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Clinical Chemistry 53: 874-881, 2007. First published March 23, 2007; 10.1373/clinchem.2006.080192
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Right arrow Proteomics and Protein Markers
(Clinical Chemistry. 2007;53:874-881.)
© 2007 American Association for Clinical Chemistry, Inc.

Proteomics and Protein Markers

Multiple Biomarker Use for Detection of Adverse Events in Patients Presenting with Symptoms Suggestive of Acute Coronary Syndrome

Fred S. Apple1,a, Lesly A. Pearce2, Adrine Chung1, Ranka Ler1 and MaryAnn M. Murakami1

1 Department of Laboratory Medicine and Pathology, Hennepin County Medical Center, University of Minnesota School of Medicine, Minneapolis, MN.
2 Biostatistical Consulting, Minot, ND.

aAddress correspondence to this author at: Hennepin County Medical Center, Clinical Labs P4, 701 Park Ave., Minneapolis, MN 55415. Fax 612-904-4229; e-mail apple004{at}umn.edu.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: We investigated multiple biomarkers of various pathophysiologic pathways to determine their relationships with adverse outcomes in patients presenting with symptoms of acute coronary syndrome.

Methods: We obtained plasma specimens from 457 patients on admission and measured 7 biomarkers: myeloperoxidase (MPO), soluble CD40 ligand (CD40L), placental growth factor (PlGF), metalloproteinase-9 (MMP-9), high-sensitivity C-reactive protein (hsCRP), cardiac troponin I (cTnI), and N-terminal pro-B-type natriuretic peptide (NT-proBNP). We used the Modification of Diet in Renal Disease formula to calculate the estimated glomerular filtration rate (eGFR). Endpoints were cardiac events (myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft, cardiac death) and all-cause mortality. We estimated cumulative event rates over a 4-month period with the Kaplan–Meier method and relative risk (RR) with the Cox proportional hazards model.

Results: Patients with increased PlGF, NT-proBNP, hsCRP, or cTnI or decreased eGFR had 11% to 20% higher all-cause mortality rates than patients with concentrations within reference intervals: 20.4% (eGFR), 16.0% (PlGF), 15.8% (hsCRP), 12.7% (NT-proBNP), and 11.3% (cTnI; all P ≤0.03). No differences in mortality rates were observed between those with increased vs normal concentrations of MPO, CD40L, or MMP-9. Decreased eGFR (RR 3.4, P = 0.004) and increased NT-proBNP (RR 7.9, P = 0.04) were independently predictive of mortality, and PlGF (RR 2.0, P = 0.08) approached significance. Patients with increased NT-proBNP (12.3%) or cTnI (33.8%) had higher cardiac event rates (each P <0.02), with increased MPO (11.1%) showing a trend (P = 0.09). Patients in whom both cTnI and MPO were increased had a cardiac event rate of 43%.

Conclusion: Multiple biomarkers that are likely indicative of different underlying pathophysiologic mechanisms are independently predictive of increased risk for adverse events in patients with acute coronary syndrome.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Recent investigations have suggested that increases in biomarkers upstream from biomarkers of necrosis [cardiac troponin I (cTnI)1 and cardiac troponin T (cTnT)] may provide earlier assessment of overall risk and aid in the identification and management of patients with symptoms suggestive of acute coronary syndrome (ACS) (1)(2)(3)(4)(5)(6)(7). These markers include inflammatory cytokines, cellular adhesion molecules, metalloproteinases (MMPs), acute-phase reactants indicative of general inflammation [high-sensitivity C-reactive protein (hsCRP)], plaque destabilization and rupture biomarkers [CD40 ligand (CD40L), placental growth factor (PlGF), pregnancy-associated plasma protein-A, and myeloperoxidase (MPO)], biomarkers of ischemia (choline, unbound free fatty acids, ischemia-modified albumin), and biomarkers of myocardial dysfunction [B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP)]. The evidence-based literature regarding biomarkers of myocardial necrosis, cTnI and cTnT, has increased clinicians’ ability to detect and exclude myocardial injury (8)(9), with cardiac troponin concentrations within reference intervals indicating a significantly lower risk of adverse events than increased concentrations (10)(11). Several of the nonnecrosis biomarkers studied in the current investigation have been shown to have potential clinical utility for identifying patients at higher risk for subsequent cardiovascular and mortality events. The majority of these findings, however, are from proof-of-principle studies performed in highly selected, high-risk ACS populations (12)(13)(14)(15)(16)(17)(18), and thus their clinical evaluation is less applicable to a more heterogeneous, nonpreselected patient group. We determined the prognostic value of multiple biomarkers indicative of myocardial damage, myocardial dysfunction, inflammation, plaque rupture, and ischemia based on renal function [estimated glomerular filtration rate (eGFR)] in a nonselected, heterogeneous population of patients with ischemic symptoms suggestive of ACS presenting to an inner-city medical center.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
After obtaining institutional review board approval, we prospectively collected a single plasma (heparin-containing) specimen (collected as leftover specimens) at presentation from 470 unselected patients presenting with symptoms suggestive of ACS admitted through the Hennepin County Medical Center’s emergency department, a 400-bed primary and tertiary care level 1 trauma center. Enrollment included a heterogeneous group of patients presenting with chest pain (63%) as well as other clinical features considered indicative of ACS who were admitted to rule in or rule out acute myocardial infarction (MI). The median time from symptom onset to presentation was 3.1 h. We obtained clinical outcomes and patient demographics from chart review over a 4-month follow-up period after patient enrollment into the study, when patients returned for clinic visits or hospitalization. Record review included up-to-date medical history of previous medical conditions and was carried out without knowledge of biomarker results. Follow-up was lost in 13 patients, leaving 457 in the data set. Plasma used in this study was separated from cells within 60 min of collection [centrifuged at 2028g (4000 rpm) for 10 min], stored initially at 4 °C, and then stored frozen (–80 °C) within 48 to 72 h. We recognize that CD40L may be unstable and that a delay of up to 72 h in processing may be a study limitation; however, stability issues have not been a documented problem in plasma (EDTA, citrate) but only with serum (19)(20). The current study used heparin-containing plasma.

Measurement of biomarkers was blind to patient histories and treatment therapies. We measured all biomarkers along manufacturer guidelines as follows: cTnI, Dade Stratus CS and Dimension RxL; NT-proBNP, Roche Elecsys 2010; hsCRP, Dade Dimension; myeloperoxidase (MPO), Assay Design ELISA; PlGF, R&D Systems ELISA; CD40L, R&D Systems ELISA; MMP-9, R&D Systems ELISA. Total imprecision (% CV) for each assay was as follows: cTnI, 9.8% at 0.15 µg/L; NT-proBNP, 7.5% at 350 ng/L; hsCRP, 5.1% at 1.8 mg/L; MPO, 8.8% at 251 µg/L; PlGF, 9.1% at 149 ng/L; CD40L, 6.4% at 437 ng/L; and MMP-9, 6.9% at 12.2 µg/L. We used previously established reference cutoff concentrations for US Food and Drug Administration (FDA)–cleared assays for risk stratification: cTnI, <0.1 µg/L (21); NT-proBNP, 125 ng/L age <75 years and 450 ng/L age ≥75 years per manufacturer’s package insert; and hsCRP, by tertile <1 mg/L, 1 to <3.0 mg/L, ≥3.0 mg/L (15). For the other non–FDA-cleared ELISA assays, we used 99th percentile reference limits established in our laboratory using nonparametric statistics based on the same normal population: MPO, ≤125.6 µg/L; PlGF, ≤17 ng/L; CD40L, ≤1.081 ng/L; and MMP-9, ≤233.7 µg/L. [An MPO assay (PrognostiX) has been cleared by the FDA since this study was carried out.] We calculated eGFR values using the National Kidney Disease Education Program Modification of Diet in Renal Disease equation [mL · min–1 · (1.73 m2)–1] based on plasma creatinine, age, sex, and whether African American (22). Values of at least 60 mL · min–1 · (1.73 m2)–1 were considered normal vs those indicative of reduced renal function [<60 mL · min–1 · (1.73 m2)–1].

