Single and multiple cardiovascular biomarkers in subjects without a previous cardiovascular event

Aims To assess the incremental value of biomarkers, including N-terminal prohormone of brain natriuretic peptide (NT-proBNP), high-sensitivity troponin T (hs-TnT), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), growth differentiation factor 15 (GDF-15), and procollagen type 1 N-terminal propeptide (P1NP), in predicting incident cardiovascular events and mortality among asymptomatic individuals from the general population, beyond traditional risk factors, including fasting glucose and renal function (cystatin C), medication use, and echocardiographic measures. Methods and results Prospective population-based cohort study of 1324 subjects without a previous cardiovascular event, who underwent baseline echocardiography and biomarker assessment between 2002 and 2006. The clinical endpoint was the composite of myocardial infarction, invasively treated stable/unstable ischemic heart disease, heart failure, stroke, or all-cause mortality. Predictive capabilities were evaluated using Cox proportional-hazards regression, Harrell’s concordance index (C-index), and net reclassification improvement. Median age was 66 (interquartile range: 60–70) years, and 413 (31%) were female. During median 8.6 (interquartile range: 8.1–9.2) follow-up years, 368 (28%) composite events occurred. NT-proBNP, hs-TnT, GDF-15, and IL-6 were significantly associated with outcome, independently of traditional risk factors, medications, and echocardiography (p < 0.05 for all). Separate addition of NT-proBNP and GDF-15 to traditional risk factors, medications, and echocardiographic measurements provided significant improvements in discriminative ability (NT-proBNP: C-index 0.714 vs. 0.703, p = 0.03; GDF-15: C-index 0.721 vs. 0.703, p = 0.02). Both biomarkers remained significant predictors of outcome upon inclusion in the same model (p < 0.05 for both). Conclusions NT-proBNP and GDF-15 each enhance prognostication beyond traditional risk factors, glucose levels, renal function, and echocardiography in individuals without known cardiovascular disease.

[1]  Deepak L. Bhatt,et al.  Prognostic implications of fasting plasma glucose in subjects with echocardiographic abnormalities. , 2017, International journal of cardiology.

[2]  T. Aye,et al.  Risk stratification in stable coronary artery disease , 2017 .

[3]  C. Cannon,et al.  Biomarkers and Coronary Lesions Predict Outcomes after Revascularization in Non-ST-Elevation Acute Coronary Syndrome. , 2017, Clinical chemistry.

[4]  L. Wallentin,et al.  Growth Differentiation Factor 15 as a Biomarker in Cardiovascular Disease. , 2017, Clinical chemistry.

[5]  C. Held,et al.  Growth Differentiation Factor 15 Predicts All-Cause Morbidity and Mortality in Stable Coronary Heart Disease. , 2017, Clinical chemistry.

[6]  Deepak L. Bhatt,et al.  Prognostic Implications of Biomarker Assessments in Patients With Type 2 Diabetes at High Cardiovascular Risk: A Secondary Analysis of a Randomized Clinical Trial. , 2016, JAMA cardiology.

[7]  E. Antman,et al.  Cardiovascular Biomarker Score and Clinical Outcomes in Patients With Atrial Fibrillation: A Subanalysis of the ENGAGE AF-TIMI 48 Randomized Clinical Trial. , 2016, JAMA cardiology.

[8]  J. Coresh,et al.  60 A comparison of HFrEF vs HFpEF’s clinical workload and cost in the first year following hospitalisation and enrollment in a disease management program , 2016, Journal of the American College of Cardiology.

[9]  Deepak L. Bhatt Troponin and the J-Curve of Diastolic Blood Pressure: When Lower Is Not Better. , 2016, Journal of the American College of Cardiology.

[10]  J. Danesh,et al.  Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis , 2016 .

[11]  Daniel F. Freitag,et al.  Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis , 2016, The lancet. Diabetes & endocrinology.

[12]  A. Hsu,et al.  Intestinal Microbial Metabolites Are Linked to Severity of Myocardial Infarction in Rats , 2016, PloS one.

[13]  S. Solomon,et al.  Six-Year Change in High-Sensitivity Cardiac Troponin T and Risk of Subsequent Coronary Heart Disease, Heart Failure, and Death. , 2016, JAMA cardiology.

[14]  R. Prager,et al.  Targeted multiple biomarker approach in predicting cardiovascular events in patients with diabetes , 2016, Heart.

[15]  Deepak L. Bhatt,et al.  Adaptive Designs for Clinical Trials. , 2016, The New England journal of medicine.

[16]  G. Collins,et al.  Prediction models for cardiovascular disease risk in the general population: systematic review , 2016, British Medical Journal.

[17]  A. Peters,et al.  Troponin I and cardiovascular risk prediction in the general population: the BiomarCaRE consortium , 2016, European heart journal.

[18]  M. Sabatine,et al.  Multimarker Risk Stratification in Patients With Acute Myocardial Infarction , 2016, Journal of the American Heart Association.

[19]  E. Hagström,et al.  Growth differentiation factor-15 level predicts major bleeding and cardiovascular events in patients with acute coronary syndromes: results from the PLATO study. , 2016, European heart journal.

[20]  A. Low,et al.  Growth differentiation factor 15 in heart failure with preserved vs. reduced ejection fraction , 2016, European journal of heart failure.

