1
|
Jiu L, Wang J, Javier Somolinos-Simón F, Tapia-Galisteo J, García-Sáez G, Hernando M, Li X, Vreman RA, Mantel-Teeuwisse AK, Goettsch WG. A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes. Diabetes Res Clin Pract 2024; 209:111574. [PMID: 38346592 DOI: 10.1016/j.diabres.2024.111574] [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: 09/15/2023] [Revised: 01/17/2024] [Accepted: 02/06/2024] [Indexed: 02/23/2024]
Abstract
This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.
Collapse
Affiliation(s)
- Li Jiu
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, Netherlands
| | - Junfeng Wang
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, Netherlands
| | - Francisco Javier Somolinos-Simón
- Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica, ETSI de Telecomunicación, Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Jose Tapia-Galisteo
- Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica, ETSI de Telecomunicación, Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223 Pozuelo de Alarcón, Madrid, Spain; CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Gema García-Sáez
- Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica, ETSI de Telecomunicación, Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223 Pozuelo de Alarcón, Madrid, Spain; CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Mariaelena Hernando
- Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica, ETSI de Telecomunicación, Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223 Pozuelo de Alarcón, Madrid, Spain; CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Parque Científico y Tecnológico de la UPM, Crta. M40, Km. 38, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Xinyu Li
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, Netherlands; University of Groningen, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, Broerstraat 5, 9712 CP Groningen, the Netherlands
| | - Rick A Vreman
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, Netherlands; National Health Care Institute (ZIN), Diemen, Willem Dudokhof 1, 1112 ZA Diemen, Netherlands
| | - Aukje K Mantel-Teeuwisse
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, Netherlands
| | - Wim G Goettsch
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, Netherlands; National Health Care Institute (ZIN), Diemen, Willem Dudokhof 1, 1112 ZA Diemen, Netherlands.
| |
Collapse
|
2
|
Luo H, Huemer MT, Petrera A, Hauck SM, Rathmann W, Herder C, Koenig W, Hoyer A, Peters A, Thorand B. Association of plasma proteomics with incident coronary heart disease in individuals with and without type 2 diabetes: results from the population-based KORA study. Cardiovasc Diabetol 2024; 23:53. [PMID: 38310303 PMCID: PMC10838466 DOI: 10.1186/s12933-024-02143-z] [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: 11/13/2023] [Accepted: 01/22/2024] [Indexed: 02/05/2024] Open
Abstract
BACKGROUND Coronary heart disease (CHD) is a major global health concern, especially among individuals with type 2 diabetes (T2D). Given the crucial role of proteins in various biological processes, this study aimed to elucidate the aetiological role and predictive performance of protein biomarkers on incident CHD in individuals with and without T2D. METHODS The discovery cohort included 1492 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 study with 147 incident CHD cases (45 vs. 102 cases in the group with T2D and without T2D, respectively) during 15.6 years of follow-up. The validation cohort included 888 participants from the KORA-Age1 study with 70 incident CHD cases (19 vs. 51 cases in the group with T2D and without T2D, respectively) during 6.9 years of follow-up. We measured 233 plasma proteins related to cardiovascular disease and inflammation using proximity extension assay technology. Associations of proteins with incident CHD were assessed using Cox regression and Mendelian randomization (MR) analysis. Predictive models were developed using priority-Lasso and were evaluated on top of Framingham risk score variables using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI). RESULTS We identified two proteins associated with incident CHD in individuals with and 29 in those without baseline T2D, respectively. Six of these proteins are novel candidates for incident CHD. MR suggested a potential causal role for hepatocyte growth factor in CHD development. The developed four-protein-enriched model for individuals with baseline T2D (ΔC-index: 0.017; cfNRI: 0.253; IDI: 0.051) and the 12-protein-enriched model for individuals without baseline T2D (ΔC-index: 0.054; cfNRI: 0.462; IDI: 0.024) consistently improved CHD prediction in the discovery cohort, while in the validation cohort, significant improvements were only observed for selected performance measures (with T2D: cfNRI: 0.633; without T2D: ΔC-index: 0.038; cfNRI: 0.465). CONCLUSIONS This study identified novel protein biomarkers associated with incident CHD in individuals with and without T2D and reaffirmed previously reported protein candidates. These findings enhance our understanding of CHD pathophysiology and provide potential targets for prevention and treatment.
Collapse
Affiliation(s)
- Hong Luo
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstaedter Landstraße 1, D-85764, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany
| | - Marie-Theres Huemer
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstaedter Landstraße 1, D-85764, Neuherberg, Germany
| | - Agnese Petrera
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Stefanie M Hauck
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine Universität, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, Neuherberg, Germany
| | - Christian Herder
- German Center for Diabetes Research (DZD), Partner Düsseldorf, Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine Universität, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine Universität, Düsseldorf, Germany
| | - Wolfgang Koenig
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Annika Hoyer
- Biostatistics and Medical Biometry, Medical School OWL, Bielefeld University, Bielefeld, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstaedter Landstraße 1, D-85764, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstaedter Landstraße 1, D-85764, Neuherberg, Germany.
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany.
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany.
| |
Collapse
|
3
|
Cai X, Xue Z, Zeng FF, Tang J, Yue L, Wang B, Ge W, Xie Y, Miao Z, Gou W, Fu Y, Li S, Gao J, Shuai M, Zhang K, Xu F, Tian Y, Xiang N, Zhou Y, Shan PF, Zhu Y, Chen YM, Zheng JS, Guo T. Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome. Cell Rep Med 2023; 4:101172. [PMID: 37652016 PMCID: PMC10518601 DOI: 10.1016/j.xcrm.2023.101172] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 09/02/2023]
Abstract
Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.
