Printer Friendly

Beyond Natriuretic Peptides for Diagnosis and Management of Heart Failure.

Heart failure (HF) [3] is a complex syndrome with a concerning rise in prevalence and incidence. Among the challenges in treating this patient population are difficulties in gauging disease severity or missed opportunities for understanding underlying pathophysiology, resulting in mismatch between risk stratification and intensity of therapy or incorrect selection of therapies. Additionally, development of new therapies has been a slow process, in part because of incomplete characterization of the various clinical phenotypes encountered in the syndrome that is HF. It has therefore become essential to improve the diagnosis, management, and treatment of HF. The role of biomarkers to supplement traditional clinical and diagnostic testing has emerged as a new avenue to explore to better refine understanding of HF and possibly improve management of this complex disease process.

Among available biomarkers, B-type natriuretic peptide (BNP) and amino-terminal pro-BNP (NT-proBNP) are the most studied and validated biomarkers used in HF and are to be considered the gold standard biomarkers against which more novel biomarkers are compared. However, BNP and NT-proBNP represent a single pathophysiologic pathway, which leaves opportunity for layering results of other biomarkers reflecting alternative biology. Additionally, BNP and NT-proBNP may be somewhat less accurate in patients with obesity, renal disease, and atrial fibrillation because all these factors influence NP concentrations; this opens the door for other biomarkers to potentially supplement information provided by the NPs. We will focus our discussion on these novel biomarkers each of which represents a particular pathophysiologic pathway and demonstrates promise in terms of improving the understanding and management of HF (Fig. 1).

The Growth of HF Biomarkers

Over recent years, with refinements in molecular techniques along with greater emphasis on in vitro diagnostics to assist in the evaluation of chronic and debilitating medical conditions such as HF, there has been a dramatic increase in the number of scientific reports regarding biomarkers in HF (1). Illustrating this point, a PubMed search of the phrase "biomarkers in heart failure" resulted in more than 9000 manuscripts published since 1990; it is reasonable to expect the volume of biomarker publications to remain very high, particularly with the recent announcement of the Precision Medicine Initiative, whose purpose is to identify tools to individualize care for disease states such as HF (2).

In this period of great growth, we have cautioned that the current state of biomarker research in HF is characterized by exponential gains in knowledge far exceeding our ability to put these findings into biological or clinical context; studies of HF biomarkers may yield interesting data, but may not provide insights to biological processes in HF, or yield clinically actionable information (3). Furthermore, the variable quality of research studies in the field has made bench-to-bedside translations rare, with very few candidates making the translation from in vitro curiosity to clinical value.

What Makes a Biomarker Useful?

We and others have focused on importance of a systematic and collaborative approach involving researchers, the in vitro diagnostics and pharma industries, and regulators to more rapidly and definitively study clinical benefits of novel biomarkers (3). Additionally, given heterogeneity of methodology, we argue quality of biomarker studies should trump results; a well-executed and analyzed "negative" study of a promising biomarker is more important than a poorly-analyzed "positive" analysis that leads to further time investment to fully understand presence or absence of clinical utility (3). Such studies should have high amounts of statistical rigor (4), with highest quality studies providing useful clinical translatability.

We previously suggested certain criteria a biomarker must possess (5), now updated:

1. The method by which a novel biomarker is judged should be thorough.

Novel assays should be evaluated across a wide range of patients typical for the diagnosis(es) for which they will be applied, and statistical methods used to evaluate these biomarkers should be contemporary and rigorous (3).

2. Assays used to measure the novel biomarker should be robust.

Assays for newly proposed biomarkers should be readily available, easily interpretable, and of reasonable cost. Results should be available in a timely manner, should have defined biological variation, and low imprecision. Reference limits must be well-defined, with preanalytic, analytic, and postanalytic vulnerabilities well understood.

3. The novel biomarker should reflect an important pathophysiologic pathway involved in the HF disease process.

This will serve to not only understand the disease process better but also to elucidate pathways that can become potential therapeutic targets. Early studies should attempt to elucidate underlying biological and clinical correlates explaining abnormal novel biomarker results.

Use of biomarkers originating outside the myocardium is acceptable as long as such biomarkers provide independently useful information involved in the diagnosis, prognosis, progression, or therapy of HF syndromes.

4. The novel biomarker should provide information other than what is already available by routine physical exam and laboratory evaluation.

If information overlap exists between a novel marker and BNP or NT-proBNP, compelling reason(s) should exist to justify use of a newer marker over the NPs.

5. The novel biomarker should add to clinical judgment for understanding diagnosis, prognosis, or management of HF.

Such information would provide the much needed clinical utility to support measurement of the biomarker, and allow for more accurate identification of the disease process and personalization of care.

Biomarker Pathways

The biomarkers we will explore represent 7 pathophysiologic pathways defined by Braunwald (6) (Fig. 1). As efforts become focused on improving the management of HF in an aim to reduce the morbidity and mortality, the role these novel biomarkers will play will become more prominent. An example of biomarkers is included in Table 1 with top candidates and their potential uses detailed in Table 2.

Myocardial Stretch/Stress


BNP and NT-proBNP are by far the most studied and validated cardiac biomarkers used in patients with HF, and whose use is reviewed elsewhere in this issue of Clinical Chemistry. Other members of the NP family have been examined, although some [such as C-type NP (CNP)] are less well studied.

Atrial NP (ANP) is a 28-amino acid peptide that is stored within cytosolic granules in the myocytes. Much in the same way BNP is converted from a propeptide, a precursor of ANP is cleaved by convertases to generate mature ANP. Testing for ANP is challenging owing to its short half-life; to overcome this limitation, a midregional assay for pro-ANP (MR-proANP) is now available, which hypothetically provides equimolar measurements for ANP.

MR-proANP has been examined in both the Biomarkers in Acute Congestive Heart Failure (BACH) and ProBNP Investigation of Dyspnea in the Emergency Department (PRIDE) studies. In the BACH Study, amongst 1641 patients presenting to the emergency department with dyspnea, MR-proANP >120 pmol/L proved noninferior to BNP >100 pg/mL for the diagnosis of acute decompensated HF (ADHF); MR-proANP added to the utility of BNP in patients with intermediate BNP values and with obesity but not in renal insufficiency, the elderly, or patients with edema (7). In the PRIDE study MR-proANP had lower area under the receiver operating characteristic curve (AUC) as compared to NT-proBNP (0.90 vs 0.94; P = 0.001 for the difference); although MR-proANP was a significant predictor of ADHF [odds ratio (OR) = 4.34; P < 0.001] it had lower weight than NT-proBNP (8); unlike in BACH, MR-proANP did not assist with clarifying intermediate values or those in patients with obesity or renal failure.

Curiously, in both trials MR-proANP appeared additive to BNP or NT-proBNP for diagnostic evaluation, suggesting the 2 NPs may have different triggers for their release. In terms of clinical applicability, MR-proANP is valuable for verification of the diagnosis of HF and for prognostication in patients with HF.

A substantial percentage of circulating NT-proBNP or BNP in HF consists of the uncleaved precursor peptide known as pro-BNP1-108 (9). ProBNP1- 108 lacks the ability to generate cyclic guanosine monophosphate (the second messenger for BNP), and thus lacks biological benefit for those with HF; studies suggest the majority of circulating "BNP" and "NT-proBNP" in those with advanced HF is actually the uncleaved precursor, but assays for BNP and NT-proBNP cannot differentiate between the free peptides (BNP or NT-proBNP) and proBNP1108 because of the fact that the peptides contain both regions recognized by the assays (10). The value of testing [proBNP.sub.1-108] over BNP and NT-proBNP remains ambiguous.


