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Haller PM, Jarolim P, Palazzolo MG, Bellavia A, Antman EM, Eikelboom J, Granger CB, Harrington J, Healey JS, Hijazi Z, Patel MR, Patel SM, Ruff CT, Wallentin L, Braunwald E, Giugliano RP, Morrow DA. Heart Failure Risk Assessment Using Biomarkers in Patients With Atrial Fibrillation: Analysis From COMBINE-AF. J Am Coll Cardiol 2024:S0735-1097(24)07965-8. [PMID: 39230543 DOI: 10.1016/j.jacc.2024.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 07/24/2024] [Accepted: 07/25/2024] [Indexed: 09/05/2024]
Abstract
BACKGROUND Heart failure (HF) is common among patients with atrial fibrillation (AF), and accurate risk assessment is clinically important. OBJECTIVES The goal of this study was to investigate the incremental prognostic performance of N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT), and growth differentiation factor (GDF)-15 for HF risk stratification in patients with AF. METHODS Individual patient data from 3 large randomized trials comparing direct oral anticoagulants (DOACs) with warfarin (ARISTOTLE [Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation], ENGAGE AF-TIMI 48 [Effective Anticoagulation With Factor Xa Next Generation in Atrial Fibrillation-Thrombolysis in Myocardial Infarction 48], and RE-LY [Randomized Evaluation of Long-Term Anticoagulation Therapy]) from the COMBINE-AF (A Collaboration Between Multiple Institutions to Better Investigate Non-Vitamin K Antagonist Oral Anticoagulant Use in Atrial Fibrillation) cohort were pooled; all patients with available biomarkers at baseline were included. The composite endpoint was hospitalization for HF (HHF) or cardiovascular death (CVD), and secondary endpoints were HHF and HF-related death. Cox regression was used, adjusting for clinical factors, and interbiomarker correlation was addressed using weighted quantile sum regression analysis. RESULTS In 32,041 patients, higher biomarker values were associated with a graded increase in absolute risk for CVD/HHF, HHF, and HF-related death. Adjusting for clinical variables and all biomarkers, NT-proBNP (HR per 1 SD: 1.68; 95% CI: 1.59-1.77), hs-cTnT (HR: 1.39; 95% CI: 1.33-1.44), and GDF-15 (HR: 1.20; 95% CI: 1.15-1.25) were significantly associated with CVD/HHF. The discrimination of the clinical model improved significantly upon addition of the biomarkers (c-index: 0.70 [95% CI: 0.69-0.71] to 0.77 [95% CI: 0.76-0.78]; likelihood ratio test, P < 0.001). Using weighted quantile sum regression analysis, the contribution to risk assessment was similar for NT-proBNP and hs-cTnT for CVD/HHF (38% and 41%, respectively); GDF-15 provided a statistically significant but lesser contribution to risk assessment. Results were similar for HHF and HF-related death, individually, and across key subgroups of patients based on history of HF, AF pattern, and reduced or preserved left ventricular ejection fraction. CONCLUSIONS NT-proBNP, hs-cTnT, and GDF-15 contributed significantly and independently to the risk stratification for HF endpoints in patients with AF, with hs-cTnT being as important as NT-proBNP for HF risk stratification. Our findings support a possible future use of these biomarkers to distinguish patients with AF at low or high risk for HF.
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Affiliation(s)
- Paul M Haller
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany. https://twitter.com/PaulMHaller
| | - Petr Jarolim
- Department of Pathology, Brigham & Women's Hospital, Boston, Massachusetts, USA
| | - Michael G Palazzolo
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Andrea Bellavia
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Elliott M Antman
- Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - John Eikelboom
- Population Health Research Institute, Hamilton, Ontario, Canada; McMaster University, Hamilton, Ontario, Canada
| | - Christopher B Granger
- Department of Medicine, Division of Cardiology, Duke University, Durham, North Carolina, USA
| | - Josephine Harrington
- Department of Medicine, Division of Cardiology, Duke University, Durham, North Carolina, USA
| | - Jeff S Healey
- Population Health Research Institute, Hamilton, Ontario, Canada; McMaster University, Hamilton, Ontario, Canada
| | - Ziad Hijazi
- Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Manesh R Patel
- Department of Medicine, Division of Cardiology, Duke University, Durham, North Carolina, USA
| | - Siddharth M Patel
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Christian T Ruff
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Lars Wallentin
- Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Eugene Braunwald
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Robert P Giugliano
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - David A Morrow
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham & Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
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Said F, Arnott C, Voors AA, Heerspink HJL, Ter Maaten JM. Prediction of new-onset heart failure in patients with type 2 diabetes derived from ALTITUDE and CANVAS. Diabetes Obes Metab 2024; 26:2741-2751. [PMID: 38584567 DOI: 10.1111/dom.15592] [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: 01/11/2024] [Revised: 03/16/2024] [Accepted: 03/21/2024] [Indexed: 04/09/2024]
Abstract
