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Figure.
Relative Risk for Coronary Heart Disease (CHD), Heart Failure (HF), or Death According to 6-Year Change in High-Sensitivity Cardiac Troponin T (hs-cTnT) Levels
Relative Risk for Coronary Heart Disease (CHD), Heart Failure (HF), or Death According to 6-Year Change in High-Sensitivity Cardiac Troponin T (hs-cTnT) Levels

Adjusted hazard ratios (95% CIs) for incident CHD, HF, or death were incrementally higher according to categories of increasing 6-year change in hs-cTnT levels between Atherosclerosis Risk in Communities Study visit 2 (1990-1992) and visit 4 (1996-1998). The visit 2 less than 0.005 ng/mL and visit 4 less than 0.005 ng/mL category is the reference group throughout. The visit 2 0.014 ng/mL or more and visit 4 less than 0.005 ng/mL category has been removed because of low numbers (6 participants). Error bars indicate 95% CIs.

Table 1.  
Characteristics of the Study Population at Visit 4a
Characteristics of the Study Population at Visit 4a
Table 2.  
Crude Incidence Rates (per 1000 Person-years) and Adjusted HRs (95% CIs) for Incident Coronary Heart Disease, Heart Failure, or Deatha
Crude Incidence Rates (per 1000 Person-years) and Adjusted HRs (95% CIs) for Incident Coronary Heart Disease, Heart Failure, or Deatha
Table 3.  
C Statistics and Differences in C Statistics for Clinical Events From Models With and Without Baseline hs-cTnT and hs-cTnT Change in the Overall Population
C Statistics and Differences in C Statistics for Clinical Events From Models With and Without Baseline hs-cTnT and hs-cTnT Change in the Overall Population
Table 4.  
Crude Incidence Rates (per 1000 Person-years) and Adjusted HRs (95% CIs) for Heart Failure Subtypes Adjudicated After 2005a
Crude Incidence Rates (per 1000 Person-years) and Adjusted HRs (95% CIs) for Heart Failure Subtypes Adjudicated After 2005a
Supplement.

eTable 1. Study Population for the Primary Analysis Evaluating the Association of hs-cTnT Change Between ARIC Visit 2 and Visit 4 With subsequent CHD, HF, or Death Occurring After Visit 4

eTable 2. Crude Incidence Rates (per 1000 Person-years) and Adjusted Hazard Ratios (95% CIs) for Incident Coronary Heart Disease, Heart Failure Hospitalization, or Death According To Sex-Specific Categories of 6-Year Change in High-Sensitivity Cardiac Troponin T (hs-cTnT) (N=8838)

eTable 3. Adjusted Hazard Ratios (95% CIs) for Incident Coronary Heart Disease, Heart Failure, or Death According to Categories of 6-Year Change in High-Sensitivity Cardiac Troponin T (hs-cTnT) With and Without Further Adjustment for Either Change in NT-proBNP or Visit 4 hs-cTnT

eTable 4. Crude Incidence Rates (per 1000 Person-years) and Adjusted Hazard Ratios (95% CIs) for Incident Coronary Heart Disease, Heart Failure Hospitalization, or Death According to 25% Relative Change of 6-Year Change in hs-cTnT

eTable 5. Net Reclassification Improvement for Coronary Heart Disease, Heart Failure and Death With the Addition of the First and Second hs-cTnT Values to a Traditional Risk Factor Prediction Model

eTable 6. Adjusted Hazard Ratios (95% CIs) for Heart Failure Subtypes (HFrEF and HFpEF) Adjudicated After 2005 According to Categories of 6-Year Change in High-Sensitivity Cardiac Troponin T (hs-cTnT): Full Model With and Without Further Adjustment for 6-Year Change in NT-proBNP

eFigure 1. Cumulative Survival Free of Coronary Heart Disease, Heart Failure Hospitalization, or Death Stratified by Categories of Change in High-Sensitivity Cardiac Troponin T (hs-cTnT) During 6 Years (Incident Detectable, Incident Elevated, or Percent Relative Change)

eFigure 2. Restricted Cubic Splines for Adjusted Hazard Ratios (95% CIs) of Incident Coronary Heart Disease (CHD), Heart Failure (HF) Hospitalization, or Death According to Absolute 6-Year Change in High-Sensitivity Cardiac Troponin T (hs-cTnT) (per Unit Increase in ng/L)

eFigure 3. Restricted Cubic Splines for Adjusted Hazard Ratios (95% CIs) of (a) Incident Coronary Heart Disease (CHD) (B) Heart Failure (HF) Hospitalization, or (C) Death According to Absolute 6-Year Change in High-Sensitivity Cardiac Troponin T (hs-cTnT) (per Unit Increase in ng/L) and Stratified by Visit 2 Concentration Groups (<5 ng/L, 5-13 ng/L, and ≥14 ng/L)

eFigure 4. Restricted Cubic Splines for Adjusted Hazard Ratios (95% CIs) of Incident HFrEF or HFpEF according to Absolute 6-Year Change in High-Sensitivity Cardiac Troponin T (hs-cTnT) (per Unit Increase in ng/L)