ST2 is a protein member of the interleukin 1 (IL-1) receptor family released under conditions of mechanical myocardial and vascular strain. ST2 consists of 2 isoforms: a transmembrane ligand (ST2L) and a soluble component (sST2) (11); the ST2 system plays an essential role in mediating myocardial and vascular remodeling and fibrosis, early atherosclerosis as well as hypertension (11).

The ligand for ST2 is IL-33, a member of the IL-1 superfamily of cytokines expressed in epithelial and endothelial cells. The biological effects of IL-33 are transduced by ST2L, resulting in reduction in programed cell death, and down-regulation of profibrotic pathways. sST2, also released under conditions of mechanical stress and myocyte injury and death appears to act as a "decoy" receptor for IL-33, preventing its binding to ST2L, leading to a circumstance favoring myocardial cell death and ventricular fibrosis and remodeling.

The IL-33/ST2 system is also expressed in aortic and coronary artery endothelial cells and similarly released under conditions of mechanical stress. Abnormalities in IL-33/ST2 signaling are associated with accelerated vascular disease, including atherosclerosis and reduced arterial compliance. Thus, as the ST2 system appears to be a pivotal regulator of cardiac and vascular risk.

Concentrations of sST2 are not useful for diagnostic evaluation of ADHF (12) but they do have strong prognostic value. In the PRIDE study, increased sST2 concentrations strongly predicted death at 1 year in dyspneic patients in general [hazard ratio (HR) 5.6, 95% CI 2.2-14.2, P < 0.001] as well as in those with ADHF (HR9.3, 95% CI 1.3-17.8, P = 0.03) above and beyond NTproBNP. A multibiomarker approach that included sST2 and NT-proBNP more accurately identified patients with the highest risk for death (13). The ability of sST2 to predict risk is similar in those with HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF; HFpEF, per ng/mL, HR 1.41; 95% CI 1.14-1.76, P = 0.002; and HFrEF, per ng/mL, HR 1.20, 95% CI 1.10-1.32, P < 0.001) (14). Unlike BNP or NT-proBNP, sST2 concentrations are not affected by obesity, age, atrial fibrillation, or renal function (15).

Serial measurements of sST2 are more useful than a single measurement for prognostication. In ADHF, serial measurements better discriminated risk for mortality than a baseline value (16). In a similar fashion, in the ambulatory HF setting, serial concentrations of sST2 predict worsening HF, rehospitalization, heart transplantation, and death, better than BNP or NT-proBNP, and independently predicted reverse myocardial remodeling (OR 1.22, 95% CI 1.04-1.43, P = 0.01) (17). The role of sST2 in guiding HF management remains unexplored but has promise. Multiple therapies have been shown to reduce sST2 concentrations, including mineralocorticoid receptor antagonists (18), [beta] blockers (19), and angiotensin receptor blockers (20).

sST2 is tested using an ELISA, but a point of care rapid quantitative assay is on the near horizon (21) and could prove to be a useful tool for clinicians for prognostication in patients with HF.


The role of growth differentiation factor 15 (GDF-15) has gained recent attention in HF. The expression of the GDF-15 gene in cardiac myocytes, vascular smooth muscle cells, and endothelial cells is upregulated in response to oxidative stress including pressure overload, HF, atherosclerosis, and inflammation (22). GDF-15 regulates inflammatory and apoptotic pathways by inhibiting macrophage activation; additionally, it also acts as a growth inhibitory molecule in tumor cells (22). GDF-15 is not only specific to cardiac myocytes but found in a wide range of tissue under conditions of stress. It is also upregulated in pregnancy and certain cancers (5).

Concentrations of GDF-15 are prognostic in HF syndromes. For example in the RELAX-AHF (Relaxin in Acute Heart Failure) study, baseline GDF-15 was not associated with adverse outcomes, however increases in GDF-15 at days 2 and 14 were associated with a greater risk of 60-day HF/renal failure rehospitalizations/cardiovascular (CV) death and CV death at 180 days. Additionally, serelaxin treatment was associated with significantly larger decreases of GDF-15 than placebo (23).

Increased GDF-15 concentrations have been associated with an adverse prognosis in patients with chronic HF (17). In a post hoc analysis from the Val-HeFT (Valsartan Heart Failure Trial), baseline GDF-15 concentrations were associated with the risks of mortality (HR 1.017, 95% CI 1.014-1.019, P < 0.001) and first morbid event (HR 1.020, 95% CI 1.017-1.023, P< 0.001). Additionally, increases in GDF-15 concentrations over 12 months were independently associated with the risks of future mortality and first morbid event even after adjustment for clinical prognostic variables, BNP, hs CRP (C-reactive protein), and hs troponin T (hsTnT) (24). GDF-15 may pay a prognostic role in HFpEF patients as well. Recently, Chan et al. demonstrated that GDF-15 was a significant independent predictor for composite outcome even after adjusting for important clinical predictors including hsTnT and NT-proBNP (adjusted HR 1.76 per 1 LnU, 95% CI 1.39-2.21, P < 0.001) in both HFrEF and HFpEF (P interaction = 0.275) (25).

GDF-15 is not yet widely available for clinical application; its future role remains unknown as of now.


Fibroblast growth factor 23 (FGF-23) is a protein member of the fibroblast growth factor family, responsible for phosphate and vitamin D metabolism. It is released in the earliest stages of renal impairment, but may play a role in myocardial disease. Gutierrez and colleagues showed that in patients with chronic kidney disease (CKD), FGF-23 is independently associated with left ventricular (LV) mass index and LV hypertrophy (26), and amongst patients with stable ischemic heart disease, Udell et al. (27) showed that after adjustment for clinical risk predictors, LV ejection fraction, markers of renal function, and established CV biomarkers, FGF-23 was independently associated with an increased risk of CV death or HF, and predicted benefit from trandolapril.

A recent review of the literature available noted that FGF-23 predicted risk of CV disease including ischemic heart disease, stroke, HF, and atrial fibrillation in various studies with mixed results, suggesting that the utility of FGF-23 as a predictive biomarker has yet to be determined (28) and its clinical applicability is undermined.

Extracellular Matrix Remodeling


Cardiac extracellular matrix remodeling is an important pathway in the development and progression of HF that takes place through degradation of collagen and other matrix proteins by collagenases and matrix metalloproteinases (MMPs), and is mediated by tissue inhibitors of metalloproteinases (TIMPs) (5). An imbalance between MMPs and TIMPs may lead to fibrosis and ventricular remodeling (6). Myocardial fibrosis is caused by increased cross-linking of collagen fibrils by the enzyme lysyl oxidase, forming thick and stiff insoluble fibers. Collagen cross-linking determines resistance of collagen fiber degradation by MMP-1. Recently, Loepez and colleagues showed that excessive collagen cross-linking is associated with hospitalization for HF in hypertensive patients (29).