AIM To create and validate a prediction model to identify patients with type 2 diabetes (T2D) at high risk of new-onset heart failure (HF), including those treated with a sodium-glucose cotransporter-2 (SGLT2) inhibitor. METHODS A prediction model was developed from the Aliskiren Trial in Type 2 Diabetes Using Cardiorenal Endpoints (ALTITUDE), a trial in T2D patients with albuminuria or cardiovascular disease. We included 5081 patients with baseline N-terminal pro B-type natriuretic peptide (NT-proBNP) measurement and no history of HF. The model was developed using Cox regression and validated externally in the placebo arm of the Canagliflozin Cardiovascular Assessment Study (CANVAS), which included 996 participants with T2D and established cardiovascular disease or high cardiovascular risk, and in patients treated with canagliflozin. RESULTS ALTITUDE participants (mean age 64 ± 9.8 years) had a median serum NT-proBNP level of 157 (25th-75th percentile 70-359) pg/mL. Higher NT-proBNP level, troponin T (TnT) level and body mass index (BMI) emerged as significant and independent predictors of new-onset HF in both cohorts. The model further contained urinary albumin-to-creatinine ratio, glycated haemoglobin, age, haematocrit, and use of calcium channel blockers. A prediction model including these variables had a C-statistic of 0.828 (95% confidence interval [CI] 0.801-0.855) in ALTITUDE and 0.800 (95% CI 0.720-0.880) in CANVAS. The C-statistic of this model increased to 0.847 (95% CI 0.792-0.902) in patients after 1 year of canagliflozin treatment. CONCLUSION In patients with T2D, higher NT-proBNP level, TnT level and BMI are independent and externally validated predictors of new-onset HF, including patients using an SGLT2 inhibitor. This newly developed model may identify patients at high risk of new-onset HF, contributing to early recognition and possibly prevention.
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Affiliation(s)
- Fatema Said
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Clare Arnott
- The George Institute for Global Health, Sydney, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Adriaan A Voors
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Hiddo J L Heerspink
- The George Institute for Global Health, Sydney, Australia
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jozine M Ter Maaten
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Berezin AE, Berezina TA, Hoppe UC, Lichtenauer M, Berezin AA. Methods to predict heart failure in diabetes patients. Expert Rev Endocrinol Metab 2024; 19:241-256. [PMID: 38622891 DOI: 10.1080/17446651.2024.2342812] [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: 01/26/2024] [Accepted: 04/10/2024] [Indexed: 04/17/2024]
Abstract
INTRODUCTION Type 2 diabetes mellitus (T2DM) is one of the leading causes of cardiovascular disease and powerful predictor for new-onset heart failure (HF). AREAS COVERED We focus on the relevant literature covering evidence of risk stratification based on imaging predictors and circulating biomarkers to optimize approaches to preventing HF in DM patients. EXPERT OPINION Multiple diagnostic algorithms based on echocardiographic parameters of cardiac remodeling including global longitudinal strain/strain rate are likely to be promising approach to justify individuals at higher risk of incident HF. Signature of cardiometabolic status may justify HF risk among T2DM individuals with low levels of natriuretic peptides, which preserve their significance in HF with clinical presentation. However, diagnostic and predictive values of conventional guideline-directed biomarker HF strategy may be non-optimal in patients with obesity and T2DM. Alternative biomarkers affecting cardiac fibrosis, inflammation, myopathy, and adipose tissue dysfunction are plausible tools for improving accuracy natriuretic peptides among T2DM patients at higher HF risk. In summary, risk identification and management of the patients with T2DM with established HF require conventional biomarkers monitoring, while the role of alternative biomarker approach among patients with multiple CV and metabolic risk factors appears to be plausible tool for improving clinical outcomes.
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Affiliation(s)
- Alexander E Berezin
- Department of Internal Medicine II, Division of Cardiology, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Tetiana A Berezina
- VitaCenter, Department of Internal Medicine & Nephrology, Zaporozhye, Ukraine
| | - Uta C Hoppe
- Department of Internal Medicine II, Division of Cardiology, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Michael Lichtenauer
- Department of Internal Medicine II, Division of Cardiology, Paracelsus Medical University of Salzburg, Salzburg, Austria
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Patel KV, Segar MW, Klonoff DC, Khan MS, Usman MS, Lam CSP, Verma S, DeFilippis AP, Nasir K, Bakker SJL, Westenbrink BD, Dullaart RPF, Butler J, Vaduganathan M, Pandey A. Optimal Screening for Predicting and Preventing the Risk of Heart Failure Among Adults With Diabetes Without Atherosclerotic Cardiovascular Disease: A Pooled Cohort Analysis. Circulation 2024; 149:293-304. [PMID: 37950893 PMCID: PMC11257100 DOI: 10.1161/circulationaha.123.067530] [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: 10/08/2023] [Accepted: 11/01/2023] [Indexed: 11/13/2023]
Abstract