1.
Thygesen  K, Alpert  JS, Jaffe  AS,  et al; Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarction.  Third universal definition of myocardial infarction.  Circulation. 2012;126(16):2020-2035.PubMedGoogle ScholarCrossref
2.
Apple  FS.  High-sensitivity cardiac troponin for screening large populations of healthy people: is there risk?  Clin Chem. 2011;57(4):537-539.PubMedGoogle ScholarCrossref
3.
Giannitsis  E, Katus  HA.  Highly sensitive troponins knocking at the door of primary prevention.  Eur Heart J. 2014;35(5):268-270.PubMedGoogle ScholarCrossref
4.
Sherwood  MW, Kristin Newby  L.  High-sensitivity troponin assays: evidence, indications, and reasonable use.  J Am Heart Assoc. 2014;3(1):e000403.PubMedGoogle ScholarCrossref
5.
Saunders  JT, Nambi  V, de Lemos  JA,  et al.  Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study.  Circulation. 2011;123(13):1367-1376.PubMedGoogle ScholarCrossref
6.
de Lemos  JA, Drazner  MH, Omland  T,  et al.  Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population.  JAMA. 2010;304(22):2503-2512.PubMedGoogle ScholarCrossref
7.
Eggers  KM, Al-Shakarchi  J, Berglund  L,  et al.  High-sensitive cardiac troponin T and its relations to cardiovascular risk factors, morbidity, and mortality in elderly men.  Am Heart J. 2013;166(3):541-548.PubMedGoogle ScholarCrossref
8.
Oluleye  OW, Folsom  AR, Nambi  V, Lutsey  PL, Ballantyne  CM; ARIC Study Investigators.  Troponin T, B-type natriuretic peptide, C-reactive protein, and cause-specific mortality.  Ann Epidemiol. 2013;23(2):66-73.PubMedGoogle ScholarCrossref
9.
Wang  TJ, Wollert  KC, Larson  MG,  et al.  Prognostic utility of novel biomarkers of cardiovascular stress: the Framingham Heart Study.  Circulation. 2012;126(13):1596-1604.PubMedGoogle ScholarCrossref
10.
deFilippi  CR, de Lemos  JA, Christenson  RH,  et al.  Association of serial measures of cardiac troponin T using a sensitive assay with incident heart failure and cardiovascular mortality in older adults.  JAMA. 2010;304(22):2494-2502.PubMedGoogle ScholarCrossref
11.
Eggers  KM, Venge  P, Lindahl  B, Lind  L.  Cardiac troponin I levels measured with a high-sensitive assay increase over time and are strong predictors of mortality in an elderly population.  J Am Coll Cardiol. 2013;61(18):1906-1913.PubMedGoogle ScholarCrossref
12.
Selvin  E, Lazo  M, Chen  Y,  et al.  Diabetes mellitus, prediabetes, and incidence of subclinical myocardial damage.  Circulation. 2014;130(16):1374-1382.PubMedGoogle ScholarCrossref
13.
Masson  S, Anand  I, Favero  C,  et al; Valsartan Heart Failure Trial (Val-HeFT) and Gruppo Italiano per lo Studio della Sopravvivenza nell’Insufficienza Cardiaca–Heart Failure (GISSI-HF) Investigators.  Serial measurement of cardiac troponin T using a highly sensitive assay in patients with chronic heart failure: data from 2 large randomized clinical trials.  Circulation. 2012;125(2):280-288.PubMedGoogle ScholarCrossref
14.
Omland  T, Røsjø  H, Giannitsis  E, Agewall  S.  Troponins in heart failure.  Clin Chim Acta. 2015;443:78-84.PubMedGoogle ScholarCrossref
15.
Borlaug  BA, Paulus  WJ.  Heart failure with preserved ejection fraction: pathophysiology, diagnosis, and treatment.  Eur Heart J. 2011;32(6):670-679.PubMedGoogle ScholarCrossref
16.
The ARIC Investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives.  Am J Epidemiol. 1989;129(4):687-702.PubMedGoogle ScholarCrossref
17.
Agarwal  SK, Avery  CL, Ballantyne  CM,  et al.  Sources of variability in measurements of cardiac troponin T in a community-based sample: the atherosclerosis risk in communities study.  Clin Chem. 2011;57(6):891-897.PubMedGoogle ScholarCrossref
18.
Giannitsis  E, Kurz  K, Hallermayer  K, Jarausch  J, Jaffe  AS, Katus  HA.  Analytical validation of a high-sensitivity cardiac troponin T assay.  Clin Chem. 2010;56(2):254-261.PubMedGoogle ScholarCrossref
19.
Parrinello  CM, Grams  ME, Couper  D,  et al.  Recalibration of blood analytes over 25 years in the atherosclerosis risk in communities study: impact of recalibration on chronic kidney disease prevalence and incidence.  Clin Chem. 2015;61(7):938-947.PubMedGoogle ScholarCrossref
20.
Casale  PN, Devereux  RB, Alonso  DR, Campo  E, Kligfield  P.  Improved sex-specific criteria of left ventricular hypertrophy for clinical and computer interpretation of electrocardiograms: validation with autopsy findings.  Circulation. 1987;75(3):565-572.PubMedGoogle ScholarCrossref
21.
Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.  Ann Intern Med. 2009;150(9):604-612.PubMedGoogle ScholarCrossref
22.
White  AD, Folsom  AR, Chambless  LE,  et al.  Community surveillance of coronary heart disease in the Atherosclerosis Risk in Communities (ARIC) Study: methods and initial two years’ experience.  J Clin Epidemiol. 1996;49(2):223-233.PubMedGoogle ScholarCrossref
23.
Rosamond  WD, Chang  PP, Baggett  C,  et al.  Classification of heart failure in the Atherosclerosis Risk in Communities (ARIC) Study: a comparison of diagnostic criteria.  Circ Heart Fail. 2012;5(2):152-159.PubMedGoogle ScholarCrossref
24.
Lindenfeld  J, Albert  NM, Boehmer  JP,  et al; Heart Failure Society of America.  HFSA 2010 Comprehensive Heart Failure Practice Guideline.  J Card Fail. 2010;16(6):e1-e194.PubMedGoogle ScholarCrossref
25.
Kelly  JP, Mentz  RJ, Mebazaa  A,  et al.  Patient selection in heart failure with preserved ejection fraction clinical trials.  J Am Coll Cardiol. 2015;65(16):1668-1682.PubMedGoogle ScholarCrossref
26.
Wu  AH, Lu  QA, Todd  J, Moecks  J, Wians  F.  Short- and long-term biological variation in cardiac troponin I measured with a high-sensitivity assay: implications for clinical practice.  Clin Chem. 2009;55(1):52-58.PubMedGoogle ScholarCrossref
27.
Everett  BM, Brooks  MM, Vlachos  HE, Chaitman  BR, Frye  RL, Bhatt  DL; BARI 2D Study Group.  Troponin and Cardiac Events in Stable Ischemic Heart Disease and Diabetes.  N Engl J Med. 2015;373(7):610-620.PubMedGoogle ScholarCrossref
28.
Harrell  FE  Jr, Lee  KL, Mark  DB.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.  Stat Med. 1996;15(4):361-387.PubMedGoogle ScholarCrossref
29.
Pencina  MJ, D’Agostino  RB  Sr, Steyerberg  EW.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.  Stat Med. 2011;30(1):11-21.PubMedGoogle ScholarCrossref
30.
Glick  D, DeFilippi  CR, Christenson  R, Gottdiener  JS, Seliger  SL.  Long-term trajectory of two unique cardiac biomarkers and subsequent left ventricular structural pathology and risk of incident heart failure in community-dwelling older adults at low baseline risk.  JACC Heart Fail. 2013;1(4):353-360.PubMedGoogle ScholarCrossref
31.
White  HD, Tonkin  A, Simes  J,  et al; LIPID Study Investigators.  Association of contemporary sensitive troponin I levels at baseline and change at 1 year with long-term coronary events following myocardial infarction or unstable angina: results from the LIPID Study (Long-Term Intervention With Pravastatin in Ischaemic Disease).  J Am Coll Cardiol. 2014;63(4):345-354.PubMedGoogle ScholarCrossref
32.
Seliger  SL, de Lemos  J, Neeland  IJ,  et al.  Older adults, “malignant” left ventricular hypertrophy, and associated cardiac-specific biomarker phenotypes to identify the differential risk of new-onset reduced versus preserved ejection fraction heart failure: CHS (Cardiovascular Health Study).  JACC Heart Fail. 2015;3(6):445-455.PubMedGoogle ScholarCrossref
33.
Ledwidge  M, Gallagher  J, Conlon  C,  et al.  Natriuretic peptide-based screening and collaborative care for heart failure: the STOP-HF randomized trial.  JAMA. 2013;310(1):66-74.PubMedGoogle ScholarCrossref
34.
O’Meara  E, de Denus  S, Rouleau  JL, Desai  A.  Circulating biomarkers in patients with heart failure and preserved ejection fraction.  Curr Heart Fail Rep. 2013;10(4):350-358.PubMedGoogle ScholarCrossref
35.
Packer  M, McMurray  JJ, Desai  AS,  et al; PARADIGM-HF Investigators and Coordinators.  Angiotensin receptor neprilysin inhibition compared with enalapril on the risk of clinical progression in surviving patients with heart failure.  Circulation. 2015;131(1):54-61.PubMedGoogle ScholarCrossref
Original Investigation
August 2016