In initial studies of MMPs, George et al. demonstrated that MMP-2 (but not MMP-3, MMP-9, or TIMP-1) concentrations were an independent predictor of mortality in patients with HF (30). Subsequently, Buralli et al. demonstrated that MMP-3 and MmP-9 were univariate predictors of all-cause mortality in patients with HFrEF; only MMP-9 emerged as an independent predictor of adverse prognosis (31). In contrast, Frank et al. found that TIMP-1 but not MMP-9 is of independent and incremental value regarding the prediction of all-cause death (32).

In a sub-study of the RALES (Randomized Aldactone Evaluation Study), 261 patients were randomized to placebo or spironolactone. Baseline procollagen type III amino-terminal peptide (PIIINP) >3.85 [micro]g/L was associated with an increased risk of death [relative risk (RR) 2.36, 95% CI 1.34-4.18] and of death/hospitalization (RR 1.83, 95% CI 1.18-2.83). At 6 months, markers decreased in the spironolactone group but remained unchanged in the placebo group. Effect of spironolactone on outcome was significant only in patients with above-median baseline concentrations of the markers (33).

As matrix remodeling may be of importance in HFpEF, these biomarkers may be of particular interest in this challenging patient group. Zile et al. demonstrated that for detection of LV hypertrophy, the panel of MMP-7, MMP-9, TIMP-1, PIIINP, and NT-proBNP combined with clinical covariates had an AUC of 0.80. Similarly, for HFpEF a multibiomarker panel consisting of increased MMP-2, TIMP-4, PIIINP, and decreased MMP-8 predicted the presence of HFpEF with an AUC of 0.79 (34).

Although measurement of MMPs, TIMPs, and collagen peptides makes intuitive sense, there are still challenges to be faced, such as their reduced specificity for CV pathologies, and therefore the clinical utility of these biomarkers remains to be determined.


Galectin-3 is a macrophage lectin product that plays a role in a cascade of events leading to tissue fibrosis. Seminal work by Sharma and colleagues demonstrated galectin-3 was up-regulated in a model of HF, and played an important role in LV remodeling (35). The first clinical data for galectin-3 in HF came from the PRIDE study. In this analysis of patients with ADHF, galectin-3 concentrations were strongly predictive of 60-day mortality (OR 10.3, P < 0.01) or the combination of death/ recurrent HF within 60 days (OR 14.3, P < 0.001), and, the combination of NT-proBNP with galectin-3 was a better predictor of mortality than either biomarker alone (36). However, unlike in ADHF, the prognostic ability of galectin-3 in the setting of chronic HF is more variable, with other biomarkers such as NT-proBNP or sST2 frequently showing superiority for prognostication (37). Further, effects of renal dysfunction may undermine prognostic accuracy of galectin-3, suggesting the biomarker may detect fibrosis in other organs, such as the kidneys.

Despite suggestion of a pathophysiologic link between galectin-3 and aldosterone-mediated fibrosis, retrospective analyses of mineralocorticoid receptor antagonist therapy did not reveal a significant benefit in patients with increased galectin-3 levels (35); on the contrary, recent data suggest any benefit of aldosterone antagonist use would be seen mainly in those with a low galectin-3 concentration (38).

Galectin-3 measurement is clinically available and galectin-3 may be provide additive information for prognostication in ADHF, although other novel biomarkers such as sST2 appear generally superior.

Myocyte Injury


High-sensitivity troponin (hsTn) assays detect more instances of myocardial necrosis as compared to conventional assays, and concentrations of troponin are very frequently detectable or frankly increased when hsTn assays are run in patients with both ADHF and chronic HF (39). Such abnormalities in troponin concentration may occur in the absence of ischemic heart disease; indeed multiple mechanisms besides coronary artery disease have been invoked for release of troponin in patients with HF, including subendocardial stress, myocyte degeneration, and toxic effects of non-CV disorders such as renal failure. Recent interest has focused on release of cardiomyocyte cell membrane bleb following hemodynamic stress and/or toxic exposures; release of such blebs, containing cytosolic contents, may hypothetically explain in creases in troponin without conventional acute myocardial infarction (MI) (40).

Regardless of their cause, concentrations of hsTn are prognostic for adverse outcomes, predict LV remodeling, and are additive to NP for prognostication. In a study of patients with ADHF without acute MI, hsTnT was detected in 98% of patients vs 56% for conventional TnT. Both assays predicted the risk of death in adjusted multivariable Cox regression analyses; however hsTnT was superior to the conventional assay in individuals with very low concentrations of the marker (39). In another study of patients with ADHF and no detectable conventional troponin but with increases of hsTnT, hsTnT was a significant predictor of death after adjustment for age, sex, ejection fraction, and creatinine concentrations (HR 1.003, 95% CI 1.001-1.005, P = 0.008) (41). Similar results may be seen with chronic HF syndromes, in which concentrations of hsTnI or hsTnT predict adverse CV outcome and chronic LV remodeling (42). Recent data suggest therapy with serelaxin may reduce hsTnT concentrations in ADHF (43), and neprilysin inhibition may similarly attenuate hsTnT in chronic HF (44).

The availability of hsTn assays has become widespread; hsTn testing may prove useful to clinicians for prediction of adverse outcomes in HF patients.

Oxidative Stress


Neutrophil activation and inflammation are important in the pathophysiology and progression of HF. Myeloperoxidase (MPO) is released from activated neutrophils and monocytes during periods of inflammation and is produced in endothelial cells in response to oxidative stress contributing to vascular inflammation, depletion of vascular nitric oxide, and promotion of low-density lipoprotein oxidation (45). MPO concentrations are increased in ADHF and stable chronic HF patients and predictive of adverse clinical outcomes. For example, in patients with ADHF, Reichlin et al. showed that MPO concentrations above 99 pmol/L were associated with significantly increased 1-year mortality (HR 1.58, P = 0.02). The combination of MPO and BNP improved the prediction of 1-year mortality (HR 2.80, P < 0.001) (45).

The clinical applicability of MPO measurements remains to be determined.



Early reports implicated tumor necrosis factor-[alpha] (TNF-[alpha]) in the process of HF; increased concentrations of the biomarker were found in patients with end stage HF and cachexia (46). Vasan et al. showed that TNF-[alpha] was associated with increased risk of development of HF in elderly patients without prior MI (47). Deswal et al. showed TNF-[alpha] concentrations predicted the progression of HF (48).


IL-6 affects communications between cardiac myocytes and fibroblasts and is associated with cardiac dysfunction and alteration of the cardiac extracellular matrix (5). IL-6 concentrations have been shown to correlate with severity of LV dysfunction and additionally have been shown to be an independent predictor of adverse CV outcome in HF patients. IL-6 however, lacks specificity for diagnosis of HF and its role in HF is as of yet uncertain.

The role of measurement of inflammatory markers in clinical practice remains uncertain.

Neurohumoral Activation


Endothelin-1 (ET-1) is produced by the endothelium in response to inflammation, neurohormonal activation, and vascular shear stress (5). ET-1 causes vasoconstriction, proinflammatory actions, proliferative effects, and stimulation of free radical formation. Tang et al. demonstrated that higher concentrations of ET-1 were associated with worse LV diastolic performance and worse clinical outcomes in patients with HFrEF (49). Although ET receptor antagonists were not found to be of benefit for management of patients with chronic HF, the specific benefit of antagonizing the ET receptor in those with marked increases in ET-1 has not been specifically examined; this deserves exploration.


