Clinical Chemistry 60:3 530–540 (2014)
Lipids, Lipoproteins, and Cardiovascular Risk Factors
Increased Soluble ST2 Predicts Long-term Mortality in Patients with Stable Coronary Artery Disease: Results from the Ludwigshafen Risk and Cardiovascular Health Study Benjamin Dieplinger,1* Margot Egger,1 Meinhard Haltmayer,1 Marcus E. Kleber,2 Hubert Scharnagl,3 Guenther Silbernagel,4 Rudolf A. de Boer,5 Winfried Maerz,2,3,6 and Thomas Mueller1
BACKGROUND: Soluble suppression of tumorigenicity 2 (sST2) has emerged as a strong prognostic biomarker in patients with heart failure and myocardial infarction. The aim of this study was to evaluate the longterm prognostic value of sST2 in patients with stable coronary artery disease (CAD). METHODS: sST2 plasma concentrations were measured in 1345 patients with stable CAD referred for coronary angiography at a single tertiary care center. The primary endpoint was all-cause mortality. RESULTS: During a median follow-up time of 9.8 years, 477 (36%) patients died. The median sST2 plasma concentration at baseline was significantly higher among decedents than survivors (21.4 vs 18.5 ng/mL; P ⬍ 0.001). In multivariate Cox proportional hazards regression analysis, sST2 was an independent predictor of all-cause mortality (risk ratio 1.16 per 1-SD increase in log-transformed values; 95% CI 1.05–1.29; P ⫽ 0.004). In the same multivariate analysis, aminoterminal pro–B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) were also independent predictors, whereas galectin-3 was not. Patients with sST2 in the highest quartile (⬎24.6 ng/mL) displayed a 2-fold increased risk of death in univariate analysis, which was attenuated but remained significant in a fully adjusted model (risk ratio 1.39; 95% CI 1.10 –1.76; P ⫽ 0.006). Further analysis showed that the prognostic impact of sST2 was additive to NT-proBNP and hs-cTnT. Us-
1
Department of Laboratory Medicine, Konventhospital Barmherzige Brueder Linz, Linz, Austria; 2 Institute of Public Health, Social and Preventive Medicine, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany; 3 Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria; 4 Department of Angiology, Swiss Cardiovascular Center, Inselspital, University of Bern, Switzerland; 5 University of Groningen, Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands; 6 Synlab Academy Mannheim, Mannheim, Germany. * Address correspondence to this author at: Department of Laboratory Medicine, Konventhospital Barmherzige Brueder, Seilerstaette 2-4, A-4020 Linz, Austria. Fax ⫹43-732-7677-3799; e-mail
[email protected]. Received May 11, 2013; accepted December 12, 2013.
530
ing a multibiomarker approach combining these 3 complementary makers, we demonstrated that patients with all 3 biomarkers in the highest quartiles had the poorest outcome. CONCLUSIONS: In this cohort of patients with stable CAD, increased sST2 was an independent predictor of long-term all-cause mortality and provided complementary prognostic information to hs-cTnT and NT-proBNP.
© 2013 American Association for Clinical Chemistry
Suppression of tumorigenicity 2 (ST2)7 is an interleukin-1 (IL-1) receptor family member with membrane bound (ST2L) and soluble (sST2) isoforms (1, 2 ). IL-33 is the functional ligand for ST2, and ST2/ IL-33 signaling was originally described in the context of inflammation and immunity (3 ). IL-33 signals through a heterodimeric receptor complex composed of ST2L and IL-1 receptor accessory protein, promoting the production of inflammatory cytokines and a T helper type 2 (Th2) immune response, whereas sST2 is known to bind to IL-33 and functions as a “decoy” receptor for IL-33, inhibiting IL-33/ST2L signaling. Therefore, increased concentrations of sST2 in the circulation attenuate the systemic biologic effects of IL-33 (4 ). Blood concentrations of sST2 are increased in inflammatory and heart diseases and are considered a valuable prognostic marker in both conditions (4 ). In
Previously published online at DOI: 10.1373/clinchem.2013.209858 Nonstandard abbreviations: ST2, suppression of tumorigenicity 2; IL, interleukin; ST2L, membrane bound ST2; sST2, soluble ST2; Th, T helper; CAD, coronary artery disease; LURIC, Ludwigshafen Risk and Cardiovascular Health; NTproBNP, amino-terminal pro–B-type natriuretic peptide; hs-cTnT, highsensitivity cardiac troponin T; ICD-9, International Classification of Diseases, Ninth Revision; IQR, interquartile range; AUC, area under the curve; NYHA, New York Heart Association; RR, risk ratio; GUSTO, Global Use of Strategies to Open Occluded Coronary Arteries in Acute Coronary Syndromes; MERLIN-TIMI, Metabolic Efficiency with Ranolazine for Less Ischemia in Non–ST-Elevation Acute Coronary Syndromes/Thrombolysis in Myocardial Infarction; CORONA, Controlled Rosuvastatin Multinational Study in Heart Failure.