Six-Year Change in High-Sensitivity Cardiac Troponin T and Risk of Subsequent Coronary Heart Disease, Heart Failure, and Death

Author Affiliations
  • 1Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
  • 2Ciccarone Center for the Prevention of Heart Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • 3Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
  • 4Michael E DeBakey Veterans Affairs Hospital, Houston, Texas
  • 5Department of Medicine, Section of Cardiovascular Research, Baylor College of Medicine, Houston, Texas
  • 6Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas
JAMA Cardiol. 2016;1(5):519-528. doi:10.1001/jamacardio.2016.0765
Abstract

Importance  High-sensitivity cardiac troponin T (hs-cTnT) is a biomarker of cardiovascular risk and could be approved in the United States for clinical use soon. However, data linking long-term temporal change in hs-cTnT to outcomes are limited, particularly in primary prevention settings.

Objective  To examine the association of 6-year change in hs-cTnT with incident coronary heart disease (CHD), heart failure (HF), and all-cause mortality.

Design, Setting, and Participants  This prospective observational cohort study, performed from January 1, 1990, to December 31, 2011, included 8838 participants with biracial representation from the Atherosclerosis Risk in Communities Study who were initially free of CHD and HF and who had hs-cTnT measured twice, 6 years apart. Data analysis was performed from October 28, 2014, to March 9, 2016.

Main Outcome and Measures  Risk factor and temporal hs-cTnT data were collected. Using Cox proportional hazards regression, we examined the association of hs-cTnT change with subsequent CHD, HF, and death during a maximum of 16 years. Improvement in discrimination was determined by the Harrell C statistic.

Results  Of the 8838 participants (mean age, 56 years; 5215 female [59.0%]; 1891 black [21.4%]) there were 1157 CHD events, 965 HF events, and 1813 deaths overall. Incident detectable hs-cTnT (baseline, <0.005 ng/mL; follow-up, ≥0.005 ng/mL) was independently associated with subsequent CHD (hazard ratio [HR], 1.4; 95% CI, 1.2-1.6), HF (HR, 2.0; 95% CI, 1.6-2.4), and death (HR, 1.5; 95% CI, 1.3-1.7), relative to an hs-cTnT level less than 0.005 ng/mL at both visits. In addition, HRs as high as 4 for CHD and death and 8 for HF were recorded among individuals with the most marked hs-cTnT increases (eg, baseline, < 0.005 ng/mL; follow-up, ≥0.014 ng/mL). Risk for subsequent outcomes was lower among those with relative hs-cTnT reductions greater than 50% from baseline. Furthermore, information on hs-cTnT change improved discrimination for HF and death when added to a model that included traditional risk factors, N-terminal pro–brain natriuretic peptide, and baseline hs-cTnT level. Among individuals with adjudicated HF hospitalizations, hs-cTnT change appeared to be similarly associated with HF with reduced and preserved ejection fraction.

Conclusions and Relevance  Temporal increases in hs-cTnT, suggestive of progressive myocardial damage, are independently associated with incident CHD, death, and, above all, HF. Serial determination of hs-cTnT trajectory adds clinically relevant information to baseline testing and may be useful in prognostic assessments and the targeting of prevention strategies to high-risk individuals, especially among persons with stage A or B HF.

Introduction

Cardiac troponin is critical to the clinical diagnosis of myocardial infarction, particularly among symptomatic persons with chest pain.1 However, with the advent of new high-sensitivity assays, the utility of this biomarker may extend to the prognostic evaluation of adults at risk for coronary heart disease (CHD) or heart failure (HF).2,3

Detectable concentrations of high-sensitivity troponin, which indicate cardiomyocyte cell damage or death, are known to be present in a substantial proportion of asymptomatic adults without any history of cardiovascular disease.4 In this context, prior studies59 have found that single measurements of high-sensitivity cardiac troponin T (hs-cTnT) are independently associated with a range of adverse outcomes, including CHD, HF, and all-cause mortality.

