Journal of Cardiac Failure Vol. 21 No. 1 2015
The Utility of Galectin-3 for Predicting Cause-Specific Death in Hospitalized Patients With Heart Failure YUHUI ZHANG, MD,1 RONGCHENG ZHANG, MM,1 TAO AN, MD,1 YAN HUANG, MB,1 XIAO GUO, MD,1 SHIJIE YIN, MM,1 YUNHONG WANG, MM,1 SHIMING JI, RN,1 RONG LV, RN,1 JIAN ZHANG, MD, PhD,1 AND ALAN MAISEL, MD2 Beijing, China; and San Diego, CA, USA
ABSTRACT Objectives: Galectin-3 has been shown to be involved in the process of cardiac fibrosis and to predict adverse events in heart failure (HF), but the association of galectin-3 with cause-specific death has not been well established. The purpose of this study was to investigate the prognostic value of baseline galectin-3 for all-cause, cardiovascular (CV), and in-hospital death in patients with HF. Methods and Results: From March 2009 to April 2013, we consecutively measured galectin-3 in a large cohort of 1,440 hospitalized patients with HF. Cox proportional hazards regression, discrimination, and reclassification analyses were used to evaluate the association between galectin-3 and death. During a median follow-up of 582 days, 283 deaths were identified, of which 64 were patients who died during hospitalization. Compared with the lowest galectin-3 tertile, the highest 2 tertiles were significantly associated with all-cause, CV, and progressive HF death, but not significant for sudden and inhospital death when analyzed by multivariable Cox regression. The utility of combining galectin-3 and N-terminal proeB-type natriuretic peptide was assessed by dichotomizing these 2 biomarkers according to their median values. The highest risk of death due to all-cause, CV, and progressive HF was observed when both biomarkers were elevated after adjustment for established risk factors. Addition of galectin-3 to the prediction model for all-cause and CV death significantly improved discrimination and reclassification. Conclusions: Galectin-3 independently predicted death and added additional prognostic value beyond established risk factors in hospitalized patients with HF. The utility of galectin-3 alone as a risk predictor was not strong enough to assess sudden or in-hospital death. (J Cardiac Fail 2015;21:51e59) Key Words: Galectin-3, heart failure, death, prognosis.
Heart failure (HF) is the end-stage condition of various cardiovascular (CV) diseases with adverse prognosis. Although advances have been made in HF diagnosis and treatment, the 1-year case fatality rates after hospitalization for HF were 22% in a community-based study.1 Assessing the risk of patients may be helpful for physicians in guiding medical decision making. Patients estimated to be at high risk may require intensified monitoring and follow-up and more advanced therapies. Currently, several biomarkers associated with cardiac fibrosis have been found to reflect the process of HF and predict adverse events.2,3 Galectin-3, a member of the galectin family, is overexpressed by activated macrophages and involved in the processes of inflammation and fibrosis that cause cardiac dysfunction.4 Studies have demonstrated that elevated concentrations of galectin-3 are associated with adverse events in both the general population,5 as well patients with acute coronary syndrome6 and HF.7e11 Although Ahmad et al
From the 1State Key Laboratory of Cardiovascular Disease, Heart Failure Center Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China and 2Department of Medicine, University of California, San Diego, CA, USA. Manuscript received July 29, 2014; revised manuscript received September 12, 2014; revised manuscript accepted October 8, 2014. Reprint requests: Jian Zhang, MD, PhD, 167 Beilishi Road, Beijing 100037, China. Tel: þ86-13911102015; Fax: þ86 (10) 8839 6180. E-mail: [email protected] The first 2 authors contributed equally to this work. See page 58 for disclosure information. Funding: Key Projects in the National Science & Technology Pillar Program of the 12th 5-Year Plan Period (no. 2011BAI11B02, Project for Heart Failure), Beijing, China. Galectin-3 assays provided by BG Medicine. NT-proBNP assays provided by Alere. Neither of these companies participated in process of the study. 1071-9164/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cardfail.2014.10.006
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52 Journal of Cardiac Failure Vol. 21 No. 1 January 2015 have investigated the association of galectin-3 with pump failure and sudden cardiac death in patients with chronic HF,12 few studies have focused on cause-specific and inhospital death in hospitalized patients with HF, another specific population of HF. Furthermore, according to the demographic characteristics, the expression of galectin-3 in patients with HF from Asia has not been well characterized. Therefore, we aimed to investigate the clinical characteristics of galectin-3 and evaluate the performance of galectin-3 for predicting death in a large cohort of Chinese hospitalized patients with HF.
patient without evidence of specific cause of death), death due to myocardial infarction or stroke, or other CV death (such as mortal complications of cardiac surgery, rupture of an aneurysm pulmonary embolism, aortic dissection, etc). All patients provided written informed consents, and the Ethics Committee of Fuwai Hospital approved the study. Biomarker Measurement Fasting venous blood samples were collected within 12 hours of hospitalization, immediately centrifuged, and stored at 80 C in plasma with EDTA. Galectin-3 and NT-proBNP were determined in blood samples with no more than one freeze-thaw cycle. Information about these 2 assays is detailed in Supplemental Table 1.
Materials and Methods Study Population and Design We consecutively enrolled patients admitted to Fuwai Hospital HF center, Beijing, from March 2009 to April 2013 with HF as their primary diagnosis. The diagnosis of HF was confirmed by 2 specialists according to Chinese guidelines.13,14 In this study, patients with both preserved ejection fraction and systolic HF (defined as a de novo presentation of HF secondary to abnormal cardiac function or worsening of previously chronic stable HF requiring unplanned hospitalization) were evaluated and enrolled if they were aged $18 years and agreed to venous blood sampling for biomarkers analysis. Patients with a diagnosis of acute coronary syndrome, cancer, or acute pulmonary embolism were excluded from this analysis. An ischemic etiology of HF was assumed if the patient had prevalent angina pectoris, a history of coronary artery bypass grafting, percutaneous coronary intervention, or acute myocardial infarction, or confirmed coronary artery obstruction according to coronary angiography or computerized tomographic angiography. Data from medical records were abstracted by trained clinicians or cardiology nurses and entered into a predefined electronic case report form with checking by another abstractor. Clinical data, including demographic characteristics, New York Heart Association (NYHA) functional class, primary HF etiologies, physical examination, preexisting comorbidities, and medical history, were obtained at the time of the hospitalization. All patients received intravenous loop diuretics at least once during the 1st 24 hours of admission. Left ventricular ejection fraction (LVEF) was assessed and interpreted by specialists trained in cardiac ultrasonography by means of echocardiography within 48 hours after admission. Posterior and septal wall thickness at end-diastole was also available and used to calculate LV mass index (LVMI) and relative wall thickness (RWT).15 Estimated glomerular filtration rate (eGFR) was calculated with the use of Cockcroft-Gault equation,16 and the Child-Turcotte-Pugh score was used to assess liver function.17 For patients with multiple admissions, only the 1st admission was included in this study. Adverse events regarding causespecific and in-hospital death were ascertained (every 3 months for discharged patients) via electronic hospital records or conversations with patients or patients’ families by telephone by an investigator who was blinded to the value of galectin-3 and N-terminal proeB-type natriuretic peptide (NT-proBNP). NonCV deaths were considered if there was no specific CV cause identified as the main trigger of death. CV death includes progressive HF death (progressive deterioration of HF in the absence of other cause), sudden death (unexpected and witnessed death in a stable
Statistical Analyses Continuous variables were tested for normal distribution by means of the Kolmogorov-Smironov test and are described as mean 6 SD for normally distributed variables and median (interquartile range [IQR]) for variables with skewed distribution. Categoric variables are described as percentages. Comparisons among galectin-3 tertiles were performed with the use of analysis of variance for normal continuous, Kruskal-Wallis H testing for nonnormal continuous, and c2 tests for categoric variables. MannWhitney U test was used to compare the difference of galectin-3 between 2 groups. Log2 transformation was performed to normalize the distribution of NT-proBNP and galectin-3. Univariate Spearman correlation was used to evaluate the relationships between galectin-3 and clinical variables. Multivariate linear regression analyses were performed with the use of a stepwise method, with logtransformed of galectin-3 levels as the dependent variables. Cox regression was used to estimate the associations between galectin-3 and death with the use of galectin-3 tertiles and logarithmic transformation. We included the following variables in the reference model: sex, age, diabetes mellitus, ischemic heart disease, systolic blood pressure, NYHA functional class, LVEF, angiotensinconverting enzyme inhibitor or angiotensin II receptor blocker (ACEI/ARB) treatment, b-blocker treatment, hemoglobin, sodium, eGFR, and NT-proBNP. Log-rank tests for Kaplan-Meier cumulative hazard curves were used for comparisons. Differences in Harrell C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were performed to evaluate the additional predictive value of galectin-3 in the reference model. Because there are no established criterion standard risk categories for HF, we used category-free NRI as described by Pencina et al.18 Confidence intervals and P values for NRI and IDI were determined by bootstrapping with 1,000 repetitions. The Hosmer-Lemeshow statistic was used to evaluate model calibration. Receiver operating characteristic (ROC) curve analyses were used to evaluate the ability of galectin-3 to classify patients who died or were alive during hospitalization. All P values of !.05 from 2-sided tests
Galectin-3 Assessment in Heart Failure
were accepted as statistically significant. Statistical analyses were conducted using SPSS version 19.0 (SPSS, Chicago, Illinois) and Stata version 11.2 (Statacorp, College Station, Texas).