BACKGROUND The optimal approach to identify individuals with diabetes who are at a high risk for developing heart failure (HF) to inform implementation of preventive therapies is unknown, especially in those without atherosclerotic cardiovascular disease (ASCVD). METHODS Adults with diabetes and no HF at baseline from 7 community-based cohorts were included. Participants without ASCVD who were at high risk for developing HF were identified using 1-step screening strategies: risk score (WATCH-DM [Weight, Age, Hypertension, Creatinine, HDL-C, Diabetes Control, QRS Duration, MI, and CABG] ≥12), NT-proBNP (N-terminal pro-B-type natriuretic peptide ≥125 pg/mL), hs-cTn (high-sensitivity cardiac troponin T ≥14 ng/L; hs-cTnI ≥31 ng/L), and echocardiography-based diabetic cardiomyopathy (echo-DbCM; left atrial enlargement, left ventricular hypertrophy, or diastolic dysfunction). High-risk participants were also identified using 2-step screening strategies with a second test to identify residual risk among those deemed low risk by the first test: WATCH-DM/NT-proBNP, NT-proBNP/hs-cTn, NT-proBNP/echo-DbCM. Across screening strategies, the proportion of HF events identified, 5-year number needed to treat and number needed to screen to prevent 1 HF event with an SGLT2i (sodium-glucose cotransporter 2 inhibitor) among high-risk participants, and cost of screening were estimated. RESULTS The initial study cohort included 6293 participants (48.2% women), of whom 77.7% without prevalent ASCVD were evaluated with different HF screening strategies. At 5-year follow-up, 6.2% of participants without ASCVD developed incident HF. The 5-year number needed to treat to prevent 1 HF event with an SGLT2i among participants without ASCVD was 43 (95% CI, 29-72). In the cohort without ASCVD, high-risk participants identified using 1-step screening strategies had a low 5-year number needed to treat (22 for NT-proBNP to 37 for echo-DbCM). However, a substantial proportion of HF events occurred among participants identified as low risk using 1-step screening approaches (29% for echo-DbCM to 47% for hs-cTn). Two-step screening strategies captured most HF events (75-89%) in the high-risk subgroup with a comparable 5-year number needed to treat as the 1-step screening approaches (30-32). The 5-year number needed to screen to prevent 1 HF event was similar across 2-step screening strategies (45-61). However, the number of tests and associated costs were lowest for WATCH-DM/NT-proBNP ($1061) compared with other 2-step screening strategies (NT-proBNP/hs-cTn: $2894; NT-proBNP/echo-DbCM: $16 358). CONCLUSIONS Selective NT-proBNP testing based on the WATCH-DM score efficiently identified a high-risk primary prevention population with diabetes expected to derive marked absolute benefits from SGLT2i to prevent HF.
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Affiliation(s)
- Kershaw V. Patel
- Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA
| | - Matthew W. Segar
- Department of Cardiology, Texas Heart Institute, Houston, TX, USA
| | - David C. Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
| | - Muhammad Shahzeb Khan
- Division of Cardiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Muhammad Shariq Usman
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Carolyn S. P. Lam
- National Heart Centre Singapore, Duke-National University of Singapore, Singapore
| | - Subodh Verma
- Division of Cardiac Surgery, St Michael’s Hospital, University of Toronto, Toronto, ON, Canada
| | - Andrew P. DeFilippis
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Khurram Nasir
- Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA
| | - Stephan J. L. Bakker
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, The Netherlands
| | - B. Daan Westenbrink
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Robin P. F. Dullaart
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, The Netherlands
| | - Javed Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
- Baylor Scott and White Research Institute, Dallas, Texas, USA
| | - Muthiah Vaduganathan
- Brigham and Women’s Hospital Heart and Vascular Center, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Ambarish Pandey
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Rubini A, Vilaplana-Prieto C, Vázquez-Jarén E, Hernández-González M, Félix-Redondo FJ, Fernández-Bergés D. Analysis and prediction of readmissions for heart failure in the first year after discharge with INCA score. Sci Rep 2023; 13:22477. [PMID: 38110472 PMCID: PMC10728208 DOI: 10.1038/s41598-023-49390-w] [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: 02/17/2023] [Accepted: 12/07/2023] [Indexed: 12/20/2023] Open
Abstract
To determine the readmissions trends and the comorbidities of patients with heart failure that most influence hospital readmission rates. Heart failure (HF) is one of the most prevalent health problems as it causes loss of quality of life and increased health-care costs. Its prevalence increases with age and is a major cause of re-hospitalisation within 30 days after discharge. INCA study had observational and ambispective design, including 4,959 patients from 2000 to 2019, with main diagnosis of HF in Extremadura (Spain). The variables examined were collected from discharge reports. To develop the readmission index, capable of discriminating the population with higher probability of re-hospitalisation, a Competing-risk model was generated. Readmission rate have increased over the period under investigation. The main predictors of readmission were: age, diabetes mellitus, presence of neoplasia, HF without previous hospitalisation, atrial fibrillation, anaemia, previous myocardial infarction, obstructive pulmonary disease (COPD) and chronic kidney disease (CKD). These variables were assigned values with balanced weights, our INCA index showed that the population with values greater than 2 for men and women were more likely to be re-admitted. Previous HF without hospital admission, CKD, and COPD appear to have the greatest effect on readmission. Our index allowed us to identify patients with different risks of readmission.