Arginine vasopressin (AVP) is an antidiuretic and vasconstrictive hormone that is released from the hypothalamus in response to hypovolemia and changes in plasma osmolality (5); concentrations of circulating AVP are increased in patients with severe HF (6). However, direct AVP measurement is difficult because the peptide is unstable. Copeptin, the C-terminal segment of preprovasopressin has been reported to be a suitable surrogate, predictive of adverse outcomes in patients with ADHF (50) and predicts CV outcomes in patients with chronic HF independent of NT-proBNP and troponin (51). Similarly, in elderly patients with symptomatic HF, the combination ofincreased copeptin and NT-proBNP concentration was associated with increased risk of all-cause mortality (52).

It is tempting to speculate copeptin measurement might have a role in specific selection and/or guidance of AVP receptor antagonists. In general, such drugs, which improve hyponatremia through mobilization of free water, have been explored for treatment of hyponatremia with mixed results (53). Maisel and colleagues showed amongst 557 patients with ADHF from the BACH trial that patients with copeptin concentrations in the highest quartile had increased 90-day mortality (P < 0.001; HR 3.85) (50), particularly in those with hyponatremia. Thus, there is potential in harnessing copeptin concentrations to identify patients with hypervolemic hyponatremia that may specifically benefit from AVP receptor modulators. Unfortunately, such trials are yet to be performed.


Adrenomedullin (ADM) is a potent vasodilatory peptide expressed in multiple tissues including the heart. With a short half-life, ADM is a challenge to measure; as such a precursor mid regional propeptide ADM (MR-proADM) assay was developed.

MR-proADM has been studied in several cohort studies in HF, and is particularly noteworthy for its accuracy in predicting short-term (e.g., <30 days) hazard. For example, Shah et al. showed that among patients with acute dyspnea MR-proADM had the highest AUC for mortality during the first year from enrollment (0.792); other biomarkers were more powerfully predictive for later death, but MR-proADM remained independently prognostic of mortality to 4 years of follow-up (8). Similarly, in chronic HF MR-proADM was shown to be an independent predictor of mortality (54). Although MR-proADM is rather prognostic, its fundamental biology is poorly understood, it lacks specificity for HF (55) because of its expression in multiple tissues (with increases occurring in sepsis and renal failure, for example), and a therapeutic trigger for an increased MR-proADM has not been identified.

The clinical applicability of biomarkers of neurohumoral biomarkers in HF is uncertain.

Renal Dysfunction


Cystatin-C is considered to be a potential alternative to serum creatinine for estimating glomerular filtration rate (eGFR) as its clearance depends entirely on glomerular filtration. A ubiquitously distributed small molecular weight protein, cystatin-C has been suggested as being less susceptible to factors that render other means for calculating eGFR less accurate. Formulas incorporating cystatin-C to estimate GFR are more sensitive for detection of milder states of renal dysfunction and may ultimately be incorporated into clinical use. The Chronic Kidney Disease Epidemiology Collaboration (CKDEPI) creatinine-cystatin equation has been shown to be superior to other methods for estimating eGFR in those with ADHF (particularly in milder forms of chronic kidney disease), and is superior for predicting mortality and/or HF hospitalization (56). Additionally, the combination of NT-proBNP and eGFR calculated with the CKD-EPI equation predicted outcomes better in patients with ADHF than either biomarker alone (57).

In studies of HF prognosis, cystatin-C has been consistently shown to be superior to creatinine (58); this may be due in part to the fact cystatin-C may also be a biomarker for inflammation and underlying heart disease (59). In patients with chronic stable HF, Dupont et al. showed that cystatin-C remained an independent predictor of major adverse CV events after adjustment for traditional risk factors and BNP (P < 0.001). Additionally, it added prognostic value to creatinine, particularly in patients with preserved renal function (60). Similarly, in ADHF cystatin C predicted mortality and morbidity better than traditional measures of renal function (61).

Renal Injury


Neutral gelatinase-associated lipocalin (NGAL) is a siderophore peptide found in renal tubular epithelium. NGAL is thought to play a pivotal role in response to renal tubular damage (including response to bacterial invasion), and its blood and urine measurement has thus been explored as a biomarker of acute renal injury. NGAL concentrations are frequently increased in patients with ADHF and may be prognostic; Maisel et al. showed that NGAL concentrations at the time of discharge were a strong prognostic indicator of 30 days outcomes in patients admitted for ADHF (62). Because NGAL is a marker for renal tubular damage, its measurement has been explored as a means to predict impending renal dysfunction in patients with HF; in 1 study, combining blood NGAL results with either BNP (OR = 2.79) or NT-proBNP (OR = 3.11) was useful for predicting impending cardio-renal syndrome (63); however, in follow up studies measurement of several renal biomarkers in urine including NGAL did not predict future renal dysfunction in this cohort (64).


Kidney injury molecule-1 (KIM-1) is a glycoprotein expressed in the proximal tubule in renal injury (6) and is common in patients with HF and mildly reduced kidney function. In patients with chronic HF, urinary KIM-1 concentrations are associated with an increased risk of death or HF hospitalizations, independent of eGFR (65). Conversely, in ADHF Grodin et al. demonstrated that circulating KIM-1 concentrations at baseline and during hospitalization were not associated with adverse clinical outcomes after adjusting for standard indices of kidney function (66).


N-Acetyl-[beta]-(D)-glucosaminidase (NAG) is a urinary marker for renal tubular dysfunction. Damman and colleagues showed that in patients with chronic HF, those that developed worsening renal failure had higher NAG concentrations at baseline (67). In another study, diuretic withdrawal resulted in increases in concentration of NAG (P = 0.01); after reinitiation of furosemide, NAG concentrations returned to baseline (P < 0.05) (68), demonstrating the potential ability of NAG to detect early renal damage due to congestion.

Newer renal dysfunction markers may be of value, but their uptake has been slow. Biomarkers of kidney injury may be valuable for prognostication in HF, but their clinical applicability remains uncertain.



Neprilysin, which is also known as neutral endopeptidase, is a zinc-dependent metalloprotease that cleaves and inactivates several vasoactive peptides, including ANP, BNP, CNP, enkephalins, substance P, and bradykinin. Neprilysin inhibition may improve prognosis of patients with HF; through its effects, neprilysin inhibition leads to significant rise in BNP concentrations, whereas NTproBNP concentrations fall in parallel with improved prognosis (69). NT-proBNP is not a substrate for neprilysin degradation (70). As articulated (70), NT-proBNP may become the biomarker of choice for monitoring therapy of patients with neprilysin inhibitors.

Recent attention has focused soluble neprilysin as a biomarker in HF. In a pilot study, admission neprilysin concentrations were associated with short- and long-term outcomes in ADHF (71). Recent data also suggest neprilysin concentrations to be additive to NT-proBNP for prognosis in chronic HF, although in a cohort study of HFpEF, neprilysin concentrations were not useful for predicting prognosis (72).


Given limitations of the NPs together with the multifaceted nature of HF pathophysiology, attention has been given to the inclusion of novel biomarker testing in conjunction with the NPs.

Addition of multiple markers to known scoring algorithms may improve discrimination; for example, addition of NT-proBNP and sST2 results to the Seattle Heart Failure Model improved score performance (73). Table 3 provides select multibiomarker HF risk scoring algorithms.

