CCA-13648; No of Pages 7 Clinica Chimica Acta xxx (2014) xxx–xxx

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Inflammatory cytokines as biomarkers in heart failure Thor Ueland a,d,e,h,⁎, Lars Gullestad b,d,f, Ståle H. Nymo a, Arne Yndestad a,d,e, Pål Aukrust a,c,d,e,h, Erik T. Askevold a,f,g a

Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Norway Department of Cardiology, Oslo University Hospital Rikshospitalet, Norway Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Norway d Faculty of Medicine, University of Oslo, Norway e K.G. Jebsen Inflammatory Research Center, University of Oslo, Norway f KG Jebsen Cardiac Research Center and Center for Heart Failure Research, University of Oslo, Norway g Clinic for Internal Medicine, Lovisenberg Diakonale Hospital, N-0027 Oslo, Norway h KG Jebsen Thrombosis Research and Expertise Center, N-9037 Tromsø, Norway b c

a r t i c l e

i n f o

Article history: Received 10 July 2014 Received in revised form 31 August 2014 Accepted 1 September 2014 Available online xxxx Keywords: Inflammation Cytokines Analytical aspects Outcome Prognosis Heart failure

a b s t r a c t Inflammation has been implicated in the pathogenesis of heart failure (HF). In addition to their direct involvement as mediators in the pathogenesis of HF, inflammatory cytokines and related mediators could also be suitable markers for risk stratification and prognostication in HF patients. Many reports have suggested that inflammatory cytokines may predict adverse outcome in these patients. However, most studies have been limited in sample size and lacking full adjustment with the most recent and strongest biochemical predictor such as NT-proBNP and high sensitivity troponins. Furthermore, a number of pre-analytical and analytical aspects of cytokine measurements may limit their use as biomarkers. This review focuses on technical, informative and practical considerations concerning the clinical use of inflammatory cytokines as prognostic biomarkers in HF. We focus on the predictive value of tumor necrosis factor (TNF) α, the TNF family receptors sTNFR1 and osteoprotegerin, interleukin (IL)-6 and its receptor gp130, the chemokines MCP-1, IL-8, CXCL16 and CCL21 and the pentraxin PTX-3 in larger prospective fully adjusted studies. No single inflammatory cytokine provides sufficient discrimination to justify the transition to everyday clinical use as a prognosticator in HF. However, while subjecting potential new HF markers to rigorous comparisons with “gold-standard” markers, such as NT-proBNP, using receiver operating characteristics (ROCs) and HF risk models, makes sense from a clinical standpoint, it may pose a threat to a broadening of mechanistic insight if the new markers are dismissed solely on account of lower statistical power. © 2014 Published by Elsevier B.V.

1. Introduction Heart failure (HF) is a complex multi-step disorder in which a number of pathophysiological processes participate [1]. The involvement of neurohormones in the progression of HF has been firmly established leading to new treatment modalities such as angiotensin converting enzyme (ACE) inhibitors, angiotensin II receptor antagonist and β-blockers [2]. However, despite state-of-the-art cardiovascular (CV) treatment, chronic HF is a progressive disease with high morbidity and mortality, suggesting that important pathogenic mechanisms remain unmodified by present treatment modalities. Persistent inflammation may represent such an unmodified mechanism [3,4]. The finding of elevated levels of tumor necrosis factor (TNF) in sera of HF patients ⁎ Corresponding author at: Room D1.2017, Research Institute for Internal Medicine, Rikshospitalet, Sognsvannsveien 20, N-0027 Oslo, Norway. Tel.: + 47 23 073626; fax: + 47 23 073630. E-mail address: [email protected] (T. Ueland).

initiated the “inflammation era” in HF research [5]. Since then, a large body of evidence has implicated activation of inflammatory pathways as an important pathological event in the initiation and progression of the syndrome [6–10]. Biomarkers are now widely used for risk stratification and for evaluation of therapeutic responses in CV disease, and in HF the N-terminal prohormone of brain natriuretic peptide (NT-proBNP) and highsensitivity cardiac troponin T (hs-cTn) have been extensively studied [11–17]. In addition to their direct involvement as mediators in the pathogenesis of HF, inflammatory cytokines and related mediators could also be suitable markers for risk stratification and prognostication in HF patients [7–10]. Numerous reports have suggested that inflammatory cytokines may predict adverse outcome in these patients. However, many studies have been limited in sample size and lacking full adjustment with the most recent and strongest biochemical predictors such as NT-proBNP, hs-cTn and C-reactive protein (CRP) [7–10,17–19], which are frequently available as routine tests. Furthermore, a number of pre-analytical and analytical aspects of cytokine measurements may

http://dx.doi.org/10.1016/j.cca.2014.09.001 0009-8981/© 2014 Published by Elsevier B.V.

