence of (unknown) invalid IVs, especially for bi-directional causal relationships. In this paper, first, we propose a CD method that performs better than existing CD methods regardless of the presence of correlated pleiotropy. Second, along with a simple but yet effective IV screening rule, we propose applying a closely related and state-of-the-art MR method in reciprocal MR, showing its almost identical performance to that of the new CD method when their model assumptions hold; however, if the modeling assumptions are violated, the new CD method is expected to better control type I errors. Notably bi-directional causal relationships impose some unique challenges beyond those for uni-directional ones, and thus requiring special treatments. For example, we point out for the first time several scenarios where a bi-directional relationship, but not a uni-directional one, can unexpectedly cause the violation of some weak modeling assumptions commonly required by many robust MR methods. We also offer some numerical support and a modeling justification for the application of our new methods (and more generally MR) to binary traits. Finally we applied the proposed methods to 12 risk factors and 4 common diseases, confirming mostly well-known uni-directional causal relationships, while identifying some novel and plausible bi-directional ones such as between body mass index and type 2 diabetes (T2D), and between diastolic blood pressure and stroke.
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Affiliation(s)
- Haoran Xue
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
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45
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Hoffmann AP, Honigberg MC. Glycated Hemoglobin as an Integrator of Cardiovascular Risk in Individuals Without Diabetes: Lessons from Recent Epidemiologic Studies. Curr Atheroscler Rep 2022; 24:435-442. [PMID: 35386092 DOI: 10.1007/s11883-022-01024-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/14/2022] [Indexed: 12/19/2022]
Abstract
PURPOSE OF REVIEW Prediabetes, or dysglycemia in the absence of diabetes, is a prevalent condition typically defined by a glycated hemoglobin (HgbA1c) of 5.7- < 6.5%. This article reviews current contemporary data examining the association between prediabetes and cardiovascular disease (CVD) as well as HgbA1c as a continuous measure of cardiovascular risk across the glycemic spectrum. RECENT FINDINGS Dysglycemia in the prediabetic range is associated with an increased risk of both subclinical and clinical CVD, including atherosclerotic CVD, chronic kidney disease, and heart failure. Several recent large, prospective studies demonstrate roughly linear risk with increasing HgbA1c, even below the threshold for prediabetes. "High-risk" patients with prediabetes have similar CVD risk as those with diabetes. HgbA1c below the threshold for diabetes stratifies CVD risk. Use of HgbA1c as a continuous measure, rather than simply dichotomized, may inform current and future prevention strategies. Given the high population attributable risk associated with prediabetes, targeted prevention strategies in this population warrant dedicated study.
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Affiliation(s)
- Alexander P Hoffmann
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Michael C Honigberg
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, USA.
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, MA, Boston, 02114, USA.
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46
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Loh M, Zhang W, Ng HK, Schmid K, Lamri A, Tong L, Ahmad M, Lee JJ, Ng MCY, Petty LE, Spracklen CN, Takeuchi F, Islam MT, Jasmine F, Kasturiratne A, Kibriya M, Mohlke KL, Paré G, Prasad G, Shahriar M, Chee ML, de Silva HJ, Engert JC, Gerstein HC, Mani KR, Sabanayagam C, Vujkovic M, Wickremasinghe AR, Wong TY, Yajnik CS, Yusuf S, Ahsan H, Bharadwaj D, Anand SS, Below JE, Boehnke M, Bowden DW, Chandak GR, Cheng CY, Kato N, Mahajan A, Sim X, McCarthy MI, Morris AP, Kooner JS, Saleheen D, Chambers JC. Identification of genetic effects underlying type 2 diabetes in South Asian and European populations. Commun Biol 2022; 5:329. [PMID: 35393509 PMCID: PMC8991226 DOI: 10.1038/s42003-022-03248-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/08/2022] [Indexed: 02/08/2023] Open
Abstract
South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.
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Affiliation(s)
- Marie Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK
| | - Hong Kiat Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Katharina Schmid
- Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, 85764, Neuherberg, Germany
- Department of Informatics, Technical University of Munich, 85748, Garching bei München, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Lin Tong
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Meraj Ahmad
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Jung-Jin Lee
- Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Medicine, Mayo Hospital, Lahore, Pakistan
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, 37215, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, 01003, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Md Tariqul Islam
- U Chicago Research Bangladesh, House#4, Road#2b, Sector#4, Uttara, Dhaka, 1230, Bangladesh
| | - Farzana Jasmine
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Anuradhani Kasturiratne
- Department of Public Health, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - Muhammad Kibriya
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Mohammad Shahriar
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Miao Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - James C Engert
- Department of Medicine, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Hertzel C Gerstein
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - K Radha Mani
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Marijana Vujkovic
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ananda R Wickremasinghe
- Department of Public Health, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Habibul Ahsan
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Dwaipayan Bharadwaj
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Donald W Bowden
- Department of Medicine, Mayo Hospital, Lahore, Pakistan
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, 37215, USA
| | - Giriraj R Chandak
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- JSS Academy of Health Education of Research, Mysuru, India
- Science and Engineering Research Board, Department of Science and Technology, Ministry of Science and technology, Government of India, New Delhi, India
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Anubha Mahajan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hosptial, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK.