Although the potential future application of such multimarker testing is in keeping with our original expectations regarding why a novel biomarker might be measured alongside the gold standard NPs, the field of promising candidates remains yet relatively narrow. Concentrations of sST2 (17), hsTn (17), and possibly biomarkers of renal dysfunction and/or injury (67) have each been well-studied in cohorts, subjected to rigorous statistical analyses, and found to be robustly additive to the NPs; in the case of sST2 and hsTn, therapeutic options may exist to blunt the risk associated with their elevation. Whether multimarker panels can be used for superior HF care remains unclear but worthy of study.


Genetic testing may play a role in the diagnosis and prognostication of patients presenting with HF, lending information regarding genetic cause of disease as well as to monitor clinical status; few studies exist demonstrating utility of genetic biomarkers in the diagnosis and risk stratification of HF. Recently, a study by Bayes-Genis and colleagues found certain circulating microRNAs to be associated with risk of death in HF patients but did not incrementally improve prognostication offered by current biomarkers (74). Similarly, Maciejak and colleagues were able to demonstrate that an altered gene expression profile in peripheral blood mononuclear cells during acute MI predicted risk of HF development (75).

Clinical Application

We have provided an overview of several of the most promising new HF biomarkers. Only a few have been shown acceptably to provide additive value to traditional clinical variables and other biomarkers. sST2 and gal-3 are clinically available biomarkers and although each has been shown to be valuable for prognostication, sST2 appears generally superior. hsTn use is becoming more widespread and is useful for prognostication with a suspected diagnosis of HF. Cystatin-C has proven to be valuable in prognostication in HF but its uptake has been slow. The remaining biomarkers remain to be tested in larger studies to determine their applicability in clinical practice. Whether the application of biomarkers in the care of patients with or suspected of having HF will prove superior to traditional clinical variables remains uncertain but worthy of further investigation.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: J.L. Januzzi, Roche Diagnostics, Critical Diagnostics, Boeringer Ingelheim, Sphingotec, and Philips.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: Siemens, Prevencio, Singulex, and Roche Diagnostics.

Expert Testimony: None declared.

Patents: None declared.


(1.) Januzzi JL, Felker GM. Surfing the biomarker tsunami at JACC: heart failure. JACC Heart Fail 2013;1:213-5.

(2.) NIH. Precision medicine initiative https://www.Nih. Gov/precision-medicine-initiative-cohort-program (Accessed January 2016).

(3.) Ahmad T, Fiuzat M, Pencina MJ, Geller NL, Zannad F, Cleland JG, et al. Charting a roadmap for heart failure biomarker studies. JACC Heart Fail 2014;2:477-88.

(4.) Januzzi JL, van Kimmenade RRJ. Importance of rigorous evaluation in comparative biomarker studies. J Am Coll Cardiol 2014;63:167-9.

(5.) van Kimmenade RRJ, Januzzi JL. Emerging biomarkers in heart failure. Clin Chem 2012;58:127-38.

(6.) Braunwald E. Heart failure. JACC Heart Fail 2013;1: 1-20.

(7.) Maisel A, Mueller C, Nowak R, Peacock WF, Landsberg JW, Ponikowski P, et al. Mid-region pro-hormone markers for diagnosis and prognosis in acute dyspnea: results from the BACH (Biomarkers in Acute Heart Failure) trial. J Am Coll Cardiol 2010;55:2062-76.

(8.) Shah RV, Truong QA, Gaggin HK, Pfannkuche J, Hartmann O, Januzzi JL. Mid-regional pro-atrial natriuretic peptide and pro-adrenomedullin testing for the diagnostic and prognostic evaluation of patients with acute dyspnoea. Eur Heart J 2012;33:2197-205.

(9.) Vodovar N, Seronde M-F, Laribi S, Gayat E, Lassus J, Boukef R, et al. Post-translational modifications enhance NT-proBNP and BNP production in acute decompensated heart failure. Eur Heart J 2014;35:3434-41.

(10.) Ibrahim N, Januzzi JL. The potential role of natriuretic peptides and other biomarkers in heart failure diagnosis, prognosisand management. Expert Rev Cardiovasc Ther 2015:1-14.

(11.) Coglianese EE, Larson MG, Vasan RS, Ho JE, Ghorbani A, McCabe EL, et al. Distribution and clinical correlates of the interleukin receptor family member soluble ST2 in the Framingham Heart Study. Clin Chem 2012;58: 1673-81.

(12.) Dieplinger B, Mueller T. Soluble ST2 in heart failure. Clin Chim Acta 2015;443:57-70.

(13.) Januzzi JJL, Peacock WF, Maisel AS, Chae CU, Jesse RL, Baggish AL, et al. Measurement of the interleukin family member ST2 in patients with acute dyspnea: results from the PRIDE (Pro-Brain Natriuretic Peptide Investigation of Dyspnea in the Emergency Department) study. J Am Coll Cardiol 2007;50:607-13.

(14.) Manzano-Fernandez S, Mueller T, Pascual-Figal D, Truong QA, Januzzi JL. Usefulness of soluble concentrations of interleukin family member ST2 as predictor of mortality in patients with acutely decompensated heart failure relative to left ventricular ejection fraction. Am J Cardiol 2011;107:259-67.

(15.) Piper S, Hipperson D, deCourcey J, Sherwood R, George Amin-Youssef AS, McDonagh T. 50 biological variability of soluble ST2 in stable chronic heart failure. Heart2014;100:A29.

(16.) Boisot S, Beede J, Isakson S, Chiu A, Clopton P, Januzzi J, et al. Serial sampling of ST2 predicts 90-day mortality following destabilized heart failure. J Card Fail 2008; 14:732-8.

(17.) Gaggin HK, Szymonifka J, Bhardwaj A, Belcher A, De Berardinis B, Motiwala S, et al. Head-to-head comparison of serial soluble ST2, growth differentiation factor15, and highly-sensitive troponin t measurements in patients with chronic heart failure. JACC Heart Fail 2014;2:65-72.

(18.) Maisel A, Xue Y, van Veldhuisen DJ, Voors AA, Jaarsma T, Pang PS, et al. Effect of spironolactone on 30-day death and heart failure rehospitalization (from the COACH Study). Am J Cardiol 2014;114:737-42.

(19.) Gaggin HK, Motiwala S, Bhardwaj A, Parks KA, Januzzi JL. Soluble concentrations of the interleukin receptor family member ST2 and [beta]-blocker therapy in chronic heart failure. Circ Heart Fail 2013;6:1206-13.

(20.) Anand IS, Rector TS, Kuskowski M, Snider J, Cohn JN. Prognostic value of soluble ST2 in the valsartan heart failure trial. Circ Heart Fail 2014;7:418-26.

(21.) Dieplinger B, Egger M, Gegenhuber A, Haltmayer M, Mueller T. Analytical and clinical evaluation of a rapid quantitative lateral flow immunoassay for measurement of soluble ST2 in human plasma. Clin ChimActa 2015;451, Part B:310-5.

(22.) Ago T, Sadoshima J. GDF15, a cardioprotective TGF-[beta] superfamily protein. Circ Res 2006;98:294-7.

(23.) Cotter G, Voors AA, Prescott MF, Felker GM, Filippatos G, Greenberg BH, et al. Growth differentiation factor 15 (GDF-15) in patients admitted for acute heart failure: results from the RELAX-AHF study. Eur J Heart Fail 2015;17:1133-43.