Please cite this article as: Ueland T, et al, Inflammatory cytokines as biomarkers in heart failure, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.09.001

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limit their use as biomarkers. This review focuses on technical, informative and practical considerations concerning the clinical use of inflammatory cytokines as prognostic biomarkers of HF. 2. Inflammatory cytokines as biomarkers 2.1. Patho-genetic role of inflammation in HF Several reports have demonstrated enhanced expression and release of inflammatory cytokines such as TNFα, IL-1, IL-6, IL-18, cardiotrophin-1 (CT-1) and Fas ligand, as well as several chemokines [e.g., monocyte chemoattractant peptide (MCP)-1/CCL2, IL-8/CXCL8, CXCL16 and CCL21 in HF patients [3,4,7–10,18,19]. Plasma levels of these molecules appear to be elevated in direct proportion to deterioration of functional class (i.e. NYHA classification) and cardiac performance [i.e. left ventricular ejection fraction (LVEF)] [3,4,7–10,18,19]. A series of experimental studies have revealed that the biological effects of cytokines may explain several aspects of the syndrome of chronic HF and the pathogenic role of inflammatory cytokines in HF is supported by various transgenic mouse models. Thus, inflammatory cytokines may modulate myocardial functions by a variety of mechanisms including stimulation of hypertrophy and fibrosis through direct effects on cardiomyocytes and fibroblasts, impairment of myocardial contractile function by influencing intracellular calcium transport and signal transduction through β-adrenergic receptors, induction of apoptosis, and stimulation of genes involved in myocardial remodeling [6,18,20]. In addition, indirect effects of inflammatory mediators could contribute to the progression of HF through impairment of bone marrow function with secondary anemia, endothelial cell activation, and skeletal muscle catabolism with secondary induction of systemic inflammation and reflex abnormalities in HF [6,18,20]. While inflammation is intrinsically a beneficial event, inadequate or excessive inflammatory response may lead to improper cellular repair, tissue damage, and dysfunction. Achieving a balanced inflammatory response has proven difficult in HF and trials focusing on specific targets, such as TNFα, have been largely unsuccessful [21]. The chimeric anti-TNF antibody (infliximab) directly binds to the transmembrane form of TNF, resulting in damage to TNF-expressing cells by different mechanisms. While such mechanisms may be beneficial in inflammatory disorders such as inflammatory bowel disease, it may result in deleterious effects in chronic HF, secondary to damage of TNF-expressing cardiomyocytes. Furthermore, while too much of inflammatory cytokines such as TNF may be harmful, too little of these mediators may also have adverse effects on the myocardium 2.2. From patho-physiology to plasma biomarker However, the most important mediators may not necessarily be the best biomarkers. The leading role of CRP as an inflammatory biomarker in CV disease is not primarily based on its pathogenic role in these disorders, but rather on its analytical stability and ability to reflect upstream inflammatory activity of several relevant pathways [22]. Furthermore, while secreted cytokine ligands often circulate at low levels increasing analytical variation and requiring expensive high sensitivity assays, their corresponding soluble receptors are frequently detected at high levels in serum and plasma, potentially increasing their reliability as biomarkers. For instance, soluble receptors for TNFα (i.e., soluble TNF receptors 1 [sTNFR1] and sTNFR2) and several other members of the TNF receptor superfamily such as CD27, FAS and osteoprotegerin (OPG) are present in the circulation at relatively high levels and are elevated in HF [19,23]. As for IL-6 signaling, circulating levels of the common receptor subunit gp130, soluble gp130 (sgp130), could potentially reflect the activity of several IL-6 family cytokines (e.g. IL-6, IL-11, CT-1, oncostatin M [OSM]) thereby reflecting several relevant pathways in relation to HF [24]. Similarly, IL-1 receptor-like 1 (IL1RL1), commonly referred to as ST2, belongs to the IL-1 receptor family and is upregulated in