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Affiliation(s)
- Alessia Rubini
- PhD Programme in Economics (DEcIDE), International Doctorate School of the National University of Distance Education (EIDUNED), 28015, Madrid, Spain.
- Research Unit of Don Benito-Villanueva de la Serena Health Area, 06700, Villanueva de la Serena, Spain.
| | | | - Elena Vázquez-Jarén
- Research Unit of Don Benito-Villanueva de la Serena Health Area, 06700, Villanueva de la Serena, Spain
- University Institute for Biosanitary Research of Extremadura (INUBE), 06080, Badajoz, Spain
| | - Miriam Hernández-González
- Research Unit of Don Benito-Villanueva de la Serena Health Area, 06700, Villanueva de la Serena, Spain
| | - Francisco Javier Félix-Redondo
- Research Unit of Don Benito-Villanueva de la Serena Health Area, 06700, Villanueva de la Serena, Spain
- University Institute for Biosanitary Research of Extremadura (INUBE), 06080, Badajoz, Spain
- Villanueva Norte Health Centre, Extremadura Health Service, 06700, Villanueva de la Serena, Spain
| | - Daniel Fernández-Bergés
- Research Unit of Don Benito-Villanueva de la Serena Health Area, 06700, Villanueva de la Serena, Spain
- University Institute for Biosanitary Research of Extremadura (INUBE), 06080, Badajoz, Spain
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Huang N, Xiao W, Lv J, Yu C, Guo Y, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Avery D, Ou T, Chen J, Chen Z, Huang T, Li L. Genome-wide polygenic risk score, cardiometabolic risk factors, and type 2 diabetes mellitus in the Chinese population. Obesity (Silver Spring) 2023; 31:2615-2626. [PMID: 37661427 DOI: 10.1002/oby.23846] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 05/26/2023] [Accepted: 05/28/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Type 2 diabetes (T2D) is caused by both genetic and cardiometabolic risk factors. However, the magnitude of the genetic predisposition of T2D in the Chinese population remains largely unknown. METHODS This study included 93,488 participants from the China Kadoorie Biobank, and multiple polygenic risk scores (PRS) were calculated. A common cardiometabolic risk score (CRS) using smoking, alcohol consumption, physical activity, diet, obesity, blood pressure, and blood lipids was constructed to investigate the effects of cardiometabolic risk factors on T2D. Furthermore, an equation based on ideal PRS, CRS, and their interaction was established to explore the combined effects on T2D. RESULTS An ideally fitting PRS model (variance explained, R2 = 7.6%) was reached based on multiple PRS calculation methods. An additive interaction between PRS and CRS (coefficient = 28%, 95% CI: 0.20-0.36, p < 0.001) was found. The R2 of the T2D predictive model could increase to 8.3% when CRS and the interaction terms of PRS × CRS were considered. In the etiological composition of T2D, the ratio of genetic risk effect, cardiometabolic risk effect, and interaction between genetic and cardiometabolic factors was 67:16:17. CONCLUSIONS This study identified an ideally fitting PRS model for identifying and predicting the risk of T2D suitable for the Chinese population. The quantified proportional structure of genetic risk factors, cardiometabolic risk factors, and their interaction was detected, which elucidated the critical effect of genetic factors.
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Affiliation(s)
- Ninghao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Wendi Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Yu Guo
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
- National Center for Cardiovascular Diseases, Beijing, China
| | - Pei Pei
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tingting Ou
- Noncommunicable Diseases Prevention and Control Department, Hainan Centers for Disease Control and Prevention, Hainan, China
| | - Junshi Chen
- China National Centre for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Centre for Intelligent Public Health, Academy for Artificial Intelligence, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, China
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Kostopoulos G, Doundoulakis I, Toulis KA, Karagiannis T, Tsapas A, Haidich AB. Prognostic models for heart failure in patients with type 2 diabetes: a systematic review and meta-analysis. Heart 2023; 109:1436-1442. [PMID: 36898704 DOI: 10.1136/heartjnl-2022-322044] [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: 10/25/2022] [Accepted: 02/07/2023] [Indexed: 03/12/2023] Open
Abstract
OBJECTIVE To provide a systematic review, critical appraisal, assessment of performance and generalisability of all the reported prognostic models for heart failure (HF) in patients with type 2 diabetes (T2D). METHODS We performed a literature search in Medline, Embase, Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Scopus (from inception to July 2022) and grey literature to identify any study developing and/or validating models predicting HF applicable to patients with T2D. We extracted data on study characteristics, modelling methods and measures of performance, and we performed a random-effects meta-analysis to pool discrimination in models with multiple validation studies. We also performed a descriptive synthesis of calibration and we assessed the risk of bias and certainty of evidence (high, moderate, low). RESULTS Fifty-five studies reporting on 58 models were identified: (1) models developed in patients with T2D for HF prediction (n=43), (2) models predicting HF developed in non-diabetic cohorts and externally validated in patients with T2D (n=3), and (3) models originally predicting a different outcome and externally validated for HF (n=12). RECODe (C-statistic=0.75 95% CI (0.72, 0.78), 95% prediction interval (PI) (0.68, 0.81); high certainty), TRS-HFDM (C-statistic=0.75 95% CI (0.69, 0.81), 95% PI (0.58, 0.87); low certainty) and WATCH-DM (C-statistic=0.70 95% CI (0.67, 0.73), 95% PI (0.63, 0.76); moderate certainty) showed the best performance. QDiabetes-HF demonstrated also good discrimination but was externally validated only once and not meta-analysed. CONCLUSIONS Among the prognostic models identified, four models showed promising performance and, thus, could be implemented in current clinical practice.