(24.) Anand IS, Kempf T, Rector TS, Tapken H, Allhoff T, Jantzen F, et al. Serial measurement of growthdifferentiationfactor-15 in heart failure: Relation to disease severity and prognosis in the valsartan heart failure trial. Circulation 2010;122:1387-95.

(25.) Chan MMY, Santhanakrishnan R, Chong JPC, Chen Z, Tai BC, Liew OW, et al. Growth differentiation factor 15 in heart failure with preserved vs. reduced ejection fraction. Eur J Heart Fail 2016;18:81-8.

(26.) Gutierrez OM, Januzzi JL, Isakova T, Laliberte K, Smith K, Collerone G, et al. Fibroblast growth factor 23 and left ventricular hypertrophy in chronic kidney disease. Circulation 2009;119:2545-52.

(27.) Udell JA, Morrow DA, Jarolim P, Sloan S, Hoffman EB, O'Donnell TF, et al. Fibroblast growth factor-23, cardiovascular prognosis, and benefit of angiotensin-converting enzyme inhibition in stable ischemic heart disease. J Am Coll Cardiol 2014;63:2421-8.

(28.) Scialla JJ. Epidemiologic insights on the role of fibroblast growth factor 23 in cardiovascular disease. Curr Opin Nephrol Hypertens 2015;24:260-7.

(29.) Lopez B, Ravassa S, Gonzalez A, Zubillaga E, Bonavila C, Berges M, et al. Myocardial collagen cross-linking is associated with heart failure hospitalization in patients with hypertensive heart failure. J Am Coll Cardiol 2016; 67:251-60.

(30.) George J, Patal S, Wexler D, Roth A, Sheps D, Keren G. Circulating matrix metalloproteinase-2 but not matrix metalloproteinase-3, matrix metalloproteinase-9, or tissue inhibitor of metalloproteinase-1 predicts outcome in patients with congestive heart failure. Am Heart J 2005;150:484-7.

(31.) Buralli S, Dini FL, Ballo P, Conti U, Fontanive P, Duranti E, et al. Circulating matrix metalloproteinase-3 and metalloproteinase-9 and tissue Doppler measures of diastolic dysfunction to risk stratify patients with systolic heart failure. Am J Cardiol 2010;105:853-6.

(32.) Frantz S, Stork S, Michels K, Eigenthaler M, Ertl G, Bauersachs J, Angermann CE. Tissue inhibitor of metalloproteinases levels in patients with chronic heart failure: an independent predictor of mortality. Eur J Heart Fail 2008;10:388-95.

(33.) Zannad F, Alla F, Dousset B, Perez A, Pitt B. Limitation of excessive extracellular matrix turnover may contribute to survival benefit of spironolactone therapy in patients with congestive heart failure: insights from the Randomized Aldactone Evaluation Study (RALES). Circulation 2000;102:2700-6.

(34.) Zile MR, DeSantis SM, Baicu CF, Stroud RE, Thompson SB, McClure CD, et al. Plasma biomarkers that reflect determinants of matrix composition identify the presence of left ventricular hypertrophy and diastolic heart failure. Circ Heart Fail 2011;4:246-56.

(35.) Sharma UC, Pokharel S, van Brakel TJ, van Berlo JH, Cleutjens JP, Schroen B, et al. Galectin-3 marks activated macrophages in failure-prone hypertrophied hearts and contributes to cardiac dysfunction. Circulation 2004;110:3121-8.

(36.) van Kimmenade RR, Januzzi JL Jr, Ellinor PT, Sharma UC, Bakker JA, Low AF, et al. Utility of amino-terminal pro-brain natriuretic peptide, galectin-3, and apelin for the evaluation of patients with acute heart failure. J Am Coll Cardiol 2006;48:1217-24.

(37.) Bayes-GenisA. Neprilysinin heart failure: from oblivion to center stage. JACC Heart Fail 2015;3:637-40.

(38.) Sanders-van Wijk S, Masson S, Milani V, Rickenbacher P, Gorini M, Tavazzi LT, et al. Interaction of galectin-3 concentrations with the treatment effects of [beta]-blockers and RAS blockade in patients with systolic heart failure: a derivation-validation study from TIME-CHF and GISSIHF. Clin Chem 2016;62:605-16.

(39.) Pascual-Figal DA, Casas T, Ordonez-Llanos J, Manzano-Fernandez S, Bonaque JC, Boronat M, et al. Highly sensitive troponin T for riskstratification of acutely destabilized heart failure. Am Heart J 2012;163:1002-10.

(40.) White HD. Pathobiology of troponin elevations: do elevations occur with myocardial ischemia as well as necrosis? J Am Coll Cardiol 2011;57:2406-8.

(41.) Parissis JT, Papadakis J, Kadoglou NPE, Varounis C, Psarogiannakopoulos P, Rafouli-Stergiou P, et al. Prognostic value of high sensitivity troponin T in patients with acutely decompensated heart failure and nondetectable conventional troponin T levels. Int J Cardiol 2013;168:3609-12.

(42.) Motiwala SR, Gaggin HK, Gandhi PU, Belcher A, Weiner RB, Baggish AL, et al. Concentrations of highly sensitive cardiac troponin-I predict poor cardiovascular outcomes and adverse remodeling in chronic heart failure. J Cardiovasc Transl Res 2015;8:164-72.

(43.) Pang PS, Teerlink JR, Voors AA, Ponikowski P, Greenberg BH, Filippatos G, et al. Use of high-sensitivity troponin T to identify patients with acute heart failure at lower risk for adverse outcomes: an exploratory analysis from the RELAX-AHF trial. JACC Heart Fail 2016;4: 591-5.

(44.) Packer M, McMurray JJ, Desai AS, Gong J, Lefkowitz MP, Rizkala AR, et al. Angiotensin receptor neprilysin inhibition compared with enalapril on the risk of clinical progression insurviving patients with heart failure. Circulation 2014;131:54-61.

(45.) Reichlin T, Socrates T, Egli P, Potocki M, Breidthardt T, Arenja N, et al. Use of myeloperoxidase for risk stratification in acute heart failure. Clin Chem 2010;56: 944-51.

(46.) Levine B, Kalman J, Mayer L, Fillit HM, Packer M. Elevated circulating levels of tumor necrosis factor in severe chronic heart failure. N Engl J Med 1990;323: 236-41.

(47.) Vasan RS, Sullivan LM, Roubenoff R, Dinarello CA, Harris T, Benjamin EJ, et al. Inflammatory markers and risk of heart failure in elderly subjects without prior myocardial infarction: the Framingham Heart Study. Circulation 2003;107:1486-91.

(48.) Deswal A, Petersen NJ, Feldman AM, Young JB, White BG, Mann DL. Cytokines and cytokine receptors in advanced heart failure: an analysis of the cytokine database from the Vesnarinone trial (VEST). Circulation 2001;103:2055-9.

(49.) Tang WH,Shrestha K, Martin MG, Borowski AG, Jasper S, Yandle TG, et al. Clinical significance of endogenous vasoactive neurohormones in chronicsystolic heart failure. J Card Fail 2010;16:635-40.

(50.) Maisel A, Xue Y, Shah K, Mueller C, Nowak R, Peacock WF, et al. Increased 90-day mortality in patients with acute heart failure with elevated copeptin: secondary results from the Biomarkers In Acute Heart Failure (BACH)study. Circ Heart Fail 2011;4:613-20.