HF, reflecting both inflammation and hemodynamic stress. The soluble, truncated IL1RL1 isoform B has been shown to provide prognostic information in HF in several large trials [25], as also covered by Dieplinger et al. in this issue. These soluble receptors may be considered as stable and reliable markers of activity in their ligand/receptor system. However, not all cytokines circulate at low levels and for example CXCL16 has been shown to give prognostic information, potentially reflecting its ability to reflect several up-stream inflammatory pathways as IL-1β, TNFα and interferon (IFN)g are potent inducers of CXCL16 release [26, 27]. Finally, although CRP has received much attention due to its prognostic power in several CV populations, it is unlikely that this molecule should reflect all aspects of complicated disorders like HF, involving several inflammatory pathways. As an example, another pentraxin, pentraxin 3 (PTX3) which unlike CRP is produced at the site of inflammation, has recently been found associated with an increased risk for cardiac events in HF patients [28,29]. 2.3. Clinical role of inflammatory biomarker Morrow and deLemos have proposed three criteria a good biomarker needs to fulfill [30]. Clinically, these criteria boil down to three key questions: (1) Can the clinician measure the biomarker? This question relates to the accuracy and reproducibility of the analytical method, the accessibility of the assay and cost-effectiveness. (2) Does the biomarker add new information? The marker needs to display consistent and solid association to the disease or outcome of interest (in multiple studies); the information the marker provides must add to or improve on existing tests. Lastly, (3) will the biomarker help the clinician to manage patients? In order to impact the latter aspect, the marker needs to either surpass performance of other diagnostic tests, provide evidence that associated risk is modifiable with specific therapy or give evidence that implementation of biomarker-guided triage or monitoring enhances care of the patient. Implicitly, these are tough criteria to meet for any biomarker. So how do inflammatory cytokines perform with regard to these criteria? Although data on an increasing number of novel inflammatory biomarkers are reported in HF populations, few have been sufficiently evaluated and, with the exception of CRP, none have made the transition into routine clinical use. In this review we focus on the inflammatory cytokines TNFα, the TNF family receptors sTNFR1 and OPG, IL-6 and its receptor gp130, the chemokines MCP-1, IL-8, CXCL16 and CCL21 and the pentraxin PTX-3. 3. Analytical performance of inflammatory cytokines There are a number of features and conditions that can influence the analytical performance of cytokines and limit the ability to use them as biomarkers in everyday practice. Biological factors such as age, gender, diurnal and postprandial variations may account for both within and between patient variability. In addition, pre-analytical factors such as sample handling (collection methods, storage, freeze–thaw cycles, and plasma versus serum) may influence the measurement of cytokines as well as analytical factors related to assay methodology and standardization. As an example, to this end there are no established normal levels for most of the relevant cytokines and the absolute levels vary largely between the different studies. Together, these factors may contribute considerably to the disparities seen among similar types of clinical studies and complicate a direct comparison of the study results. 3.1. Patient related variability Multiple studies have evaluated the effect of age- and gender specific variables on circulating cytokine levels. In general, low-grade inflammation is associated with an age-related 2–4 fold increase in circulating levels of inflammatory cytokines [31] due to changes in life-style factors, infections and physiological changes, such as increased fat mass and

Please cite this article as: Ueland T, et al, Inflammatory cytokines as biomarkers in heart failure, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.09.001

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physical inactivity, as well as increased risk of age-related diseases [32]. In fact, aging and development of CV diseases share several common mechanisms including inflammation, often referred to as inflammaging [33,34]. As shown in Table 1, most of the cytokines and corresponding secreted receptors discussed in this review increase with advancing age, and in addition, estrogen deprivation may account for particularly high levels in postmenopausal women [35]. While age- and gender may be accounted for when evaluating the independent contribution of an inflammatory biomarker in survival models, the lack of international standardization of age-adjusted normal ranges of many cytokines may make it difficult to interpret the biological significance of minor variations in cytokine levels in patients in a routine setting [36–38]. Other factors that may affect the measurability of a given cytokine are diurnal variation and effects of food intake. Most of the inflammatory markers present a circadian pattern partly related to the rhythm of plasma cortisol and melatonin [39,40], although there is limited data on many of the chemokines (Table 1). Ideally, diurnal variation and effects of food intake should be taken into account when evaluating a marker for clinical use. In addition, cytokines may be produced by many cell types including muscle cells, and for example physical exercise and stress may influence circulating levels of certain inflammatory markers such as IL-6 which has been termed a myokine because of its high levels in skeletal muscles [39,41–43]. Certainly, strict adherence to protocols for sampling (i.e. fasting samples at a standardized time-point) may be more achievable in homogenous monocenter studies than in more challenging multicenter studies. Indeed, the increased variability attributed to a more heterogeneous sampling procedure in multicenter trials will likely attenuate the predictive value of an inflammatory marker modified by the above mentioned mechanisms. Still, a biomarker viable for use in clinical practice needs to be relatively stable, and should ideally not be affected by day-to-day, postprandial and diurnal variation. 3.2. Pre-analytical considerations Numerous factors may influence the results of plasma or serum analyses. Correct collection, processing and storage of blood samples are important considerations when evaluating the applicability of an inflammatory biomarker in the clinic [36,37,44–46]. The choice of serum or plasma may influence cytokine levels since platelets activated during clotting for serum preparation may release significant amounts of cytokines [36,37,44,45]. Importantly, this may not necessarily result in higher levels in HF patients. In fact, platelets from these patients are activated in vivo and the degranulated platelets may release lower levels of cytokines from their α-granules when serum is coagulated on the bench as illustrated by low serum levels of RANTES/CCL5 in various CV disorders. In addition, sample processing time may be an important factor since cytokines, and ligands in particular, have a relatively short half-life [46], may be produced by immune cells after collection or may be bound by receptors or affected by enzymatic activities [41,47]. Table 1 Circulating levels of inflammatory cytokines and association with age, gender and diurnal and postprandial variation.

TNFα sTNFR1 OPG IL6 Gp130 MCP-1 IL-8 CXCL16 CCL21 PTX-3

Typical range*

Age/gender**

Postprandial

Diurnal

0.5–5.0 pg/mL 1–5 ng/mL 0.5–10.0 ng/mL 1.0–30 pg/mL 100–800 ng/mL 0.01–0.5 ng/mL 1–10 pg/mL 0.5–2.0 ng/mL 0.2–1.0 ng/mL 0.5–140 ng/mL

↔[60,62] ↑[62] ↑[62,63]/♀[63] ↔[60] ↔[66] ↔[62,67] ↔[62] ↑♀[68] ↑[69] ↑♀[24]

↑[71,72] ↓[75] ↔[77] ↑[71,72] NA ↔[80] ↑[79] NA NA NA

↕[73,74] ↕[76] ↕ [78] ↕[74] ↔[79] NA NA NA NA NA

Range of *circulating levels and **association with age/gender in the prospective trials evaluated in the review. ↑increase, ↓decrease, ↔no association, diurnal variation, and ♀increased in females

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Also plasma collection offers some challenges as cytokine measurements may be affected by choice of anticoagulant. First, citrate and heparin plasma have been shown to affect IL-6 and TNFα levels [47–49]. Second, endotoxin may induce release of IL-6 and TNF in contaminated vacutainer tubes while EDTA inhibits endotoxin [49]. Third, heparin may release cytokines bound to heparin-sulfate on the surface of blood cells [50]. Finally, cytokine stability seems to be superior in EDTA plasma due to its function as a protease inhibitor [46]. Still, a recent study of a large cytokine panel in spiked serum and plasma samples, using collection tubes with different additives, showed that recovery ranged between 80% and 120% for all cytokines [51]. Furthermore, the study showed that serum might be the preferred medium for some cytokines, while plasma might be more suited for others. Therefore, quick processing may be important to accurately determine levels of cytokines and plasma collection with use of EDTA seems to bring the most consistent results, although no single sample type may be optimal for all cytokines. Nonetheless, these strict requirements are a limitation for the use of inflammatory cytokines in clinical practice. In general, most cytokines are quite stable during long-term storage at −80 °C [44,46,51]. The stability is to a larger degree affected by repeated freeze–thaw cycles. In some reports, cytokine concentrations have been found to be stable when blood is subjected to less than three freeze–thaw cycles, while others report great variability [44,46]. Certain cytokines (e.g. IL-6) are stable throughout multiple cycles, others might rise (e.g. TNFα) or fall (e.g. CXCL8) after only one or more rounds of freeze-thawing [44,46]. The issue of cytokine stability during long-term storage may not be relevant when evaluating the applicability of a biomarker for clinical use where bench life (i.e. stability at room temperature after separation) may be more crucial. However, the issue of long-term storage stability is highly relevant when evaluating the predictive value of cytokines since retrospectively analyzed prospective studies are a main source of information in this setting. As mentioned above, biomarkers that demonstrate desirable qualities for a clinically useful biomarker such as stability and durability will likely be affected less by these pre-analytical aspects. However, although a candidate marker may fail to make the transition to everyday clinical use, it may still prove useful in elucidating the biological underpinnings of HF. Thus, the pathophysiological relevance of a marker may be overlooked if it displays poor pre-analytical assay characteristics. Although more limited in their number of observations, monocenter studies, with stricter adherence to specific sampling protocols, may hold an advantage for evaluating markers in a biological context. 3.3. Analytical considerations Due to ease of use, high sensitivity (most cytokines can be detected at picogram levels) and, in general, high specificity, enzyme-linked innumosorbent assays (ELISAs) have, since their introduction in the 1970s, become the most widely used and best validated method for quantifying circulating cytokine concentrations [44,52]. In spite of its favorable traits, some caveats and considerations regarding the use of ELISAs are worth mentioning. Firstly, the quality and precision of ELISA antibodies and kits of different origin may show considerable variation, making direct comparisons of cytokine levels unreliable if the assays are not supplied by the same manufacturer [37,53]. Additionally, even kits from different production batches supplied by a single manufacturer may give rise to unwanted variability [53]. The lack of international standardization of age-adjusted normal ranges of many cytokines can also be a hindrance to ELISA interpretation [37,53,54]. In fact, there is a large gap between those measurements that are performed routinely at hospitals world-wide with international standardization and routine daily assessment of the procedure, and ELISA measurements that are performed in relation to different research project including those on biomarkers. Another limitation of immunoassays is related to what is actually being measured. Although the antibodies of an ELISA may very well be specific, the assay does not

Please cite this article as: Ueland T, et al, Inflammatory cytokines as biomarkers in heart failure, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.09.001

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necessarily differentiate between free cytokine, cytokine-soluble receptor complexes or cytokines associated with other binding proteins [41, 54]. Also, the nature of the cytokine itself might be of importance [41]. For instance, some cytokines are only active in a glycosylated form, whereas an antibody may be directed against the non-glycosylated form. Similarly, whether or not a cytokine is present in monomeric or in multimeric form might be biologically important, but the assay might not discriminate between these different states. Furthermore, the range of linear association between cytokine concentration and absorbance reading (i.e. the dynamic assay range) is often quite narrow, forcing the issue of sample dilution, which may influence the level of the cytokine itself, as well as its potential soluble receptors and natural inhibitors [52]. Multiplex assays have recently received attention since many different cytokines can be measured simultaneously in one specimen, which may be advantageous in a multimarker approach. However, as multiplex assays are designed to accommodate simultaneous measurement of many analytes, compromises are inevitably made with regard to incubation time, buffers, specimen dilution and specimen type for the individual analytes. Multiplex assays may therefore be most useful as screening tools. At present, the major limitation for cytokine measurements, regardless of analytical methodology, is the lack of availability of commercial assays. Furthermore, none of the cytokines or cytokine receptors mentioned above are available on an automated assay platform typically utilized at a hospital laboratory, although a benchtop immunoassay analyzer with assays for IL-1β, IL-6, IL-8 and TNFα exists. However, the analyte volume is large and sensitivity average [55]. 4. Cytokines and cytokine receptors as predictors of long-term adverse outcome in HF 4.1. Current circulating biomarkers in HF In addition to displaying the qualities above related to analytical issues, a marker viable for implementation in the clinic needs to display consistent and solid association to the disease or outcome of interest and add to or improve on existing tests. Currently, only a limited number of markers are frequently used in the HF setting [56,57], with natriuretic peptides, namely BNP/NT-proBNP, being the only HF markers achieving ESC guideline recommendation [11]. In fact, few other markers have impacted HF care since the clinical implementation of BNP/NT-proBNP over the last decade. However, in spite of their prognostic power, even the use of natriuretic peptides has limitations related to poor performance in certain demographics, such as patients with obesity and renal impairment, as well as the interpretation of midrange “gray-zone” levels [58]. In addition, the natriuretic peptides do not reflect all underlying pathological processes in the failing myocardium, and only modestly improve well-constructed multivariable risk models [59]. Indeed, measurement of hs-cTn provides significant prognostic information independent of NT-proBNP in chronic HF, and simultaneous measurement of hs-cTn and NT-proBNP improves mortality risk stratification in these patients [15–17]. 4.2. Statistical considerations Any work with potential biomarkers requires a statistical evaluation of the marker in question. Such evaluations might be simple comparisons of patient versus control levels, where a simple Student t-test or Mann–Whitney U-test is sufficient. However, when inherent properties of the marker are to be assessed, more advanced statistical methods are needed. The Cox proportional hazard regression model is the most widely used and accepted method to evaluate survival in clinical medicine, and using a stepwise approach allows for evaluation of effect or loss of effect of these variables on the outcome [60]. Caution must be exercised when including variables into multivariable regression models so that the number of events per variable maintains a