Collapse
Affiliation(s)
- Xue Cai
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Zhangzhi Xue
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Fang-Fang Zeng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510080, China
| | - Jun Tang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Liang Yue
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Bo Wang
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Yuting Xie
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Zelei Miao
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Wanglong Gou
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Yuanqing Fu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Sainan Li
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Jinlong Gao
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Menglei Shuai
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Ke Zhang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Fengzhe Xu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Yunyi Tian
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Nan Xiang
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Yan Zhou
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Peng-Fei Shan
- Department of Endocrinology, the Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, Zhejiang 310009, China
| | - Yi Zhu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China.
| | - Yu-Ming Chen
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Ju-Sheng Zheng
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China.
| | - Tiannan Guo
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China.
| |
Collapse
|
4
|
Kuang N, Shu B, Yang F, Li S, Zhang M. TRAIL or TRAIL-R2 as a Predictive Biomarker for Mortality or Cardiovascular Events: A Systematic Review and Meta-analysis. J Cardiovasc Pharmacol 2023; 81:348-354. [PMID: 36888983 DOI: 10.1097/fjc.0000000000001415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/10/2023] [Indexed: 03/10/2023]
Abstract
ABSTRACT Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and TRAIL-receptor-2 (TRAIL-R2) are associated with atherosclerosis. This meta-analysis aimed to investigate the potential association between TRAIL/TRAIL-R2 with mortality or cardiovascular (CV) events. PubMed, Embase, and Cochrane Library were searched for reports published up to May 2021. Reports were included when the association between TRAIL or TRAIL-R2 and mortality or CV events was reported. Considering the heterogeneity between studies, we used the random-effects model for all analyses. Ultimately, the meta-analysis included 18 studies (16,295 patients). The average follow-up ranged from 0.25 to 10 years. Decreased TRAIL levels were negatively associated with all-cause mortality [rank variable, hazard ratio (HR), 95% CI, 2.93, 1.94-4.42; I2 = 0.0%, Pheterogeneity = 0.835]. Increased TRAIL-R2 levels were positively associated with all-cause mortality (continuous variable, HR, 95% CI, 1.43, 1.23-1.65; I2 = 0.0%, Pheterogeneity = 0.548; rank variable, HR, 95% CI, 7.08, 2.70-18.56; I2 = 46.5%, Pheterogeneity = 0.154), CV mortality (continuous variable, HR, 95% CI, 1.33, 1.14-1.57; I2 = 0.0%, Pheterogeneity = 0.435), myocardial infarction (continuous variable, HR, 95% CI, 1.23, 1.02-1.49; rank variable, HR, 95% CI, 1.49, 1.26-1.76; I2 = 0.7%, Pheterogeneity = 0.402), and new-onset heart failure (rank variable, HR, 95% CI, 3.23, 1.32-7.87; I2 = 83.0%, Pheterogeneity = 0.003). In conclusion, decreased TRAIL was negatively associated with all-cause mortality, and increased TRAIL-R2 was positively associated with all-cause mortality, CV mortality, myocardial infarction, and heart failure.
Collapse
Affiliation(s)
- Na Kuang
- Department of Cardiology, Wuhan Hospital of Traditional Chinese Medicine, Wuhan, China
| | | | | | | | | |
Collapse
|
5
|
Kelland E, Patil MS, Patel S, Cartland SP, Kavurma MM. The Prognostic, Diagnostic, and Therapeutic Potential of TRAIL Signalling in Cardiovascular Diseases. Int J Mol Sci 2023; 24:ijms24076725. [PMID: 37047698 PMCID: PMC10095395 DOI: 10.3390/ijms24076725] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/07/2023] Open
Abstract
TNF-related apoptosis-inducing ligand (TRAIL) was originally discovered, almost 20 years ago, for its ability to kill cancer cells. More recent evidence has described pleiotropic functions, particularly in the cardiovascular system. There is potential for TRAIL concentrations in the circulation to act as prognostic and/or diagnostic factors for cardiovascular diseases (CVD). Pre-clinical studies also describe the therapeutic capacity for TRAIL signals, particularly in the context of atherosclerotic disease and diseases of the myocardium. Because diabetes mellitus significantly contributes to the progression and pathogenesis of CVDs, in this review we highlight recent evidence for the prognostic, diagnostic, and therapeutic potential of TRAIL signals in CVDs, and where relevant, the impact of diabetes mellitus. A greater understanding of how TRAIL signals regulate cardiovascular protection and pathology may offer new diagnostic and therapeutic avenues for patients suffering from CVDs.
Collapse
Affiliation(s)
- Elaina Kelland
- Heart Research Institute, The University of Sydney, Sydney 2042, Australia
| | - Manisha S. Patil
- Heart Research Institute, The University of Sydney, Sydney 2042, Australia
| | - Sanjay Patel
- Heart Research Institute, The University of Sydney, Sydney 2042, Australia
- Royal Prince Alfred Hospital, Sydney 2006, Australia
| | - Siân P. Cartland
- Heart Research Institute, The University of Sydney, Sydney 2042, Australia
| | - Mary M. Kavurma
- Heart Research Institute, The University of Sydney, Sydney 2042, Australia
| |
Collapse
|
6
|
Emerging Biomarkers for Predicting Clinical Outcomes in Patients with Heart Disease. LIFE (BASEL, SWITZERLAND) 2023; 13:life13010230. [PMID: 36676179 PMCID: PMC9864006 DOI: 10.3390/life13010230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/31/2022] [Accepted: 01/09/2023] [Indexed: 01/18/2023]
Abstract
Cardiovascular disease is most frequently caused by the development and progression of atherosclerosis. When coronary arteries are afflicted, and the stenoses caused by atherosclerotic plaques are severe enough, the metabolic supply-and-offer balance is disturbed, leading to myocardial ischemia. If atherosclerotic plaques become unstable and local thrombosis develops, a myocardial infarction occurs. Sometimes, myocardial ischemia and infarction may result in significant and irreversible heart failure. To prevent severe complications, such as acute coronary syndromes and ischemia-related heart failure, extensive efforts have been made for developing biomarkers that would help identify patients at increased risk for cardiovascular events. In this two-part study, we attempted to provide a review of existing knowledge of blood biomarkers that may be used in this setting. The first part of this work was dedicated to conventional biomarkers, which are already used in clinical practice. In the second part, here presented, we discuss emerging biomarkers which have not yet become mainstream.