However, little is known about the implications of temporal change in hs-cTnT. The few studies10,11 to date among primary prevention cohorts were conducted exclusively in elderly individuals. Furthermore, although studies5,1214 have consistently found hs-cTnT to be strongly associated with HF, much less is known about the extent to which hs-cTnT change is a risk marker for HF with reduced ejection fraction (HFrEF) vs HF with preserved ejection fraction (HFpEF). A biomarker with the capacity to stratify risk for HFpEF could inform the phenotyping and, ultimately, the prevention of this vexing clinical syndrome.15

Thus, the goal of this study was to determine, among participants in the Atherosclerosis Risk in Communities (ARIC) Study16 who were initially free of CHD or HF, the association between 6-year change in serial hs-cTnT levels and the following outcomes of interest: incident CHD, incident HF, and death. In addition, among those with an adjudicated HF hospitalization, we conducted secondary analyses evaluating the association between hs-cTnT change and incident HFrEF vs HFpEF.

Box Section Ref ID

Key Points

  • Question What is the association between temporal change in high-sensitivity cardiac troponin T (hs-cTnT), which reflects trajectory of subclinical myocardial damage, and subsequent cardiovascular outcomes?

  • Findings In this cohort of 8838 adults, 6-year change in hs-cTnT was associated with subsequent coronary heart disease, heart failure (HF) (including both HF with reduced ejection fraction and HF with preserved ejection fraction subtypes), and death. Repeat hs-cTnT testing improved discrimination and net reclassification of HF and death, adding to traditional risk factors, N-terminal pro–brain natriuretic peptide, and baseline hs-cTnT level.

  • Meaning Two measurements of hs-cTnT appear to be better than 1 measurement for characterizing risk and may augment targeted prevention efforts, especially among adults with stage A or B HF.

Methods
Study Population

The ARIC Study is a community-based prospective cohort of 15 792 adults sampled from 4 US communities (Forsyth County, North Carolina; Jackson, Mississippi; suburban Minneapolis, Minnesota; and Washington County, Maryland). Details of the study design have been published elsewhere.16 The institutional review boards of the University of North Carolina, University of Mississippi Medical Center, University of Minnesota, and the Johns Hopkins School of Public Health reviewed and approved the study. Written informed consent was obtained from all participants. All data were deidentified before analysis. Of the 11 656 persons who had both a first (visit 2, 1990-1992) and a second (visit 4, 1996-1998) hs-cTnT measurement, we excluded those who had CHD or HF at or before visit 4 (n = 1308) and those who were missing other variables of interest (n = 1510). Thus, 8838 persons were included in our main analytic sample (eTable 1 in the Supplement). Our study was performed from January 1, 1990, to December 31, 2011. Data analysis was performed from October 28, 2014, to March 9, 2016.

Measurement of hs-cTnT and Other Exposure Variables

Measurement of hs-cTnT occurred at 2 time points, 6 years apart. The measurement range of the assay is 0.003 to 100 ng/mL (to convert to micrograms per liter, multiply by 1) with levels below limit of blank (levels that are not measurable and are recorded as blank) of 0.003 ng/mL.17 Of note, hs-cTnT levels between 0.003 and 0.005 ng/mL are measurable but with lower precision than levels of 0.005 ng/mL or greater.5 Thus, 0.005 ng/mL is the limit of detection of this assay and was used as the cutoff for our detectable hs-cTnT category. Levels of 0.014 ng/mL or greater represent the 90th percentile in the ARIC Study sample and the 99th percentile value for a healthy reference group aged 20 to 70 years.18

The first hs-cTnT level was measured in stored serum samples from visit 2 at the University of Minnesota in 2012-2013 using a sandwich immunoassay method with a Roche Elecsys 2010 Analyzer (Roche Diagnostics). Intra-assay coefficients of variation (CVs) were 2.1% at a mean hs-cTnT level of 0.026 ng/mL and 1.0% at 1.99 ng/mL. Interassay CVs were 6.0% at a mean hs-cTnT level of 0.025 ng/mL and 3.7% at 1.94 ng/mL.

The second hs-cTnT level was measured in stored supernatant plasma samples from visit 4 at Baylor College of Medicine in 2010 using an electrochemiluminescence immunoassay implemented on a Roche Cobas e411 analyzer. Intra-assay CVs were 2.1% at a mean hs-cTnT level of 0.029 ng/mL and 0.76% at 2.378 ng/mL. Interassay CVs were 6.9% and 2.6% at mean cTnT levels of 0.029 ng/mL and 2.378 ng/mL. A formal calibration study19 evaluating heterogeneity in hs-cTnT across specimen type and laboratory has been conducted, and no significant differences were observed.

Demographic and cardiac risk factor values from ARIC Study visit 4 (the baseline for outcomes assessment) were used as covariates for the analysis. All measurements were obtained using standardized protocols. Participants self-reported medications, alcohol use, smoking status, and race (with options for the latter defined by the investigator). Body mass index was calculated as the weight in kilograms divided by height in meters squared. Blood pressure was recorded as the mean of 2 seated measurements. Hypertension was defined as systolic blood pressure of 140 mm Hg or higher, diastolic blood pressure of 90 mm Hg or higher, or the use of antihypertensive medications. Left ventricular hypertrophy was assessed using resting 12-lead electrocardiograms and defined by the Cornell criteria.20 Diagnosed diabetes mellitus was defined as a self-reported physician diagnosis of diabetes or current use of diabetic medications.

Highly sensitive C-reactive protein was measured in 2010 from stored plasma collected at visit 4 (Siemens Dade Behring BN II). The glomerular filtration rate was estimated using visit 4 serum creatinine and the Chronic Kidney Disease Epidemiology Research Group 2009 equation.21 N-terminal pro–brain natriuretic peptide (NT-proBNP) was measured in stored serum samples from visit 2 on a Roche Elecsys 2010 analyzer and in stored plasma from visit 4 using a Cobas e411 analyzer.