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Affiliation(s)
- Georgios Kostopoulos
- Department of Endocrinology, 424 General Military Hospital, Thessaloniki, Greece
| | - Ioannis Doundoulakis
- Department of Cardiology, 424 General Military Hospital, Thessaloniki, Greece
- First Department of Cardiology, National and Kapodistrian University, "Hippokration" Hospital, Athens, Greece
| | - Konstantinos A Toulis
- Department of Endocrinology, 424 General Military Hospital, Thessaloniki, Greece
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Thomas Karagiannis
- Diabetes Centre, Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Clinical Research and Evidence-Based Medicine Unit, Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Apostolos Tsapas
- Diabetes Centre, Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Clinical Research and Evidence-Based Medicine Unit, Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Harris Manchester College, University of Oxford, Oxford, Oxfordshire, UK
| | - Anna-Bettina Haidich
- Department of Hygiene, Social-Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Yang H, Luo YM, Ma CY, Zhang TY, Zhou T, Ren XL, He XL, Deng KJ, Yan D, Tang H, Lin H. A gender specific risk assessment of coronary heart disease based on physical examination data. NPJ Digit Med 2023; 6:136. [PMID: 37524859 PMCID: PMC10390496 DOI: 10.1038/s41746-023-00887-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html .
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Affiliation(s)
- Hui Yang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ya-Mei Luo
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China
- Medical Engineering & Medical Informatics Integration and Transformational Medicine Key Laboratory of Luzhou City, Luzhou, 646000, China
| | - Cai-Yi Ma
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tian-Yu Zhang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tao Zhou
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiao-Lei Ren
- Sichuan Chuanjiang Science and Technology Research Institute Co., Ltd, Luzhou, 646000, China
| | - Xiao-Lin He
- Sichuan Chuanjiang Science and Technology Research Institute Co., Ltd, Luzhou, 646000, China
| | - Ke-Jun Deng
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dan Yan
- Beijing Institute of Clinical Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, 646000, China.
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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9
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Yeung AM, Huang J, Pandey A, Hashim IA, Kerr D, Pop-Busui R, Rhee CM, Shah VN, Bally L, Bayes-Genis A, Bee YM, Bergenstal R, Butler J, Fleming GA, Gilbert G, Greene SJ, Kosiborod MN, Leiter LA, Mankovsky B, Martens TW, Mathieu C, Mohan V, Patel KV, Peters A, Rhee EJ, Rosano GMC, Sacks DB, Sandoval Y, Seley JJ, Schnell O, Umpierrez G, Waki K, Wright EE, Wu AHB, Klonoff DC. Biomarkers for the Diagnosis of Heart Failure in People with Diabetes: A Consensus Report from Diabetes Technology Society. Prog Cardiovasc Dis 2023; 79:65-79. [PMID: 37178991 DOI: 10.1016/j.pcad.2023.05.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/08/2023] [Indexed: 05/15/2023]
Abstract
Diabetes Technology Society assembled a panel of clinician experts in diabetology, cardiology, clinical chemistry, nephrology, and primary care to review the current evidence on biomarker screening of people with diabetes (PWD) for heart failure (HF), who are, by definition, at risk for HF (Stage A HF). This consensus report reviews features of HF in PWD from the perspectives of 1) epidemiology, 2) classification of stages, 3) pathophysiology, 4) biomarkers for diagnosing, 5) biomarker assays, 6) diagnostic accuracy of biomarkers, 7) benefits of biomarker screening, 8) consensus recommendations for biomarker screening, 9) stratification of Stage B HF, 10) echocardiographic screening, 11) management of Stage A and Stage B HF, and 12) future directions. The Diabetes Technology Society panel recommends 1) biomarker screening with one of two circulating natriuretic peptides (B-type natriuretic peptide or N-terminal prohormone of B-type natriuretic peptide), 2) beginning screening five years following diagnosis of type 1 diabetes (T1D) and at the diagnosis of type 2 diabetes (T2D), 3) beginning routine screening no earlier than at age 30 years for T1D (irrespective of age of diagnosis) and at any age for T2D, 4) screening annually, and 5) testing any time of day. The panel also recommends that an abnormal biomarker test defines asymptomatic preclinical HF (Stage B HF). This diagnosis requires follow-up using transthoracic echocardiography for classification into one of four subcategories of Stage B HF, corresponding to risk of progression to symptomatic clinical HF (Stage C HF). These recommendations will allow identification and management of Stage A and Stage B HF in PWD to prevent progression to Stage C HF or advanced HF (Stage D HF).