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disease. The median (IQR) galectin-3 concentration was 21.9 ng/mL (16.4e29.4). Patients without blood samples available (n 5 285) did not differ in age, sex, and LVEF from included patients. Distribution and Correlations of Galectin-3
Results
Median galectin-3 concentrations had an increasing trend in patients with worse symptoms defined by NYHA functional class (P ! .001). Patients with nonischemic HF etiology did not have a significantly different galectin-3 concentration compared with ischemic HF patients (P 5 .816). A significant association was detected between galetin-3 concentrations and severity of liver function defined by Child-Turcotte-Pugh score (P ! .001). After dichotomizing renal function as a function of eGFR of 60 mL min1 1.73 m2, galectin-3 concentrations were significantly higher in
Baseline Characteristics
A total of 1,940 patients presenting with HF were admitted from March 2009 to April 2013; 1,440 patients were included by design (Supplemental Fig. 1). Baseline characteristics across the included patients according to galectin-3 tertile are presented in Table 1. The mean age was 57.8 6 15.6 years, the majority were male (70.3%), and 55% of the patients had LVEF O40%. Approximately one-half of the patients had a history of ischemic heart
Table 1. Baseline Characteristics According to Galectin-3 Tertile Galectin-3 Tertile
Variable Age, y Male, n (%) History, n (%) Hypertension Diabetes mellitus Ischemic heart disease Dilated cardiomyopathy Valvular heart disease Congenital heart disease Physical examination Heart rate, beats/min Systolic blood pressure, mm Hg Diastolic blood pressure, mm Hg Body mass index, kg/m2 NYHA functional class, n (%) II III IV LVEF (%)* LVDD (mm) LV mass index (g/m2) Relative wall thickness Current smoking, n (%) Medication on presentation, n (%) Loop diuretics ACEI/ARB b-blockers Aldosterone antagonists Digoxin Laboratory results Hemoglobin, g/dL Sodium, mmol/L eGFR, mL min-1 1.73 m-2 Child-Turcotte-Pugh score Child A Child B Child C NT-proBNP, pg/mL
All Patients (n 5 1,440) 57.8 6 15.6 1,013 (70.3) 693 366 687 296 230 48
(48.1) (25.4) (47.7) (20.6) (16.0) (3.3)
78 6 17 119 6 20 71 6 13 24.2 6 4.2 435 603 402 40
(30.2) (41.9) (27.9) (30e56) 59 6 13 131.1 (103.6e168.5) 0.31 (0.26e0.38) 392 (27.2) 955 745 1093 885 687
(66.3) (51.7) (75.9) (61.5) (47.7)
133.6 6 22.7 139.4 6 3.5 76.5 6 34.5 1217 204 19 1546
(84.5) (14.2) (1.3) (778e3272)
Tertile 1: #18.1 ng/mL (n 5 480)
Tertile 2: O18.1 to #26.4 ng/mL (n 5 480)
Tertile 3: O26.4 ng/mL (n 5 480)
P Value
52.6 6 15.2 363 (75.6)
57.3 6 15.1 341 (71)
63.5 6 14.5 309 (64.4)
!.001 !.001
202 94 225 113 69 17
230 114 219 102 73 16
261 158 243 81 88 15
.001 !.001 .272 .034 .211 .937
(42.1) (19.6) (46.9) (23.5) (14.4) (3.5)
77 6 16 120 6 19 71 6 13 24.5 6 4.6 201 198 81 44
(41.9) (41.3) (16.9) (31e59) 59 6 12 126.4 (99.0e159.4) 0.32 (0.26e0.38) 123 (25.6) 284 274 379 281 205
(59.2) (57.1) (79.0) (58.5) (42.7)
137.0 6 19.5 140.0 6 3.0 92.0 6 32.4 442 37 1 1062
(92.1) (7.7) (0.2) (640e2102)
(47.9) (23.8) (45.6) (21.3) (15.2) (3.3)
78 6 17 119 6 20 72 6 13 24.2 6 3.9 143 214 123 40
(29.8) (44.6) (25.6) (29e55) 59 6 14 132.9 (105.8e172.6) 0.30 (0.25e0.37) 131 (27.3) 327 257 370 306 243
(68.1) (53.5) (77.1) (63.8) (50.6)
135.5 6 22.2 139.4 6 3.2 77.4 6 29.8 410 66 4 1485
(85.4) (13.8) (0.8) (760e2944)
(54.4) (32.9) (50.6) (16.9) (18.3) (3.1)
78 6 19 120 6 21 70 6 12 23.9 6 4.2 91 191 198 40
(19.0) (39.8) (41.3) (30e55) 59 6 14 134.2 (106.4e177.0) 0.32 (0.26e0.38) 138 (28.8) 344 214 344 298 239
(71.7) (44.6) (71.7) (62.1) (49.8)
128.4 6 25.1 138.8 6 4.1 60.1 6 33.7 365 101 14 2664
(76.0) (21.0) (2.9) (1238e5038)
.467 .459 .042 .147 !.001 .304 !.001 .006 .821 .011 .225 .553 !.001 !.001 .023 .238 .026 !.001 !.001 !.001 !.001 !.001 !.001 !.001
ACEI, angiotension-converting enzyme inhibitor; ARB, angiotensin receptor blocker; eGFR, estimated glomerular filtration rate; LVDD, left ventricular diastolic diameter; LVEF, left ventricular ejection fraction; NT-proBNP, N-terminal proeB-type natriuretic peptide; NYHA, New York Heart Association. Values are presented as mean 6 SD, n (%), or median (interquartile range). *Patients with the lowest galectin-3 levels have the highest LVEF according to pairwise comparisons of Bonferroni test (tertile 1 vs tertile 2: P 5 .011; tertile 1 vs tertile 3: P 5 .034).
54 Journal of Cardiac Failure Vol. 21 No. 1 January 2015 other clinical indexes (Supplemental Fig. 2). A multivariable linear regression model with log-transformed galectin-3 as a dependent variable showed that galectin-3 concentrations were predicted by sex, body mass index, ACEI/ARB, NYHA functional class, sodium, eGFR, Child-Turcotte-Pugh score, and log NT-proBNP. The R2 was 0.31.
Associations Between Galectin-3 and Death
Fig. 1. Box plots of galectin-3 according to left ventricular ejection fraction (LVEF) in patients with different levels of New York Heart Association functional class. P values indicate the differences among groups stratified by LVEF.
patients with low values of eGFR than in patients with high values (P ! .001). Patients with the lowest galectin-3 levels had the highest LVEF according to pairwise comparisons of the Bonferroni test (Table 1). Given the difference of pathophysiologic processes linked to fibrosis, hypertrophy, and dysfunction between HF with reduced ejection fraction (HF-rEF) and with preserved ejection fraction (HF-pEF), we categorized LVEF according to the cutoff points of EF $50%, 50% O EF O 40%, and EF #40% and found that galectin-3 concentration was not different in patients with different categories of LVEF after considering NYHA functional class (Fig. 1). Univariate correlations of galectin-3 with other continuous variables showed that galectin-3 concentrations were correlated with LV mass index (r 5 0.083; P 5 .003) and
During a median follow-up of 582 days, 283 deaths were indentified: 259 of all deaths resulted from CV cause (91.5%), 169 were due to progressive HF (59.7%), 43 were due to sudden death (15.2%) (Supplemental Fig. 1). Galectin-3 concentrations were significantly higher in patients who died compared with those who were alive (P ! .001). In addition, increasing tertiles of galectin-3 were associated with increasing risk of 1-year mortality due to all-cause and CV death in patients with different categories of LVEF (Fig. 2). The interaction between LVEF (EF $50%, 50% O EF O 40%, EF #40%) and prognostic value of sST2 for death also was investigated. We found that LVEF did not significantly interact with the prognostic value of sST2 for allcause (P 5 .586) and CV (P 5 .267) death. The prognostic values of log-transformed galectin-3 and galectin-3 tertile analyzed by Cox regression are presented in Table 2. Compared with the lowest tertile of galectin-3, the highest 2 tertiles had significant unadjusted hazard ratios (HRs) for all-cause, CV, and progressive HF death, and remained robust when adjusted for established risk factors. There was not a significant association for sudden death. Kaplan-Meier cumulative hazard curves depicting time to cause-specific death showed the highest galectin-3 tertile to be significantly associated with end points related to all-cause, CV, progressive HF, and sudden death (Supplemental Fig. 2).