Follow-up for Outcomes of Interest

The baseline for events is visit 4 (1996-1998), which is when the second hs-cTnT measurement was obtained. The ascertainment of deaths and classification of CHD and HF events in the ARIC Study have been described previously.22,23 Briefly, hospitalizations were reported annually by study participants or their proxy and also identified through surveillance of hospitals in each ARIC Study community. The CHD events were adjudicated by an ARIC Study end points committee and were defined as a definite or probable myocardial infarction, death from CHD, or cardiac procedure.22 The HF hospitalization cases were identified from diagnosis codes and HF death (as well as all-cause mortality) from hospital discharge records for inpatient deaths and death certificates for deaths that occurred outside the hospital.23

Beginning in 2005, the ARIC Study conducted retrospective surveillance and adjudication of hospitalized HF events. Hospitalized medical records that indicated signs or symptoms of HF were fully abstracted and reviewed.23 Although the primary definition of HF hospitalization (defined above) did not change before or after 2005, we were able to determine HF subtype in cases that occurred after 2005 using the abstracted ejection fraction from inpatient diagnostic tests (91% of these were based on transthoracic echocardiograms) or, when absent, preadmission imaging studies within 90 days. The HFpEF was classified by normal systolic function (ejection fraction, ≥50%), whereas persons admitted with HF and an ejection fraction less than 50% were considered to have HFrEF.24,25 Of the 965 incident HF hospitalizations overall, a total of 563 occurred after 2005 and were adjudicated. Of these, there were 306 HFrEF cases, 225 HFpEF cases, and 32 HF events with missing ejection fraction values.

Statistical Analysis

Visit 4 characteristics of the participants were compared across categories of 6-year change in hs-cTnT. To create these categories, hs-cTnT concentrations at each time point (visits 2 and 4) were grouped as undetectable (hs-cTnT, <0.005 ng/mL), detectable (hs-cTnT, ≥0.005 ng/mL and <0.014 ng/mL), or elevated (hs-cTnT, ≥0.014 ng/mL).6,7 Temporal change across these groups was then categorized using each participant’s baseline and follow-up concentration. We conducted comparisons using 1-way analysis of variance for gaussian continuous variables, Wilcoxon rank sum test for nonnormally distributed continuous variables, and χ2 test for proportions.

To model the association between absolute 6-year hs-cTnT change and subsequent events that occurred after visit 4, we defined the following binary exposure categories: (1) incident detectable (hs-cTnT progression from <0.005 ng/mL at visit 2 to ≥0.005 ng/mL at visit 4 six years later) and (2) incident elevation (hs-cTnT progression from <0.014 ng/mL at visit 2 to ≥0.014 ng/mL at visit 4). Among all participants who had measurable hs-cTnT levels of 0.003 ng/mL or higher (n = 3448), we also modeled the association of percentage of relative change (visit 4 hs-cTnT 4 minus visit 2 hs-cTnT, divided by visit 2 hs-cTnT) with subsequent events. For this, we modeled the following exposure categories proposed by deFilippi et al10: relative hs-cTnT change less than 50%, relative increase of 50% or greater, and relative decrease of 50% or greater from the visit 2 level.10,26 We also evaluated 25% relative change cut points.27

Cumulative incidence of CHD, HF, and death among categories of hs-cTnT change were graphed using the Kaplan-Meier method. Multivariable analyses were performed using Cox proportional hazards regression models with follow-up through December 31, 2011 (median follow-up, 14 years; maximum follow-up, 16 years). All models were adjusted for age, race, sex, body mass index, C-reactive protein, smoking (current, former, or never), alcohol intake (current, former, or never), systolic blood pressure, hypertension medication use, left ventricular hypertrophy by electrocardiography, diagnosed diabetes, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, current use of cholesterol-lowering medication, and estimated glomerular filtration rate. We verified the proportionality of the hazards visually and with Schoenfeld residuals. We tested for statistical interaction by age, race, or sex. In models that evaluated categories of relative change in hs-cTnT as the exposure, we further adjusted for visit 2 hs-cTnT (in addition to conducting sensitivity analyses adjusting instead for visit 4 hs-cTnT). Furthermore, in models that evaluated absolute hs-cTnT change categories, sensitivity analyses were conducted with NT-proBNP change values between visits 2 and 4 added to the full model.

Among persons with measurable hs-cTnT at both visits (≥0.003 ng/mL), we also graphed the hazard ratio (HR) for events using restricted cubic splines that were centered at the median of hs-cTnT change and truncated at the 1st and 99th percentiles. These spline models were adjusted using the variables in the main model described above (with the further addition of baseline visit 2 hs-cTnT). Model discrimination was assessed using the Harrell C statistic,28 and we estimated improvement in the C statistic and net reclassification improvement (NRI)29 for the addition of, first, visit 4 hs-cTnT level and, second, visit 2 hs-cTnT level to adjusted models (note that persons with hs-cTnT levels <0.003 ng/mL had levels imputed as 0.0015 ng/mL in the discrimination and NRI models).

Finally, our secondary analysis consisted of Cox proportional hazards regression models and restricted cubic splines that evaluated the adjusted association between categories of hs-cTnT change and incident HFpEF or HFrEF. Given that HF adjudication commenced in 2005, which was after our baseline of visit 4 (1996-1998), patients were not at risk for either HF subtype until observation for these outcomes started in 2005 and, thus, were treated as late entries for this analysis (ie, follow-up time was left truncated at 2005). All analyses were conducted with STATA statistical software, version 13 (StataCorp). The nominal level of significance was defined as P < .05 (2-sided).

Results

Differences in demographics and cardiac risk factors, according to 6-year temporal change in hs-cTnT, are given in Table 1. In the sample overall, the mean age was 56 years at visit 2 and 62 years at visit 4, 5215 (59.0%) were female, and 1891 (21.4%) were black. In general, persons who had hs-cTnT levels less than 0.005 ng/mL at both visits were younger, more likely to be female or white, and more likely to have a lower burden of cardiac risk factors.

During a median of 14 years of follow-up, there were 1157 CHD events, 965 HF hospitalizations, and 1813 deaths overall. Crude incidence rates (IRs) per 1000 person-years for all 3 events were higher among persons with 6-year temporal increases in hs-cTnT (Table 2). Crude IRs were lower among those with a greater than 50% decrease in follow-up hs-cTnT level. Kaplan-Meier cumulative survival curves were consistent with these IR findings by binary categories of absolute temporal change and categories of percentage of relative temporal change (eFigure 1 in the Supplement).