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Affiliation(s)
- Andrea M Yeung
- Diabetes Technology Society, Burlingame, CA, United States of America
| | - Jingtong Huang
- Diabetes Technology Society, Burlingame, CA, United States of America
| | - Ambarish Pandey
- UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Ibrahim A Hashim
- UT Southwestern Medical Center, Dallas, TX, United States of America
| | - David Kerr
- Diabetes Technology Society, Burlingame, CA, United States of America
| | | | - Connie M Rhee
- Division of Nephrology, Hypertension, and Kidney Transplantation, University of California Irvine, Orange, CA, United States of America
| | - Viral N Shah
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Lia Bally
- Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Antoni Bayes-Genis
- Hospital Universitari Germans Trias I Pujol, CIBERCV, Universitat Autonoma Barcelona, Spain
| | | | - Richard Bergenstal
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN, United States of America
| | - Javed Butler
- Baylor Scott and White Research Institute, Dallas, TX and University of Mississippi, Jackson, MS, United States of America
| | | | - Gregory Gilbert
- Mills-Peninsula Medical Center, Burlingame, CA, United States of America
| | - Stephen J Greene
- Division of Cardiology, Duke University School of Medicine, Durham, NC, United States of America
| | - Mikhail N Kosiborod
- Saint Luke's Mid America Heart Institute, University of Missouri-Kansas City School of Medicine, Kansas City, MO, United States of America
| | - Lawrence A Leiter
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | | | - Thomas W Martens
- International Diabetes Center and Park Nicollet Clinic, Minneapolis, MN, United States of America
| | | | - Viswanathan Mohan
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Chennai, India
| | - Kershaw V Patel
- Department of Cardiology, Houston Methodist DeBakey Heart and Vascular Center, Houston, TX, United States of America
| | - Anne Peters
- University of Southern California Keck School of Medicine, Los Angeles, CA, United States of America
| | - Eun-Jung Rhee
- Department of Endocrinology and Metabolism, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | | | - David B Sacks
- National Institutes of Health, Bethesda, MD, United States of America
| | - Yader Sandoval
- Minneapolis Heart Institute, Abbott Northwestern Hospital and Minneapolis Heart Institute Foundation, Minneapolis, MN, United States of America
| | | | - Oliver Schnell
- Forschergruppe Diabetes e.V., Munich-, Neuherberg, Germany
| | | | - Kayo Waki
- The University of Tokyo, Tokyo, Japan
| | - Eugene E Wright
- Charlotte Area Health Education Center, Charlotte, NC, United States of America
| | - Alan H B Wu
- University of California, San Francisco, San Francisco, CA, United States of America
| | - David C Klonoff
- Mills-Peninsula Medical Center, San Mateo, CA, United States of America.
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10
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Wiviott SD, Berg DD. SGLT2 Inhibitors Reduce Heart Failure Hospitalization and Cardiovascular Death: Clarity and Consistency. J Am Coll Cardiol 2023; 81:2388-2390. [PMID: 37344039 DOI: 10.1016/j.jacc.2023.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 06/23/2023]
Affiliation(s)
- Stephen D Wiviott
- TIMI (Thrombolysis In Myocardial Infarction) Study Group, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| | - David D Berg
- TIMI (Thrombolysis In Myocardial Infarction) Study Group, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. https://twitter.com/ddbergMD
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11
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Pandey A, Khan MS, Patel KV, Bhatt DL, Verma S. Predicting and preventing heart failure in type 2 diabetes. Lancet Diabetes Endocrinol 2023:S2213-8587(23)00128-6. [PMID: 37385290 DOI: 10.1016/s2213-8587(23)00128-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 04/25/2023] [Accepted: 05/04/2023] [Indexed: 07/01/2023]
Abstract
The burden of heart failure among people with type 2 diabetes is increasing globally. People with comorbid type 2 diabetes and heart failure often have worse outcomes than those with only one of these conditions-eg, higher hospitalisation and mortality rates. Therefore, it is essential to implement optimal heart failure prevention strategies for people with type 2 diabetes. A detailed understanding of the pathophysiology underlying the occurrence of heart failure in type 2 diabetes can aid clinicians in identifying relevant risk factors and lead to early interventions that can help prevent heart failure. In this Review, we discuss the pathophysiology and risk factors of heart failure in type 2 diabetes. We also review the risk assessment tools for predicting heart failure incidence in people with type 2 diabetes as well as the data from clinical trials that have assessed the efficacy of lifestyle and pharmacological interventions. Finally, we discuss the potential challenges in implementing new management approaches and offer pragmatic recommendations to help overcome these challenges.
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Affiliation(s)
- Ambarish Pandey
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Kershaw V Patel
- Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA
| | - Deepak L Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY, USA
| | - Subodh Verma
- Division of Cardiac Surgery, St Michael's Hospital, University of Toronto, Toronto, ON, Canada.