Fig. 2. One-year mortality according to tertiles of galectin-3 and categories of left ventricular ejection fraction (LVEF). Increasing tertiles of galectin-3 were associated with 1-year mortality due to (A) all causes and (B) cardiovascular death at every level of LVEF.
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Table 2. Hazard Ratios for Death According to Log-Transformed Galectin-3 and Galectin-3 Tertile Unadjusted
All cause death Log galectin-3 Tertile 1 Tertile 2 Tertile 3 CV death Log galectin-3 Tertile 1 Tertile 2 Tertile 3 Progressive HF death Log galectin-3 Tertile 1 Tertile 2 Tertile 3 Sudden death Log galectin-3 Tertile 1 Tertile 2 Tertile 3 In-hospital mortality Log galectin-3 Tertile 1 Tertile 2 Tertile 3
Adjusted*
HR (95% CI)
P Value
HR (95% CI)
P Value
4.36 (3.49e5.45) reference 2.94 (1.95e4.44) 6.76 (4.60e9.92)
!.001
2.17 (1.63e2.89) reference 1.98 (1.30e3.02) 2.88 (1.90e4.36)
!.001
4.58 (3.62e5.80) reference 2.96 (1.88e4.64) 7.34 (4.84e11.13)
!.001
!.001
!.001 !.001
2.32 (1.71e3.15) reference 2.00 (1.26e3.17) 3.17 (2.02e4.98)
!.001
!.001 !.001
2.46 (1.71e3.54) reference 2.69 (1.41e5.12) 4.43 (2.36e8.33) 1.95 (0.91e4.17) reference 0.94 (0.38e2.32) 1.66 (0.69e3.99)
.085
2.00 (1.19e3.39) reference 1.96 (0.72e5.33) 2.18 (0.83e5.72)
.009
reference 4.27 (2.27e8.05) 11.25 (6.19e20.42) 3.30 (1.83e5.97) reference 1.34 (0.56e3.24) 3.21 (1.48e6.95) 3.72 (2.44e5.69) reference 3.44 (1.29e9.17) 6.33 (2.49e16.10)
!.001 !.001
!.001 !.001 !.001 !.001 .014 !.001
.002 !.001
.003 !.001
.003 !.001
.894 .243
.190 .115
CI, confidence interval; CV, cardiovascular; HF, heart failure; HR, hazard ratio. *Covariates for adjusted model: sex, age, diabetes mellitus, ischemic heart disease, systolic blood pressure, New York Heart Association functional class, left ventricular ejection fraction, angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker treatment, b-blockers treatment, hemoglobin, sodium, estimated glomerular filtration rate, and N-terminal proeB-type natriuretic peptide.
The combined use of galectin-3 with NT-proBNP was assessed by dichotomizing these 2 biomarkers according to their median values. According to Cox regression, the highest risk of death due to all-cause, CV, and progressive HF was observed if both biomarkers were elevated in a model with or without risk factors adjusted for (Fig. 3). Again, there was no significant association for sudden death. Harrell C-statistic calculated from established risk factors significantly improved with addition of galectin-3 to the prediction model for all-cause and CV death. The same improvement occurred in reclassification analysis as assessed by category-free NRI and IDI. However, only category-free NRI improved for progressive HF when galectin-3 was the only added biomarker (Table 3). Associations Between Galectin-3 and In-Hospital Death
In 1,440 patients, 64 patients (4.4%) died during hospitalization. The median hospital stay for all patients was 13 days. In univariable Cox regression, the highest 2 galectin-3 tertiles were significantly associated with inhospital death compared with the lowest tertile, but the association was not significant in multivariable analysis (Table 2). In ROC curve analyses, the optimal cutoff point for galectin-3 as a predictor for in-hospital death was 22.4 ng/mL, associated with a sensitivity of 0.69 and specificity of 0.62, and the cutoff for NT-proBNP was 2,472 pg/mL. Based on the ROC-optimal cutoffs for NT-proBNP and
galectin-3 levels, patients with both galectin-3 and NT-proBNP elevated had a significant adjusted HR of 3.01 (95% confidence interval [CI] 1.26e7.20; P ! .001) compared with patients with lower values for both biomarkers (Fig. 4). The area under the ROC curve (AUC) for galectin-3 was 0.71 (95% CI 0.65e0.77), which was significantly lower than that of NT-proBNP (AUC 0.79, 95% CI 0.74e0.85; P 5 .027). The combination of these 2 biomarkers slightly improved the AUC (0.81, 95% CI 0.76e0.86) compared with NT-proBNP alone (P 5 .087).
Discussion This study represents the largest-scale analysis of galectin-3 measurement in Asia. We demonstrated that galectin-3 was correlated both with the severity of HF renal function and liver disease, suggesting a galectin-3e mediated mechanism of organ fibrosis. Cox regression showed that the highest 2 galectin-3 tertiles independently predicted all-cause, CV, and progressive HF death. Patients with both galectin-3 and NT-proBNP elevation had the highest risk of death due to all causes, CV cause, and progressive HF. Addition of galectin-3 to the prediction model significantly improved discrimination and reclassification for all-cause and CV death. In ROC analyses for inhospital death, galectin-3 did not improve the AUC for NT-proBNP when combining these 2 biomarkers.
56 Journal of Cardiac Failure Vol. 21 No. 1 January 2015
Fig. 3. KaplaneMeier curves for cause-specific death according to the combination of galectin-3 and N-terminal proeB-type natriuretic peptide (NT-proBNP) median values. Unadjusted Kaplan-Meier curves: (A) all-cause death; (B) cardiovascular death; (C) progressive HF death; (D) sudden death. Adjusted Kaplan-Meier curves by sex, age, diabetes mellitus, ischemic heart disease, systolic blood pressure, New York Heart Association functional class, left ventricular ejection fraction, angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker treatment, b-blockers treatment, hemoglobin, sodium, and estimated glomerular filtration rate: (E) all-cause death; (F) cardiovascular death; (G) progressive HF death; (H) sudden death. HR, hazard ratio (95% confidence interval); GN1, low galectin-3 and low NT-proBNP; GN2, low galectin-3 and high NT-proBNP; GN3, high galectin-3 and low NT-proBNP; GN4, high galectin-3 and high NT-proBNP.
Galectin-3 is a b-galactosideebinding lectin and expressed in a wide range of species and tissues.19,20 Under pathophysiologic conditions, the expression of galectin-3 may change substantially and act as a promoter to interact with relative receptors, including cardiac remodelingerelated
proteins and molecules. Studies have showed that recombinant galectin-3 induces cardiac fibroblast proliferation, collagen deposition, and ventricular dysfunction,3 which might be partly attributed to the degradation of N-acetylseryl-aspartyl-lysyl-proline.21 Besides the increase of
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P 5 .063
(0.28e0.62) (0.05e0.30) (0.19e0.35) (0.003e0.03) (0.001e0.03) (0.000e0.004)
!.001 .003 !.001 .055 .055 .059
CI, confidence interval; HF, heart failure; H-L, Hosmer-Lemeshow statistic; IDI, integrated discrimination improvement; NRI, net reclassification improvement.
P 5 .380 P 5 .435 P 5 .161 P 5 .162 P 5 .638
Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference
0.40 0.16 0.24 0.02 0.01 0.003
(0.27e0.54) (0.06e0.25) (0.18e0.31) (0.001e0.03) (0.004e0.03) (0.001e0.01)
!.001 .001 !.001 .035 .036 .036
0.43 0.18 0.25 0.02 0.01 0.003
(0.28e0.58) (0.07e0.28) (0.19e0.32) (0.001e0.03) (0.001e0.03) (0.001e0.006)
0.45 0.18 0.27 0.01 0.01 0.002 Reference Reference Reference Reference Reference Reference !.001 .001 !.001 .035 .036 .035
.051 0.85 (0.83e0.88) 0.85 (0.82e0.88) .030 0.83 (0.80e0.85)
Discrimination C-Statistic Reclassification Category-free all NRI Category-free event NRI Category-free nonevent NRI All IDI Event IDI Nonevent IDI Calibration H-L
0.82 (0.79e0.84)
0.83 (0.80e0.85)
.011
0.83 (0.81e0.86)
With Galectin-3 (95% CI) Reference Model (95% CI) P Value With Galectin-3 (95% CI) Reference Model (95% CI) Reference Model (95% CI)
With Galectin-3 (95% CI)
P Value
Cardiovascular Death All-Cause Death
Table 3. Improvement of Adding Galectin-3 to Model for Predicting All-Cause and Cardiovascular Death
Progressive HF Death
P Value
Galectin-3 Assessment in Heart Failure
Fig. 4. Hazard ratios for in-hospital mortality on the basis of receiver operating characteristiceoptimal cutoffs for NT-proBNP and galectin-3 levels. Abbreviations as in Figure 3.
angiotensin II signaling, up-regulation of galectin-3 is associated with myocardial apoptosis, cardiac hypertrophy, and fibrosis.22 Other putative mechanism for galectin-3 in prompting the development of HF might be explained by the potential role in the dysfunction of other organs, because galectin-3 is also involved in the process of hepatic and renal fibrosis.23,24 Our finding that patients with severe liver and renal function were more likely to have high levels of galectin-3 might indicate that elevated galectin-3 may not only be involved in the process of HF, but also may play an important role in the impairment of liver and renal function.25 LVMI and relative wall RWT were first used to assess whether galectin-3 was associated with eccentric or concentric hypertrophy according to American Society of Echocardiography recommendations.15 We found that galectin-3 elevation was associated with increase of LVMI rather than RWT, indicating that galectin-3 might be involved in the process of eccentric hypertrophy. Although higher levels of galectin-3 have been shown to be associated with an increased risk for incident HF in the general population, the association between galectin-3 and CV death was not significant after adjustment for clinical variables and BNP.26 A similar negative result for CV death was found also in patients with chronic HF.27 On the other hand, earlier reports concerning patients with acute HF mainly focused on all-cause death7,9,10,28 and rehospitalization.28 Only 1 study, from Ahmad et al, reported that galectin-3 provided insignificant incremental contributions to predictors along with NT-proBNP for predicting pump failure and sudden cardiac death in chronic HF.12 Therefore, whether galectin-3 was useful to predict end points related to CV-specific death in hospitalized patients with HF needs to be further validated. In the present study, galectin-3 was independently associated with all-cause, CV, and progressive HF death after adjustment for established risk factors (including NT-proBNP). This means that galectin-3 might be an independent risk predictor for developing HF in hospitalized patients with HF. However, after analysis with the use of discrimination and IDI
58 Journal of Cardiac Failure Vol. 21 No. 1 January 2015 methods, galectin-3 did not provide additional prognostic value beyond established risk factors in predicting progressive HF, which is consistent with its performance in patients with chronic HF.12 Given the different pathophysiologic processes of HF between galectin-3 and NT-proBNP, the combined use of these 2 biomarkers was also tested. Patients with both biomarkers elevated exhibited the highest adjusted HRs for all-cause, CV, and progressive HF death. Francia et al reported that galectin-3 retained its prognostic power for ventricular tachycardia/fibrillation during a median follow-up period of 2.5 years after correction for risk factors in HF patients at high risk for sudden cardiac death.28 However, the association between galectin-3 and sudden death was insignificant after adjustment for clinical variables. The difference probably indicated that galectin-3 might be more meaningful to predict sudden death in long-term observation and special populations. We also evaluated the prognostic value of galectin-3 for in-hospital death. Log-transformed galectin-3 was significantly associated with in-hospital death after adjustment for established risk factors. The combination of galectin3 and NT-proBNP according to their ROC-optimal cutoffs was also useful for clinicians, because patients with both biomarkers elevated had the highest risk of in-hospital death. ROC analysis showed no significant results when galectin-3 and NT-proBNP were combined. The prognostic value of galectin-3 for in-hospital death was not as strong as it for longer-term death. The main explanation for this might be to the fact that galectin-3 levels were not significantly elevated from the values before onset of HF at the baseline of its pathophysiologic characteristics. De Boer et al also found that galectin-3 levels were very stable over a 6-month time period after an acute HF admission.10 Study Strengths and Limitations Strengths of this study include the large cohort of Chinese hospitalized patients with HF, sensitivity analysis of death prediction as a function of LVEF, multiple causes of death for the end points, and adjustment for multivariable risk factors. Our study also has several limitations, including 110 patients lost during longer-term follow up. However, those patients completed the first 6 months’ follow-up and provided useful information for this analysis. Furthermore, we used the same prediction model to analyze the association of galectin-3 with all end points. Some invasive therapies (such as percutaneous coronary intervention, biventricular pacemaker, internal cardiac defibrillator, and so on) might affect the end points, especially for sudden death. Finally, although our analysis is one of the largest studies of galectin-3 in HF, the biomarker was not measured serially after discharge. Although galectin-3 concentrations have been proved to be very stable over a 6-month time period,10 future studies of serial galectin-3 measurement are needed in longer intervals.
Conclusion In this large cohort of Chinese hospitalized patients with HF, galectin-3 was significantly associated with death due to all causes, CV cause, and progressive HF. Incorporation of galectin-3 in the prediction model added additional prognostic value beyond established risk factors for all-cause and CV death. On the other hand, the prognostic value of galectin-3 was not strong for sudden or in-hospital death. Disclosures Alan Maisel is a consultant for Alere and has received speaking honoraria from Alere.
Supplementary Data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.cardfail.2014.10.006
References 1. Loehr LR, Rosamond WD, Chang PP, Folsom AR, Chambless LE. Heart failure incidence and survival (from the Atherosclerosis Risk in Communities study). Am J Cardiol 2008;101:1016e22. 2. Sanada S, Hakuno D, Higgins LJ, Schreiter ER, McKenzie AN, Lee RT. IL-33 and ST2 comprise a critical biomechanically induced and cardioprotective signaling system. J Clin Invest 2007;117: 1538e49. 3. Sharma UC, Pokharel S, van Brakel TJ, van Berlo JH, Cleutjens JP, Schroen B, et al. Galectin-3 marks activated macrophages in failureprone hypertrophied hearts and contributes to cardiac dysfunction. Circulation 2004;110:3121e8. 4. de Boer RA, Yu L, van Veldhuisen DJ. Galectin-3 in cardiac remodeling and heart failure. Curr Heart Fail Rep 2010;7:1e8. 5. Socrates T, deFilippi C, Reichlin T, Twerenbold R, Breidhardt T, Noveanu M, et al. Interleukin family member ST2 and mortality in acute dyspnoea. J Intern Med 2010;268:493e500. 6. Grandin EW, Jarolim P, Murphy SA, Ritterova L, Cannon CP, Braunwald E, et al. Galectin-3 and the development of heart failure after acute coronary syndrome: pilot experience from PROVE ITe TIMI 22. Clin Chem 2012;58:267e73. 7. van Kimmenade RR, Januzzi JL Jr, Ellinor PT, Sharma UC, Bakker JA, Low AF, et al. Utility of amino-terminal proebrain natriuretic peptide, galectin-3, and apelin for the evaluation of patients with acute heart failure. J Am Coll Cardiol 2006;48:1217e24. 8. Lok DJ, van der Meer P, de la Porte PW, Lipsic E, Van Wijngaarden J, Hillege HL, et al. Prognostic value of galectin-3, a novel marker of fibrosis, in patients with chronic heart failure: data from the DEALHF study. Clin Res Cardiol 2010;99:323e8. 9. Shah RV, Chen-Tournoux AA, Picard MH, van Kimmenade RR, Januzzi JL. Galectin-3, cardiac structure and function, and long-term mortality in patients with acutely decompensated heart failure. Eur J Heart Fail 2010;12:826e32. 10. de Boer RA, Lok DJ, Jaarsma T, van der Meer P, Voors AA, Hillege HL, et al. Predictive value of plasma galectin-3 levels in heart failure with reduced and preserved ejection fraction. Ann Med 2011; 43:60e8. 11. Felker GM, Fiuzat M, Shaw LK, Clare R, Whellan DJ, Bettari L, et al. Galectin-3 in ambulatory patients with heart failure: results from the HF-ACTION study. Circ Heart Fail 2012;5:72e8.