After full adjustment in our Cox proportional hazards regression models, increasing hs-cTnT levels remained a robust risk factor for all 3 events (Table 2). Of note, the association between categories of absolute 6-year hs-cTnT change (eg, incident detectable and incident elevated) and adverse events was strongest for HF, with more modest associations for CHD and death. Indeed, among those with the most marked increases in hs-cTnT during 6 years, we found HRs as high as 8 for HF and 4 for both CHD and death (Figure and eFigure 2 in the Supplement). There was no interaction based on age, race, or sex. Findings were quantitatively and qualitatively similar using sex-specific cutoffs for incident elevated hs-cTnT level (eTable 2 in the Supplement) and also with further adjustment for absolute NT-proBNP change between visits 2 and 4 (eTable 3 in the Supplement). The associations depicted in our restricted cubic splines were qualitatively similar after stratification by visit 2 hs-cTnT concentration (eFigure 3 in the Supplement).

Analyses that modeled the exposure of percentage of relative 6-year hs-cTnT change (which were further adjusted for visit 2 hs-cTnT level) were consistent with the results for hs-cTnT change modeled in absolute terms, revealing that those individuals with a greater than 50% increase in hs-cTnT had HRs (95% CIs) of 1.28 (1.09-1.52), 1.60 (1.35-1.91), and 1.39 (1.22-1.59) for CHD, HF, and death, respectively. Furthermore, those with a greater than 50% decrease in hs-cTnT had generally lower risk for all 3 events (Table 2). Results were similar, but attenuated, in sensitivity analyses modeling relative change after adjustment for visit 4 hs-cTnT level (instead of visit 2 levels) and also in models using a 25% relative change cut point27 (eTable 3 and eTable 4 in the Supplement).

The addition of visit 4 hs-cTnT level (testing the effect of baseline values) and visit 2 hs-cTnT level (testing the effect of temporal change) significantly improved discrimination for HF and death when added to fully adjusted base models that included NT-proBNP (Table 3). In contrast, although the most recent visit 4 hs-cTnT level improved discrimination for CHD events, information on hs-cTnT change did not add further prognostic value for CHD. Furthermore, the addition of both hs-cTnT levels from visits 2 and 4 improved the continuous NRI for all 3 events, whereas the baseline visit 4 level alone did not (eTable 5 in the Supplement).

In secondary analysis that evaluated the association between 6-year hs-cTnT change and subsequent HF hospitalization subtype (occurring after 2005), we found that an increasing hs-cTnT level appears to be associated with HFrEF and HFpEF outcomes (Table 4). These associations were grossly unchanged after further adjusting for NT-proBNP (eTable 6 in the Supplement). The restricted cubic splines, modeling mean absolute hs-cTnT change as a continuous variable, were consistent with findings from the categorical analyses, suggesting an increased risk of HFrEF and HFpEF, particularly among those with absolute 6-year increases ranging from 0.001 to 0.01 ng/mL (eFigure 4 in the Supplement).

Discussion

Temporal increases in hs-cTnT were independently associated with incident CHD, death, and, above all, HF events in this prospective biracial sample of middle-aged adults. In persons with decreasing hs-cTnT levels (eg, 6-year reductions >50% from baseline), there was also evidence suggestive of lower risk for outcomes compared with persons with stable or increasing concentrations.

To our knowledge, only 2 prior studies10,30 have examined the association between temporal change in hs-cTnT and subsequent events in primary prevention settings. The Cardiovascular Health Study (CHS) found that the percentage of relative change in hs-cTnT is an independent risk factor for HF and death among elderly ambulatory adults. Despite some important differences (eg, the CHS evaluated the effect of 2- to 3-year change and was conducted in an older sample), our results from the ARIC Study were generally consistent with those from the CHS. Our results are also consistent with mortality data from asymptomatic elderly persons enrolled in the Prospective Investigation of the Vasculature in Uppsala Seniors study,11 as well as findings from higher-risk persons with established cardiovascular disease.13,27,31

As such, our results extend prior research to include asymptomatic middle-aged adults and also demonstrate, for the first time to our knowledge, an association between temporal hs-cTnT change and incident CHD events. In addition, although our analysis is not designed to derive a risk prediction equation that incorporates hs-cTnT change, our discrimination and NRI results highlight the additive prognostic value of additional hs-cTnT testing, which captures the trajectory of myocardial damage over and above one-time measurements. Furthermore, participants with the most marked increases in hs-cTnT were at highest risk for all 3 events, suggesting that accelerated progression of myocardial damage represents an extreme risk phenotype.

The association between hs-cTnT change and subsequent HF was most striking, raising the question of the effect of such change on HF subtypes. We found that, among those with adjudicated HF hospitalizations, hs-cTnT increases appear to be associated with HFrEF and HFpEF. A prior analysis32 from the CHS reported that serial increases in hs-cTnT levels during 2 to 3 years were associated with HFrEF but not with HFpEF. However, a major limitation of this CHS analysis was that a significant proportion (43%) of HF hospitalizations did not have information on ejection fraction and were excluded. This finding contrasts with just 6% of our adjudicated HF cases being excluded because of lack of ejection fraction data.

Our results have a number of clinical implications. Together with the wealth of data supporting single baseline hs-cTnT measurements,5,6 we found improved prognostic performance for serial measurement of hs-cTnT and add to a compelling argument that serial hs-cTnT monitoring, alone or with other biomarkers, may identify high-risk individuals and guide the prevention of CHD or HF. Clinical trials are necessary to confirm this hypothesis. Our data also suggest that serial hs-cTnT monitoring could personalize the approach to HF prevention by stratifying risk for HFrEF and HFpEF hospitalization in persons with stage A or B HF. Importantly, our results were independent of NT-proBNP, suggesting that hs-cTnT monitoring could add to an NT-proBNP–based HF prevention strategy.33

Although there are a number of established biomarkers of risk for HFrEF events, fewer biomarkers increase the risk of HFpEF events.34 Our results indicate that serial hs-cTnT monitoring, likely added to NT-proBNP, could allow future HFpEF trials to be enriched with individuals at high risk. Another potential clinical and research application of serial hs-cTnT measurement may be as a modifiable surrogate marker of treatment benefit on the myocardium. Indeed, significant reductions in hs-cTnT have been found for sacubitril and valsartan relative to placebo in established HF.35

Our study has some limitations. These data are observational in nature, and despite rigorous adjustment for confounding, residual confounding may be present. Hospitalization for HF was determined by diagnosis codes, which may misclassify some cases, and our outcome may not capture the effect of hs-cTnT change on outpatient HF. Furthermore, HF adjudication only began in 2005. As such, it is possible that HF events that occurred after visit 4, but before 2005, may have differed in their HF subtype distribution with respect to the hs-cTnT change exposure. Because we only have hs-cTnT data from 2 time points, 6 years apart, this analysis cannot define the optimal interval or number of serial hs-cTnT measurements necessary for assessing risk. Although high-sensitivity troponin assays are commercially available for clinical use in many countries, they have yet to be approved in the United States by the US Food and Drug Administration.