7
sST2 Predicts Long-Term Mortality in Stable CAD
multiple clinical trials, sST2 has emerged as a clinically useful prognostic biomarker in patients with cardiovascular diseases, including myocardial infarction (5– 9 ), heart failure (10 –15 ), and acute dyspnea (16 –19 ). Interestingly, sST2 even provided prognostic information in a low-risk community-based population (20 ). However, to our knowledge, there are no data on the prognostic value of sST2 in patients with stable coronary artery disease (CAD). Therefore, the aim of our study was to evaluate the long-term prognostic value of sST2 in patients with stable CAD undergoing coronary angiography in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. In this context, we also compared the prognostic value of sST2 with other biomarkers such as amino-terminal pro–B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT), and galectin-3. Methods STUDY POPULATION
LURIC is a prospective study designed to determine biochemical and genetic risk factors for CAD in a hospital-based cohort of white individuals referred to coronary angiography and to evaluate the predictive value of potential markers on long-term outcome. LURIC study objectives, recruitment procedures, and characteristics have been described in detail previously (21 ). In brief, 3316 patients, who were referred for coronary angiography to a single tertiary care medical center in southwest Germany (Herzzentrum, Ludwigshafen), were enrolled between July 1997 and January 2000. Inclusion criteria were availability of a coronary angiogram, German ancestry, and clinical stability, with the exception of acute coronary syndromes. Individuals suffering from any acute illness other than acute coronary syndromes, namely chronic polymorbidity of noncardiac origin or malignant disease within 5 years preceding study entry, were excluded. The study protocol was approved by the ethics committee at the Landesärztekammer Rheinland-Pfalz (Mainz, Germany) in accordance with the Declaration of Helsinki, and written informed consent was obtained from each participant. The original LURIC cohort comprised 49 individuals with no data on coronary angiography and 3267 individuals with full data on coronary angiography. Of the latter, 665 patients had no CAD, 1535 patients had stable CAD, and 989 patients had unstable CAD (i.e., unstable angina, non–ST-elevation myocardial infarction, and ST-elevation myocardial infarction). The aim of our present study was to investigate the prognostic value of sST2 in patients with stable CAD. Therefore, we only included the 1535 patients with stable CAD in the present post hoc analysis. We excluded 190 partici-
pants in this group who had missing clinical or biochemical data, resulting in a subgroup of 1345 patients with stable CAD available for this study. Details of the LURIC baseline examination have been described previously (21 ) and are outlined in the Supplemental Data, which accompanies the online version of this article at http://www.clinchem.org/content/ vol60/issue3. BIOCHEMICAL ANALYSES
Venous blood was drawn from study participants after overnight fasting before coronary angiography under standardized conditions. Routine laboratory parameters were measured as previously described (21 ) and outlined in the online Supplemental Data. Within 30 min after venipuncture, the remaining blood samples were centrifuged at 3000g for 10 min, aliquoted, frozen, and stored at ⫺80 °C until further analysis. We measured NT-proBNP in plasma by chemiluminescent microparticle immunoassay on an Elecsys 2010 (Roche Diagnostics); CVs were 3.2% and 2.0% at mean values of 157 and 5125 ng/L, respectively. We measured hs-cTnT in plasma by chemiluminescent microparticle immunoassay on a Modular E170 series automated analyzer (Roche Diagnostics); the CV was ⱕ10% at 13 ng/L. We measured galectin-3 in plasma by chemiluminescent microparticle immunoassay on an ARCHITECT (Abbott Diagnostics); CVs were ⱕ2.8% and ⱕ6% at mean values of 9.1 and 74.1 ng/mL, as described recently (22 ). We used 1 of the EDTA plasma aliquots for the determination of sST2, and all patient samples were measured in 1 batch approximately 12 years after the recruitment period of the LURIC study was closed. We measured sST2 on a fully automated BEP® 2000 instrument (Siemens Healthcare Diagnostics) with the Presage™ ST2 sandwich immunoassay assay (Critical Diagnostics); CVs were ⬍4.0%, as previously reported by our group (23 ). MORTALITY ASCERTAINMENT
All 1345 patients with stable CAD received follow-up observation. We obtained mortality data for the entire cohort from local community registries through May 31, 2009. We extracted information on the causes of death from death certificates obtained from local health authorities, encoded either before 2002 according to the International Classification of Diseases, Ninth Revision (ICD-9) or after 2002 according to ICD-10. The primary endpoint of this study was all-cause mortality, and the secondary endpoint was cardiovascular mortality. Cardiovascular mortality was defined with ICD-9 codes 390 – 448 and ICD-10 codes I00 –I79. Clinical Chemistry 60:3 (2014) 531
STATISTICAL ANALYSIS
Data were analyzed with SPSS 18.0.0 software and the MedCalc 11.5.0.0 package. Dichotomous data were given as absolute numbers (percent), and continuous variables were presented as median [interquartile range (IQR)] if not otherwise indicated. Univariate comparisons between groups were performed with the Fisher exact test or the 2 test for categorical variables and with the nonparametric Mann–Whitney U-test for continuous variables (respective P values were not adjusted for multiple comparisons and are therefore descriptive only). We tested the association between sST2 and angiographic disease severity with Spearman coefficient of rank correlation (rs) analysis. We performed ROC analyses for sST2 at different follow-up time points (i.e., after 2 years, 5 years, and a median of 9.8 years), and areas under the curve (AUCs) were calculated. We used univariate and multivariate Cox proportional hazards regression analyses to analyze the effect of sST2 and several potential confounders on survival. To report robust information in the multivariate analyses, we limited the number of baseline variables included in the multivariate models to established clinical and biochemical markers. Continuous variables were not normally distributed and were thus normalized by log transformation. Risk ratios refer to a 1-SD rise in log-transformed units. In addition, after establishing a cutoff value for sST2 by quartile analysis, we performed Cox proportional hazards regression analyses with sST2 as a dichotomized variable. Kaplan–Meier estimates of the distribution of times from baseline to death were computed according to quartiles of sST2, and log-rank test for trend was performed to compare the survival curves between the groups. To examine a possible combined effect of sST2, NT-proBNP, and hs-cTnT on survival, we computed risk ratios by Cox proportional hazards regression analyses, in which study participants were stratified into quartiles of sST2, NT-proBNP, and hs-cTnT. Further, we performed a simple combined multibiomarker approach stratifying the entire cohort according to cutoffs between the third and fourth quartiles for sST2, NT-proBNP, and hs-cTnT. Using these cutoffs, we reported Kaplan–Meier curves of survival according to the presence of 0, 1, 2, or 3 increased biomarkers. Results Baseline patient characteristics of the 1345 patients with stable CAD are shown in Table 1. The study comprised 1008 (75%) men and 337 (25%) women with a median age of 65 years. Median sST2 plasma concentrations were significantly higher in men compared 532 Clinical Chemistry 60:3 (2014)
with women (20.0 ng/mL, IQR 16.2–25.2 ng/mL vs 17.4 ng/mL, IQR 14.8 –22.6 ng/mL; P ⬍ 0.001). Evaluation of angiographic disease severity in the entire cohort revealed 229 (17%) patients with at least 1 stenosis between ⱖ20% and 49%, whereas 323 (24%) patients presented with 1-vessel, 300 (22%) with 2-vessel, and 493 (37%) with 3-vessel disease at baseline. Spearman rank correlation analysis revealed no association of sST2 with angiographic disease severity of CAD (rs ⫽ 0.038; P ⫽ 0.159). Traditional cardiovascular risk factors as well as the use of cardiovascular medication were highly prevalent in this cohort. Among the 129 patients with heart failure, 60 (47%) presented with New York Heart Association (NYHA) class II, 59 (46%) class III, and 10 (7.8%) class IV. During follow-up, 477 (36%) patients died from all causes, including 305 (23%) deaths from cardiovascular causes. The median follow-up time for the entire cohort (n ⫽ 1345) was 9.8 years (range 0.01–11.8 years). The median follow-up for the survivors (n ⫽ 868) was 10.4 years (range 9.3–11.8 years). The median time to mortality for decedents (n ⫽ 477) was 4.7 years (range 4 days to 11.1 years). The median sST2 plasma concentration at baseline was significantly higher among decedents from all causes than survivors (21.4 ng/mL, IQR 16.6 –28.3 ng/mL vs 18.5 ng/mL, IQR 15.4 –23.2 ng/mL; P ⬍ 0.001). ROC curve analyses for sST2 and prediction of allcause mortality at different follow-up time points (i.e., after 2 years, 5 years, and a median of 9.8 years) were performed. The numbers of deaths were 108 (8%) after a follow-up time of 2 years, 242 (18%) after 5 years, and 477 (35%) after a median of 9.8 years. The AUC of sST2 for the prediction of all-cause mortality was highest after 2-year follow-up (0.69, 95% CI 0.66 – 0.71), followed by the AUC at 5-year follow-up (0.64, 95% CI 0.61– 0.66), and lowest after 9.8 years of follow-up (0.61, 95% CI 0.59 – 0.64). Cox proportional hazards regression analyses evaluating the prognostic value of sST2, clinical variables, and several other biomarkers are shown in Table 2. In univariate analyses, sST2 (per 1-SD increase in log-transformed values) displayed a risk ratio (RR) of 1.49 (95% CI 1.38 –1.61; P ⬍ 0.001) for all-cause mortality and an RR of 1.47 (95% CI 1.33–1.62; P ⬍ 0.001) for cardiovascular mortality. After adjustment for all clinical variables and biomarkers listed in Table 2, the prognostic value was attenuated but sST2 remained an independent predictor of mortality, with an RR of 1.16 (95% CI 1.05–1.29; P ⫽ 0.004) for all-cause mortality and an RR of 1.15 (95% CI 1.01–1.31; P ⫽ 0.040) for cardiovascular mortality (Table 2). In the same multivariate analyses, NT-proBNP and hs-cTnT were also independent predictors, whereas galectin-3 was not found to be an independent predictor (Table 2).
sST2 Predicts Long-Term Mortality in Stable CAD
Table 1. Baseline characteristics of all patients with stable CAD according to all-cause mortality.a All
Survivors
Decedents
n
1345
868
477
Male sex
1008 (75)
636 (73)
372 (78)
Age, years Body mass index, kg/m2 Arterial hypertension
65 (58–71) 27 (25–30) 1046 (78)
63 (57–69) 27 (25–30)
Pb
0.057
69 (63–75)
⬍0.001
27 (24–30)
0.054
658 (76)
388 (81)
Systolic blood pressure, mmHg
145 (129–162)
142 (128–159)
149 (134–166)
Diastolic blood pressure, mmHg
82 (75–90)
83 (75–90)
82 (74–89)
0.020 ⬍0.001 0.497
Dyslipidemia
917 (68)
587 (68)
330 (69)
0.582
Diabetes mellitus
576 (43)
306 (35)
270 (57)
⬍0.001
Current smoking
207 (15)
133 (15)
74 (16)
0.937
Family history of cardiovascular disease
502 (37)
356 (41)
146 (31)
⬍0.001
Prior myocardial infarction
599 (45)
358 (41)
241 (51)
0.001
Prior percutaneous coronary intervention
316 (26)
235 (27)
111 (23)
0.293
Prior coronary artery bypass graft
221 (16)
108 (13)
113 (24)
⬍0.001
Peripheral artery disease
168 (13)
64 (7)
104 (22)
⬍0.001
Cerebrovascular disease
130 (10)
Resting heart rate, beats/min
68 (60–76)
53 (6)
77 (16)
⬍0.001
66 (60–75)
71 (62–80)
⬍0.001
Arterial fibrillation
167 (12)
73 (8)
94 (20)
⬍0.001
Heart failure
129 (10)
42 (5)
87 (18)
⬍0.001 ⬍0.001c
Left ventricular ejection fraction ⱖ55%
871 (65)
639 (74)
45–55%
219 (16)
132 (15)
87 (18)
30–44%
183 (14)
80 (9)
103 (22)
72 (5)
17 (2)
55 (11)
Platelet inhibitors
978 (73)
648 (75)
327 (69)
0.011
Vitamin K antagonists
109 (8)
52 (6)
57 (12)
⬍0.001
Beta-blockers
828 (62)
576 (66)
252 (53)
⬍0.001
Calcium channel blockers
250 (19)
144 (17)
106 (22)
0.002
Angiotensin-converting enzyme inhibitors
718 (53)
423 (49)
295 (62)
⬍0.001
70 (5)
37 (4)
33 (7)
Diuretics
422 (31)
191 (22)
231 (48)
⬍0.001
Digitalis
235 (17)
86 (10)
149 (31)
⬍0.001
Nitrates
468 (35)
305 (35)
164 (34)
0.909
Oral antidiabetics
124 (9)
59 (7)
65 (14)
⬍0.001 ⬍0.001
⬍30%
232 (49)
Medication
Angiotensin receptor blockers
0.060
Insulin
83 (6)
25 (3)
58 (12)
Statins
673 (50)
454 (52)
219 (46)
0.041
42 (3)
30 (3)
12 (3)
0.531
701 (52)
474 (55)
227 (48)
0.014
Total cholesterol, mg/dL
192 (166–182)
193 (166–219)
190 (168–215)
0.488
LDL cholesterol, mg/dL
114 (93–138)
115 (93–139)
113 (93–136)
0.662
HDL cholesterol, mg/dL
38 (32–44)
38 (32–44)
37 (31–44)
0.016
Nonstatins Lipid-lowering therapy Biochemical markers
Continued on page 534
Clinical Chemistry 60:3 (2014) 533
Table 1. Baseline characteristics of all patients with stable CAD according to all-cause mortality.a (Continued from page 533) Pb
All
Survivors
Decedents
Triglycerides, mg/dL
148 (111–202)
150 (112–202)
147 (106–201)
Glycosylated hemoglobin, %
6.1 (5.7–6.7)
6.0 (5.6–6.5)
6.3 (5.7–7.3)
⬍0.001
0.497
Glucose, mg/dL
93 (83–109)
92 (83–104)
97 (85–125)
⬍0.001
Creatinine, mg/dL
0.9 (0.8–1.1)
0.9 (0.8–1.0)
1.0 (0.8–1.1)
⬍0.001
Estimated glomerular filtration rate, mL 䡠 min⫺1 䡠 (1.73 m2)⫺1
81 (70–92)
83 (72–93)
78 (65–90)
⬍0.001
5.81 (4.93–6.67)
5.94 (5.11–6.80)
5.44 (4.69–6.35)
⬍0.001
hs-CRP, mg/L
2.8 (1.2–6.7)
2.3 (1.0–5.5)
4.1 (1.6–9.3)
⬍0.001
IL-6, ng/L
3.0 (1.7–5.7)
2.6 (1.7–4.5)
4.3 (2.3–7.6)
⬍0.001
NT-proBNP, ng/L
295 (113–874)
201 (86–486)
751 (256–1822)
⬍0.001
Cholinesterase, kU/L
hs-cTnT, ng/L
10 (6–19)
8 (5–14)
17 (9–31)
⬍0.001
Galectin-3, ng/mL
14.7 (11.6–18.8)
14.2 (11.3–17.8)
15.6 (12.3–20.7)
⬍0.001
sST2, ng/mL
19.4 (15.7–24.6)
18.5 (15.4–23.2)
21.4 (16.6–28.3)
⬍0.001
d
Mortality rates All-cause mortality
477 (36)
Cardiovascular mortality
305 (23)
a
Dichotomous data are given as absolute numbers (%), and continuous variables are presented as median (interquartile range). Baseline biochemical data expressed in SI units are available in online Supplemental Table 1. b Univariate comparisons between survivors and decedents were performed with the Fisher exact test for categorical variables and the nonparametric Mann–Whitney U-test for continuous variables. Respective P values were not adjusted for multiple comparisons and are therefore descriptive only. c For left ventricular ejection fraction, univariate comparison between survivors and decedents was performed with the 2 test. d Plasma concentrations for galectin-3 were available in 1025 patients.