(51.) Tentzeris I, Jarai R, Farhan S, Perkmann T, Schwarz MA, Jakl G, et al. Complementary role of copeptin and high-sensitivity troponin in predicting outcome in patients with stable chronic heart failure. Eur J Heart Fail 2011; 13:726-33.

(52.) Alehagen U, Dahlstrom U, Rehfeld JF, Goetze JP. Association of copeptin and N-terminal proBNP concentrations with risk of cardiovascular death in older patients with symptoms of heart failure. JAMA 2011;305: 2088-95.

(53.) Finley JJ, Konstam MA, Udelson JE. Arginine vasopressin antagonists for the treatment of heart failure and hyponatremia. Circulation 2008;118:410-21.

(54.) von Haehling S, Filippatos GS, Papassotiriou J, Cicoira M, Jankowska EA, Doehner W, etal. Mid-regional proadrenomedullin as a novel predictor of mortality in patients with chronic heart failure. Eur J Heart Fail 2010; 12:484-91.

(55.) Gaggin HK, Januzzi JL Jr. Biomarkers and diagnostics in heart failure. Biochim Biophys Acta 2013;1832: 2442-50.

(56.) Manzano-Fernandez S, Flores-Blanco PJ, Perez-Calvo JI, Ruiz-Ruiz FJ, Carrasco-Sanchez FJ, Morales-Rull JL, et al. Comparison of risk prediction with the CKD-EPI and MDRD equations in acute decompensated heart failure. J Card Fail 2013;19:583-91.

(57.) Flores-Blanco PJ, Manzano-Fernandez S, Perez-Calvo JI, Pastor-Perez FJ, Ruiz-Ruiz FJ, Carrasco-Sanchez FJ, et al. Cystatin c-based CKD-EPI equations and N-terminal pro-B-type natriuretic peptide for predicting outcomes in acutely decompensated heart failure. Clin Cardiol 2015;38:106-13.

(58.) Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin c. N Engl J Med 2012;367:20-9.

(59.) Curhan G. Cystatin C: a marker of renal function or something more? Clin Chem 2005;51:293-4.

(60.) Dupont M, Wu Y, Hazen SL, Tang WH. Cystatin C identifies patients with stable chronic heart failure at increased risk for adverse cardiovascular events. Circ Heart Fail 2012;5:602-9.

(61.) Manzano-Fernandez S, Januzzi JL Jr, Boronat-Garcia M, Bonaque-Gonzalez JC, Truong QA, Pastor-Perez FJ, et al. [beta]-trace protein and cystatin Cas predictors of longterm outcomes in patients with acute heart failure. J Am Coll Cardiol 2011;57:849-58.

(62.) Maisel AS, Mueller C, Fitzgerald R, Brikhan R, Hiestand BC, Iqbal N, et al. Prognostic utility of plasma neutrophil gelatinase-associated lipocalin in patients with acute heart failure: the NGAL EvaLuation Along with B-Type NaTriuretic Peptide In Acutely Decompensated Heart Failure (GALLANT)trial. Eur J Heart Fail 2011;13: 846-51.

(63.) De Berardinis B, Gaggin Hanna K, Magrini L, Belcher A, Zancla B, Femia A, et al. Comparison between admission natriuretic peptides, NGAL and sST2 testing for the prediction of worsening renal function in patients with acutely decompensated heart failure. Clin Chem Lab Med 2015;53:613-21.

(64.) Legrand M, De Berardinis B, Gaggin HK, Magrini L, Belcher A, Zancla B, et al. Evidence of uncoupling between renal dysfunction and injury in cardiorenal syndrome: insights from the BIONICS study. PLoS One 2014;9:e112313.

(65.) Damman K, Van Veldhuisen DJ, Navis G, Vaidya VS, Smilde TD, Westenbrink BD, et al. Tubular damage in chronicsystolic heart failure is associated with reduced survival independent of glomerular filtration rate. Heart 2010;96:1297-302.

(66.) Grodin JL, Perez AL, Wu Y, Hernandez AF, Butler J, Metra M, etal. Circulating kidney injury molecule-1 levels in acute heart failure: Insights from the ASCEND-HF trial (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure). JACC Heart Fail 2015;3:777-85.

(67.) Damman K, Masson S, Hillege HL, Voors AA, van Veldhuisen DJ, Rossignol P, et al. Tubular damage and worsening renal function in chronic heart failure. JACC Heart Fail 2013;1:417-24.

(68.) Damman K, Ng Kam Chuen MJ, MacFadyen RJ, Lip GY, Gaze D, Collinson PO, et al. Volume status and diuretic therapy in systolic heart failure and the detection of early abnormalities in renal and tubular function. J Am Coll Cardiol 2011;57:2233-41.

(69.) McMurray JJ, Packer M, Desai AS, Gong J, Lefkowitz MP, Rizkala AR, et al. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med 2014; 371:993-1004.

(70.) Januzzi JL. B-type natriuretic peptide testing in the era of neprilysin inhibition: are the winds of change blowing? Clin Chem 2016;62:663-5.

(71.) Bayes-Genis A, Barallat J, Pascual-Figal D, Nunez J, Minana G, Sanchez-Mas J, et al. Prognostic value and kinetics of soluble neprilysin in acute heart failure: a pilot study. JACC Heart Fail 2015;3:641-4.

(72.) Goliasch G, Pavo N, Zotter-Tufaro C, Kammerlander A, Duca F, Mascherbauer J, Bonderman D. Soluble neprilysin does not correlate with outcome in heart failure with preserved ejection fraction. Eur J Heart Fail 2016; 18:89-93.

(73.) Levy WC, Anand IS. Heart failure risk prediction models: what have we learned? JACC Heart Fail 2014; 2:437-9.

(74.) 2015 AHA late-breaking basic science abstracts. Circ Res2015;117:e121-e7.

(75.) Maciejak A, Kiliszek M, Michalak M, Tulacz D, Opolski G, Matlak K, et al. Gene expression profiling reveals potential prognostic biomarkers associated with the progression of heart failure. Genome Med 2015;7:1-15.

(76.) Ky B, French B, McCloskey K, Rame JE, McIntosh E, Shahi P, et al. High-sensitivity ST2 for prediction of adverse outcomes in chronic heart failure. Circ Heart Fail 2011;4:180-7.

(77.) Ky B, French B, Levy WC, Sweitzer NK, Fang JC, Wu AH, et al. Multiple biomarkers for risk prediction in chronic heart failure. Circ Heart Fail 2012;5:183-90.

(78.) Richter B, Koller L, Hohensinner PJ, Zorn G, Brekalo M, Berger R, et al. A multi-biomarker risk score improves prediction of long-term mortality in patients with advanced heart failure. Int J Cardiol 2013;168:1251-7.

(79.) Lupon J, de Antonio M, Vila J, Penafiel J, Galan A, Zamora E, et al. Development of a novel heart failure risktool: the Barcelona Bio-Heart Failure Risk calculator (BCN BIO-HF calculator). PLoS One 2014;9:e85466.

(80.) Berezin AE, Kremzer AA, Martovitskaya YV, Berezina TA, Samura TA. The utility of biomarker risk prediction score in patients with chronic heart failure. Clin Hypertens 2016;22:1-11.