Conclusions

Our results indicate that 2 measurements of hs-cTnT appear to be better than 1 for characterizing risk and that large increases in hs-cTnT are particularly deleterious. Temporal change in hs-cTnT may help guide the preventive management of asymptomatic persons at risk for CHD and adults with stage A or B HF (both the HFrEF and HFpEF subtypes).

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Article Information

Accepted for Publication: March 12, 2016.

Corresponding Author: Elizabeth Selvin, PhD, MPH, Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E Monument St, Ste 2-600, Baltimore, MD (eselvin@jhu.edu).

Published Online: June 8, 2016. doi:10.1001/jamacardio.2016.0765.

Author Contributions: Drs McEvoy and Selvin had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: McEvoy, Chen, Ndumele, Solomon, Selvin.

Acquisition, analysis, or interpretation of data: McEvoy, Chen, Ndumele, Nambi, Ballantyne, Blumenthal, Coresh, Selvin.

Drafting of the manuscript: McEvoy.

Critical revision of the manuscript for important intellectual content: Chen, Ndumele, Solomon, Nambi, Ballantyne, Blumenthal, Coresh, Selvin.

Statistical analysis: McEvoy, Chen, Coresh, Selvin.

Obtained funding: Solomon, Ballantyne, Selvin.

Administrative, technical, or material support: McEvoy, Selvin.

Study supervision: Ndumele, Blumenthal, Selvin.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Ballantyne reported received grant support from Roche Diagnostics and the National Institutes of Health. Drs Ballantyne and Nambi reported being coinvestigators on a provisional patent filed by Roche for use of biomarkers in heart failure prediction. Drs Ballantyne and Selvin reported serving on an advisory board for Roche Diagnostics. The other authors reported receiving National Institutes of Health grant funding. Reagents for the hs-cTnT and C-reactive protein assays were donated by Roche Diagnostics. No other disclosures were reported.

Funding/Support: This research was supported by grants R01DK089174 and K24DK106414 from the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health (Dr Selvin), and the PJ Schafer fund for early career investigators and the Magic That Matters Fund at Johns Hopkins (Dr McEvoy). The ARIC Study is performed as a collaborative study supported by contracts HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C from the National Heart, Lung, and Blood Institute.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Additional Contributions: Michael Steffes, MD, PhD, Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Rochester, provided significant contributions to this article. We thank the staff and participants of the ARIC Study for their important contributions.