Rates of all-cause mortality as a function of sST2 quartiles are depicted in Fig. 1, displaying a relationship between rising sST2 plasma concentrations and increased rates of all-cause mortality, indicating a clear threshold effect between the third and fourth quartiles (⬎24.6 ng/mL) of the entire cohort. Fig. 2 shows Kaplan–Meier plots according to sST2 quartiles, confirming the threshold effect between the third and fourth quartile for prediction of all-cause and cardiovascular mortality in patients with stable CAD (log-rank test for trend, P ⬍ 0.001). With this threshold (⬎24.6 ng/mL) as cutoff to dichotomize sST2 in univariate Cox proportionalhazard regression analysis, patients with sST2 ⬎ 24.6 ng/mL had a 2-fold increased risk of death, with RRs of 2.01 (95% CI 1.74 –2.53; P ⬍ 0.001) for all-cause mortality and 2.19 (95% CI 1.73–2.76; P ⬍ 0.001) for cardiovascular mortality, which was attenuated but remained significant in the multivariate models after adjustment for all clinical variables and biomarkers listed in Table 2, with RR of 1.39 (95% CI 1.10 –1.76; P ⫽ 0.006) for all-cause and RR of 1.44 (95% CI 1.08 – 1.93; P ⫽ 0.013) for cardiovascular mortality, respectively. 534 Clinical Chemistry 60:3 (2014)
To explore whether sST2 adds to the prognostic value of NT-proBNP and hs-cTnT, we computed RRs of mortality by Cox proportional-hazards regression analyses in which the study participants were stratified into quartiles of sST2 and NT-proBNP as well as quartiles of sST2 and hs-cTnT. As shown in Fig. 3, the prognostic impact of sST2 was additive to NT-proBNP and hs-cTnT; RRs were highest among patients in the highest quartiles of both sST2 and NT-proBNP as well in the highest quartiles of both sST2 and hs-cTnT for allcause and cardiovascular mortality, respectively. Finally, we performed a simple combined multibiomarker approach stratifying the entire cohort according to cutoffs between the third and fourth quartiles for sST2 (⬎24.6 ng/mL), NT-proBNP (⬎874 ng/ L), and hs-cTnT (⬎19 ng/L). Fig. 4 displays the Kaplan–Meier curves of survival according to the presence of 0, 1, 2, or 3 biomarkers above the cutoff for the prediction of all-cause and cardiovascular mortality (log-rank tests for trend, P ⬍ 0.001). Of the 1345 patients with stable CAD, 707 patients presented with no increased biomarkers, 356 patients with 1, 191 patients with 2, and 91 patients with 3 increased biomarkers at baseline. For each additional biomarker found to be
sST2 Predicts Long-Term Mortality in Stable CAD
Table 2. Results of Cox proportional hazards regression analyzing the effect of baseline variables on all-cause mortality and cardiovascular mortality in patients with stable CAD.a Variable
Univariate analyses
P
Multivariate modelb
P
All-cause mortality Sex
0.80 (0.64–0.99)
0.041
0.91 (0.70–1.18)
0.460
Age
1.79 (1.56–2.06)
⬍0.001
1.57 (1.35–1.82)
⬍0.001
Body mass index
0.90 (0.82–0.99)
0.030
0.97 (0.87–1.09)
0.617
Arterial hypertension
1.27 (1.01–1.59)
0.046
0.91 (0.69–1.20)
0.499
Dyslipidemia
1.06 (0.87–1.29)
0.570
0.93 (0.74–1.17)
0.526
Diabetes mellitus
2.09 (1.74–2.50)
⬍0.001
1.56 (1.24–1.96)
⬍0.001
Current smoking
0.99 (0.77–1.27)
0.921
1.37 (1.00–1.86)
0.049
Prior myocardial infarction
1.36 (1.13–1.62)
0.001
1.02 (0.82–1.27)
0.881
Peripheral artery disease
2.40 (1.93–2.98)
⬍0.001
1.55 (1.18–2.03)
0.001
Cerebrovascular disease
2.23 (1.75–2.85)
⬍0.001
1.56 (1.16–2.11)
0.004
Resting heart rate
1.28 (1.17–1.41)
⬍0.001
1.14 (1.02–1.28)
0.020 ⬍0.001
Left ventricular ejection fraction
1.72 (1.58–1.88)
⬍0.001
1.26 (1.11–1.43)
Estimated glomerular filtration rate
0.76 (0.71–0.81)
⬍0.001
1.00 (0.89–1.12)
0.988
Cholinesterase
0.65 (0.57–0.73)
⬍0.001
1.01 (0.85–1.20)
0.895
hs-CRP
1.49 (1.36–1.63)
⬍0.001
1.01 (0.89–1.16)
0.836
IL-6
1.63 (1.49–1.78)
⬍0.001
1.13 (1.00–1.28)
0.055
NT-proBNP
2.21 (2.01–2.43)
⬍0.001
1.29 (1.10–1.52)
0.002
hs-cTnT
1.95 (1.78–2.14)
⬍0.001
1.20 (1.04–1.39)
0.014
Galectin-3c
1.38 (1.24–1.53)
⬍0.001
1.07 (0.95–1.21)
0.280
sST2
1.49 (1.38–1.61)
⬍0.001
1.16 (1.05–1.29)
0.004
Cardiovascular mortality Sex
0.79 (0.60–1.03)
0.081
0.94 (0.68–1.29)
0.684
Age
1.79 (1.56–2.06)
⬍0.001
1.51 (1.26–1.81)
⬍0.001
Body mass index
1.00 (0.89–1.13)
0.963
1.09 (0.95–1.24)
0.220
Arterial hypertension
1.21 (0.91–1.60)
0.195
0.94 (0.66–1.33)
0.724
Dyslipidemia
1.17 (0.91–1.49)
0.228
0.95 (0.71–1.26)
0.699
Diabetes mellitus
2.19 (1.74–2.75)
⬍0.001
1.53 (1.16–2.03)
0.003
Current smoking
0.96 (0.70–1.31)
0.782
1.22 (0.82–1.80)
0.331
Prior myocardial infarction
1.46 (1.17–1.83)
0.001
1.04 (0.80–1.37)
0.753
Peripheral artery disease
1.94 (1.45–2.59)
⬍0.001
1.29 (0.92–1.82)
0.138
Cerebrovascular disease
2.02 (1.48–2.77)
⬍0.001
1.41 (0.96–2.07)
0.083
Resting heart rate
1.26 (1.12–1.41)
⬍0.001
1.08 (0.94–1.25)
0.254
Left ventricular ejection fraction
1.87 (1.69–2.07)
⬍0.001
1.29 (1.10–1.51)
0.001
Estimated glomerular filtration rate
0.76 (0.70–0.82)
⬍0.001
1.09 (0.94–1.27)
0.274
Cholinesterase
0.66 (0.57–0.78)
⬍0.001
1.14 (0.93–1.41)
0.214
hs-CRP
1.59 (1.42–1.78)
⬍0.001
1.06 (0.90–1.25)
0.498
IL-6
1.70 (1.51–1.92)
⬍0.001
1.14 (0.98–1.33)
0.090
NT-proBNP
2.39 (2.13–2.69)
⬍0.001
1.46 (1.19–1.79)
⬍0.001
hs-cTnT
2.17 (1.94–2.43)
⬍0.001
1.30 (1.09–1.56)
0.004
Galectin-3c
1.36 (1.19–1.55)
⬍0.001
1.05 (0.90–1.22)
0.561
sST2
1.47 (1.33–1.62)
⬍0.001
1.15 (1.01–1.31)
0.040
a
Data are RR (95% CI). Age, body mass index, resting heart rate, and biochemical markers were normalized by log transformation, and RRs refer to a 1-SD increase in the log-transformed units. b Multivariate model without variable selection technique (all independent variables listed above were included simultaneously into the model). c Plasma concentrations for galectin-3 were available in 1025 patients.
Clinical Chemistry 60:3 (2014) 535
Fig. 1. Rates of all-cause mortality as a function of sST2 quartiles in patients with stable CAD.
increased in this combined multibiomarker approach, we found a steeply rising incremental risk of death. During follow-up, the lowest rates of death were observed in patients with no increased biomarkers, with a 21% (n ⫽ 145) death rate from all-cause and 11% (n ⫽ 77) death rate from cardiovascular mortality. In patients with 1 increased biomarker, death rates of 39% (n ⫽ 14) from all-cause and 27% (n ⫽ 95) from cardiovascular mortality were observed. The patients with 2 increased biomarkers displayed death rates of 60% (n ⫽ 114) from all-cause and 41% (n ⫽ 78) from cardiovascular mortality. Finally, the highest death rates were observed in patients with 3 increased biomarkers, with an 86% (n ⫽ 78) rate from all-cause and a 60% (n ⫽ 78) rate from cardiovascular mortality. Discussion In the present analysis of a large and well-characterized cohort, we demonstrated that sST2 is an independent predictor for long-term all-cause and cardiovascular mortality in patients with stable CAD. Furthermore, we reported that sST2 adds prognostic value to the well-established cardiac biomarkers NT-proBNP and hs-cTnT. Using a simple multibiomarker approach combing these 3 complementary biomarkers, we identified patients at low, intermediate, and high risk for all-cause and cardiovascular mortality. 536 Clinical Chemistry 60:3 (2014)
Clinical studies in patients with acute myocardial infarction or acute coronary syndrome unequivocally have demonstrated that increased sST2 is associated with adverse outcome (5–9 ). Although stable CAD and unstable CAD represent 2 entities of the same disease, they differ distinctly in their progression and long-term prognosis (24 ). The present study is the first report on the long-term prognostic value of sST2 in patients with stable CAD. We found that increased sST2 at baseline is associated with long-term all-cause and cardiovascular mortality in patients with stable CAD. Even after extensive adjustments for clinical variables and several other biomarkers, sST2 remained an independent prognostic marker. In the same multivariate analyses, NTproBNP and hs-cTnT were also independent predictors, whereas galectin-3, an emerging prognostic biomarker in cardiovascular diseases (22 ), was not found to be an independent predictor. We further found that when a cutoff value (⬎24.6 ng/mL) was used for prediction of long-term mortality in patients with stable CAD, patients with sST2 above this cutoff at baseline displayed a 2-fold increased risk of all-cause and cardiovascular death. Therefore, the present study extends the prognostic value of sST2 to patients with stable CAD. The cutoff value we found for sST2 in the present study on patients with stable CAD is lower than the cutoff of ⬎35 ng/mL recom-
sST2 Predicts Long-Term Mortality in Stable CAD
Fig. 2. Kaplan–Meier plots showing survival according to sST2 quartiles in patients with stable CAD.
mended by the manufacturer for risk stratification of patients with heart failure. Research studies in animal models of cardiac overload and myocardial infarction have reported that the IL-33/ST2 signaling axis shields the myocardium against maladaptive hypertrophy, fibrosis, and cardiomyocyte apoptosis and thereby reduces cardiac dysfunction and improves survival (25–27 ). Furthermore, IL-33/ST2 signaling has also been implicated in modulating atherogenesis (28 ). A mouse model of developing atherosclerosis demonstrated that treatment with IL-33 significantly reduced atherosclerotic lesion size (29 ). Conversely, sST2-treated mice developed significantly larger atherosclerotic plaques, indicating that sST2 counteracts the atheroprotective effects of IL-33/ ST2 signaling (29 ). Atherosclerosis is a chronic inflammatory disease that appears to be driven by a Th1 immune response (30 –32 ). It was hypothesized that IL33/ST2 signaling may have protective effects during atherosclerosis by inducing a Th1-to-Th2 switch of immune responses (28 ). Furthermore, IL-33 and ST2 mRNA and protein expression were found in human
atherosclerotic plaque (33 ). Very recently, differential expression of components of the IL-33/ST2 signaling was reported in adult human cardiac cells and cells of the cardiac vasculature (34 ). IL-33 was constitutively expressed in cardiac fibroblasts and myocytes, whereas only minor expression of mRNA of both ST2 isoforms (ST2L and sST2) was found in these cells. In contrast, vascular endothelial cells were the predominant source of mRNA expression for both ST2 isoforms and secretion of sST2 protein (34 ). These findings are consistent with previous work, which demonstrated that the myocardium is not the major source of increased sST2 in patients with heart failure, but that sST2 is rapidly synthesized and released from endothelial cells in response to an inflammatory stimulus, supporting the notion that vascular endothelium is the major source of increased circulating sST2 (35 ). The functional role of sST2 in vivo has not fully been elucidated. However, it is tempting to speculate that increased sST2 in patients with stable CAD may be a result of the chronic inflammation present in atherosclerosis. The median sST2 plasma concentrations of our present evaluation of patients with stable CAD (19.4 ng/mL) are markedly increased compared with median concentrations we previously reported in healthy blood donors (11.1 ng/mL) (23 ), and