Collapse
|
7
|
Regan JA, Truby LK, Tahir UA, Katz DH, Nguyen M, Kwee LC, Deng S, Wilson JG, Mentz RJ, Kraus WE, Hernandez AF, Gerszten RE, Peterson ED, Holman RR, Shah SH. Protein biomarkers of cardiac remodeling and inflammation associated with HFpEF and incident events. Sci Rep 2022; 12:20072. [PMID: 36418363 PMCID: PMC9684116 DOI: 10.1038/s41598-022-24226-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/11/2022] [Indexed: 11/24/2022] Open
Abstract
There is increasing evidence that HFpEF is a heterogeneous clinical entity and distinct molecular pathways may contribute to pathophysiology. Leveraging unbiased proteomics to identify novel biomarkers, this study seeks to understand the underlying molecular mechanisms of HFpEF. The discovery cohort consisted of HFpEF cases and non-HF controls from the CATHGEN study (N = 176); the validation cohort consisted of participants from the TECOS trial of patients with diabetes (N = 109). Proteins associated with HFpEF were included in a LASSO model to create a discriminative multi-protein model and assessed in the validation cohort. Survival models and meta-analysis were used to test the association of proteins with incident clinical outcomes, including HF hospitalization, mortality and HFpEF hospitalization in CATHGEN, TECOS and the Jackson Heart Study. In the derivation set, 190 proteins were associated with HFpEF in univariate analysis, of which 65 remained significant in the multivariate model. Twenty (30.8%) of these proteins validated in TECOS, including LCN2, U-PAR, IL-1ra, KIM1, CSTB and Gal-9 (OR 1.93-2.77, p < 0.01). LASSO regression yielded a 13-protein model which, when added to a clinical model inclusive of NT-proBNP, improved the AUC from 0.82 to 0.92 (p = 1.5 × 10-4). Five proteins were associated with incident HF hospitalization, four with HFpEF hospitalization and eleven with mortality (p < 0.05). We identified and validated multiple circulating biomarkers associated with HFpEF as well as HF outcomes. These biomarkers added incremental discriminative capabilities beyond clinical factors and NT-proBNP.
Collapse
Affiliation(s)
- Jessica A Regan
- Duke Molecular Physiology Institute (DUMC), 300 N. Duke Street, Box 104775, Durham, NC, 27701, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Lauren K Truby
- Duke Molecular Physiology Institute (DUMC), 300 N. Duke Street, Box 104775, Durham, NC, 27701, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Maggie Nguyen
- Duke Molecular Physiology Institute (DUMC), 300 N. Duke Street, Box 104775, Durham, NC, 27701, USA
| | - Lydia Coulter Kwee
- Duke Molecular Physiology Institute (DUMC), 300 N. Duke Street, Box 104775, Durham, NC, 27701, USA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert J Mentz
- Department of Medicine, Duke University, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | - William E Kraus
- Duke Molecular Physiology Institute (DUMC), 300 N. Duke Street, Box 104775, Durham, NC, 27701, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Adrian F Hernandez
- Department of Medicine, Duke University, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Eric D Peterson
- Department of Medicine, University of Texas Southwestern, Dallas, TX, USA
| | - Rury R Holman
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Svati H Shah
- Duke Molecular Physiology Institute (DUMC), 300 N. Duke Street, Box 104775, Durham, NC, 27701, USA.
- Department of Medicine, Duke University, Durham, NC, USA.
- Duke Clinical Research Institute, Durham, NC, USA.
| |
Collapse
|
8
|
Png G, Gerlini R, Hatzikotoulas K, Barysenka A, Rayner NW, Klarić L, Rathkolb B, Aguilar-Pimentel JA, Rozman J, Fuchs H, Gailus-Durner V, Tsafantakis E, Karaleftheri M, Dedoussis G, Pietrzik C, Wilson JF, Angelis MH, Becker-Pauly C, Gilly A, Zeggini E. Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing. Hum Mol Genet 2022; 32:1266-1275. [PMID: 36349687 PMCID: PMC10077504 DOI: 10.1093/hmg/ddac275] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Cardiometabolic diseases, such as type 2 diabetes and cardiovascular disease, have a high public health burden. Understanding the genetically-determined regulation of proteins that are dysregulated in disease can help to dissect the complex biology underpinning them. Here, we perform a protein quantitative trait locus (pQTL) analysis of 255 serum proteins relevant to cardiometabolic processes in 2893 individuals. Meta-analysing whole-genome sequencing (WGS) data from two Greek cohorts, MANOLIS (n = 1356; 22.5x WGS) and Pomak (n = 1537; 18.4x WGS), we detect 302 independently-associated pQTL variants for 171 proteins, including 12 rare variants (minor allele frequency [MAF] < 1%). We additionally find 15 pQTL variants that are rare in non-Finnish European populations, but have drifted up in frequency in the discovery cohorts here. We identify proteins causally associated with cardiometabolic traits, including MEP1B for high-density lipoprotein levels; and describe a knock-out Mep1b mouse model. Our findings furnish insights into the genetic architecture of the serum proteome, identify new protein-disease relationships, and demonstrate the importance of isolated populations in pQTL analysis.
Collapse
Affiliation(s)
- Grace Png
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Technical University of Munich (TUM), School of Medicine, Munich, Germany
| | - Raffaele Gerlini
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Andrei Barysenka
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - N William Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Lucija Klarić
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Birgit Rathkolb
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute of Molecular Animal Breeding and Biotechnology, Gene Center, Ludwig-Maximilians University Munich, Munich, Germany
| | - Juan A Aguilar-Pimentel
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Jan Rozman
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute of Molecular Genetics of the Czech Academy of Sciences, Czech Centre for Phenogenomics, Vestec, Czech Republic
| | - Helmut Fuchs
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Valerie Gailus-Durner
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | | | | | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | - Claus Pietrzik
- Institute for Pathobiochemistry, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Martin Hrabe Angelis
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,Chair of Experimental Genetics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Christoph Becker-Pauly
- Institute of Biochemistry, Unit for Degradomics of the Protease Web, University of Kiel, Kiel, Germany
| | - Arthur Gilly
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| |
Collapse
|
9
|
Liu Z, Bian N, Wu S, Fan Y, Li H, Yu J, Guo J, Chen D. A meta-analysis evaluating indirectly GLP-1 receptor agonists and arrhythmias in patients with type 2 diabetes and myocardial infarction. Front Cardiovasc Med 2022; 9:1019120. [PMID: 36277800 PMCID: PMC9581215 DOI: 10.3389/fcvm.2022.1019120] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Aims At present, the effects of Glucagon-Like Peptide 1 Receptor agonists (GLP-1RAs) on arrhythmia in patients with type 2 diabetes mellitus (T2DM) and myocardial infarction (MI) are still unclear. Hence, this systematic review and meta-analysis aimed to investigate this association. Methods and results PubMed, Embase, Cochrane Library, and Web of Science were searched from inception to 30 April 2022. Randomized controlled trials (RCTs) that compared GLP-1RAs with placebo and met the critical criterion of a proportion of patients with T2DM and MI > 30% were included to verify our purpose indirectly. The outcomes of interest included atrial arrhythmias, ventricular arrhythmias, atrioventricular block (AVB), sinus arrhythmia, and cardiac arrest. Relative risk (RR) and 95% confidence intervals (CI) were pooled using a random-effects model. We included five RCTs with altogether 31,314 patients. In these trials, the highest proportion of patients with T2DM and MI was 82.6%, while the lowest was 30.7%. Compared to placebo, GLP-1RAs were associated with a lower risk of atrial arrhythmias (RR 0.81, 95% CI 0.70-0.95). There was no significant difference in the risk of ventricular arrhythmias (RR 1.26, 95% CI 0.87-1.80), AVB (RR 0.95, 95% CI 0.63-1.42), sinus arrhythmia (RR 0.62, 95% CI 0.26-1.49), and cardiac arrest (RR 0.97, 95% CI 0.52-1.83) between groups. Conclusion GLP-1RAs may be associated with reduced risk for atrial arrhythmias, which seems more significant for patients with T2DM combined with MI. More studies are needed to clarify the definitive anti-arrhythmic role of this drug.
Collapse
|
10
|
Mihalovic M, Mikulenka P, Línková H, Neuberg M, Štětkářová I, Peisker T, Lauer D, Tousek P. Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand (TRAIL) in Patients after Acute Stroke: Relation to Stroke Severity, Myocardial Injury, and Impact on Prognosis. J Clin Med 2022; 11:jcm11092552. [PMID: 35566677 PMCID: PMC9103556 DOI: 10.3390/jcm11092552] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/21/2022] [Accepted: 04/28/2022] [Indexed: 02/05/2023] Open
Abstract
Background: Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is known to be associated with poor prognosis after cardiovascular events. We aimed to assess the dynamic changes in TRAIL levels and the relation of TRAIL level to stroke severity, its impact on the short-term outcomes, and its association with markers of cardiac injury in patients after acute stroke. Methods: Between August 2020 and August 2021, 120 consecutive patients, 104 after acute ischemic stroke (AIS), 76 receiving reperfusion therapy, and 16 patients after intracerebral hemorrhage (ICH) were enrolled in our study. Blood samples were obtained from patients at the time of admission, 24 h later, and 48 h later to determine the plasma level of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and high-sensitive Troponin I (hs-TnI). Twelve-lead ECGs were obtained at the time of admission, 24 h later, 48 h later, and at the release of the patients. Evaluations were performed using the National Institutes of Health Stroke Scale (NIHSS) at the time of admission and using the modified Rankin Scale (mRS) 90 days following the patient’s discharge from the hospital. Results: We observed a connection between lower TRAIL levels and stroke severity evaluated using the NIHSS (p = 0.044) on the first day. Lower TRAIL showed an association with severe disability and death as evaluated using the mRS at 90 days, both after 24 (p = 0.0022) and 48 h (p = 0.044) of hospitalization. Moreover, we observed an association between lower TRAIL and NT-proBNP elevation at the time of admission (p = 0.039), after 24 (p = 0.043), and after 48 h (p = 0.023) of hospitalization. In the ECG analysis, lower TRAIL levels were associated with the occurrence of premature ventricular extrasystoles (p = 0.043), and there was an association with prolonged QTc interval (p = 0.052). Conclusions: The results show that lower TRAIL is associated with stroke severity, unfavorable functional outcome, and short-term mortality in patients after acute ischemic stroke. Moreover, we described the association with markers of cardiac injury and ECG changes.
Collapse
Affiliation(s)
- Michal Mihalovic
- Cardiocenter, Third Faculty of Medicine, Charles University, 100 34 Prague, Czech Republic
| | - Petr Mikulenka
- Department of Neurology, Third Faculty of Medicine, University Hospital Kralovske Vinohrady, Charles University, 100 34 Prague, Czech Republic
| | - Hana Línková
- Cardiocenter, Third Faculty of Medicine, Charles University, 100 34 Prague, Czech Republic
| | - Marek Neuberg
- Medtronic Czechia, Partner of INTERCARDIS Project, 190 00 Prague, Czech Republic
| | - Ivana Štětkářová
- Department of Neurology, Third Faculty of Medicine, University Hospital Kralovske Vinohrady, Charles University, 100 34 Prague, Czech Republic
| | - Tomáš Peisker
- Department of Neurology, Third Faculty of Medicine, University Hospital Kralovske Vinohrady, Charles University, 100 34 Prague, Czech Republic
| | - David Lauer
- Department of Neurology, Third Faculty of Medicine, University Hospital Kralovske Vinohrady, Charles University, 100 34 Prague, Czech Republic
| | - Petr Tousek
- Cardiocenter, Third Faculty of Medicine, Charles University, 100 34 Prague, Czech Republic
- Correspondence:
| |
Collapse
|
11
|
Ferreira JP, Rossignol P, Bakris G, Mehta C, White WB, Zannad F. Body weight changes in patients with type 2 diabetes and a recent acute coronary syndrome: an analysis from the EXAMINE trial. Cardiovasc Diabetol 2021; 20:187. [PMID: 34521390 PMCID: PMC8442423 DOI: 10.1186/s12933-021-01382-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/07/2021] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Patients with type 2 diabetes (T2D) may experience frequent body weight changes over time. The prognostic impact of these weight changes (gains or losses) requires further study. AIMS To study the associations between changes in body weight (intentional or unintentional) with subsequent outcomes. METHODS The EXAMINE trial included 5380 patients with T2D and a recent acute coronary syndrome, who were randomized to alogliptin or placebo. Time-updated Cox models and mixed effects models were used to test the associations between changes in body weight and subsequent outcomes over a median follow-up of 1.6 (1.0-2.1) years. RESULTS During the post-randomization follow-up period, 1044 patients (19.4%) experienced a weight loss ≥ 5% of baseline weight, 2677 (49.8%) had a stable weight, and 1659 (30.8%) had a ≥ 5 % weight gain. Patients with weight loss were more frequently women and had more co-morbid conditions. In contrast, patients who gained ≥ 5% weight were more frequently men with less co-morbid conditions. A weight loss ≥ 5% was independently associated with a higher risk of subsequent adverse outcomes, including all-cause mortality: adjusted HR (95% CI) = 1.79 (1.33-2.42), P < 0.001. Similar associations were found for cardiovascular mortality, the composite of cardiovascular mortality or heart failure hospitalization, and the primary outcome. A weight gain ≥ 5% was independently associated with an increase in the risk of subsequent cardiovascular mortality or heart failure hospitalization only: adjusted HR (95% CI) = 1.34 (1.02-1.76), P = 0.033. CONCLUSIONS In patients with T2D who had a recent ACS/MI, a ≥ 5% loss of body weight was associated with a higher risk of subsequent cardiovascular events and mortality.
Collapse
Affiliation(s)
- João Pedro Ferreira
- Centre DInvestigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Université de Lorraine, Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre lès Nancy, France. .,Cardiovascular Research and Development Center, Faculty of Medicine, University of Porto, Porto, Portugal.
| | - Patrick Rossignol
- Centre DInvestigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Université de Lorraine, Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre lès Nancy, France
| | - George Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, University of Chicago, Chicago, IL, USA
| | | | - William B White
- Calhoun Cardiology Center, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Faiez Zannad
- Centre DInvestigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Université de Lorraine, Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre lès Nancy, France
| |
Collapse
|
12
|
Tayanloo-Beik A, Roudsari PP, Rezaei-Tavirani M, Biglar M, Tabatabaei-Malazy O, Arjmand B, Larijani B. Diabetes and Heart Failure: Multi-Omics Approaches. Front Physiol 2021; 12:705424. [PMID: 34421642 PMCID: PMC8378451 DOI: 10.3389/fphys.2021.705424] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/08/2021] [Indexed: 12/16/2022] Open
Abstract
Diabetes and heart failure, as important global issues, cause substantial expenses to countries and medical systems because of the morbidity and mortality rates. Most people with diabetes suffer from type 2 diabetes, which has an amplifying effect on the prevalence and severity of many health problems such as stroke, neuropathy, retinopathy, kidney injuries, and cardiovascular disease. Type 2 diabetes is one of the cornerstones of heart failure, another health epidemic, with 44% prevalence. Therefore, finding and targeting specific molecular and cellular pathways involved in the pathophysiology of each disease, either in diagnosis or treatment, will be beneficial. For diabetic cardiomyopathy, there are several mechanisms through which clinical heart failure is developed; oxidative stress with mediation of reactive oxygen species (ROS), reduced myocardial perfusion due to endothelial dysfunction, autonomic dysfunction, and metabolic changes, such as impaired glucose levels caused by insulin resistance, are the four main mechanisms. In the field of oxidative stress, advanced glycation end products (AGEs), protein kinase C (PKC), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) are the key mediators that new omics-driven methods can target. Besides, diabetes can affect myocardial function by impairing calcium (Ca) homeostasis, the mechanism in which reduced protein phosphatase 1 (PP1), sarcoplasmic/endoplasmic reticulum Ca2+ ATPase 2a (SERCA2a), and phosphorylated SERCA2a expressions are the main effectors. This article reviewed the recent omics-driven discoveries in the diagnosis and treatment of type 2 diabetes and heart failure with focus on the common molecular mechanisms.
