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Kula AJ, Katz R, Zelnick LR, Soliman E, Go A, Shlipak M, Deo R, Ky B, DeBoer I, Anderson A, Christenson R, Seliger SL, Defilippi C, Feldman HI, Wolf M, Kusek J, Shafi T, He J, Bansal N. Association of circulating cardiac biomarkers with electrocardiographic abnormalities in chronic kidney disease. Nephrol Dial Transplant 2020; 36:2282-2289. [PMID: 33367652 DOI: 10.1093/ndt/gfaa296] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Among patients with chronic kidney disease (CKD), the circulating cardiac biomarkers soluble ST2 (SST2), galectin-3, growth differentiation factor-15 (GDF-15), N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity troponin-T (hsTnT) possibly reflect pathophysiologic processes and are associated with clinical cardiovascular disease. Whether these biomarkers are associated with electrocardiographic findings is not known. The aim of this study was to test the association between serum cardiac biomarkers and the presence of electrocardiographic changes potentially indicative of subclinical myocardial disease in patients with CKD. METHODS We performed a cross-sectional analysis using 3048 participants from the Chronic Renal Insufficiency Cohort (CRIC) without atrial fibrillation, atrioventricular block, bundle branch block or a pacemaker at the baseline visit. Using logistic regression, we tested the association of each of the five cardiac biomarkers with baseline electrocardiogram (ECG) findings: PR interval >200 ms, QRS interval >100 ms and a prolonged QTc interval. Models were adjusted for demographic variables, measures of kidney function, prevalent cardiovascular disease and cardiovascular risk factors. RESULTS In adjusted models, hsTnT levels associated with prolonged PR {odds ratio [OR] 1.23 [95% confidence interval (CI) 1.08-1.40]}, QRS [OR 1.28 (95% CI 1.16-1.42)] and QTc [OR 1.94 (95% CI 1.50-2.51)] intervals. NT-proBNP levels were associated with prolonged QRS [OR 1.11 (95% CI 1.06-1.16)] and QTc [OR 1.82 (95% CI 1.58-2.10)] intervals. SST2, galectin-3 and GDF-15 were not significantly associated with any of the ECG parameters. CONCLUSIONS hsTnT and NT-proBNP were associated with ECG measures indicative of subclinical myocardial dysfunction. These results may support future research investigating the significance of myocardial ischemia and volume overload in the pathogenesis of dysfunctional myocardial conduction in CKD.
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Affiliation(s)
- Alexander J Kula
- Division of Pediatric Nephrology, Seattle Children's Hospital, University of Washington, Seattle, WA, USA
| | - Ronit Katz
- Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Leila R Zelnick
- Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Elsayed Soliman
- Department of Medicine, School of Medicine, Wake Forest University, Winston-Salem, NC, USA
| | - Alan Go
- Division of Research, Kaiser Permanente Northern California, San Francisco, CA, USA
| | - Michael Shlipak
- Department of General Internal Medicine, San Francisco VA Medical Center, San Francisco, CA, USA
| | - Rajat Deo
- Departments of Medicine and Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bonnie Ky
- Departments of Medicine and Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian DeBoer
- Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Amanda Anderson
- Translational Science Institute School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Rob Christenson
- Department of Medicine, University of Maryland School of Medicine, Baltimore, LA, USA
| | - Stephen L Seliger
- Department of Medicine, University of Maryland School of Medicine, Baltimore, LA, USA
| | | | - Harold I Feldman
- Departments of Medicine and Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Myles Wolf
- Department of Medicine, School of Medicine, Duke University, Durham, NC, USA
| | - John Kusek
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Tariq Shafi
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Jiang He
- Translational Science Institute School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Nisha Bansal
- Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, WA, USA
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Deo R, Shou H, Soliman EZ, Yang W, Arkin JM, Zhang X, Townsend RR, Go AS, Shlipak MG, Feldman HI. Electrocardiographic Measures and Prediction of Cardiovascular and Noncardiovascular Death in CKD. J Am Soc Nephrol 2015; 27:559-69. [PMID: 26160896 DOI: 10.1681/asn.2014101045] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 05/01/2015] [Indexed: 11/03/2022] Open
Abstract
Limited studies have assessed the resting 12-lead electrocardiogram (ECG) as a screening test in intermediate risk populations. We evaluated whether a panel of common ECG parameters are independent predictors of mortality risk in a prospective cohort of participants with CKD. The Chronic Renal Insufficiency Cohort (CRIC) study enrolled 3939 participants with eGFR<70 ml/min per 1.73 m(2) from June 2003 to September 2008. Over a median follow-up of 7.5 years, 750 participants died. After adjudicating the initial 497 deaths, we identified 256 cardiovascular and 241 noncardiovascular deaths. ECG metrics were independent risk markers for cardiovascular death (hazard ratio, 95% confidence interval): PR interval ≥200 ms (1.62, 1.19-2.19); QRS interval 100-119 ms (1.64, 1.20-2.25) and ≥120 ms (1.75, 1.17-2.62); corrected QT (QTc) interval ≥450 ms in men or ≥460 ms in women (1.72, 1.19-2.49); and heart rate 60-90 beats per minute (1.21, 0.89-1.63) and ≥90 beats per minute (2.35, 1.03-5.33). Most ECG measures were stronger markers of risk for cardiovascular death than for all-cause mortality or noncardiovascular death. Adding these intervals to a comprehensive model of cardiorenal risk factors increased the C-statistic for cardiovascular death from 0.77 to 0.81 (P<0.001). Furthermore, adding ECG metrics to the model adjusted for standard risk factors resulted in a net reclassification of 12.1% (95% confidence interval 8.1%-16.0%). These data suggest common ECG metrics are independent risk factors for cardiovascular death and enhance the ability to predict death events in a population with CKD.
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Affiliation(s)
- Rajat Deo
- Cardiac Electrophysiology Section, Division of Cardiovascular Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania;
| | - Haochang Shou
- Center for Clinical Epidemiology and Biostatistics and the Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Elsayed Z Soliman
- Epidemiological Cardiology Research Center (EPICARE), Department of Epidemiology and Prevention, and Department of Internal Medicine, Cardiology Section, Wake Forest University School of Medicine, Winston Salem, North Carolina
| | - Wei Yang
- Center for Clinical Epidemiology and Biostatistics and the Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joshua M Arkin
- Cardiac Electrophysiology Section, Division of Cardiovascular Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Xiaoming Zhang
- Center for Clinical Epidemiology and Biostatistics and the Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Raymond R Townsend
- Renal Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Alan S Go
- Division of Research, Kaiser Permanent Northern California, Oakland, California; Department of Health Research and Policy, Stanford University, Palo Alto, California; Department of Epidemiology, Biostatistics, and Medicine, University of California San Francisco, San Francisco, California; and
| | - Michael G Shlipak
- Department of Epidemiology, Biostatistics, and Medicine, University of California San Francisco, San Francisco, California; and Department of General Internal Medicine, San Francisco VA Medical Center, San Francisco, California
| | - Harold I Feldman
- Center for Clinical Epidemiology and Biostatistics and the Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Renal Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
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