[21]  P. Nilsson,et al.  Untreated diabetes mellitus, but not impaired fasting glucose, is associated with increased left ventricular mass and concentric hypertrophy in an elderly, healthy, Swedish population , 2015 .

[22]  A. Gavazzi,et al.  Role of biomarkers in cardiac structure phenotyping in heart failure with preserved ejection fraction: critical appraisal and practical use , 2015, European journal of heart failure.

[23]  Deepak L. Bhatt,et al.  Troponin and Cardiac Events in Stable Ischemic Heart Disease and Diabetes. , 2015, The New England journal of medicine.

[24]  P. Nilsson,et al.  Worsening diastolic function is associated with elevated fasting plasma glucose and increased left ventricular mass in a supra-additive fashion in an elderly, healthy, Swedish population. , 2015, International journal of cardiology.

[25]  Victor Mor-Avi,et al.  Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. , 2015, European heart journal cardiovascular Imaging.

[26]  Jennifer G. Robinson,et al.  2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines , 2014, Circulation.

[27]  J. Mariani,et al.  Evaluating the Utility of Circulating Biomarkers of Collagen Synthesis in Hypertrophic Cardiomyopathy , 2014, Circulation. Heart failure.

[28]  D. DeMets,et al.  Management of patients with atrial fibrillation (compilation of 2006 ACCF/AHA/ESC and 2011 ACCF/AHA/HRS recommendations): a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines. , 2013, Circulation.

[29]  E. Falk,et al.  Traditional SCORE-based health check fails to identify individuals who develop acute myocardial infarction. , 2013, Danish medical journal.

[30]  J. Ioannidis,et al.  Bias in associations of emerging biomarkers with cardiovascular disease. , 2013, JAMA internal medicine.

[31]  C. Ayers,et al.  Biomarkers of chronic cardiac injury and hemodynamic stress identify a malignant phenotype of left ventricular hypertrophy in the general population. , 2013, Journal of the American College of Cardiology.

[32]  D. Goff,et al.  Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. , 2012, JAMA.

[33]  V. Salomaa,et al.  A multiple biomarker risk score for guiding clinical decisions using a decision curve approach , 2012, European journal of preventive cardiology.

[34]  J. Ioannidis,et al.  Minimal and null predictive effects for the most popular blood biomarkers of cardiovascular disease. , 2012, Circulation research.

[35]  J. Ludvigsson,et al.  External review and validation of the Swedish national inpatient register , 2011, BMC public health.

[36]  E. Barrett-Connor,et al.  Growth-Differentiation Factor-15 Is a Robust, Independent Predictor of 11-Year Mortality Risk in Community-Dwelling Older Adults: The Rancho Bernardo Study , 2011, Circulation.

[37]  Ruth M. Pfeiffer,et al.  The impact of sample storage time on estimates of association in biomarker discovery studies , 2011, Journal of Clinical Bioinformatics.

[38]  Ewout W Steyerberg,et al.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers , 2011, Statistics in medicine.

[39]  D. Lloyd‐Jones,et al.  Risk Prediction in Cardiovascular Medicine Cardiovascular Risk Prediction Basic Concepts, Current Status, and Future Directions , 2010 .

[40]  V. Gudnason,et al.  Myocardial structure and function by echocardiography in relation to glucometabolic status in elderly subjects from 2 population-based cohorts: a cross-sectional study. , 2010, American heart journal.

[41]  Paul M Ridker,et al.  Inflammation in atherosclerosis: from pathophysiology to practice. , 2009, Journal of the American College of Cardiology.

[42]  L. Lind,et al.  Growth-differentiation factor-15 is an independent marker of cardiovascular dysfunction and disease in the elderly: results from the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) Study. , 2009, European heart journal.

[43]  M. Pencina,et al.  Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. , 2009, JAMA.

[44]  G. Boysen,et al.  European Guidelines on Cardiovascular Disease Prevention , 2009, International journal of stroke : official journal of the International Stroke Society.

[45]  Arturo Evangelista,et al.  Recommendations for the evaluation of left ventricular diastolic function by echocardiography. , 2009, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[46]  J. Sundström,et al.  Use of multiple biomarkers to improve the prediction of death from cardiovascular causes , 2008 .

[47]  T. Hansen,et al.  N-terminal pro-brain natriuretic peptide, but not high sensitivity C-reactive protein, improves cardiovascular risk prediction in the general population. , 2007, European heart journal.

[48]  K. Bibbins-Domingo,et al.  N-terminal fragment of the prohormone brain-type natriuretic peptide (NT-proBNP), cardiovascular events, and mortality in patients with stable coronary heart disease. , 2007, JAMA.

[49]  D. Levy,et al.  Multiple biomarkers for the prediction of first major cardiovascular events and death. , 2006, The New England journal of medicine.

[50]  H. Tunstall-Pedoe,et al.  Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. , 2003, European heart journal.

[51]  D. Levy,et al.  Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.

[52]  Pickering,et al.  Relation of arterial pressure level and variability to left ventricular geometry in normotensive and hypertensive adults. , 1996, Blood pressure monitoring.

[53]  Ørnulf Borgan,et al.  A method for checking regression models in survival analysis based on the risk score , 1996, Lifetime data analysis.

[54]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.