Collapse
Affiliation(s)
- Akram Tayanloo-Beik
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Peyvand Parhizkar Roudsari
- Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mahmood Biglar
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ozra Tabatabaei-Malazy
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Babak Arjmand
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
13
|
Patient Phenotypes and SGLT-2 Inhibition in Type 2 Diabetes: Insights From the EMPA-REG OUTCOME Trial. JACC-HEART FAILURE 2021; 9:568-577. [PMID: 34325887 DOI: 10.1016/j.jchf.2021.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/15/2021] [Accepted: 03/15/2021] [Indexed: 11/23/2022]
Abstract
OBJECTIVES Using latent class analysis (LCA) of EMPA-REG OUTCOME (BI 10773 [Empagliflozin] Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients), this study identified distinct phenotypes in subjects with type 2 diabetes (T2D) and cardiovascular (CV) disease and explored treatment effects across phenotypes. BACKGROUND In the EMPA-REG OUTCOME trial, empagliflozin reduced risk of CV death or hospitalization for heart failure (HHF) by 34% in subjects with T2D and CV disease. Among such subjects, there has been limited evaluation of clinical phenotypes. METHODS Overall, 7,020 participants were treated with empagliflozin 25 mg, 10 mg, or placebo. For this post hoc analysis, participants were randomly separated into training (two-thirds of patients) and validation (remaining one-third) sets. LCA identified 3 phenotype groups (n = 6,639 with complete data). The phenotype association with CV death or HHF and empagliflozin treatment effect across groups was explored by Cox regression (in training and validation sets). RESULTS In the training set, phenotype group 1 (n = 1,463; 33.1%) included younger patients with shorter T2D duration and the highest estimated glomerular filtration rate (eGFR). Phenotype group 2 (n = 1,172; 26.5%) included more women with non-coronary artery disease. Phenotype group 3 (n = 1,785; 40.4%) included older patients with advanced coronary disease and the lowest eGFR. The risk of CV death varied across phenotypes (group 2 vs. 1: hazard ratio [HR]; 1.83; 95% confidence interval [CI]: 1.23 to 2.71; group 3 vs. 1: HR: 1.86; 95% CI: 1.30 to 2.67) with similar patterns for CV death or HHF. Consistent treatment effects of empagliflozin were seen across phenotypes in the training and validation sets (interaction p > 0.30). CONCLUSIONS Among participants with T2D, this study identified 3 phenotypes with varying CV risk. The treatment effect across phenotypes reaffirms the robustness of CV death or HHF reduction with empagliflozin. (BI 10773 [Empagliflozin] Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients [EMPA-REG OUTCOME]; NCT01131676).
Collapse
|
14
|
Kresoja KP, Rommel KP, Wachter R, Henger S, Besler C, Klöting N, Schnelle M, Hoffmann A, Büttner P, Ceglarek U, Thiele H, Scholz M, Edelmann F, Blüher M, Lurz P. Proteomics to improve phenotyping in obese patients with heart failure with preserved ejection fraction. Eur J Heart Fail 2021; 23:1633-1644. [PMID: 34231954 DOI: 10.1002/ejhf.2291] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/28/2021] [Accepted: 07/02/2021] [Indexed: 12/17/2022] Open
Abstract
AIMS Recent evidence points towards a distinct obese phenotype among patients with heart failure with preserved ejection fraction (HFpEF). We aimed to identify differentially expressed circulating biomarkers in obese HFpEF patients and link them to disease severity and outcomes. METHODS AND RESULTS From the LIFE-Heart study, 999 patients with HFpEF and 999 patients without heart failure (no-HF) were selected and 92 circulating serum biomarkers were measured using a proximity extension assay. Elevation of identified biomarkers was validated in 220 patients from the Aldo-DHF trial with diagnosed HFpEF. HFpEF patients were older and had more comorbidities including coronary artery disease and type 2 diabetes as compared to no-HF patients (P < 0.05 for all). After adjusting for covariates, adrenomedullin (ADM), galectin-9 (Gal-9), thrombospondin-2 (THBS-2), CD4, and tumour necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL-R2) were significantly higher in obese HFpEF patients [body mass index (BMI) ≥30 kg/m2 , n = 464] as compared to lean HFpEF (BMI <30 kg/m2 , n = 535) and obese no-HF patients (BMI ≥30 kg/m2 , n = 387) (P < 0.001 for both); these findings were verified in the Aldo-DHF validation cohort (P < 0.001). Except for CD4 these proteins were associated with increased estimates of left atrial pressure in a linear fashion. Importantly, ADM and CD4 were associated with increased mortality in obese HFpEF patients after adjusting for covariates. CONCLUSION Obese HFpEF patients exhibit higher circulating biomarkers of volume expansion (ADM), myocardial fibrosis (THBS-2) and systemic inflammation (Gal-9, CD4) compared to obese non-HFpEF or lean HFpEF patients. These findings support the clinical definition of a distinct obese HFpEF phenotype and might merit further investigation.
Collapse
Affiliation(s)
- Karl-Patrik Kresoja
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Leipzig, Germany
| | - Karl-Philipp Rommel
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Clinic and Policlinic for Cardiology, University Hospital, Leipzig, Germany
| | - Sylvia Henger
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Christian Besler
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Leipzig, Germany
| | - Nora Klöting
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.,Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Moritz Schnelle
- Institute for Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany
| | - Anne Hoffmann
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.,Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Petra Büttner
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
| | - Holger Thiele
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Frank Edelmann
- Department of Internal Medicine and Cardiology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,German Centre for Cardiovascular Research, partner site Berlin, Germany
| | - Matthias Blüher
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.,Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Philipp Lurz
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Leipzig, Germany
| |
Collapse
|
15
|
Ferreira JP, Lamiral Z, Bakris G, Mehta C, White WB, Zannad F. Red cell distribution width in patients with diabetes and myocardial infarction: An analysis from the EXAMINE trial. Diabetes Obes Metab 2021; 23:1580-1587. [PMID: 33687751 DOI: 10.1111/dom.14371] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 02/19/2021] [Accepted: 03/01/2021] [Indexed: 12/16/2022]
Abstract
AIM To determine the clinical correlates of increased red blood cell distribution width (RDW), its potential mechanistic association with multiple circulating biomarkers, and its prognostic value in patients with type 2 diabetes (T2D) who had a recent acute coronary syndrome. METHODS We used time-updated Cox models applied to patients enrolled in the Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care (EXAMINE) trial. RESULTS A total of 5380 patients were included, the median age was 61 years and 32% were women. Patients with higher RDW were older, more frequently women, with a longer diabetes duration and increased co-morbidities. An RDW of more than 16.1% (both baseline and time-updated) was independently associated with the study primary composite outcome of non-fatal myocardial infarction, non-fatal stroke or cardiovascular death (time-updated adjusted HR = 1.36, 95% CI = 1.16-1.61, p < .001), all-cause death (time-updated adjusted HR = 2.01, 95% CI = 1.60-2.53, p < .001), as well as mortality from non-cardiovascular causes (time-updated adjusted HR = 2.67, 95% CI = 1.72-4.15, p < .001). RDW had a weak-to-moderate correlation with haemoglobin and circulating markers that reflected inflammation, apoptosis, fibrosis and congestion. Alogliptin did not alter RDW values. CONCLUSIONS RDW is a marker of disease severity associated with a multitude of poor outcomes, including both cardiovascular and non-cardiovascular death. RDW correlated modestly with inflammatory, pro-apoptotic, pro-fibrotic and congestion markers, and its levels were not affected by alogliptin during the course of the trial.
Collapse
Affiliation(s)
- João Pedro Ferreira
- Centre d'Investigations Cliniques Plurithématique Inserm 1433, Université de Lorraine, CHRU de Nancy, Inserm U1116, FCRIN INI-CRCT, Nancy, France
| | - Zohra Lamiral
- Centre d'Investigations Cliniques Plurithématique Inserm 1433, Université de Lorraine, CHRU de Nancy, Inserm U1116, FCRIN INI-CRCT, Nancy, France
| | - George Bakris
- Department of Medicine, University of Chicago Medicine, Chicago, Illinois, USA
| | | | - William B White
- Calhoun Cardiology Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Faiez Zannad
- Centre d'Investigations Cliniques Plurithématique Inserm 1433, Université de Lorraine, CHRU de Nancy, Inserm U1116, FCRIN INI-CRCT, Nancy, France
| |
Collapse
|
16
|
Kakareko K, Rydzewska-Rosołowska A, Zbroch E, Hryszko T. TRAIL and Cardiovascular Disease-A Risk Factor or Risk Marker: A Systematic Review. J Clin Med 2021; 10:jcm10061252. [PMID: 33803523 PMCID: PMC8002847 DOI: 10.3390/jcm10061252] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/12/2021] [Accepted: 03/15/2021] [Indexed: 12/17/2022] Open
Abstract
Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a pro-apoptotic protein showing broad biological functions. Data from animal studies indicate that TRAIL may possibly contribute to the pathophysiology of cardiomyopathy, atherosclerosis, ischemic stroke and abdominal aortic aneurysm. It has been also suggested that TRAIL might be useful in cardiovascular risk stratification. This systematic review aimed to evaluate whether TRAIL is a risk factor or risk marker in cardiovascular diseases (CVDs) focusing on major adverse cardiovascular events. Two databases (PubMed and Cochrane Library) were searched until December 2020 without a year limit in accordance to the PRISMA guidelines. A total of 63 eligible original studies were identified and included in our systematic review. Studies suggest an important role of TRAIL in disorders such as heart failure, myocardial infarction, atrial fibrillation, ischemic stroke, peripheral artery disease, and pulmonary and gestational hypertension. Most evidence associates reduced TRAIL levels and increased TRAIL-R2 concentration with all-cause mortality in patients with CVDs. It is, however, unclear whether low TRAIL levels should be considered as a risk factor rather than a risk marker of CVDs. Further studies are needed to better define the association of TRAIL with cardiovascular diseases.
Collapse
Affiliation(s)
- Katarzyna Kakareko
- 2nd Department of Nephrology and Hypertension with Dialysis Unit, Medical University of Białystok, 15-276 Białystok, Poland; (A.R.-R.); (T.H.)
- Correspondence:
| | - Alicja Rydzewska-Rosołowska
- 2nd Department of Nephrology and Hypertension with Dialysis Unit, Medical University of Białystok, 15-276 Białystok, Poland; (A.R.-R.); (T.H.)
| | - Edyta Zbroch
- Department of Internal Medicine and Hypertension, Medical University of Białystok, 15-276 Białystok, Poland;
| | - Tomasz Hryszko
- 2nd Department of Nephrology and Hypertension with Dialysis Unit, Medical University of Białystok, 15-276 Białystok, Poland; (A.R.-R.); (T.H.)