Nasrien E. Ibrahim [1] and James L. Januzzi Jr. [1,2] *

[1] Cardiology Division, Massachusetts General Hospital, Boston, MA; [2] Harvard Clinical Research Institute, Boston, MA.

* Address correspondence to this author at: Massachusetts General Hospital, 32 Fruit St., Yawkey 5984, Boston, MA, 02114. Fax 1-617-643-1620; e-mail

Received April 19,2016; accepted July 18,2016.

Previously published online at DOI: 10.1373/clinchem.2016.259564

[3] Nonstandard abbreviations: HF, heart failure; BNP, B-type natriuretic peptide; NTproBNP, amino-terminal pro-BNP; CNP, C-type NP; ANP, atrial NP; MR-proANP, midregional assay for pro-ANP; BACH, Biomarkers in Acute Congestive Heart Failure; PRIDE, ProBNP Investigation of Dyspnea in the Emergency Department; ADHF, acute decompensated HF; AUC, area under the receiver operating characteristic curve; OR, odds ratio; IL-1, interleukin 1; ST2L, ST2 transmembrane ligand;sST2,solubleST2; HR, hazard ratio; HFpEF, HF with preserved ejection fraction; HFrEF, HF with reduced ejection fraction; GDF-15, growth differentiation factor 15; CV, cardiovascular; hsTnT, high-sensitivity troponin T; FGF-23, fibroblast growth factor 23; CKD, chronic kidney disease; LV, left ventricular; MMP, matrix metalloproteinase; TIMP, tissue inhibitors of metalloproteinase; PIIINP, procollagen type III amino-terminal peptide; RR, relative risk; hsTn, high-sensitivity troponin; MI, myocardial infarction; MPO, myeloperoxidase; TNF-[alpha], tumor necrosis factor-[alpha]; ET-1, endothelin-1; AVP, arginine vasopressin; ADM, adrenomedullin; MRproADM, mid regional propeptide ADM; eGFR, estimated glomerularfiltration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; NGAL, neutral gelatinase-associated lipocalin; KIM-1, kidney injury molecule-1; NAG, Ai-acetyl-[beta]-(D)-glucosaminidase.

Caption: Fig. 1. Various pathophysiologic pathways for HF biomarkers.
Table 1. Biomarkers in HF divided by pathophysiologic pathway. (a)

Myocardial stretch
  ANP, BNP, NT-proBNP, MR-proBNP           Neuregulin
  GDF-15                                   sST2
Extracellular matrix remodeling
  MMPs (2,3,4,8, and 9)                    Myostatin
  TIMP1                                    Syndecan-4
  IL-6                                     Galectin-3
  Collagen propeptides                     sST2
  N-terminal collagen type III peptide     Osteopontin
Myocyte injury
  TnT, TnI, hsTn                           Heart shock protein 60
  CK-MB                                    sTRAIL
  Myosin light-chain kinase I              Pentraxin 3
  Heart-type fatty acid-binding protein    sFAS
Oxidative stress
  MPO                                      Urinary and plasma
  Oxidized LDLs                            Urinary 8-hydpoxy-2%-
  Urinary biopryyins                       Plasma malondialdehyde
  CRP                                      Leucine-ricj2-glycoprotein
  TNF-[alpha]                              Cytokines
  LP-PLA2                                  Prolactinon
  TWEAK (b)                                Adinopectine
  IL-6 (1,10,18)                           Soluble endoglin
  Adipokines                               Serine protease PR3
  FAS (APO-1)                              S100A8/A9 complex
  Osteoprotegerin                          CA-125
  Soluble TNF receptors 1 and 2            Pentraxin-3
  YKL-40                                   Midkine
  IL-1 receptor antagonist
Neurohumoral activation
  Norepinephrine                           Adrenomedullin
  Renin                                    MR-proADM
  Angiotensin II                           Chromagranin A and B
  Aldosterone                              Urocortin
  Aginine vasopressin                      Endothelin
Renal dysfunction
  Neutral gelatinase-associated lipocalin  FGF-23
  NAG                                      Cystatin C
  Kidney injury molecule-1                 B-trace protein
  Quiescin Q6
  RDW                                      MicroRNA
  [beta]2-microglobulin                    Neprilysin
  Triiodothyronine                         Urinary albumin-to-
                                           creatinine ratio

(a) Adapted with permission from van Kimmenade and Januzzi (5).

(b) TWEAK, umor necrosis factor-like weak inducer of apoptosis;
RDW, red cell distribution width.

Table 2. Promising novel biomarkers. (a)

Biomarker            Diagnosis   Prognosis   Therapy

NT-proBNP, BNP         ++++        ++++         ++
MR-proANP               +++        ++++         ?
[proBNP.SUB.1-108]       ?          +++         ?
sST2                     +         ++++         ?
GDF-15                   -          +++         ?
Gal-3                    -          +++         ?
hsTn                     +         ++++         ?
MPO                      -          ++          ?
CRP                      -          ++          ?
TNF-[alpha]              -          + +         ?
IL-6                     -          ++          ?
ET-1                     -          ++          ?
Copeptin                 -          ++          ?
MR-proADM                -         ++++         ?
Cystatin-C               -         ++++         ?
NGAL                     -          +++         ?
Neprilysin               ?           ?          ?

Biomarker            Cardiac production

NT-proBNP, BNP       Solely
MR-proANP            Solely
[proBNP.SUB.1-108]   Solely
sST2                 Not exclusively
GDF-15               Not exclusively
Gal-3                Not exclusively
hsTn                 Solely
MPO                  Not exclusively
CRP                  No
TNF-[alpha]          No
IL-6                 No
ET-1                 Not exclusively
Copeptin             No
MR-proADM            No
Cystatin-C           No
NGAL                 No
Neprilysin           No

(a) Adapted with permission from van
Kimmenade and Januzzi (5).

Table 3. Select multibiomarker HF risk scores.

Reference             Year   Biomarkers

Ky et al. (76)        2011   NT-proBNP
Ky et al. (77)        2012   hs-CRP

                             sFlt-1 (a)
                             Uric acid
Richteret al. (78)    2013   NT-proBNP
Lupon et al. (79)     2014   NT-proBNP
Berezin et al. (80)   2016   NT-proBNP
                             [CD31.sup.+]/annexin [V.sup.+]
                             EMPs to [CD14.sup.+]
                             [CD309.sup.+]MPCs ratio

Reference             Outcome predicted

Ky et al. (76)        Death, heart transplantation

Ky et al. (77)        Death, cardiac
                      transplantation, or
                      ventricular assist device

Richteret al. (78)    Death

Lupon et al. (79)     Death

Berezin et al. (80)   Cumulative cardiovascular

(a) sFlt-1, soluble fms-like tyrosine kinase receptor-1; HGF,
angiogenic and mitogenic hepatocyte growth factor; sFAS, soluble
apoptosis-stimulating fragment, sTRAIL, soluble TNF- related
apoptosis-inducing ligand; EMP, endothelial apoptotic
microparticle; MPC, monuclear progenitor cell.
COPYRIGHT 2017 American Association for Clinical Chemistry, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2017 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Ibrahim, Nasrien E.; Januzzi, James L., Jr.
Publication:Clinical Chemistry
Article Type:Report
Date:Jan 1, 2017
Previous Article:The Changing Face of HDL and the Best Way to Measure It.
Next Article:Application of Cardiac Troponin in Cardiovascular Diseases Other Than Acute Coronary Syndrome.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |