REVIEWS Clinical usefulness of novel prognostic biomarkers in patients on hemodialysis Alberto Ortiz, Ziad A. Massy, Danilo Fliser, Bengt Lindholm, Andrzej Wiecek, Alberto Martínez-Castelao, Adrian Covic, David Goldsmith, Gültekin Süleymanlar, Gérard M. London and Carmine Zoccali Abstract | Prognosis, risk stratification and monitoring the effects of treatment are fundamental elements in the decision-making process when implementing prevention strategies for chronic kidney disease. The use of biomarkers is increasingly proposed as a method to refine risk stratification and guide therapy. In this Review, we present basic concepts regarding the validation of biomarkers and highlight difficulties inherent to the identification of useful new biomarkers in patients on hemodialysis. We focus on prognostic biomarkers that have been consistently linked to survival in this group of patients. To date, no biomarker has had sufficient full-scale testing to qualify as a useful addition to standard prognostic factors or to guide the prescription of specific treatments in this population. Furthermore, little information exists on the relative strength of various biomarkers for their prediction of mortality. A multimarker approach might refine prognosis in patients on hemodialysis, but this concept needs to be properly evaluated in large longitudinal studies and clinical trials. The potential of proteomics for the identification and study of new biomarkers in the pathophysiology of cardiovascular disease in patients with end-stage renal disease is also discussed. Ortiz, A. et al. Nat. Rev. Nephrol. 8, 141–150 (2012); published online 1 November 2011; doi:10.1038/nrneph.2011.170
Introduction Prognosis and risk stratification are fundamental elements in the decision-making process when implementing prevention strategies in clinical practice. This tenet applies both to individuals at high risk of adverse clinical outcomes and to populations at low or intermediate risk. Interventions at the community level that aim to prevent cardiovascular events, for example, rely on estimates of the probability of these events occurring, as calculated by standard risk scores such as the Framingham Risk Score.1 The intensity of interventions needed to prevent the recurrence of events in individuals who have had a myocardial infarction can be tailored to the individual’s risk as calculated by scores specific to this condition.2 Traditional risk factors explain a large fraction of the risk of future events in the community.3 Similarly, simple and inexpensive risk scores explain most of the risk of new (repeated) cardiovascular events in patients who have had a myocardial infarction. However, risk estimates based on traditional risk factors and simple clinical information remain fairly imprecise instruments for establishing prognosis. Therefore, intensive research on biomarkers is being pursued to complement risk stratification based on risk scores. In addition to prognosis, biomarker research has the potential for improving disease monitoring and for providing objective measures of the effect of treatments on targeted pathophysiological phenomena. End-stage renal disease (ESRD) is a condition with a unique risk factor profile and predictive models Competing interests The authors declare no competing interests.
developed in the general population cannot therefore be applied to patients with ESRD. Moreover, the patho physiology of major comorbidities, such as athero sclerotic complications in patients with chronic kidney disease (CKD), differs substantially from that of the general population. Patients on hemodialysis represent a highly selected population with a high prevalence of elderly individuals who have multiple comorbidities and have survived long enough to develop ESRD. Specific risk stratification methods such as the Khan Index have been proposed for patients with ESRD;4 however, these scores remain imperfect prognostic instruments. Biomarker research on novel risk factors that specifically increase the accuracy of prognosis in patients with ESRD is a flourishing enterprise, with the number of publications in this field growing at an exponential rate. In this Review, we will present basic concepts about the accuracy and clinical usefulness of novel biochemical markers for the prognosis of patients on hemodialysis, highlighting current difficulties inherent to the identifica tion of new prognostic biomarkers in those patients. We also consider biomarkers that have been consistently linked to survival in ESRD and describe current efforts to increase the precision of clinical predictions by using a multimarker approach. We specifically focus on survival because the definition of nonfatal events is generally heterogeneous and subjective, and the adjudication of events is a complex undertaking, even in the framework of clinical trials that adopt central adjudication of such events.5 The potential of proteomics for the identification and study of new biomarkers in the pathophysiology
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Universidad Autónoma de Madrid, Madrid, Spain (A. Ortiz). University of Picardie Jules Verne, Amiens, France (Z. A. Massy). Saarland University Medical Center, Homburg/Saar, Germany (D. Fliser). Karolinska Institutet, Stockholm, Sweden (B. Lindholm). Medical University of Silesia, Katowice, Poland (A. Wiecek). Hospital Universitario de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain (A. Martínez-Castelao). C. I. Parhon University Hospital, Gr. T. Popa University of Medicine and Pharmacy, Iasi, Romania (A. Covic). Guy’s and St Thomas’ NHS Foundation Hospital, King’s Health Partners, London, UK (D. Goldsmith). Akdeniz University Medical School, Antalya, Turkey (G. Süleymanlar). Hôpital Européen Georges Pompidou, Paris, France (G. M. London). Nephrology, Dialysis and Transplantation Unit and CNR-IBIM Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, Reggio Calabria, Italy (C. Zoccali). Correspondence to: C. Zoccali
[email protected]
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REVIEWS Key points ■■ Novel biomarkers have the potential to refine risk stratification based on standard risk scores and to guide therapy in patients on hemodialysis ■■ Biomarkers of chronic kidney disease-related mineral and bone disorders, protein–energy wasting, inflammation and myocardial injury or dysfunction have been linked with decreased survival ■■ To date, no biomarker has had sufficient full-scale testing to qualify as a useful addition to standard prognostic factors or to guide therapy in patients on hemodialysis ■■ A multimarker approach holds potential for refining prognosis in patients on hemodialysis, but this concept still needs to be properly evaluated in large cohorts and in clinical trials ■■ Proteomics enables the simultaneous identification and evaluation of new biomarkers in the pathophysiology of established complications of kidney failure ■■ Biomarkers can be applied to improve the design of clinical trials and to target specific subpopulations among patients on hemodialysis
of cardiovascular disease, particularly cardiovascular disease in patients with ESRD, is also discussed.
Biomarkers in clinical practice The NIH defines a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”.6 In theory, biomarkers can be useful in all major components of clinical medicine, from diagnosis to prognosis, therapy and prevention. However, it should be noted that an individual biomarker might not be suitable for all of these components. For example, a biomarker might fail to predict future events, but could still be helpful for disease monitoring if it adequately reflects a relevant under lying mechanism implicated in the pathophysiology of disease. Prognosis is by far the most investigated area in biomarker research in patients on hemodialysis. The progression of disease and future complications are often predicted on the basis of simple, easily obtainable patient characteristics (biomarkers), such as blood pressure, serum glucose levels, serum cholesterol, proteinuria and alterations in urine sediment. The predictive power of single biomarkers can be improved by combining these with other factors, such as age and sex, to form risk scores. The Framingham Risk Score, for example, is based on age, sex, smoking habits, blood pressure and total cholesterol levels, and represents the simplest score applied at a community level to calculate the 10-year risk of myocardial infarction and coronary death in a given individual.1 In general, biomarkers are selected because they reflect alterations that are part of a process whereby a noxious exposure leads to clinical events. They might also be useful for tailoring therapeutic decisions and for monitoring the effect of treatments. Hyperglycemia is itself a causal risk factor for cardiovascular and renal damage and it can be corrected by administration of insulin and hypoglycemic drugs. Therefore, serum glucose levels are used as a biomarker of the severity of diabetes mellitus 142 | MARCH 2012 | VOLUME 8
and for monitoring the effect of therapy in this disease. However, biomarkers are not necessarily causal risk factors. For example, creatinine levels are an excellent biomarker of the severity of CKD and are of proven utility for monitoring therapies applied to patients with this condition, but abnormal creatinine levels are not a cause of CKD. In prognostic research it is the precision of the prediction rather than the nature (causal versus not causal) of the link between the biomarker and the clinical outcome of interest that is important. The fundamental difference between prognostic and etiological research is described elsewhere. 7 Whereas etiological research investigates the causal relationship between the determinants of a given disease or a given clinical outcome, the focus of prognostic research is that of producing estimates of the probability of a given clinical outcome, independently of the nature of the link between the predictor and the outcome. The analytical approaches to etiology and prognosis partly overlap because multivariate analyses are applied both to explore etiological hypotheses and to estimate the risk associated with disease. However, the approach and interpretation of statistical modeling in these two areas of clinical epidemiology differ substantially.7 Four criteria are currently applied for the validation of prognostic biomarkers:8 accuracy, which is the ability to reliably identify individuals at excessive risk of the adverse clinical outcome of interest; simplicity, such as ease of measurement; cost, as biomarkers should be reasonably inexpensive and cost-effective; and relevance of the information provided by the biomarker, as this information should be additive to that conveyed by established risk factors. The first of these criteria, accuracy, refers to the agreement between the prediction of a given outcome by the biomarker and the actual occurrence of the same outcome.9 Accuracy implies that the biomarker discriminates individuals who go on to develop the outcome of interest from those who do not (discrimination), that it correctly estimates the probability of the same outcome at an individual level (calibration), and that it increases the proportion of individuals correctly classified as having, or not having, the outcome of interest as compared with categorization by risk scores and established biomarkers (reclassification). Discrimination is tested by receiver operating characteristic (ROC) curves.9 The relevance of information provided by the biomarker is of paramount importance. Prognostic biomarkers should possess predictive power beyond that of the Framingham Risk Score when applied in the community, and beyond that of other (specific) risk scores such as the APACHE score in intensive care patients10 or the Khan Index in patients with ESRD.4 As mentioned previously, the Framingham Risk Score enables a reasonably high discrimination (about 75%) of individuals at excessive cardiovascular risk in the general population. Studies performed so far show that new biomarkers add modest (~1–2%) predictive power to standard risk factors.4 For example, in the Framingham Heart Study cohort, C‑reactive protein (CRP) added www.nature.com/nrneph
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REVIEWS just 0.002% discrimination power to predictions made on the basis of Framingham risk factors, but was useful for risk reclassificat ion of coronary heart disease events.11 However, these findings were not replicated in other studies and a meta-analysis showed that this biomarker does not consistently improve performance of the Framingham Risk Score in terms of discrimination and reclassification.12 Furthermore, cost-effectiveness analyses showed that implementing therapeutic strategies based on measurement of CRP levels, such as in the Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) study,13 would make statin prescribing for primary prevention more common and less efficient than it is with current guidelines.14 An accurate definition of the clinical phenotype being investigated is fundamental in biomarker research. The complexity of many conditions, such as atherosclerotic complications, can make this definition difficult. Atherosclerosis is a multistage process that includes initiation and progression of the atherosclerotic plaque, plaque fissure and rupture, thrombosis and organ-specific clinical events (for example, myocardial infarction). The use of composite end points (such as cardiovascular events encompassing disparate complications that include coronary artery disease events, cerebrovascular events and peripheral vascular disease events) in biomarker research can be problematic, particularly when it is not possible to sensibly capture the individual links between the biomarker being tested and discrete clinical events, as often applies to major studies in ESRD.15,16
Biomarkers in patients on hemodialysis Patients with ESRD exhibit accelerated aging and are therefore likely to have a unique biomarker profile.17,18 For this reason, biomarker research in patients on hemodialysis has flourished over the past 20 years. In this section we focus on biochemical markers relevant to the prognosis of patients with ESRD and, wherever possible, we highlight the relevance of these biomarkers for cardiovascular risk prediction and for monitoring the use of treatment in these patients. We have categorized these biomarkers into four groups: biomarkers related to the CKD-related mineral and bone disorder (CKD– MBD), which is an internationally accepted designation on which a Kidney Disease: Improving Global Outcomes guideline has focused;19 biomarkers of protein–energy wasting and inflammation (risk factors for mortality that are of paramount importance in patients with ESRD);20 biomarkers of myocardial injury and dysfunction; and undefined or mixed biomarkers.
CKD–MBD Although the list of biomarkers of CKD–MBD is long and growing, only a few studies link individual biomarkers in this category to death. For example, osteoprotegerin has been intensively studied in patients with ESRD, but only four studies have reported on the association between circulating levels of this compound and all-cause or cardio vascular death in patients on hemodialysis.21–24 Since only
one of these analyses reported data on all-cause death and focused exclusively on hemodialysis patients,21 we do not discuss osteoprotegerin further. Similarly, the association of osteopontin and bone morphogenetic protein 7 with vascular calcification in patients with ESRD has been investigated, but in no study has the association of these proteins with survival been examined. Fibroblast growth factor 23 (FGF23) and alkaline phosphatase are the only biomarkers whose prognostic potential for death has been confirmed in at least two different study populations. FGF23 is a phosphaturic hormone synthesized in bone cells that inhibits renal production of 1,25-dihydroxyvitamin D.25,26 Increased FGF23 levels are independently associated with mortality in patients on hemodialysis.25 Remarkably, in a large cohort study by Gutiérrez et al., multivariate analyses adjusted for demographic factors, standard laboratory values, etiology of renal failure, blood pressure, BMI, coexisting conditions, vascular access and serum phosphate level only modestly affected the strength of the association between FGF23 and death.25 However, given the observational nature of this study, it remains uncertain whether FGF23 is a causal risk factor for death. High FGF23 levels might cause vascular damage by the nonselective activation of receptors implicated in left ventricular hypertrophy and atherosclerosis,27 but high FGF23 levels could also reflect damage attributable to prolonged exposure to high phosphate levels. Because reducing phosphate intake can lower FGF23 levels, the measurement of this biomarker may be useful to set individual phosphate targets in patients with ESRD. In other words, if the associa tion between FGF23 and death is causal, patients with normal phosphate levels but high FGF23 levels might benefit from a lower phosphate level than that currently recommended, a hypothesis that could be examined in a clinical trial. No study has as yet tested the prognostic value of FGF23 using appropriate statistical analyses that consider discrimination, calibration and reclassification. Furthermore, in a small study in a Northern European cohort, FGF23 levels predicted mortality only in men who had experienced cardiovascular events.28 Serum alkaline phosphatase is an established marker of bone turnover that has emerged as an independent risk factor for death.29,30 In the seminal study by Regidor et al., alkaline phosphatase concentration had a doseresponse relationship with mortality that was independent of other CKD–MBD biomarkers, such as serum levels of phosphate, calcium and parathyroid hormone (PTH), and was not confounded by the presence of liver disease.29 Of note, the prognostic power of alkaline phosphatase was robust and validated in various subgroups of patients on hemodialysis, including those with a history of cardiovascular events, those with low serum albumin levels and patients with diabetes mellitus. The simplicity and ease of measurement of this biomarker is remarkable. In contrast to PTH, alkaline phosphatase is not confounded by obesity and the measurement of serum levels of this enzyme is better standardized than current PTH assays. Randomized trials are needed to test the nature (causal or not causal) of the relationship
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REVIEWS between alkaline phosphatase and survival in this group of patients. Furthermore, the prognostic accuracy of alkaline phosphatase for death should be formally evaluated by discrimination testing as well as by appropriate calibration and reclassification analyses.
Protein–energy wasting and inflammation The most widely studied marker associated with protein– energy wasting and inflammation is CRP.31–40 In a review of studies examining the relationship between serum proteins and inflammatory markers with mortality in patients on hemodialysis, high CRP levels were found to predict all-cause and cardiovascular mortality in 57% and 38% of studies, respectively; a meta-analysis of these studies showed a weak but significant association of CRP with all-cause mortality but not with cardiovascular mortality.35 However, substantial heterogeneity was found between the studies and the degree of statistical adjustment was very variable, with some studies including two or three biomarkers of inflammation simultaneously, an approach that is likely to produce an underestimation of the predictive value of CRP. In a study involving three European centers, CRP emerged as a strong predictor of death in males but not in females with ESRD.33 In the Netherlands Cooperative Study on the Adequacy of Dialysis‑2 (NECOSAD‑2),34 the risk of death associated with high CRP levels was particularly strong in patients with malnutrition and past cardiovascular events. In the Mapping of Inflammatory Markers in Chronic Kidney Disease (MIMICK) Study, serial measurements of CRP levels in patients on hemodialysis provided additional information compared with a single measurement and the value of repeated measurement of CRP levels was subsequently confirmed in an analysis based on two separate dialysis cohorts.40 As mentioned earlier, heterogeneity limits the value of meta-analyses of available studies and can even distort the appreciation of the true relationship between CRP and clinical events. Overall, studies in well-characterized hemodialysis populations indicate that CRP levels are a consistent predictor of survival, particularly in males and in high-risk patients with protein–energy wasting and cardiovascular complications. The CRP assay is relatively inexpensive and well standardized,36 but it remains uncertain whether this biomarker adds discrimination power to standard risk scores. In studies that reported the area under the ROC curve, the discrimination of CRP for mortality ranged from 0.69 to 0.74. 37–39 However, all of these studies failed to test whether CRP adds prognostic value to simple clinical risk scores and no study has tested CRP as a prognostic marker by the reclassification method. Importantly, the placebo-controlled, randomized clinical trial An Assessment of Survival and Cardiovascular Events (AURORA),38 which tested the effect of rosuva statin—a lipid-lowering drug with anti-inflammatory effects—in patients on hemodialysis, failed to show an improvement in mortality and cardiovascular complica tions with rosuvastatin treatment. The lack of effect was equally evident across CRP strata. This finding indicates 144 | MARCH 2012 | VOLUME 8
that CRP levels may not help nephrologists decide whether to prescribe this drug, or other drugs with the same properties, in patients on hemodialysis. Interleukin (IL)‑6 is a stronger marker of risk of death than is CRP in patients with ESRD.41,42 A direct link between IL‑6 and mortality was reported in five studies that tested this association.41–45 The area under the ROC curve for IL‑6 for predicting mortality in patients on hemodialysis ranges from 0.59 to 0.71.38,41 However, as is the case for CRP, studies that document the usefulness of this biomarker as compared with simple, inexpensive risk scores are still lacking. This consideration is relevant because the cost of measuring IL‑6 is high (about US$100). Tumor necrosis factor (TNF) is a weaker predictor of mortality than is IL‑641 and is at least as expensive to test. Furthermore, IL‑6 or TNF levels have not yet been tested as a method to guide therapy in patients on hemodialysis. High or low plasma levels of a host of other inflammatory molecules have been associated with mortality in patients on hemodialysis (Box 1), but independent confirmation of their prognostic power in a second, external cohort (generalizability criterion) is still lacking. Fetuin A is a unique biomarker because it sits at the crossroads between CKD–MBD and inflammation. This protein is an inverse marker of inflammation (low levels denote inflammation) and a circulating inhibitor of ectopic calcification. Plasma fetuin A levels are low in patients on hemodialysis and have been associated with all-cause and cardiovascular mortality in four studies,46–49 but studies that document its usefulness in clinical practice are lacking. A complex of fetuin A and calcium was more strongly associated with coronary calcification than fetuin A alone in a series of 73 patients with CKD and diabetes mellitus.50 Only one study provided information on the area under the ROC curve (0.71)49 but again there was a lack of comparison with simple risk scores and no reclassification analysis. Therefore, the prognostic implication of fetuin A for mortality remains unclear.
Myocardial injury or dysfunction The prevalence of left ventricular hypertrophy is very high (about 75%) in patients on hemodialysis and about 30–40% of these patients have clinical evidence of heart failure and/or coronary artery disease. Cardiac bio markers have been measured in numerous studies in patients with ESRD and a search of the literature limited to major biomarkers produced over 400 hits, the vast majority of which did not test the association of these biomarkers with hard clinical end points. The application of prognostic cardiac biomarkers in patients with ESRD has been reviewed elsewhere.51 The steady-state plasma concentration of troponin T (TnT, an indicator of myocardial cell injury) is distinctly elevated in patients with ESRD and therefore the interpreta tion of TnT measurements in the acute setting demands knowledge of a baseline, steady-state value. Similarly, both brain natriuretic peptide (BNP) and N‑terminal (NT)-proBNP levels are universally increased in asymptomatic and symptomatic patients on hemodialysis. www.nature.com/nrneph
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REVIEWS However, plasma accumulation of these peptides attributable to abolished renal clearance levels does not detract from their potential value for risk stratification. Indeed, BNP, NT‑proBNP and TnT predict decreased survival and cardiovascular events.52–59 Troponin I was tested in some studies,37,60–62 but assays of this protein are still less rigorously standardized than they are for TnT.63 The area under the ROC curve for these biomarkers ranges from 0.59 to 0.76,37,39,52 but studies are still lacking. Echocardiography is recommended by current guidelines as a fundamental tool for profiling cardiovascular disease in patients with ESRD. Both BNP (or NT‑proBNP) and TnT levels displayed predictive value for adverse clinical outcomes additive to echocardio graphy in one study.55 As alluded to before, for a biomarker to be recommended in clinical practice, formal proof is needed that its systematic use leads to improved clinical outcomes. Even though this issue remains highly controversial,64 such proof has been provided for BNP in one study of non-uremic patients with heart failure.65 No trial has tested whether a treatment policy based on BNP/NT-proBNP and/or TnT levels reduces the high risk of death and cardiovascular complications in patients on hemodialysis. For this reason, no firm recommendation has been made about the use of cardiac biomarkers in patients with ESRD. Even after 20 years of intensive clinical research, doubts remain about the diagnostic utility of cardiac natriuretic peptides outside the acute setting and about their usefulness for guiding the complex pharmacotherapy of left ventricular disorders and heart failure.66
Box 1 | Current and novel prognostic biomarkers
Other biomarkers Plasma free triiodothyronine (fT3) is an inverse acutephase reactant, which predicts risk of mortality in patients on hemodialysis.67 Interestingly, the first study that tested low fT3 levels as a predictor of mortality demonstrated that this thyroid hormone captured most of the predictive power of IL‑6.68 In a subsequent study in patients with predialysis stage 5 CKD initiating renal replacement therapy, low T3 levels had a stronger link with mortality than did fT3 levels;69 the area under the ROC curve for the two hormones were 0.69 and 0.61, respectively. fT3 reflects thyroid function better than the bound form and differences between assays and alterations in protein binding in patients with ESRD (which are partly corrected by hemodialysis) explain this apparent discrepancy. The relevance of subclinical alterations in thyroid function in patients with ESRD still remains undefined. fT3 levels can be normalized by correction of acidosis in ESRD.70 Thus, mechanistic studies based on this safe intervention (for example, with bicarbonate administration) can be envisaged to improve our understanding of the link between low fT3 levels and inflammatory processes in patients with ESRD. Asymmetric dimethylarginine (ADMA) is an endo genous inhibitor of nitric oxide synthase. High plasma levels of ADMA are indicative of endothelial dysfunction and atherosclerosis, and predict mortality in the general population 71 and in patients with various diseases,
■■ Troponin I
CKD–MBD ■■ Alkaline phosphatase ■■ Calcium ■■ Fetuin A ■■ FGF23 ■■ 25-hydroxyvitamin D ■■ Parathyroid hormone ■■ Phosphate
Protein–energy wasting and inflammation ■■ Albumin ■■ CRP ■■ E-selectin ■■ Fibrinogen ■■ Gelsolin ■■ HGF ■■ ICAM1/VCAM1 ■■ Interleukin 6 ■■ Mannose-binding lectin ■■ Myeloperoxidase ■■ Pentraxin‑3 ■■ Soluble CD14 ■■ Soluble CD154 ■■ TNF
Myocardial injury/dysfunction ■■ NT-proBNP and BNP ■■ Soluble Fas ■■ Troponin T ■■ TWEAK
Other biomarkers ■■ ADMA ■■ AGEs ■■ Bicarbonate ■■ Glycemia ■■ Homoarginine ■■ Homocysteine ■■ Lipoprotein a ■■ Neuropeptide Y ■■ Norepinephrine ■■ p-Cresol ■■ Serum lipid levels ■■ Triiodothyronine Abbreviations: ADMA, asymmetric dimethylarginine; AGEs, advanced glycation end products; BNP, brain natriuretic peptide; CKD, chronic kidney disease; CRP, C‑reactive protein; FGF23, fibroblast growth factor 23; HGF, hepatocyte growth factor; ICAM1, intercellular adhesion molecule 1; MBD, mineral and bone disorder; NT‑proBNP, N‑terminal proBNP; TNF, tumor necrosis factor; TWEAK, TNF ligand superfamily member 12; VCAM1, vascular cell adhesion protein 1.