Primary endpoints evaluated after discharge were (a) cardiac event, defined as 1st of recurrent MI, percutaneous coronary intervention, coronary artery bypass graft, or cardiac death, and (b) all-cause mortality (cardiac and noncardiac death). Diagnosis of ACS or unstable angina was not part of the cardiac event endpoints. Using the Student t-test and the {chi}2 test, we compared differences in patient characteristics and biomarkers between those with and without a subsequent event. Event-free survival curves by biomarker group were estimated using the Kaplan–Meier method and compared between groups using the log-rank statistic. We computed exposure from date of blood draw until date of 1st event, with censor at 4 months of follow-up (122 days). We estimated relative risk (RR) and 95% CI using a Cox proportional hazard model and fitted a multivariate model to adjust for covariates. We used forward stepwise modeling techniques to identify independent variables. Statistical significance was accepted at the 0.05 level, and all statistical tests were 2 sided. Statistical analyses were performed with SPSS for Windows 11.5 (SPSS).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
During follow-up, there were 25 deaths, 9 of which were cardiac related (6 cardiac arrests, 1 MI, 1 congestive heart failure, and 1 complication of bypass surgery). The noncardiac deaths included sepsis, respiratory failure, cancer, stroke, and liver disease. Patients who died were more often white but otherwise demographically similar to those who lived beyond 4 months (see Table 1 in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol53/issue5). Patients who died had increased PlGF, NT-proBNP, hsCRP, or cTnI concentration or decreased eGFR at time of blood draw (see Table 1 in the online Data Supplement).

Mortality rates, as shown in Table 1 , were significantly higher (each P ≤0.03) for patients with increased PlGF, NT-proBNP, hsCRP, and cTnI and decreased eGFR vs patients with normal concentrations. Fig. 1 shows the Kaplan–Meier cumulative event rate curves for the biomarkers with significant findings. Event rates were 20.4% for decreased eGFR, 16.0% for increased PlGF, 15.8% for highest hsCRP, 12.7% for increased NT-proBNP, and 11.3% for increased cTnI. The 149 patients with normal NT-proBNP had the lowest death rate, 0.8%. We observed no significant differences in all-cause mortality rates between increased vs normal concentrations of MPO, CD40L, or MMP-9. RR of all-cause mortality associated with increased PlGF, NT-proBNP, hsCRP, or cTnI as well as decreased eGFR ranged from 2.3 to 10.6 and remained relatively unchanged after adjustment for age, sex, diabetes, and history of renal failure. Patients with normal NT-proBNP, regardless of eGFR, had a low event rate (0.8%), whereas patients with an increased NT-proBNP and decreased eGFR had a significantly higher event rate than those with an increased NT-proBNP and normal eGFR [24.7% (n = 190) vs 7.3% (n = 114), P = 0.001].


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  		Table 1. End points of all-cause mortality events.


Figure 1
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  		Figure 1. Kaplan–Meier all-cause mortality curves.

Biomarkers are stratified according to 99th percentile reference limit concentrations, demonstrating significant event differences (PlGF, hsCRP, cTnI, NT-proBNP, and eGFR).

During follow-up, 36 patients had at least 1 cardiac event—25 MI (3 ST-elevated MI), 5 percutaneous coronary intervention, 2 coronary artery bypass graft, and 4 cardiac death. Cardiac event patients were older, more often white, and presented more often with chest pain symptoms than non–cardiac event patients (see Table 2 in the online Data Supplement). NT-proBNP and cTnI were the only biomarkers significantly higher in the cardiac event group vs the no-event group (see Table 2 in the online Data Supplement). Cardiac event rates, as shown in Table 2 , were significantly higher for patients with an increased concentration of NT-proBNP (12.3% vs 3.9%, P = 0.02) or cTnI (33.8% vs 5.4%, P <0.0001); MPO concentrations approached statistical significance (11.1% vs 7.0%, P = 0.09). Fig. 2 shows the Kaplan–Meier cumulative event rate curves for the biomarkers with significant findings. Of the biomarkers, cTnI best stratified patients by risk, with NT-proBNP and MPO each adding further to the risk stratification (Table 3 ). For patients with normal cTnI, increased NT-proBNP was associated with more than a 4-fold higher cardiac event rate than normal NT-proBNP (7.4% vs 1.6%, P = 0.008). For patients with increased cTnI, increased MPO was associated with the highest cardiac event rate of 42.6% vs 14.6% for normal MPO, nearly a 3-fold difference.


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  		Table 2. End point of cardiac event analysis.


Figure 2
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  		Figure 2. Kaplan–Meier cardiac event curves.

Biomarkers are stratified according to 99th percentile reference limit concentrations, demonstrating significant event differences (cTnI, NT-proBNP, and MPO).


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  		Table 3. Cardiac event endpoints by cTnI and NT-proBNP or MPO.

The bimodal distribution shown in Fig. 3 for MPO was unique compared with the skewed distributions of the other biomarkers (data not shown). Patients with MPO >250 µg/L (n = 204) were of similar age (mean 57 years in both groups), and a similar proportion had a history of renal failure (5% vs 10%, P = 0.1) and hypertension (58% vs 59%, P = 0.8) compared with those having MPO ≤250 µg/L. Those with MPO values >250 µg/L, however, more often were male (62% vs 53%, P = 0.05) and had a history of coronary artery disease (29% vs 21%, P = 0.06), MI (18% vs 12%, P = 0.04), and diabetes (30% vs 21%, P = 0.04).


Figure 3
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  		Figure 3. Histogram of myeloperoxidase concentrations for all patients presenting with symptoms suggestive of ACS.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Understanding of the importance of biomarkers for the identification and therapy management of patients acutely presenting with symptoms suggestive of ACS is rapidly growing. Reports of biomarkers addressing myocardial injury and dysfunction and differentiating the pathophysiology of inflammation, plaque vulnerability, and ischemic myocardium are appearing with increasing numbers (1)(2)(3)(4)(5)(6)(7)(23)(24)(25). The majority of these studies report on biomarkers used to identify high-risk patients who clearly benefit from aggressive management. Few studies, however, have presented experimental evidence of the role of novel biomarkers alongside established biomarkers, linking the spectrum of the pathophysiology of coronary events from inflammation through myocardial cell death and dysfunction. We captured the risk stratification for all-cause mortality and cardiac events both (a) in a heterogeneous group of low- to high-risk patients presenting with symptoms suggestive of ACS and (b) through monitoring the independence of a spectrum of biomarkers representing inflammation, plaque destabilization, plaque rupture, myocardial necrosis, and myocardial dysfunction.

Our findings in the heterogeneous group both confirm and contrast with observations previously described in high-risk ACS patients, depending on what outcomes were examined. First, we demonstrate and complement previous studies (1)(6)(8)(11)(21)(22) showing that increased baseline concentrations of NT-proBNP (biomarker of myocardial dysfunction) (13), cTnI (biomarker of myocardial necrosis) (26), PlGF (biomarker of vascular inflammation) (24), and hsCRP (biomarker of general inflammation) (15) are independently associated with adverse all-cause mortality outcomes in patients presenting with ischemic symptoms suggestive of ACS. Our findings for MPO (biomarker of inflammation), MMP-9 (biomarker of plaque instability), and CD40L (biomarker of platelet activation), however, do not demonstrate risk stratification ability for all-cause mortality, contradicting studies that have shown these biomarkers to be independent predictors in high-risk ACS patients (14)(16)(17)(23). Our data confirm that increased cardiac troponin [cTnI, as in the current study, or cTnT, as studied previously in high-risk patients (16)(17)] is the single biomarker best able to identify patients at highest risk of events. Second, NT-proBNP (P = 0.03), cTnI (P <0.001), and MPO (P = 0.09) were associated with an increased risk of cardiac events, whereas PlGF, hsCRP, MMP-9, CD40L, and eGFR were not. Increased cTnI along with increased MPO showed the highest event rate (42.6%). Our data complement findings that increased MPO is associated with increased cardiac event risk in ACS (16)(17) and coronary artery disease (18) patients, but they contrast with the finding that normal cTnT and increased MPO identify patients at higher risk of cardiac events (16)(17).

It is possible that these data do not confirm previously reported findings in high-risk patients because of the limited size and number of events included in this study. Identification of weaker relationships between biomarkers and endpoints may be underpowered, whereas there is adequate power in this study to detect stronger relationships such as those for cTnI and NT-proBNP. Specimen processing before measuring CD40L may have affected its stability; however, using EDTA and citrate plasma separated from cells within 1 h, as performed in the current study for heparin plasma, does appear to be a suitable process for specimen stability (19)(20). We acknowledge that platelet-poor plasma is the only way to absolutely minimize the potential variable contribution of platelets to the actual CD40L concentration. Third, our findings are unique in that we used biomarker reference limits independently calculated or confirmed in our laboratory using the same healthy population for measurement of the cutoffs for the novel biomarkers, instead of accepting non–peer-reviewed cutoff concentrations found in assay package inserts. Thus preselected cutoffs were used and not ROC curve–measured cutoffs. Fourth, our findings emphasize the importance of the role of renal function (eGFR) in stratification (27).

We view our novel biomarker findings as preliminary, because our heterogeneous population was relatively small. Our findings are important in that they demonstrate that initial proof-of-principle for novel risk biomarkers has to be interpreted with caution when assessed in a heterogeneous patient group. As noted by Manolio (28), "crossing the boundary from research to clinical application, will require replication in multiple settings, experimental evidence supporting a pathophysiologic role, and ideally, intervention trials demonstrating that modification improves the outcome." Our findings challenge investigators to perform additional prospective studies in larger and diverse groups of patients presenting with ischemia to better understand the nuances of the spectrum of multiple biomarker findings and how to interpret them.

