Download Increased Soluble ST2 Predicts Long-term Mortality in Patients with

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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