including heart failure, coronary artery disease, dia betes mellitus, liver disease and predialysis CKD.72 The prognostic power of ADMA has been confirmed in at
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REVIEWS
Gain in predictive power for mortality (%)
10 –
8–
6–
4–
2–
0– IL-1β
TNF
IL-18
CRP
IL-6
Combined
Inflammation markers
Figure 1 | Gain in predictive power for mortality derived from the inclusion of individual and combined markers of inflammation. The prediction model (Cox regression analysis) included age, sex, smoking, diabetes mellitus, systolic blood pressure, cholesterol levels, previous cardiovascular events, treatment modality (hemodialysis or peritoneal dialysis) and asymmetric dimethylarginine. The gain in predictive power resulting from the combined use of all inflammatory biomarkers (+9.1%) was only marginally superior to that of IL‑6 alone (+6.1%). Abbreviations: CRP, C‑reactive protein; IL, interleukin; TNF, tumor necrosis factor.41
least three separate hemodialysis cohorts.73–75 However, proper intervention studies specifically aimed at reducing ADMA levels are lacking, and it remains uncertain whether accumulation of ADMA is causally implicated in the high risk of death seen in patients on hemodialysis. Apart from high performance liquid chromatography and mass spectrometry, ADMA levels can be measured by ELISA,76 but this assay is at least as expensive as cytokine assays. Currently, no proof that measuring ADMA levels is useful in clinical practice exists, and therefore the application of this biomarker can be considered only in the context of clinical studies.
A multimarker approach Comparing the ability of various biomarkers to predict death requires the measurement of these biomarkers to be expressed in equivalent units, that is, in terms of standard deviations or categorized into quantiles. In the Women’s Health Study,77 Ridker et al. categorized various biomarkers into quartiles and then compared the risk associated with the increase of one quartile in CRP levels with the corresponding risk associated with a comparable increase in levels of the other biomarkers. Of the biomarkers studied, the investigators found CRP to be associated with the highest hazard ratio for mortality. Analyses of this kind have rarely been performed in patients with ESRD. In the only head-to-head comparison of a set of inflammatory cytokines with CRP, IL‑6 emerged as the cytokine with the highest hazard ratio for death.41,78 The addition of other cytokines and CRP into a multivariate Cox model that included IL‑6 produced 146 | MARCH 2012 | VOLUME 8
only a modest increase in its predictive power. No similar analyses have been performed in patients with ESRD to compare the various novel biomarkers and it therefore remains unclear whether a prognostic biomarker superior to IL‑6 exists. In theory, measurement of several biomarkers simultaneously (a multimarker approach) could improve risk stratification both in the general population and in patients at high risk of adverse events. In the Framingham Study cohort, a multimarker approach based on CRP, BNP, albumin-to-creatinine ratio, homocysteine and renin identified patients at high risk of cardiovascular events. However, the addition of this multimarker score to conventional risk factors produced only a minuscule increase in the ability to classify the risk of future cardiovascular events.79 In the Olmsted study, the combined use of two biomarkers (BNP and CRP) in patients who presented with heart failure increased the discrimination of individuals who died during the follow-up by 5% as compared to the prediction made on the basis of standard risk factors (age, BMI, creatinine clearance, New York Heart Association functional class, serum sodium level