are similar to the results of several previous studies on patients with coronary artery disease, by use of the same Presage™ ST2 assay. Median sST2 concentrations of 28.4 ng/mL at baseline were observed, followed by a subsequent decrease to 21.8 ng/mL after 72 h in patients with non– ST-elevation acute coronary syndromes from the GUSTO IV (Global Use of Strategies to Open Occluded Coronary Arteries in Acute Coronary Syndromes) substudy on inflammatory markers (7 ). In the MERLINTIMI 36 (Metabolic Efficiency with Ranolazine for Less Ischemia in Non–ST-Elevation Acute Coronary Syndromes/Thrombolysis in Myocardial Infarction) biomarkers substudy on patients with non–ST-elevation acute coronary syndromes, median sST2 plasma concentration at baseline was 24.4 ng/mL (8 ), whereas patients with chronic heart failure of ischemic etiology from CORONA (Controlled Rosuvastatin Multinational Study in Heart Failure) displayed a median sST2 concentration at baseline of 17.8 ng/mL (15 ). Contrasting results have, however, recently been published from the Framingham Heart Study, a large populationbased cohort of apparently healthy individuals, in which comparatively high median plasma concentrations of 23.6 ng/mL for males and 18.8 ng/mL for females were reported (20 ). In the present study, we further evaluated the additive prognostic value of sST2 to established cardiac biomarkers, namely cardiac natriuretic peptides (NTproBNP) and cardiac troponins (hs-cTnT). Cardiac Clinical Chemistry 60:3 (2014) 537
Fig. 3. RRs of all-cause mortality (A) and cardiovascular mortality (B) in patients with stable CAD for sST2 in addition to NT-proBNP and hs-cTnT. sST2 quartiles: Q1 ⬍15.6 ng/mL, Q2 15.6 –19.3 ng/mL, Q3 ⬎19.3–24.6 ng/mL, Q4 ⬎24.6 ng/mL; NT-proBNP quartiles: Q1 ⬍113 ng/L, Q2 113–294 ng/L, Q3 ⬎294 – 874 ng/L, Q4 ⬎874 ng/L; hs-cTNT quartiles: Q1 ⬍6 ng/L, Q2 6 –10 ng/L, Q3 ⬎10 –19 ng/L, Q4 ⬎19 ng/L.
natriuretic peptides (specific markers of cardiac stretch) and cardiac troponins (specific markers of myocardial necrosis) are widely used in clinical practice for the diagnosis and/or risk stratification of patients with heart failure or acute myocardial infarction (36, 37 ). Both have proven to be strong and independent prognostic cardiac biomarkers in patients with stable CAD (38 – 40 ). In our study the prognostic impact of sST2 was additive to NT-proBNP and hs-cTnT, where risk was highest among patients in the highest quartiles of both sST2 and NT-proBNP as well as in the highest quartiles of both sST2 and hs-cTnT for allcause and cardiovascular mortality. Using a simple multibiomarker approach combining these 3 complementary biomarkers, we identified patients at low, intermediate, and high risk for long-term mortality. For each additional biomarker 538 Clinical Chemistry 60:3 (2014)
increased in this combined model, we found a steeply rising incremental risk of death. Patients with no increased biomarkers displayed all-cause death rates of only 21%, whereas patients with increases in all 3 biomarkers experienced the poorest outcome, with all-cause death rates of 86%. Thus a combination of these 3 complementary biomarkers might provide a tool for outcome prediction in patients with stable CAD. Because atherosclerosis is a chronic inflammatory disease with ongoing vascular remodeling, it is conceivable that sST2 detects prognostic information not identified by the established cardiac biomarkers NT-proBNP and hs-cTnT. Of note, sST2 alone was not as predictive as NTproBNP or hs-cTnT, but had the most benefit when combined with these established prognostic biomarkers.
sST2 Predicts Long-Term Mortality in Stable CAD
since then, diagnostic algorithms and secondary prevention strategies for patients with stable CAD have changed. Furthermore, this cohort of patients was predominantly male, so we were not able to evaluate sexspecific cutoffs for sST2, NT-proBNP, and hs-cTnT. In conclusion, among a large cohort of wellcharacterized patients with stable CAD, sST2 was an independent predictor for long-term all-cause and cardiovascular mortality. We found that increased sST2 detects risk elements not identified by NT-proBNP and hs-cTnT. Thus, a combination of these 3 complementary biomarkers may provide a promising tool for outcome prediction in patients with stable CAD.
Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:
Fig. 4. Kaplan–Meier plots showing survival according to a combined-biomarker approach in patients with stable CAD, where the entire cohort was stratified according to the number of increased biomarkers (ST2 >24.6 ng/mL, NT-proBNP >874 ng/L, and hscTnT >19 ng/L).
We acknowledge that patients were included into the present study between the years 1997 and 2000 and
Employment or Leadership: W. Maerz, Synlab Services GmbH. Consultant or Advisory Role: R. de Boer, BG Medicine. Stock Ownership: R. de Boer, Pectacea, Sc Pharma. Honoraria: R. de Boer, BG Medicine, Abbott. Research Funding: The LURIC study has received funding from the 6th Framework Program (integrated project Bloodomics, grant LSHM-CT-2004-503485) and 7th Framework Program (integrated project AtheroRemo, Grant Agreement number 201668 and RISKYCAD, grant agreement number 305739) of the European Union. Critical Diagnostics provided reagents for sST2 measurement free of charge. R. de Boer, Abbott, BG Medicine. Expert Testimony: None declared. Patents: None declared. Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript.
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