- Imperial College Healthcare NHS Trust, Imperial College London, London, W12 0HS, UK.
- MRC-PHE Centre for Enviroment and Health, Imperial College London, London, W2 1PG, UK.
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK.
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - John C Chambers
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK.
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK.
- Imperial College Healthcare NHS Trust, Imperial College London, London, W12 0HS, UK.
- MRC-PHE Centre for Enviroment and Health, Imperial College London, London, W2 1PG, UK.
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Shi LJ, Chagari B, An A, Chen MH, Bao Y, Shi W. Genetic Connection between Hyperglycemia and Carotid Atherosclerosis in Hyperlipidemic Mice. Genes (Basel) 2022; 13:genes13030510. [PMID: 35328064 PMCID: PMC8950324 DOI: 10.3390/genes13030510] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 01/09/2023] Open
Abstract
Type 2 diabetes (T2D) is a major risk for atherosclerosis and its complications. Apoe-null (Apoe−/−) mouse strains exhibit a wide range of variations in susceptibility to T2D and carotid atherosclerosis, with the latter being a major cause of ischemic stroke. To identify genetic connections between T2D and carotid atherosclerosis, 145 male F2 mice were generated from LP/J and BALB/cJ Apoe−/− mice and fed 12 weeks of a Western diet. Atherosclerotic lesions in the carotid arteries, fasting, and non-fasting plasma glucose levels were measured, and genotyping was performed using miniMUGA arrays. Two significant QTL (quantitative trait loci) on chromosomes (Chr) 6 and 15 were identified for carotid lesions. The Chr15 QTL coincided precisely with QTL Bglu20 for fasting and non-fasting glucose levels. Carotid lesion sizes showed a trend toward correlation with fasting and non-fasting glucose levels in F2 mice. The Chr15 QTL for carotid lesions was suppressed after excluding the influence from fasting or non-fasting glucose. Likely candidate genes for the causal association were Tnfrsf11b, Deptor, and Gsdmc2. These results demonstrate a causative role for hyperglycemia in the development of carotid atherosclerosis in hyperlipidemic mice.
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Affiliation(s)
- Lisa J. Shi
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA 22908, USA; (L.J.S.); (B.C.); (A.A.); (M.-H.C.)
| | - Bilhan Chagari
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA 22908, USA; (L.J.S.); (B.C.); (A.A.); (M.-H.C.)
| | - Alexander An
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA 22908, USA; (L.J.S.); (B.C.); (A.A.); (M.-H.C.)
| | - Mei-Hua Chen
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA 22908, USA; (L.J.S.); (B.C.); (A.A.); (M.-H.C.)
| | - Yongde Bao
- Department of Microbiology, University of Virginia, Charlottesville, VA 22908, USA;
| | - Weibin Shi
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA 22908, USA; (L.J.S.); (B.C.); (A.A.); (M.-H.C.)
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
- Correspondence:
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48
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Grace C, Hopewell JC, Watkins H, Farrall M, Goel A. Robust estimates of heritable coronary disease risk in individuals with type 2 diabetes. Genet Epidemiol 2022; 46:51-62. [PMID: 34672391 PMCID: PMC8983061 DOI: 10.1002/gepi.22434] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/12/2021] [Accepted: 10/04/2021] [Indexed: 11/21/2022]
Abstract
Type 2 diabetes (T2D) is an important heritable risk factor for coronary artery disease (CAD), the risk of both diseases being increased by metabolic syndrome (MS). With the availability of large-scale genome-wide association data, we aimed to elucidate the genetic burden of CAD risk in T2D predisposed individuals within the context of MS and their shared genetic architecture. Mendelian randomization (MR) analyses supported a causal relationship between T2D and CAD [odds ratio (OR) = 1.13 per log-odds unit 95% confidence interval (CI): 1.10-1.16; p = 1.59 × 10-17 ]. Simultaneously adjusting MR analyses for the effects of the T2D instrument including blood pressure, dyslipidaemia, and obesity attenuated the association between T2D and CAD (OR = 1.07, 95% CI: 1.04-1.11). Bayesian locus-overlap analysis identified 44 regions with the same causal variant underlying T2D and CAD genetic signals (FDR < 1%) at a posterior probability >0.7; five (MHC, LPL, ABO, RAI1 and MC4R) of these regions contain genome-wide significant (p < 5 × 10-8 ) associations for both traits. Given the small effect sizes observed in genome-wide association studies for complex diseases, even with 44 potential target regions, this has implications for the likely magnitude of CAD risk reduction that might be achievable by pure T2D therapies.