reasonable ratio, often no lower than 10 [61,62]. However, the evaluation of circulating biomarkers is frequently performed retrospectively on a prospectively conducted study, where the endpoints as well as the statistical approach to assess their predictive power are prespecified. This is an advantage as these trials typically include appropriate predetermined clinical and biochemical covariates (e.g. NT-proBNP) and the number of covariates is adjusted to the prevalence of events for the different outcome measures. An important feature of diagnostic biomarkers is their ability to discriminate between diseased and non-diseased individuals. This characteristic is given by the c statistic (i.e. area under the ROC curve) [63]. However, the c statistic is not necessarily well suited in a prognostic setting or in risk stratification where, as opposed to the diagnostic setting, the future disease has not yet occurred, and can only be estimated as a probability or risk [63]. It is inherently difficult to improve on already good risk prediction models and even a perfectly calibrated model can only achieve c statistic values well below the theoretical maximum of 1 (range 0.5–1.0). Hence, sole reliance on ROC might preclude the implementation of novel risk markers of clinical usefulness [63]. In response to this challenge, several new methods of assessing the additive or incremental value of novel biomarkers have been proposed [64]. One of these new approaches is the calculation of the Net Reclassification Improvement (NRI), which indicates how a new marker might aid in reclassifying patients to a higher or lower risk grade [64]. While this method might provide information beyond multivariable regression and c statistics, and is not affected by calibration or goodness of the baseline model, its ranges of meaningful improvement are still undetermined [64]. 4.3. Search strategy Although there is firm and consistent evidence that the inflammatory markers focused in this review are associated with adverse outcome in a broad range of patients with HF, most studies are limited in size and lack appropriate adjustment with NT-proBNP. As for the diagnosis of HF, there is no evidence that these cytokines can surpass NT-proBNP [65,66]. In this review we focus on the prognostic performance of inflammatory cytokines in larger prospective studies with appropriate multivariable adjustments including conventional risk factors and NT-proBNP. Search strategy was “heart failure AND cytokine name AND (biomarker OR marker OR serum OR plasma OR circulating OR systemic) AND (outcome OR death OR mortality OR adverse OR endpoint)”. We originally included studies with 500 or more patients to allow sufficient power for interaction tests (i.e. cytokine interaction with NT-proBNP), although even this number is quite low depending on outcome variable. However, due to few studies fulfilling the criteria we have included a studies with above 300 patients. As shown in Table 2 there are relatively few large studies relating cytokine levels to long-term adverse outcome in HF patients. Our group and others have analyzed the role of a number of inflammatory cytokines and related molecules in prediction of predefined outcomes in substudies of the Controlled Rosuvastatin Multinational Study in Heart Failure (CORONA), comprising approximately 1460 elderly patients with chronic HF of ischemic etiology [67], and the GISSI-Heart Failure (GISSI-HF) trial with 1230 patients with chronic and stable HF of any etiology [68]. 4.4. Evaluation of inflammatory biomarkers Miettinen et al. studied the prognostic role of TNFα and IL-6 using high sensitivity assays in 465 patients with acute HF and found that TNFα predicted all-cause mortality in an adjusted model [69], with a stronger association in patients without severe cardiac and kidney dysfunction [70]. In chronic HF, Nymo et al. failed to detect an association between TNFα levels and multiple adverse outcomes in CORONA although the study was limited by a first generation multiplex assay for

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Table 2 Inflammatory cytokines as predictors of adverse outcome.

TNFα sTNFR1 OPG IL-6

Gp130 MCP-1 IL-8 CXCL16 CCL21 PTX-3

Author, year

Size, follow-up years

Outcome

Adjustment

Assay

Finding

Miettinen 2008 [60] Nymo 2014 [62] Nymo 2014 [62] Røysland 2010 [63] Ueland 2011 [64] Miettinen 2008 [60] Liu 2011 [65] Askevold 2013 [66] Askevold 2013 [66]

n n n n n n n n n

= = = = = = = = =

465, 1 yr 1464, 2.8 yr 1464, 2.8 yr 1229, 3.9 yr 1464, 2.8 yr 465, 1 yr 548, 1.5 yr 1464, 2.8 yr 1452, 2.8 yr

AHF, All-cause death Multiple Multiple All-cause death/rehosp. CV Multiple AHF, All-cause death Death/WHF Multiple Multiple

Conv. +NT-proBNP Conv. + NT-proBNP, CRP Conv. + NT-proBNP, CRP Conv. + BNP, CRP Conv. + NT-proBNP, CRP Conv. +NT-proBNP Stepwise with NT-proBNP Conv. + NT-proBNP, CRP Conv. + NT-proBNP, CRP

ELISA MP assay* ELISA ELISA ELISA ELISA Proteome array MP assay** ELISA

Hohensinner 2010 [67] Nymo 2014 [62] Nymo 2014 [62] Dahl 2013 [68] Ueland 2013 [69] Latini 2012 [24]

n n n n n n

= = = = = =

351, 1.3 yr 1464, 2.8 yr 1464, 2.8 yr 1457, 2.8 yr 2601, 2.8 & 3.9 yr 2690, 2.8 & 3.9 yr

All-cause death Multiple Multiple Multiple Multiple Multiple

Conv. + BNP Conv. + NT-proBNP, CRP Conv. + NT-proBNP, CRP Conv. + NT-proBNP, CRP Conv. + NT-proBNP, CRP Conv. + NT-proBNP, CRP

ELISA MP assay MP assay ELISA ELISA ELISA

↑All-cause death ↔ ↑All-cause mortality ↔ ↑All-cause/WHF ↑All-cause death ↔ ↔ ↑All-cause, CV mortality, death from WHF ↑All-cause death ↔ ↑All outcome ↔ ↑All-cause and CV mortality ↑All-cause and CV mortality, WHF

MP, multiplex assay *85% below LLD, **18% below LLD; AHF, acute heart failure; CRP, C-reactive protein; CV, cardiovascular; ELISA, enzyme-linked immunosorbent assay; NT-proBNP, N-terminal prohormone of brain natriuretic peptide; WHF, rehospitalization for worsening of heart failure; Conv., forced adjustment with conventional risk factors (e.g. age, gender, diabetes,eGFR, NYHA, medical history and current medication.