Galectin-3 Assessment in Heart Failure 12. Ahmad T, Fiuzat M, Neely B, Neely ML, Pencina MJ, Kraus WE, et al. Biomarkers of myocardial stress and fibrosis as predictors of mode of death in patients with chronic heart failure. JACC Heart Fail 2014;2:260e8. 13. Chinese Society of Cardiology of Chinese Medical Association, Editorial Board of Chinese Journal of Cardiology. Guidelines for the diagnosis and management of chronic heart failure. Zhonghua Xin Xue Guan Bing Za Zhi 2007;35:1076e95. 14. Chinese Society of Cardiology of Chinese Medical Association, Editorial Board of Chinese Journal of Cardiology Practice Guideline. Guideline for diagnosis and treatment of acute heart failure. Zhonghua Xin Xue Guan Bing Za Zhi 2010;38:195e208. 15. Lang RM, Bierig M, Devereux RB, Flachskampf FA, Foster E, Pellikka PA, et al. Recommendations for chamber quantification. Eur J Echocardiogr 2006;7:79e108. 16. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976;16:31e41. 17. Pugh RN, Murray-Lyon IM, Dawson JL, Pietroni MC, Williams R. Transection of the oesophagus for bleeding oesophageal varices. Br J Surg 1973;60:646e9. 18. Pencina MJ, d’Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 2011;30:11e21. 19. Barondes SH, Cooper DN, Gitt MA, Galectins Leffler H. Structure and function of a large family of animal lectins. J Biol Chem 1994;269: 20807e10. 20. Cooper DN. Galectinomics: finding themes in complexity. Biochim Biophys Acta 2002;1572:209e31. 21. Liu YH, d’Ambrosio M, Liao TD, Peng H, Rhaleb NE, Sharma U, et al. N-Acetyl-seryl-aspartyl-lysyl-proline prevents cardiac
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remodeling and dysfunction induced by galectin-3, a mammalian adhesion/growth-regulatory lectin. Am J Physiol Heart Circ Physiol 2009;296:H404e12. Thandavarayan RA, Watanabe K, Ma M, Veeraveedu PT, Gurusamy N, Palaniyandi SS, et al. 14e3-3 protein regulates Ask1 signaling and protects against diabetic cardiomyopathy. Biochem Pharmacol 2008;75:1797e806. Henderson NC, Mackinnon AC, Farnworth SL, Poirier F, Russo FP, Iredale JP, et al. Galectin-3 regulates myofibroblast activation and hepatic fibrosis. Proc Natl Acad Sci U S A 2006;103:5060e5. Henderson NC, Mackinnon AC, Farnworth SL, Kipari T, Haslett C, Iredale JP, et al. Galectin-3 expression and secretion links macrophages to the promotion of renal fibrosis. Am J Pathol 2008;172: 288e98. Gopal DM, Kommineni M, Ayalon N, Koelbl C, Ayalon R, Biolo A, et al. Relationship of plasma galectin-3 to renal function in patients with heart failure: effects of clinical status, pathophysiology of heart failure, and presence or absence of heart failure. J Am Heart Assoc 2012;1:e000760. Ho JE, Liu C, Lyass A, Courchesne P, Pencina MJ, Vasan RS, et al. Galectin-3, a marker of cardiac fibrosis, predicts incident heart failure in the community. J Am Coll Cardiol 2012;60:1249e56. Bayes-Genis A, de Antonio M, Vila J, Penafiel J, Galan A, Barallat J, et al. Head-to-head comparison of 2 myocardial fibrosis biomarkers for long-term heart failure risk stratification: ST2 versus galectin-3. J Am Coll Cardiol 2014;63:158e66. Meijers WC, Januzzi JL, deFilippi C, Adourian AS, Shah SJ, van Veldhuisen DJ, et al. Elevated plasma galectin-3 is associated with near-term rehospitalization in heart failure: a pooled analysis of 3 clinical trials. Am Heart J 2014;167:853e860.e4.
The Utility of Galectin-3 for Predicting Cause-Specific Death in Hospitalized Patients With Heart Failure YUHUI ZHANG, MD,1 RONGCHENG ZHANG, MM,1 TAO AN, MD,1 YAN HUANG, MB,1 XIAO GUO, MD,1 SHIJIE YIN, MM,1 YUNHONG WANG, MM,1 SHIMING JI, RN,1 RONG LV, RN,1 JIAN ZHANG, MD, PhD,1 AND ALAN MAISEL, MD2 Beijing, China; and San Diego, CA, USA
ABSTRACT Objectives: Galectin-3 has been shown to be involved in the process of cardiac fibrosis and to predict adverse events in heart failure (HF), but the association of galectin-3 with cause-specific death has not been well established. The purpose of this study was to investigate the prognostic value of baseline galectin-3 for all-cause, cardiovascular (CV), and in-hospital death in patients with HF. Methods and Results: From March 2009 to April 2013, we consecutively measured galectin-3 in a large cohort of 1,440 hospitalized patients with HF. Cox proportional hazards regression, discrimination, and reclassification analyses were used to evaluate the association between galectin-3 and death. During a median follow-up of 582 days, 283 deaths were identified, of which 64 were patients who died during hospitalization. Compared with the lowest galectin-3 tertile, the highest 2 tertiles were significantly associated with all-cause, CV, and progressive HF death, but not significant for sudden and inhospital death when analyzed by multivariable Cox regression. The utility of combining galectin-3 and N-terminal proeB-type natriuretic peptide was assessed by dichotomizing these 2 biomarkers according to their median values. The highest risk of death due to all-cause, CV, and progressive HF was observed when both biomarkers were elevated after adjustment for established risk factors. Addition of galectin-3 to the prediction model for all-cause and CV death significantly improved discrimination and reclassification. Conclusions: Galectin-3 independently predicted death and added additional prognostic value beyond established risk factors in hospitalized patients with HF. The utility of galectin-3 alone as a risk predictor was not strong enough to assess sudden or in-hospital death. (J Cardiac Fail 2015;21:51e59) Key Words: Galectin-3, heart failure, death, prognosis.
Heart failure (HF) is the end-stage condition of various cardiovascular (CV) diseases with adverse prognosis. Although advances have been made in HF diagnosis and treatment, the 1-year case fatality rates after hospitalization for HF were 22% in a community-based study.1 Assessing the risk of patients may be helpful for physicians in guiding medical decision making. Patients estimated to be at high risk may require intensified monitoring and follow-up and more advanced therapies. Currently, several biomarkers associated with cardiac fibrosis have been found to reflect the process of HF and predict adverse events.2,3 Galectin-3, a member of the galectin family, is overexpressed by activated macrophages and involved in the processes of inflammation and fibrosis that cause cardiac dysfunction.4 Studies have demonstrated that elevated concentrations of galectin-3 are associated with adverse events in both the general population,5 as well patients with acute coronary syndrome6 and HF.7e11 Although Ahmad et al
From the 1State Key Laboratory of Cardiovascular Disease, Heart Failure Center Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China and 2Department of Medicine, University of California, San Diego, CA, USA. Manuscript received July 29, 2014; revised manuscript received September 12, 2014; revised manuscript accepted October 8, 2014. Reprint requests: Jian Zhang, MD, PhD, 167 Beilishi Road, Beijing 100037, China. Tel: þ86-13911102015; Fax: þ86 (10) 8839 6180. E-mail: [email protected] The first 2 authors contributed equally to this work. See page 58 for disclosure information. Funding: Key Projects in the National Science & Technology Pillar Program of the 12th 5-Year Plan Period (no. 2011BAI11B02, Project for Heart Failure), Beijing, China. Galectin-3 assays provided by BG Medicine. NT-proBNP assays provided by Alere. Neither of these companies participated in process of the study. 1071-9164/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cardfail.2014.10.006
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52 Journal of Cardiac Failure Vol. 21 No. 1 January 2015 have investigated the association of galectin-3 with pump failure and sudden cardiac death in patients with chronic HF,12 few studies have focused on cause-specific and inhospital death in hospitalized patients with HF, another specific population of HF. Furthermore, according to the demographic characteristics, the expression of galectin-3 in patients with HF from Asia has not been well characterized. Therefore, we aimed to investigate the clinical characteristics of galectin-3 and evaluate the performance of galectin-3 for predicting death in a large cohort of Chinese hospitalized patients with HF.
patient without evidence of specific cause of death), death due to myocardial infarction or stroke, or other CV death (such as mortal complications of cardiac surgery, rupture of an aneurysm pulmonary embolism, aortic dissection, etc). All patients provided written informed consents, and the Ethics Committee of Fuwai Hospital approved the study. Biomarker Measurement Fasting venous blood samples were collected within 12 hours of hospitalization, immediately centrifuged, and stored at 80 C in plasma with EDTA. Galectin-3 and NT-proBNP were determined in blood samples with no more than one freeze-thaw cycle. Information about these 2 assays is detailed in Supplemental Table 1.
Materials and Methods Study Population and Design We consecutively enrolled patients admitted to Fuwai Hospital HF center, Beijing, from March 2009 to April 2013 with HF as their primary diagnosis. The diagnosis of HF was confirmed by 2 specialists according to Chinese guidelines.13,14 In this study, patients with both preserved ejection fraction and systolic HF (defined as a de novo presentation of HF secondary to abnormal cardiac function or worsening of previously chronic stable HF requiring unplanned hospitalization) were evaluated and enrolled if they were aged $18 years and agreed to venous blood sampling for biomarkers analysis. Patients with a diagnosis of acute coronary syndrome, cancer, or acute pulmonary embolism were excluded from this analysis. An ischemic etiology of HF was assumed if the patient had prevalent angina pectoris, a history of coronary artery bypass grafting, percutaneous coronary intervention, or acute myocardial infarction, or confirmed coronary artery obstruction according to coronary angiography or computerized tomographic angiography. Data from medical records were abstracted by trained clinicians or cardiology nurses and entered into a predefined electronic case report form with checking by another abstractor. Clinical data, including demographic characteristics, New York Heart Association (NYHA) functional class, primary HF etiologies, physical examination, preexisting comorbidities, and medical history, were obtained at the time of the hospitalization. All patients received intravenous loop diuretics at least once during the 1st 24 hours of admission. Left ventricular ejection fraction (LVEF) was assessed and interpreted by specialists trained in cardiac ultrasonography by means of echocardiography within 48 hours after admission. Posterior and septal wall thickness at end-diastole was also available and used to calculate LV mass index (LVMI) and relative wall thickness (RWT).15 Estimated glomerular filtration rate (eGFR) was calculated with the use of Cockcroft-Gault equation,16 and the Child-Turcotte-Pugh score was used to assess liver function.17 For patients with multiple admissions, only the 1st admission was included in this study. Adverse events regarding causespecific and in-hospital death were ascertained (every 3 months for discharged patients) via electronic hospital records or conversations with patients or patients’ families by telephone by an investigator who was blinded to the value of galectin-3 and N-terminal proeB-type natriuretic peptide (NT-proBNP). NonCV deaths were considered if there was no specific CV cause identified as the main trigger of death. CV death includes progressive HF death (progressive deterioration of HF in the absence of other cause), sudden death (unexpected and witnessed death in a stable
Statistical Analyses Continuous variables were tested for normal distribution by means of the Kolmogorov-Smironov test and are described as mean 6 SD for normally distributed variables and median (interquartile range [IQR]) for variables with skewed distribution. Categoric variables are described as percentages. Comparisons among galectin-3 tertiles were performed with the use of analysis of variance for normal continuous, Kruskal-Wallis H testing for nonnormal continuous, and c2 tests for categoric variables. MannWhitney U test was used to compare the difference of galectin-3 between 2 groups. Log2 transformation was performed to normalize the distribution of NT-proBNP and galectin-3. Univariate Spearman correlation was used to evaluate the relationships between galectin-3 and clinical variables. Multivariate linear regression analyses were performed with the use of a stepwise method, with logtransformed of galectin-3 levels as the dependent variables. Cox regression was used to estimate the associations between galectin-3 and death with the use of galectin-3 tertiles and logarithmic transformation. We included the following variables in the reference model: sex, age, diabetes mellitus, ischemic heart disease, systolic blood pressure, NYHA functional class, LVEF, angiotensinconverting enzyme inhibitor or angiotensin II receptor blocker (ACEI/ARB) treatment, b-blocker treatment, hemoglobin, sodium, eGFR, and NT-proBNP. Log-rank tests for Kaplan-Meier cumulative hazard curves were used for comparisons. Differences in Harrell C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were performed to evaluate the additional predictive value of galectin-3 in the reference model. Because there are no established criterion standard risk categories for HF, we used category-free NRI as described by Pencina et al.18 Confidence intervals and P values for NRI and IDI were determined by bootstrapping with 1,000 repetitions. The Hosmer-Lemeshow statistic was used to evaluate model calibration. Receiver operating characteristic (ROC) curve analyses were used to evaluate the ability of galectin-3 to classify patients who died or were alive during hospitalization. All P values of !.05 from 2-sided tests
Galectin-3 Assessment in Heart Failure
were accepted as statistically significant. Statistical analyses were conducted using SPSS version 19.0 (SPSS, Chicago, Illinois) and Stata version 11.2 (Statacorp, College Station, Texas).
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disease. The median (IQR) galectin-3 concentration was 21.9 ng/mL (16.4e29.4). Patients without blood samples available (n 5 285) did not differ in age, sex, and LVEF from included patients. Distribution and Correlations of Galectin-3
Results
Median galectin-3 concentrations had an increasing trend in patients with worse symptoms defined by NYHA functional class (P ! .001). Patients with nonischemic HF etiology did not have a significantly different galectin-3 concentration compared with ischemic HF patients (P 5 .816). A significant association was detected between galetin-3 concentrations and severity of liver function defined by Child-Turcotte-Pugh score (P ! .001). After dichotomizing renal function as a function of eGFR of 60 mL min1 1.73 m2, galectin-3 concentrations were significantly higher in
Baseline Characteristics
A total of 1,940 patients presenting with HF were admitted from March 2009 to April 2013; 1,440 patients were included by design (Supplemental Fig. 1). Baseline characteristics across the included patients according to galectin-3 tertile are presented in Table 1. The mean age was 57.8 6 15.6 years, the majority were male (70.3%), and 55% of the patients had LVEF O40%. Approximately one-half of the patients had a history of ischemic heart
Table 1. Baseline Characteristics According to Galectin-3 Tertile Galectin-3 Tertile
Variable Age, y Male, n (%) History, n (%) Hypertension Diabetes mellitus Ischemic heart disease Dilated cardiomyopathy Valvular heart disease Congenital heart disease Physical examination Heart rate, beats/min Systolic blood pressure, mm Hg Diastolic blood pressure, mm Hg Body mass index, kg/m2 NYHA functional class, n (%) II III IV LVEF (%)* LVDD (mm) LV mass index (g/m2) Relative wall thickness Current smoking, n (%) Medication on presentation, n (%) Loop diuretics ACEI/ARB b-blockers Aldosterone antagonists Digoxin Laboratory results Hemoglobin, g/dL Sodium, mmol/L eGFR, mL min-1 1.73 m-2 Child-Turcotte-Pugh score Child A Child B Child C NT-proBNP, pg/mL
All Patients (n 5 1,440) 57.8 6 15.6 1,013 (70.3) 693 366 687 296 230 48
(48.1) (25.4) (47.7) (20.6) (16.0) (3.3)
78 6 17 119 6 20 71 6 13 24.2 6 4.2 435 603 402 40
(30.2) (41.9) (27.9) (30e56) 59 6 13 131.1 (103.6e168.5) 0.31 (0.26e0.38) 392 (27.2) 955 745 1093 885 687
(66.3) (51.7) (75.9) (61.5) (47.7)
133.6 6 22.7 139.4 6 3.5 76.5 6 34.5 1217 204 19 1546
(84.5) (14.2) (1.3) (778e3272)
Tertile 1: #18.1 ng/mL (n 5 480)
Tertile 2: O18.1 to #26.4 ng/mL (n 5 480)
Tertile 3: O26.4 ng/mL (n 5 480)
P Value
52.6 6 15.2 363 (75.6)
57.3 6 15.1 341 (71)
63.5 6 14.5 309 (64.4)
!.001 !.001
202 94 225 113 69 17
230 114 219 102 73 16
261 158 243 81 88 15
.001 !.001 .272 .034 .211 .937
(42.1) (19.6) (46.9) (23.5) (14.4) (3.5)
77 6 16 120 6 19 71 6 13 24.5 6 4.6 201 198 81 44
(41.9) (41.3) (16.9) (31e59) 59 6 12 126.4 (99.0e159.4) 0.32 (0.26e0.38) 123 (25.6) 284 274 379 281 205
(59.2) (57.1) (79.0) (58.5) (42.7)
137.0 6 19.5 140.0 6 3.0 92.0 6 32.4 442 37 1 1062
(92.1) (7.7) (0.2) (640e2102)
(47.9) (23.8) (45.6) (21.3) (15.2) (3.3)
78 6 17 119 6 20 72 6 13 24.2 6 3.9 143 214 123 40
(29.8) (44.6) (25.6) (29e55) 59 6 14 132.9 (105.8e172.6) 0.30 (0.25e0.37) 131 (27.3) 327 257 370 306 243
(68.1) (53.5) (77.1) (63.8) (50.6)
135.5 6 22.2 139.4 6 3.2 77.4 6 29.8 410 66 4 1485
(85.4) (13.8) (0.8) (760e2944)
(54.4) (32.9) (50.6) (16.9) (18.3) (3.1)
78 6 19 120 6 21 70 6 12 23.9 6 4.2 91 191 198 40
(19.0) (39.8) (41.3) (30e55) 59 6 14 134.2 (106.4e177.0) 0.32 (0.26e0.38) 138 (28.8) 344 214 344 298 239
(71.7) (44.6) (71.7) (62.1) (49.8)
128.4 6 25.1 138.8 6 4.1 60.1 6 33.7 365 101 14 2664
(76.0) (21.0) (2.9) (1238e5038)
.467 .459 .042 .147 !.001 .304 !.001 .006 .821 .011 .225 .553 !.001 !.001 .023 .238 .026 !.001 !.001 !.001 !.001 !.001 !.001 !.001
ACEI, angiotension-converting enzyme inhibitor; ARB, angiotensin receptor blocker; eGFR, estimated glomerular filtration rate; LVDD, left ventricular diastolic diameter; LVEF, left ventricular ejection fraction; NT-proBNP, N-terminal proeB-type natriuretic peptide; NYHA, New York Heart Association. Values are presented as mean 6 SD, n (%), or median (interquartile range). *Patients with the lowest galectin-3 levels have the highest LVEF according to pairwise comparisons of Bonferroni test (tertile 1 vs tertile 2: P 5 .011; tertile 1 vs tertile 3: P 5 .034).
54 Journal of Cardiac Failure Vol. 21 No. 1 January 2015 other clinical indexes (Supplemental Fig. 2). A multivariable linear regression model with log-transformed galectin-3 as a dependent variable showed that galectin-3 concentrations were predicted by sex, body mass index, ACEI/ARB, NYHA functional class, sodium, eGFR, Child-Turcotte-Pugh score, and log NT-proBNP. The R2 was 0.31.
Associations Between Galectin-3 and Death
Fig. 1. Box plots of galectin-3 according to left ventricular ejection fraction (LVEF) in patients with different levels of New York Heart Association functional class. P values indicate the differences among groups stratified by LVEF.
patients with low values of eGFR than in patients with high values (P ! .001). Patients with the lowest galectin-3 levels had the highest LVEF according to pairwise comparisons of the Bonferroni test (Table 1). Given the difference of pathophysiologic processes linked to fibrosis, hypertrophy, and dysfunction between HF with reduced ejection fraction (HF-rEF) and with preserved ejection fraction (HF-pEF), we categorized LVEF according to the cutoff points of EF $50%, 50% O EF O 40%, and EF #40% and found that galectin-3 concentration was not different in patients with different categories of LVEF after considering NYHA functional class (Fig. 1). Univariate correlations of galectin-3 with other continuous variables showed that galectin-3 concentrations were correlated with LV mass index (r 5 0.083; P 5 .003) and
During a median follow-up of 582 days, 283 deaths were indentified: 259 of all deaths resulted from CV cause (91.5%), 169 were due to progressive HF (59.7%), 43 were due to sudden death (15.2%) (Supplemental Fig. 1). Galectin-3 concentrations were significantly higher in patients who died compared with those who were alive (P ! .001). In addition, increasing tertiles of galectin-3 were associated with increasing risk of 1-year mortality due to all-cause and CV death in patients with different categories of LVEF (Fig. 2). The interaction between LVEF (EF $50%, 50% O EF O 40%, EF #40%) and prognostic value of sST2 for death also was investigated. We found that LVEF did not significantly interact with the prognostic value of sST2 for allcause (P 5 .586) and CV (P 5 .267) death. The prognostic values of log-transformed galectin-3 and galectin-3 tertile analyzed by Cox regression are presented in Table 2. Compared with the lowest tertile of galectin-3, the highest 2 tertiles had significant unadjusted hazard ratios (HRs) for all-cause, CV, and progressive HF death, and remained robust when adjusted for established risk factors. There was not a significant association for sudden death. Kaplan-Meier cumulative hazard curves depicting time to cause-specific death showed the highest galectin-3 tertile to be significantly associated with end points related to all-cause, CV, progressive HF, and sudden death (Supplemental Fig. 2).
Fig. 2. One-year mortality according to tertiles of galectin-3 and categories of left ventricular ejection fraction (LVEF). Increasing tertiles of galectin-3 were associated with 1-year mortality due to (A) all causes and (B) cardiovascular death at every level of LVEF.
Galectin-3 Assessment in Heart Failure
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Table 2. Hazard Ratios for Death According to Log-Transformed Galectin-3 and Galectin-3 Tertile Unadjusted
All cause death Log galectin-3 Tertile 1 Tertile 2 Tertile 3 CV death Log galectin-3 Tertile 1 Tertile 2 Tertile 3 Progressive HF death Log galectin-3 Tertile 1 Tertile 2 Tertile 3 Sudden death Log galectin-3 Tertile 1 Tertile 2 Tertile 3 In-hospital mortality Log galectin-3 Tertile 1 Tertile 2 Tertile 3
Adjusted*
HR (95% CI)
P Value
HR (95% CI)
P Value
4.36 (3.49e5.45) reference 2.94 (1.95e4.44) 6.76 (4.60e9.92)
!.001
2.17 (1.63e2.89) reference 1.98 (1.30e3.02) 2.88 (1.90e4.36)
!.001
4.58 (3.62e5.80) reference 2.96 (1.88e4.64) 7.34 (4.84e11.13)
!.001
!.001
!.001 !.001
2.32 (1.71e3.15) reference 2.00 (1.26e3.17) 3.17 (2.02e4.98)
!.001
!.001 !.001
2.46 (1.71e3.54) reference 2.69 (1.41e5.12) 4.43 (2.36e8.33) 1.95 (0.91e4.17) reference 0.94 (0.38e2.32) 1.66 (0.69e3.99)
.085
2.00 (1.19e3.39) reference 1.96 (0.72e5.33) 2.18 (0.83e5.72)
.009
reference 4.27 (2.27e8.05) 11.25 (6.19e20.42) 3.30 (1.83e5.97) reference 1.34 (0.56e3.24) 3.21 (1.48e6.95) 3.72 (2.44e5.69) reference 3.44 (1.29e9.17) 6.33 (2.49e16.10)
!.001 !.001
!.001 !.001 !.001 !.001 .014 !.001
.002 !.001
.003 !.001
.003 !.001
.894 .243
.190 .115
CI, confidence interval; CV, cardiovascular; HF, heart failure; HR, hazard ratio. *Covariates for adjusted model: sex, age, diabetes mellitus, ischemic heart disease, systolic blood pressure, New York Heart Association functional class, left ventricular ejection fraction, angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker treatment, b-blockers treatment, hemoglobin, sodium, estimated glomerular filtration rate, and N-terminal proeB-type natriuretic peptide.
The combined use of galectin-3 with NT-proBNP was assessed by dichotomizing these 2 biomarkers according to their median values. According to Cox regression, the highest risk of death due to all-cause, CV, and progressive HF was observed if both biomarkers were elevated in a model with or without risk factors adjusted for (Fig. 3). Again, there was no significant association for sudden death. Harrell C-statistic calculated from established risk factors significantly improved with addition of galectin-3 to the prediction model for all-cause and CV death. The same improvement occurred in reclassification analysis as assessed by category-free NRI and IDI. However, only category-free NRI improved for progressive HF when galectin-3 was the only added biomarker (Table 3). Associations Between Galectin-3 and In-Hospital Death
In 1,440 patients, 64 patients (4.4%) died during hospitalization. The median hospital stay for all patients was 13 days. In univariable Cox regression, the highest 2 galectin-3 tertiles were significantly associated with inhospital death compared with the lowest tertile, but the association was not significant in multivariable analysis (Table 2). In ROC curve analyses, the optimal cutoff point for galectin-3 as a predictor for in-hospital death was 22.4 ng/mL, associated with a sensitivity of 0.69 and specificity of 0.62, and the cutoff for NT-proBNP was 2,472 pg/mL. Based on the ROC-optimal cutoffs for NT-proBNP and
galectin-3 levels, patients with both galectin-3 and NT-proBNP elevated had a significant adjusted HR of 3.01 (95% confidence interval [CI] 1.26e7.20; P ! .001) compared with patients with lower values for both biomarkers (Fig. 4). The area under the ROC curve (AUC) for galectin-3 was 0.71 (95% CI 0.65e0.77), which was significantly lower than that of NT-proBNP (AUC 0.79, 95% CI 0.74e0.85; P 5 .027). The combination of these 2 biomarkers slightly improved the AUC (0.81, 95% CI 0.76e0.86) compared with NT-proBNP alone (P 5 .087).
Discussion This study represents the largest-scale analysis of galectin-3 measurement in Asia. We demonstrated that galectin-3 was correlated both with the severity of HF renal function and liver disease, suggesting a galectin-3e mediated mechanism of organ fibrosis. Cox regression showed that the highest 2 galectin-3 tertiles independently predicted all-cause, CV, and progressive HF death. Patients with both galectin-3 and NT-proBNP elevation had the highest risk of death due to all causes, CV cause, and progressive HF. Addition of galectin-3 to the prediction model significantly improved discrimination and reclassification for all-cause and CV death. In ROC analyses for inhospital death, galectin-3 did not improve the AUC for NT-proBNP when combining these 2 biomarkers.
56 Journal of Cardiac Failure Vol. 21 No. 1 January 2015
Fig. 3. KaplaneMeier curves for cause-specific death according to the combination of galectin-3 and N-terminal proeB-type natriuretic peptide (NT-proBNP) median values. Unadjusted Kaplan-Meier curves: (A) all-cause death; (B) cardiovascular death; (C) progressive HF death; (D) sudden death. Adjusted Kaplan-Meier curves by sex, age, diabetes mellitus, ischemic heart disease, systolic blood pressure, New York Heart Association functional class, left ventricular ejection fraction, angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker treatment, b-blockers treatment, hemoglobin, sodium, and estimated glomerular filtration rate: (E) all-cause death; (F) cardiovascular death; (G) progressive HF death; (H) sudden death. HR, hazard ratio (95% confidence interval); GN1, low galectin-3 and low NT-proBNP; GN2, low galectin-3 and high NT-proBNP; GN3, high galectin-3 and low NT-proBNP; GN4, high galectin-3 and high NT-proBNP.
Galectin-3 is a b-galactosideebinding lectin and expressed in a wide range of species and tissues.19,20 Under pathophysiologic conditions, the expression of galectin-3 may change substantially and act as a promoter to interact with relative receptors, including cardiac remodelingerelated
proteins and molecules. Studies have showed that recombinant galectin-3 induces cardiac fibroblast proliferation, collagen deposition, and ventricular dysfunction,3 which might be partly attributed to the degradation of N-acetylseryl-aspartyl-lysyl-proline.21 Besides the increase of
Zhang et al
57
P 5 .063
(0.28e0.62) (0.05e0.30) (0.19e0.35) (0.003e0.03) (0.001e0.03) (0.000e0.004)
!.001 .003 !.001 .055 .055 .059
CI, confidence interval; HF, heart failure; H-L, Hosmer-Lemeshow statistic; IDI, integrated discrimination improvement; NRI, net reclassification improvement.
P 5 .380 P 5 .435 P 5 .161 P 5 .162 P 5 .638
Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference Reference
0.40 0.16 0.24 0.02 0.01 0.003
(0.27e0.54) (0.06e0.25) (0.18e0.31) (0.001e0.03) (0.004e0.03) (0.001e0.01)
!.001 .001 !.001 .035 .036 .036
0.43 0.18 0.25 0.02 0.01 0.003
(0.28e0.58) (0.07e0.28) (0.19e0.32) (0.001e0.03) (0.001e0.03) (0.001e0.006)
0.45 0.18 0.27 0.01 0.01 0.002 Reference Reference Reference Reference Reference Reference !.001 .001 !.001 .035 .036 .035
.051 0.85 (0.83e0.88) 0.85 (0.82e0.88) .030 0.83 (0.80e0.85)
Discrimination C-Statistic Reclassification Category-free all NRI Category-free event NRI Category-free nonevent NRI All IDI Event IDI Nonevent IDI Calibration H-L
0.82 (0.79e0.84)
0.83 (0.80e0.85)
.011
0.83 (0.81e0.86)
With Galectin-3 (95% CI) Reference Model (95% CI) P Value With Galectin-3 (95% CI) Reference Model (95% CI) Reference Model (95% CI)
With Galectin-3 (95% CI)
P Value
Cardiovascular Death All-Cause Death
Table 3. Improvement of Adding Galectin-3 to Model for Predicting All-Cause and Cardiovascular Death
Progressive HF Death
P Value
Galectin-3 Assessment in Heart Failure
Fig. 4. Hazard ratios for in-hospital mortality on the basis of receiver operating characteristiceoptimal cutoffs for NT-proBNP and galectin-3 levels. Abbreviations as in Figure 3.
angiotensin II signaling, up-regulation of galectin-3 is associated with myocardial apoptosis, cardiac hypertrophy, and fibrosis.22 Other putative mechanism for galectin-3 in prompting the development of HF might be explained by the potential role in the dysfunction of other organs, because galectin-3 is also involved in the process of hepatic and renal fibrosis.23,24 Our finding that patients with severe liver and renal function were more likely to have high levels of galectin-3 might indicate that elevated galectin-3 may not only be involved in the process of HF, but also may play an important role in the impairment of liver and renal function.25 LVMI and relative wall RWT were first used to assess whether galectin-3 was associated with eccentric or concentric hypertrophy according to American Society of Echocardiography recommendations.15 We found that galectin-3 elevation was associated with increase of LVMI rather than RWT, indicating that galectin-3 might be involved in the process of eccentric hypertrophy. Although higher levels of galectin-3 have been shown to be associated with an increased risk for incident HF in the general population, the association between galectin-3 and CV death was not significant after adjustment for clinical variables and BNP.26 A similar negative result for CV death was found also in patients with chronic HF.27 On the other hand, earlier reports concerning patients with acute HF mainly focused on all-cause death7,9,10,28 and rehospitalization.28 Only 1 study, from Ahmad et al, reported that galectin-3 provided insignificant incremental contributions to predictors along with NT-proBNP for predicting pump failure and sudden cardiac death in chronic HF.12 Therefore, whether galectin-3 was useful to predict end points related to CV-specific death in hospitalized patients with HF needs to be further validated. In the present study, galectin-3 was independently associated with all-cause, CV, and progressive HF death after adjustment for established risk factors (including NT-proBNP). This means that galectin-3 might be an independent risk predictor for developing HF in hospitalized patients with HF. However, after analysis with the use of discrimination and IDI
58 Journal of Cardiac Failure Vol. 21 No. 1 January 2015 methods, galectin-3 did not provide additional prognostic value beyond established risk factors in predicting progressive HF, which is consistent with its performance in patients with chronic HF.12 Given the different pathophysiologic processes of HF between galectin-3 and NT-proBNP, the combined use of these 2 biomarkers was also tested. Patients with both biomarkers elevated exhibited the highest adjusted HRs for all-cause, CV, and progressive HF death. Francia et al reported that galectin-3 retained its prognostic power for ventricular tachycardia/fibrillation during a median follow-up period of 2.5 years after correction for risk factors in HF patients at high risk for sudden cardiac death.28 However, the association between galectin-3 and sudden death was insignificant after adjustment for clinical variables. The difference probably indicated that galectin-3 might be more meaningful to predict sudden death in long-term observation and special populations. We also evaluated the prognostic value of galectin-3 for in-hospital death. Log-transformed galectin-3 was significantly associated with in-hospital death after adjustment for established risk factors. The combination of galectin3 and NT-proBNP according to their ROC-optimal cutoffs was also useful for clinicians, because patients with both biomarkers elevated had the highest risk of in-hospital death. ROC analysis showed no significant results when galectin-3 and NT-proBNP were combined. The prognostic value of galectin-3 for in-hospital death was not as strong as it for longer-term death. The main explanation for this might be to the fact that galectin-3 levels were not significantly elevated from the values before onset of HF at the baseline of its pathophysiologic characteristics. De Boer et al also found that galectin-3 levels were very stable over a 6-month time period after an acute HF admission.10 Study Strengths and Limitations Strengths of this study include the large cohort of Chinese hospitalized patients with HF, sensitivity analysis of death prediction as a function of LVEF, multiple causes of death for the end points, and adjustment for multivariable risk factors. Our study also has several limitations, including 110 patients lost during longer-term follow up. However, those patients completed the first 6 months’ follow-up and provided useful information for this analysis. Furthermore, we used the same prediction model to analyze the association of galectin-3 with all end points. Some invasive therapies (such as percutaneous coronary intervention, biventricular pacemaker, internal cardiac defibrillator, and so on) might affect the end points, especially for sudden death. Finally, although our analysis is one of the largest studies of galectin-3 in HF, the biomarker was not measured serially after discharge. Although galectin-3 concentrations have been proved to be very stable over a 6-month time period,10 future studies of serial galectin-3 measurement are needed in longer intervals.
Conclusion In this large cohort of Chinese hospitalized patients with HF, galectin-3 was significantly associated with death due to all causes, CV cause, and progressive HF. Incorporation of galectin-3 in the prediction model added additional prognostic value beyond established risk factors for all-cause and CV death. On the other hand, the prognostic value of galectin-3 was not strong for sudden or in-hospital death. Disclosures Alan Maisel is a consultant for Alere and has received speaking honoraria from Alere.
Supplementary Data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.cardfail.2014.10.006
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