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Affiliation(s)
- Christopher Grace
- Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
| | - Jemma C. Hopewell
- Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
| | - Anuj Goel
- Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
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Ding Q, Qin L, Wojeck B, Inzucchi SE, Ibrahim A, Bravata DM, Strohl KP, Yaggi HK, Zinchuk AV. Polysomnographic Phenotypes of Obstructive Sleep Apnea and Incident Type 2 Diabetes: Results from the DREAM Study. Ann Am Thorac Soc 2021; 18:2067-2078. [PMID: 34185617 PMCID: PMC8641817 DOI: 10.1513/annalsats.202012-1556oc] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 06/08/2021] [Indexed: 11/20/2022] Open
Abstract
Rationale: Obstructive sleep apnea (OSA) is associated with cardiovascular disease and incident type 2 diabetes (T2DM). Seven OSA phenotypes, labeled on the basis of their most distinguishing polysomnographic features, have been shown to be differentially associated with incident cardiovascular disease. However, little is known about the relevance of polysomnographic phenotypes for the risk of T2DM. Objectives: To assess whether polysomnographic phenotypes are associated with incident T2DM and to compare the predictive value of baseline polysomnographic phenotypes with the Apnea-Hypopnea Index (AHI) for T2DM. Methods: The study included 840 individuals without baseline diabetes from a multisite observational U.S. veteran cohort who underwent OSA evaluation between 2000 and 2004, with follow-up through 2012. The primary outcome was incident T2DM, defined as no diagnosis at baseline and a new physician diagnosis confirmed by fasting blood glucose >126 mg/dL during follow-up. Relationships between the seven polysomnographic phenotypes (1. mild, 2. periodic limb movements of sleep [PLMS], 3. non-rapid eye movement and poor sleep, 4. rapid eye movement and hypoxia, 5. hypopnea and hypoxia, 6. arousal and poor sleep, and 7. combined severe) and incident T2DM were investigated using Cox proportional hazards regression and competing risk regression models with and without adjustment for baseline covariates. Likelihood ratio tests were conducted to compare the predictive value of the phenotypes with the AHI. Results: During a median follow-up period of 61 months, 122 (14.5%) patients developed incident T2DM. After adjustment for baseline sociodemographics, fasting blood glucose, body mass index, comorbidities, and behavioral risk factors, hazard ratios among persons with "hypopnea and hypoxia" and "PLMS" phenotypes as compared with persons with "mild" phenotype were 3.18 (95% confidence interval [CI], 1.53-6.61] and 2.26 (95% CI, 1.06-4.83) for incident T2DM, respectively. Mild OSA (5 ⩽ AHI < 15) (vs. no OSA) was directly associated with incident T2DM in both unadjusted and multivariable-adjusted regression models. The addition of polysomnographic phenotypes, but not AHI, to known T2DM risk factors greatly improved the predictive value of the computed prediction model. Conclusions: Polysomnographic phenotypes "hypopnea and hypoxia" and "PLMS" independently predict risk of T2DM among a predominantly male veteran population. Polysomnographic phenotypes improved T2DM risk prediction comared with the use of AHI.
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Affiliation(s)
- Qinglan Ding
- College of Health and Human Sciences, Purdue University, West Lafayette, Indiana
| | - Li Qin
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut
| | - Brian Wojeck
- Section of Endocrinology, and
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Silvio E. Inzucchi
- Section of Endocrinology, and
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Ahmad Ibrahim
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Dawn M. Bravata
- Department of Internal Medicine, Richard L. Roudenbush VA Medical Center, Indianapolis, Indiana
- Indiana University School of Medicine, Indianapolis, Indiana
| | - Kingman P. Strohl
- Section of Pulmonary, Critical Care, and Sleep Medicine, Case Western Reserve University, Cleveland, Ohio; and
| | - Henry K. Yaggi
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Veterans Affairs Clinical Epidemiology Research Center, VA Connecticut Healthcare System, West Haven, Connecticut
| | - Andrey V. Zinchuk
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
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Fernandes Silva L, Vangipurapu J, Laakso M. The "Common Soil Hypothesis" Revisited-Risk Factors for Type 2 Diabetes and Cardiovascular Disease. Metabolites 2021; 11:metabo11100691. [PMID: 34677406 PMCID: PMC8540397 DOI: 10.3390/metabo11100691] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/21/2021] [Accepted: 10/07/2021] [Indexed: 12/12/2022] Open
Abstract
The prevalence and the incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance, as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic variants in interaction with lifestyle and environmental factors. Cardiovascular disease (CVD) is the major cause of morbidity and mortality. Detailed understanding of molecular mechanisms underlying in CVD events is still largely missing. Several risk factors are shared between T2D and CVD, including obesity, insulin resistance, dyslipidemia, and hyperglycemia. CVD can precede the development of T2D, and T2D is a major risk factor for CVD, suggesting that both conditions have common genetic and environmental antecedents and that they share “common soil”. We analyzed the relationship between the risk factors for T2D and CVD based on genetics and population-based studies with emphasis on Mendelian randomization studies.
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