We recognize that our study is not without potential limitations. First, the data set includes only 457 patients, which decreases generalizability, and a limited number of events were observed, which decreases statistical power. Second, analysis does not include adjustment for electrocardiogram findings or other clinical risk indicators such as heart failure, heart rate, blood pressure, or type of ACS, and no information was available on long-term medical treatment that might have influenced patient outcomes. Adjustment for many covariates in models is marginal in a study of this size, as statistical models become unstable with more covariates when there are a limited number of events. We report the adjusted RRs (Tables 1Up and 2Up ) to illustrate that the univariate RR estimates were relatively unchanged with adjustments for covariates. Also, we were unable to include recurrence of ischemic events, such as unstable angina, as an outcome measure.

Third, only a single biomarker measurement at enrollment was obtained, and several measurements would potentially be of great interest for better understanding the differences in biomarker kinetics, as well as for future elucidation of effective therapeutic strategies. Fourth, we would have liked to have included one of the biomarkers of ischemia, such as choline or ischemia-modified albumin, but did not have the specimen volume needed to perform these assays—nor are commercial assays available.

Finally, as novel biomarkers emerge independently or as part of multimarker strategies for individual proteomic profiling of risk in ACS patients, whether using single cutoff values or independent risk ratios based on the number of biomarkers, quality specifications need to be developed regarding each assay used to measure these biomarkers. As we have learned from cardiac troponin (29) and the natriuretic peptide (30) assays, not all assays are created equal. The complexity of assay validation will be extremely important for biomarker implementation into clinical practice as single tests or part of a multimarker panel.


   Acknowledgments
 
Grant/funding support: This study was supported in part by Ortho-Clinical Diagnostics.

Financial disclosures: Dr. Apple has consulted for Ortho-Clinical Diagnostics as well as received research funding for biomarker research from Abbott, Beckman, Biosite, Bayer, DPC, Roche, MKI, Response Biomedical, and bioMerieux.


   Footnotes
 
1 Nonstandard abbreviations: cTnI, cardiac troponin I; cTnT, cardiac troponin T; ACS, acute coronary syndrome; MMP, metalloproteinase; hsCRP, high-sensitivity C-reactive protein; CD40L, CD40 ligand; PlGF, placental growth factor; MPO, myeloperoxidase; BNP, B-type natriuretic peptide; NT-proBNP, N-terminal proBNP; eGFR, estimated glomerular filtration rate; MI, myocardial infarction; FDA, US Food and Drug Administration; RR, relative risk.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Apple FS, Wu AHB, Mair J, Ravkilde J, Panteghini M, Tate J, et al. Future biomarkers for detection of ischemia and risk stratification in acute coronary syndrome. Clin Chem 2005;51:810-824.[Abstract/Free Full Text]
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  21. Lin JC, Apple FS, Murakami MM, Luepker RV. Rates of positive cardiac troponin I and creatine kinase MB among patients hospitalized for suspected acute coronary syndromes. Clin Chem 2004;50:333-338.[Abstract/Free Full Text]
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Assessment of the Multiple-Biomarker Approach for Diagnosis of Myocardial Infarction in Patients Presenting with Symptoms Suggestive of Acute Coronary Syndrome
Clin. Chem., January 1, 2009; 55(1): 93 - 100.
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J. Shih, S. A. Datwyler, S. C. Hsu, M. S. Matias, D. P. Pacenti, C. Lueders, C. Mueller, O. Danne, and M. Mockel
Effect of Collection Tube Type and Preanalytical Handling on Myeloperoxidase Concentrations
Clin. Chem., June 1, 2008; 54(6): 1076 - 1079.
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D. A. Morrow, M. S. Sabatine, M.-L. Brennan, J. A. de Lemos, S. A. Murphy, C. T. Ruff, N. Rifai, C. P. Cannon, and S. L. Hazen
Concurrent evaluation of novel cardiac biomarkers in acute coronary syndrome: myeloperoxidase and soluble CD40 ligand and the risk of recurrent ischaemic events in TACTICS-TIMI 18
Eur. Heart J., May 1, 2008; 29(9): 1096 - 1102.
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M. Weber and C. Hamm
Novel biomarkers--the long march from bench to bedside
Eur. Heart J., May 1, 2008; 29(9): 1079 - 1081.
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