TNFα with poor sensitivity [71]. However, the study demonstrated an association between sTNFR1 levels and all-cause mortality although discriminatory properties (i.e. C-index and NRI) were not improved [71]. For the TNF receptor superfamily member OPG, which circulates at high levels making detection easy, Røysland et al. found no association between circulating levels and all-cause death or hospitalization due to CV causes following adjustment for NT-proBNP and CRP in GISSI [72]. However, in CORONA, representing a more homogenous population (i.e., elderly and all ischemic HF) we found an association between serum OPG levels and hospitalization due to worsening of HF as well as the composite of all-cause mortality and HF hospitalization with an increase in discrimination by C-statistics [73]. For the IL-6 family, Meittinen et al. demonstrated that IL-6 above a certain cut-off predicted all-cause mortality in 465 patients with acute HF, while Liu et al. found no association between IL-6 levels, determined by a proteasome array, and all-cause mortality or HF hospitalization in 548 patients with chronic HF [74]. Correspondingly, Askevold et al. found no association between serum IL-6 and multiple outcome measured in CORONA although the findings were again limited by a multiplex assay with limited sensitivity [75]. In contrast, gp130 levels predicted multiple fatal outcomes in the study by Askevold [75]. Hohensinner et al., found an association between high levels of the prototypical chemokine MCP-1 and all-cause mortality in 351 patients with HF, although discriminatory powers were not tested [76]. IL-8 remained a significant predictor of all outcomes measured in CORONA (except the coronary endpoint) after adjustment and led to a significant improvement in net reclassification for all-cause mortality and CV hospitalization, but only a borderline significant improvement for the primary endpoint, CV mortality, and the composite endpoint WHF hospitalization or CV mortality [71]. In contrast, no association was found between levels of MCP-1 [71] or CXCL16 [77] and outcome after full multivariable adjustment in the same population, although an association with mortality persisted after adjustment for change in CXCL16 from baseline to 3 months. CCL21 was associated with higher risk of all-cause and CV mortality in the combined GISSI and CORONA trials, with similar influence on risk prediction when analyzed separately, and with modest but significant impact on the discriminatory properties [78]. Finally, baseline and 3 month changes in PTX3 were associated with a higher risk of all-cause and CV mortality, and hospitalization for WHF in a combined analysis of the GISSI and CORONA trials, with marginal improvements in discrimination [28]. To summarize these studies, several cytokines are associated with fatal outcome and/or HF hospitalization. Different results were obtained in acute vs. chronic HF (e.g. for TNFα and IL-6) but may also be related to type and sensitivity of assay (e.g. high sensitivity ELISA vs. multiplex). In

addition, patient demographics differed (e.g. age and etiology) between the studies. For cytokines that remain significant in multivariable analysis with NT-proBNP and CRP, the gain in discriminatory power is modest at best. However, the marginal incremental value of inflammatory cytokines in HF progression is not surprising given the limited value even NT-proBNP offers beyond any well-constructed multivariable risk model [59]. 5. Multimarker strategies Although NT-proBNP and hs-cTn are strong biomarkers in HF, they do not reflect all pathogenic processes underlying this complex disorder. While measurements of individual cytokines are unlikely to impact risk stratification of HF patients in a clinically meaningful way, assaying global patterns of cytokines and other biomarkers may yield more relevant biological information. As several mediators are involved in the development and progression of HF, at least partly through different mechanisms and at different levels, a combination of circulating levels of multiple markers could potentially identify subjects with clinically significant risk with a high degree of accuracy. Such an approach could also be beneficial for the selection of individualized therapy. In the study by Mietinnen et al., a combination of cytokines (i.e. IL-6 or TNFα) and NT-proBNP enabled a more comprehensive risk stratification in acute HF [69], while no clear evidence of enhanced risk prediction was found when combing IL-8 and NT-proBNP [71]. Although, a dual or multiple biomarker analysis may appear to be useful tool in the prognostic assessment of HF, caution is needed when interpreting the results. First, the markers should demonstrate increased discriminatory power compared to the individual markers alone. Second, when combining markers based on cut-offs (e.g. third tertile NT-proBNP and third tertile IL-6), the increased predictive capacity often comes from correlation between the weakest and the strongest marker, and the model still performs worse than a model including the strongest marker in an optimal fashion (e.g. continuous). 6. The search for new inflammatory biomarkers in HF Several of the cytokines, and in particular ligands, discussed in this review demonstrate poor analytical characteristics. In particular, low level ligands such as TNF and IL-6 are challenging to measure accurately and display considerable diurnal and post-prandial variation. However, there is little doubt that IL-6 may play an important role in HF progression and may constitute a mediator rather than a viable marker in HF. Hence, although the discrimination between marker and mediator of disease may be difficult, the search for putative markers is still