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12
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Khan MS, Segar MW, Usman MS, Patel KV, Van Spall HGC, DeVore AD, Vaduganathan M, Lam CSP, Zannad F, Verma S, Butler J, Tang WHW, Pandey A. Effect of Canagliflozin on Heart Failure Hospitalization in Diabetes According to Baseline Heart Failure Risk. JACC. HEART FAILURE 2023:S2213-1779(23)00186-5. [PMID: 37227388 DOI: 10.1016/j.jchf.2023.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND In the CANVAS (Canagliflozin Cardiovascular Assessment Study) program, canagliflozin reduced the risk of heart failure (HF) hospitalization among individuals with type 2 diabetes mellitus (T2DM). OBJECTIVES The purpose of this study was to evaluate heterogeneity in absolute and relative treatment effects of canagliflozin on HF hospitalization according to baseline HF risk as assessed by diabetes-specific HF risk scores (WATCH-DM [Weight (body mass index), Age, hyperTension, Creatinine, HDL-C, Diabetes control (fasting plasma glucose) and QRS Duration, MI and CABG] and TRS-HFDM [TIMI Risk Score for HF in Diabetes]). METHODS Participants in the CANVAS trial were categorized into low, medium, and high risk for HF using the WATCH-DM score (for participants without prevalent HF) and the TRS-HFDM score (for all participants). The outcome of interest was time to first HF hospitalization. The treatment effect of canagliflozin vs placebo for HF hospitalization was compared across risk strata. RESULTS Among 10,137 participants with available HF data, 1,446 (14.3%) had HF at baseline. Among participants without baseline HF, WATCH-DM risk category did not modify the treatment effect of canagliflozin (vs placebo) on HF hospitalization (P interaction = 0.56). However, the absolute and relative risk reduction with canagliflozin was numerically greater in the high-risk group (cumulative incidence, canagliflozin vs placebo: 8.1% vs 12.7%; HR: 0.62 [95% CI: 0.37-0.93]; P = 0.03; number needed to treat: 22) than in the low- and intermediate-risk groups. When overall study participants were categorized according to the TRS-HFDM score, a statistically significant difference in the treatment effect of canagliflozin across risk strata was observed (P interaction = 0.04). Canagliflozin significantly reduced the risk of HF hospitalization by 39% in the high-risk group (HR: 0.61 [95% CI: 0.48-0.78]; P < 0.001; number needed to treat: 20) but not in the intermediate- or low-risk groups. CONCLUSIONS Among participants with T2DM, the WATCH-DM and TRS-HFDM can reliably identify those at high risk for HF hospitalization and most likely to benefit from canagliflozin.
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Affiliation(s)
- Muhammad Shahzeb Khan
- Division of Cardiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Matthew W Segar
- Department of Cardiology, Texas Heart Institute, Houston, Texas, USA
| | - Muhammad Shariq Usman
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Kershaw V Patel
- Department of Cardiology, Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA
| | - Harriette G C Van Spall
- Department of Medicine, Population Health Research Institute, Research Institute of St. Joseph's, McMaster University, Hamilton, Ontario, Canada
| | - Adam D DeVore
- Division of Cardiology, Duke University School of Medicine, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Muthiah Vaduganathan
- Brigham and Women's Hospital Heart and Vascular Center, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Carolyn S P Lam
- National Heart Centre Singapore, Duke-National University of Singapore, Singapore
| | - Faiez Zannad
- Université de Lorraine, CIC Insert, CHRU, Nancy, France
| | - Subodh Verma
- Division of Cardiac Surgery, St Michael's Hospital, University of Toronto, Ontario, Canada
| | - Javed Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA; Baylor Scott and White Research Institute, Dallas, Texas, USA
| | - W H Wilson Tang
- Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Ambarish Pandey
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
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13
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Schechter M, Melzer Cohen C, Yanuv I, Rozenberg A, Chodick G, Bodegård J, Leiter LA, Verma S, Lambers Heerspink HJ, Karasik A, Mosenzon O. Epidemiology of the diabetes-cardio-renal spectrum: a cross-sectional report of 1.4 million adults. Cardiovasc Diabetol 2022; 21:104. [PMID: 35689214 PMCID: PMC9188046 DOI: 10.1186/s12933-022-01521-9] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 04/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background Type-2 diabetes (T2D), chronic kidney disease, and heart failure (HF) share epidemiological and pathophysiological features. Although their prevalence was described, there is limited contemporary, high-resolution, epidemiological data regarding the overlap among them. We aimed to describe the epidemiological intersections between T2D, HF, and kidney dysfunction in an entire database, overall and by age and sex. Methods This is a cross-sectional analysis of adults ≥ 25 years, registered in 2019 at Maccabi Healthcare Services, a large healthcare maintenance organization in Israel. Collected data included sex, age, presence of T2D or HF, and last estimated glomerular filtration rate (eGFR) in the past two years. Subjects with T2D, HF, or eGFR < 60 mL/min/1.73 m2 were defined as within the diabetes-cardio-renal (DCR) spectrum. Results Overall, 1,389,604 subjects (52.2% females) were included; 445,477 (32.1%) were 25– < 40 years, 468,273 (33.7%) were 40– < 55 years, and 475,854 (34.2%) were ≥ 55 years old. eGFR measurements were available in 74.7% of the participants and in over 97% of those with T2D or HF. eGFR availability increased in older age groups. There were 140,636 (10.1%) patients with T2D, 54,187 (3.9%) with eGFR < 60 mL/min/1.73m2, and 11,605 (0.84%) with HF. Overall, 12.6% had at least one condition within the DCR spectrum, 2.0% had at least two, and 0.23% had all three. Cardiorenal syndrome (both HF and eGFR < 60 mL/min/1.73m2) was prevalent in 0.40% of the entire population and in 2.3% of those with T2D. In patients with both HF and T2D, 55.2% had eGFR < 60 mL/min/1.73m2 and 15.8% had eGFR < 30 mL/min/1.73m2. Amongst those within the DCR spectrum, T2D was prominent in younger participants, but was gradually replaced by HF and eGFR < 60 mL/min/1.73m2 with increasing age. The congruence between all three conditions increased with age. Conclusions This large, broad-based study provides a contemporary, high-resolution prevalence of the DCR spectrum and its components. The results highlight differences in dominance and degree of congruence between T2D, HF, and kidney dysfunction across ages. Supplementary Information The online version contains supplementary material available at 10.1186/s12933-022-01521-9.
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Affiliation(s)
- Meir Schechter
- Diabetes Unit, Department of Endocrinology and Metabolism, Hadassah Medical Center, P.O. Box 12000, 9112001, Jerusalem, Israel.,Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Cheli Melzer Cohen
- Maccabi Institute for Research and Innovation, Maccabi Healthcare Services, Tel-Aviv, Israel
| | - Ilan Yanuv
- Diabetes Unit, Department of Endocrinology and Metabolism, Hadassah Medical Center, P.O. Box 12000, 9112001, Jerusalem, Israel.,Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aliza Rozenberg
- Diabetes Unit, Department of Endocrinology and Metabolism, Hadassah Medical Center, P.O. Box 12000, 9112001, Jerusalem, Israel.,Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gabriel Chodick
- Maccabi Institute for Research and Innovation, Maccabi Healthcare Services, Tel-Aviv, Israel.,School of Public Health Sackler, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Johan Bodegård
- Cardiovascular, Renal and Metabolism, Medical Department, BioPharmaceuticals, AstraZeneca, Oslo, Norway
| | - Lawrence A Leiter
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Subodh Verma
- Division of Cardiac Surgery, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Hiddo J Lambers Heerspink
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Avraham Karasik
- Maccabi Institute for Research and Innovation, Maccabi Healthcare Services, Tel-Aviv, Israel.,Tel Aviv University, Tel Aviv, Israel
| | - Ofri Mosenzon
- Diabetes Unit, Department of Endocrinology and Metabolism, Hadassah Medical Center, P.O. Box 12000, 9112001, Jerusalem, Israel. .,Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
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14
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Segar MW, Patel KV, Hellkamp AS, Vaduganathan M, Lokhnygina Y, Green JB, Wan SH, Kolkailah AA, Holman RR, Peterson ED, Kannan V, Willett DL, McGuire DK, Pandey A. Validation of the WATCH-DM and TRS-HF DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis. J Am Heart Assoc 2022; 11:e024094. [PMID: 35656988 PMCID: PMC9238735 DOI: 10.1161/jaha.121.024094] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background The WATCH-DM (weight [body mass index], age, hypertension, creatinine, high-density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS-HFDM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH-DM was developed to predict incident HF, whereas TRS-HFDM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer-based WATCH-DM and TRS-HFDM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer-based WATCH-DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood-Nam-D'Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person-years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer-based WATCH-DM and TRS-HFDM scores had similar discrimination and calibration for predicting 5-year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood-Nam-D'Agostino P>0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood-Nam-D'Agostino P<0.001 for both). In the electronic health record cohort, the integer-based WATCH-DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood-Nam-D'Agostino P=0.96). TRS-HFDM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH-DM and TRS-HFDM risk scores can discriminate risk of HF among intermediate-risk populations with type 2 diabetes.
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Affiliation(s)
| | - Kershaw V Patel
- Department of Cardiology Houston Methodist DeBakey Heart and Vascular Center Houston TX
| | - Anne S Hellkamp
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | - Muthiah Vaduganathan
- Brigham and Women's Hospital Heart and Vascular Center Department of Medicine Harvard Medical School Boston MA
| | - Yuliya Lokhnygina
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | - Jennifer B Green
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | - Siu-Hin Wan
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
| | - Ahmed A Kolkailah
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
| | - Rury R Holman
- Diabetes Trials Unit Radcliffe Department of Medicine University of Oxford Oxford UK
| | - Eric D Peterson
- Duke Clinical Research Institute Duke University School of Medicine Durham NC.,Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX.,Parkland Health and Hospital System Dallas TX
| | - Vaishnavi Kannan
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
| | - Duwayne L Willett
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
| | - Darren K McGuire
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX.,Parkland Health and Hospital System Dallas TX
| | - Ambarish Pandey
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
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15
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Forecasting Heart Failure Risk in Diabetes. J Am Coll Cardiol 2022; 79:2294-2297. [DOI: 10.1016/j.jacc.2022.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 04/14/2022] [Indexed: 11/23/2022]
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