References
1.
Thygesen  K, Alpert  JS, Jaffe  AS,  et al; Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarction.  Third universal definition of myocardial infarction.  Circulation. 2012;126(16):2020-2035.PubMedGoogle ScholarCrossref
2.
Apple  FS.  High-sensitivity cardiac troponin for screening large populations of healthy people: is there risk?  Clin Chem. 2011;57(4):537-539.PubMedGoogle ScholarCrossref
3.
Giannitsis  E, Katus  HA.  Highly sensitive troponins knocking at the door of primary prevention.  Eur Heart J. 2014;35(5):268-270.PubMedGoogle ScholarCrossref
4.
Sherwood  MW, Kristin Newby  L.  High-sensitivity troponin assays: evidence, indications, and reasonable use.  J Am Heart Assoc. 2014;3(1):e000403.PubMedGoogle ScholarCrossref
5.
Saunders  JT, Nambi  V, de Lemos  JA,  et al.  Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study.  Circulation. 2011;123(13):1367-1376.PubMedGoogle ScholarCrossref
6.
de Lemos  JA, Drazner  MH, Omland  T,  et al.  Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population.  JAMA. 2010;304(22):2503-2512.PubMedGoogle ScholarCrossref
7.
Eggers  KM, Al-Shakarchi  J, Berglund  L,  et al.  High-sensitive cardiac troponin T and its relations to cardiovascular risk factors, morbidity, and mortality in elderly men.  Am Heart J. 2013;166(3):541-548.PubMedGoogle ScholarCrossref
8.
Oluleye  OW, Folsom  AR, Nambi  V, Lutsey  PL, Ballantyne  CM; ARIC Study Investigators.  Troponin T, B-type natriuretic peptide, C-reactive protein, and cause-specific mortality.  Ann Epidemiol. 2013;23(2):66-73.PubMedGoogle ScholarCrossref
9.
Wang  TJ, Wollert  KC, Larson  MG,  et al.  Prognostic utility of novel biomarkers of cardiovascular stress: the Framingham Heart Study.  Circulation. 2012;126(13):1596-1604.PubMedGoogle ScholarCrossref
10.
deFilippi  CR, de Lemos  JA, Christenson  RH,  et al.  Association of serial measures of cardiac troponin T using a sensitive assay with incident heart failure and cardiovascular mortality in older adults.  JAMA. 2010;304(22):2494-2502.PubMedGoogle ScholarCrossref
11.
Eggers  KM, Venge  P, Lindahl  B, Lind  L.  Cardiac troponin I levels measured with a high-sensitive assay increase over time and are strong predictors of mortality in an elderly population.  J Am Coll Cardiol. 2013;61(18):1906-1913.PubMedGoogle ScholarCrossref
12.
Selvin  E, Lazo  M, Chen  Y,  et al.  Diabetes mellitus, prediabetes, and incidence of subclinical myocardial damage.  Circulation. 2014;130(16):1374-1382.PubMedGoogle ScholarCrossref
13.
Masson  S, Anand  I, Favero  C,  et al; Valsartan Heart Failure Trial (Val-HeFT) and Gruppo Italiano per lo Studio della Sopravvivenza nell’Insufficienza Cardiaca–Heart Failure (GISSI-HF) Investigators.  Serial measurement of cardiac troponin T using a highly sensitive assay in patients with chronic heart failure: data from 2 large randomized clinical trials.  Circulation. 2012;125(2):280-288.PubMedGoogle ScholarCrossref
14.
Omland  T, Røsjø  H, Giannitsis  E, Agewall  S.  Troponins in heart failure.  Clin Chim Acta. 2015;443:78-84.PubMedGoogle ScholarCrossref
15.
Borlaug  BA, Paulus  WJ.  Heart failure with preserved ejection fraction: pathophysiology, diagnosis, and treatment.  Eur Heart J. 2011;32(6):670-679.PubMedGoogle ScholarCrossref
16.
The ARIC Investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives.  Am J Epidemiol. 1989;129(4):687-702.PubMedGoogle ScholarCrossref
17.
Agarwal  SK, Avery  CL, Ballantyne  CM,  et al.  Sources of variability in measurements of cardiac troponin T in a community-based sample: the atherosclerosis risk in communities study.  Clin Chem. 2011;57(6):891-897.PubMedGoogle ScholarCrossref
18.
Giannitsis  E, Kurz  K, Hallermayer  K, Jarausch  J, Jaffe  AS, Katus  HA.  Analytical validation of a high-sensitivity cardiac troponin T assay.  Clin Chem. 2010;56(2):254-261.PubMedGoogle ScholarCrossref
19.
Parrinello  CM, Grams  ME, Couper  D,  et al.  Recalibration of blood analytes over 25 years in the atherosclerosis risk in communities study: impact of recalibration on chronic kidney disease prevalence and incidence.  Clin Chem. 2015;61(7):938-947.PubMedGoogle ScholarCrossref
20.
Casale  PN, Devereux  RB, Alonso  DR, Campo  E, Kligfield  P.  Improved sex-specific criteria of left ventricular hypertrophy for clinical and computer interpretation of electrocardiograms: validation with autopsy findings.  Circulation. 1987;75(3):565-572.PubMedGoogle ScholarCrossref
21.
Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate.  Ann Intern Med. 2009;150(9):604-612.PubMedGoogle ScholarCrossref
22.
White  AD, Folsom  AR, Chambless  LE,  et al.  Community surveillance of coronary heart disease in the Atherosclerosis Risk in Communities (ARIC) Study: methods and initial two years’ experience.  J Clin Epidemiol. 1996;49(2):223-233.PubMedGoogle ScholarCrossref
23.
Rosamond  WD, Chang  PP, Baggett  C,  et al.  Classification of heart failure in the Atherosclerosis Risk in Communities (ARIC) Study: a comparison of diagnostic criteria.  Circ Heart Fail. 2012;5(2):152-159.PubMedGoogle ScholarCrossref
24.
Lindenfeld  J, Albert  NM, Boehmer  JP,  et al; Heart Failure Society of America.  HFSA 2010 Comprehensive Heart Failure Practice Guideline.  J Card Fail. 2010;16(6):e1-e194.PubMedGoogle ScholarCrossref
25.
Kelly  JP, Mentz  RJ, Mebazaa  A,  et al.  Patient selection in heart failure with preserved ejection fraction clinical trials.  J Am Coll Cardiol. 2015;65(16):1668-1682.PubMedGoogle ScholarCrossref
26.
Wu  AH, Lu  QA, Todd  J, Moecks  J, Wians  F.  Short- and long-term biological variation in cardiac troponin I measured with a high-sensitivity assay: implications for clinical practice.  Clin Chem. 2009;55(1):52-58.PubMedGoogle ScholarCrossref
27.
Everett  BM, Brooks  MM, Vlachos  HE, Chaitman  BR, Frye  RL, Bhatt  DL; BARI 2D Study Group.  Troponin and Cardiac Events in Stable Ischemic Heart Disease and Diabetes.  N Engl J Med. 2015;373(7):610-620.PubMedGoogle ScholarCrossref
28.
Harrell  FE  Jr, Lee  KL, Mark  DB.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.  Stat Med. 1996;15(4):361-387.PubMedGoogle ScholarCrossref
29.
Pencina  MJ, D’Agostino  RB  Sr, Steyerberg  EW.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.  Stat Med. 2011;30(1):11-21.PubMedGoogle ScholarCrossref
30.
Glick  D, DeFilippi  CR, Christenson  R, Gottdiener  JS, Seliger  SL.  Long-term trajectory of two unique cardiac biomarkers and subsequent left ventricular structural pathology and risk of incident heart failure in community-dwelling older adults at low baseline risk.  JACC Heart Fail. 2013;1(4):353-360.PubMedGoogle ScholarCrossref
31.
White  HD, Tonkin  A, Simes  J,  et al; LIPID Study Investigators.  Association of contemporary sensitive troponin I levels at baseline and change at 1 year with long-term coronary events following myocardial infarction or unstable angina: results from the LIPID Study (Long-Term Intervention With Pravastatin in Ischaemic Disease).  J Am Coll Cardiol. 2014;63(4):345-354.PubMedGoogle ScholarCrossref
32.
Seliger  SL, de Lemos  J, Neeland  IJ,  et al.  Older adults, “malignant” left ventricular hypertrophy, and associated cardiac-specific biomarker phenotypes to identify the differential risk of new-onset reduced versus preserved ejection fraction heart failure: CHS (Cardiovascular Health Study).  JACC Heart Fail. 2015;3(6):445-455.PubMedGoogle ScholarCrossref
33.
Ledwidge  M, Gallagher  J, Conlon  C,  et al.  Natriuretic peptide-based screening and collaborative care for heart failure: the STOP-HF randomized trial.  JAMA. 2013;310(1):66-74.PubMedGoogle ScholarCrossref
34.
O’Meara  E, de Denus  S, Rouleau  JL, Desai  A.  Circulating biomarkers in patients with heart failure and preserved ejection fraction.  Curr Heart Fail Rep. 2013;10(4):350-358.PubMedGoogle ScholarCrossref
35.
Packer  M, McMurray  JJ, Desai  AS,  et al; PARADIGM-HF Investigators and Coordinators.  Angiotensin receptor neprilysin inhibition compared with enalapril on the risk of clinical progression in surviving patients with heart failure.  Circulation. 2015;131(1):54-61.PubMedGoogle ScholarCrossref
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