Please cite this article as: Ueland T, et al, Inflammatory cytokines as biomarkers in heart failure, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.09.001

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important to illuminate disease mechanisms poorly reflected by today's state-of-the-art biomarkers. As most current biomarkers participate in pathways known to be associated with HF progression, the greatest increments in predictive value and new insight might be gained by searching outside of established pathological pathways [79]. Although some of the cytokine receptors such as gp130 and OPG may hold promise, their benefit in prognostication is limited compared to the natriuretic peptides and cardiac troponins. While logical from a clinical point of view, such comparisons may be unfortunate from a mechanistic standpoint. Despite their usefulness, the natriuretic peptides have several limitations [58,59]. However, they perform quite well in statistical evaluations of prognostic utility, and may therefore preclude further investigations into novel markers if novel markers are evaluated on statistical basis alone. Clearly, in a complex clinical entity such as HF, no single marker is capable of reflecting all ongoing pathological processes. Therefore, even statistically inferior markers could be worth pursuing if they expand our current understanding of HF pathophysiology. Moreover, as in cancer medicine, not all therapy is suitable in all individuals and it is possible that cytokine profiling could be part of strategy in HF patients when it comes to personalized medicine. 7. Conclusion No single inflammatory cytokine provides sufficient discrimination to justify the transition to everyday clinical use as a prognosticator in HF. However, while subjecting potential new HF markers to rigorous comparisons with “gold-standard” markers, such as NT-proBNP, using receiver operating characteristics (ROCs) and HF risk models, makes sense from a clinical standpoint, it may pose a threat to a broadening of mechanistic insight if the new markers are dismissed solely on account of lower statistical power. Indeed, the “inflammation era” in HF research was triggered the by measurement of circulating inflammatory factors. References [1] Jessup M, Brozena S. Heart failure. N Engl J Med 2003;348:2007–18. [2] Mann DL, Deswal A, Bozkurt B, Torre-Amione G. New therapeutics for chronic heart failure. Annu Rev Med 2002;53:59–74. [3] Aukrust P, Ueland T, Muller F, et al. Elevated circulating levels of C-C chemokines in patients with congestive heart failure. Circulation 1998;97:1136–43. [4] Aukrust P, Ueland T, Lien E, et al. Cytokine network in congestive heart failure secondary to ischemic or idiopathic dilated cardiomyopathy. Am J Cardiol 1999;83: 376–82. [5] 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. [6] Hofmann U, Frantz S. How can we cure a heart “in flame”? A translational view on inflammation in heart failure. Basic Res Cardiol 2013;108:356. [7] Bozkurt B, Mann DL, Deswal A. Biomarkers of inflammation in heart failure. Heart Fail Rev 2010;15:331–41. [8] von HS, Schefold JC, Lainscak M, Doehner W, Anker SD. Inflammatory biomarkers in heart failure revisited: much more than innocent bystanders. Heart Fail Clin 2009;5: 549–60. [9] Vistnes M, Christensen G, Omland T. Multiple cytokine biomarkers in heart failure. Expert Rev Mol Diagn 2010;10:147–57. [10] Hartupee J, Mann DL. Positioning of inflammatory biomarkers in the heart failure landscape. J Cardiovasc Transl Res 2013;6:485–92. [11] McMurray JJ, Adamopoulos S, Anker SD, et al. ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur Heart J 2012;33:1787–847. [12] Maisel AS, Krishnaswamy P, Nowak RM, et al. Rapid measurement of B-type natriuretic peptide in the emergency diagnosis of heart failure. N Engl J Med 2002;347: 161–7. [13] Felker GM, Hasselblad V, Hernandez AF, O'Connor CM. Biomarker-guided therapy in chronic heart failure: a meta-analysis of randomized controlled trials. Am Heart J 2009;158:422–30. [14] Masson S, Latini R. Amino-terminal pro-B-type natriuretic peptides and prognosis in chronic heart failure. Am J Cardiol 2008;101:56–60. [15] de Antonio M, Lupon J, Galan A, et al. Head-to-head comparison of high-sensitivity troponin T and sensitive-contemporary troponin I regarding heart failure risk stratification. Clin Chim Acta 2013;426:18–24.

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Please cite this article as: Ueland T, et al, Inflammatory cytokines as biomarkers in heart failure, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.09.001