| |
Collapse
|
17
|
Vázquez-Sánchez S, Poveda J, Navarro-García JA, González-Lafuente L, Rodríguez-Sánchez E, Ruilope LM, Ruiz-Hurtado G. An Overview of FGF-23 as a Novel Candidate Biomarker of Cardiovascular Risk. Front Physiol 2021; 12:632260. [PMID: 33767635 PMCID: PMC7985069 DOI: 10.3389/fphys.2021.632260] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 02/15/2021] [Indexed: 12/12/2022] Open
Abstract
Fibroblast growth factor-23 (FGF)-23 is a phosphaturic hormone involved in mineral bone metabolism that helps control phosphate homeostasis and reduces 1,25-dihydroxyvitamin D synthesis. Recent data have highlighted the relevant direct FGF-23 effects on the myocardium, and high plasma levels of FGF-23 have been associated with adverse cardiovascular outcomes in humans, such as heart failure and arrhythmias. Therefore, FGF-23 has emerged as a novel biomarker of cardiovascular risk in the last decade. Indeed, experimental data suggest FGF-23 as a direct mediator of cardiac hypertrophy development, cardiac fibrosis and cardiac dysfunction via specific myocardial FGF receptor (FGFR) activation. Therefore, the FGF-23/FGFR pathway might be a suitable therapeutic target for reducing the deleterious effects of FGF-23 on the cardiovascular system. More research is needed to fully understand the intracellular FGF-23-dependent mechanisms, clarify the downstream pathways and identify which could be the most appropriate targets for better therapeutic intervention. This review updates the current knowledge on both clinical and experimental studies and highlights the evidence linking FGF-23 to cardiovascular events. The aim of this review is to establish the specific role of FGF-23 in the heart, its detrimental effects on cardiac tissue and the possible new therapeutic opportunities to block these effects.
Collapse
Affiliation(s)
- Sara Vázquez-Sánchez
- Cardiorenal Translational Laboratory, Institute of Research i+12, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Jonay Poveda
- Cardiorenal Translational Laboratory, Institute of Research i+12, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - José Alberto Navarro-García
- Cardiorenal Translational Laboratory, Institute of Research i+12, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Laura González-Lafuente
- Cardiorenal Translational Laboratory, Institute of Research i+12, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Elena Rodríguez-Sánchez
- Cardiorenal Translational Laboratory, Institute of Research i+12, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Luis M. Ruilope
- Cardiorenal Translational Laboratory, Institute of Research i+12, Hospital Universitario 12 de Octubre, Madrid, Spain
- CIBER-CV, Hospital Universitario 12 de Octubre, Madrid, Spain
- School of Doctoral Studies and Research, European University of Madrid, Madrid, Spain
| | - Gema Ruiz-Hurtado
- Cardiorenal Translational Laboratory, Institute of Research i+12, Hospital Universitario 12 de Octubre, Madrid, Spain
- CIBER-CV, Hospital Universitario 12 de Octubre, Madrid, Spain
| |
Collapse
|
18
|
Elharram M, Ferreira JP, Huynh T, Ni J, Giannetti N, Verma S, Zannad F, Sharma A. Prediction of heart failure outcomes in patients with type 2 diabetes mellitus: Validation of the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (TRS-HF DM ) in patients in the ACCORD trial. Diabetes Obes Metab 2021; 23:782-790. [PMID: 33269511 DOI: 10.1111/dom.14283] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/17/2020] [Accepted: 11/28/2020] [Indexed: 12/17/2022]
Abstract
AIM To investigate the ability of the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (TRS-HFDM ) to stratify patients with type 2 diabetes mellitus (T2DM) and high cardiovascular risk for heart failure (HF) hospitalization. MATERIALS AND METHODS We used data from the control group of the Action to Control Cardiovascular Risk in Diabetes Study Group (ACCORD) trial (n = 5123; mean follow-up 4.8 years). The TRS-HFDM includes: prior HF (2 points), atrial fibrillation (1 point), coronary artery disease (1 point), estimated glomerular filtration rate <60 mL/min/1.73 m2 (1 point), and urine albumin-to-creatinine ratio (>300 mg/g: 2 points; 30-300 mg/g: 1 point). We evaluated the discrimination (Harrell's C-index) and calibration (Nam-D'Agostino calibration statistic) of the TRS-HFDM with regard to time to HF hospitalization or death due to HF. RESULTS The mean age of the participants was 62.8 ± 6.6 years, and 38% were women. The prevalences of TRS-HFDM 0, 1, 2, 3 and ≥4 were 42.1%, 34.9%, 14.6%, 6.0% and 2.5%, respectively. Increasing TRS-HFDM corresponded to an increasing HF risk: 1.3 per 1000 person-years for a TRS-HFDM of 0 to 64.7 per 1000 person-years for TRS-HFDM of ≥4. The TRS-HFDM demonstrated robust discrimination of HF outcomes (C-index 0.78). Furthermore, the score was well calibrated for HF outcomes (calibration statistic P = 0.13). Similar results were seen in participants without baseline HF (C-index 0.75). CONCLUSION The TRS-HFDM discriminates HF-specific risk among people with T2DM. The use of TRS-HFDM to identify those who would maximally benefit from therapies that reduce HF risk warrants evaluation.
Collapse
Affiliation(s)
- Malik Elharram
- Division of Cardiology, McGill University Health Centre, Montreal, Quebec, Canada
| | - João Pedro Ferreira
- Université de Lorraine, Centre D'Investigation Clinique- Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy Hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France
| | - Thao Huynh
- Montreal General Hospital, Montreal, Quebec, Canada
| | - Jiayi Ni
- Division of Cardiology, McGill University Health Centre, Montreal, Quebec, Canada
- Montreal General Hospital, Montreal, Quebec, Canada
| | - Nadia Giannetti
- Division of Cardiology, McGill University Health Centre, Montreal, Quebec, Canada
| | - Subodh Verma
- Division of Cardiac Surgery, Li Ka Shing Knowledge Institute of St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Faiez Zannad
- Université de Lorraine, Centre D'Investigation Clinique- Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy Hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France
| | - Abhinav Sharma
- Division of Cardiology, McGill University Health Centre, Montreal, Quebec, Canada
- DREAM-CV Lab, McGill University Health Centre, Montreal, Quebec, Canada
| |
Collapse
|