Galectin-3 changes from admission to discharge and its prognostic value for in-hospital mortality in heart failure: A prospective observational study : Medicine

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Research Article: Observational Study

Galectin-3 changes from admission to discharge and its prognostic value for in-hospital mortality in heart failure: A prospective observational study

Bui, Thanh-Hien Thi MD, PhDa; Dinh, Nhan Hieu MD, PhDb,*

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Medicine 102(20):p e33804, May 19, 2023. | DOI: 10.1097/MD.0000000000033804
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1. Introduction

Heart failure is a complex clinical syndrome that presents a significant global health challenge, affecting over 64 million individuals worldwide[1] with an increasing incidence among the elderly population.[2] Heart failure usually progress silently until extensive cardiac remodeling occurs and results in symptomatic phase that associates with poor prognosis.[3] Despite recent advances in treatment techniques, the mortality rate among patients with heart failure remains high. Nearly 30% to 40% of patients die within 1 year and 60% to 70% die within 5 years of diagnosis.[4,5] In addition to well-known mechanisms of heart failure, the process of cardiac remodeling significantly impacts the progression and clinical outcomes of heart failure through necrosis and programmed cell death.[4,6] Managing the disease by an efficient combination of clinical symptoms, imaging, hemodynamic state and biomarkers is crucial for physicians in clinical decision making and prognosis.[4]

Galectin-3 (Gal-3), a member of the beta-galactoside-binding animal lectins, is an inflammatory biomarker highly associated to cardiac fibrosis.[7] In normal condition, Gal-3 is synthesized and stored in the cytoplasm, and function in both nuclear and cytoplasmic compartments.[8,9] In response to tissue damage or infection, Gal-3 can be released from dying cells or activated myeloid cells such as monocytes, macrophages, dendritic cells and neutrophils.[8] In addition, studies conducted in vitro suggest that cardiomyocytes may also secrete Gal-3.[9] Gal-3 has been found to have significant implications in the development of heart failure, as its sustained overexpression and release in cardiac tissue can contribute to adverse cardiac remodeling, characterized by progressive fibrosis.[9] This role of Gal-3 in the pathogenesis of heart failure was supported by 2 studies conducted on large cohorts of healthy individuals, which demonstrated that Gal-3 independently predicted the occurrence of heart failure and subsequent mortality.[3,10] Gal-3 level was found to be elevated in chronic heart failure which independent of etiology.[11] The circulating levels of Gal-3 have been shown to play a role in stratifying the risk of heart failure, aiding in treatment decision-making, and influencing prognosis, particularly when used in conjunction with other biomarkers like B-type natriuretic peptide (BNP) or NT-proBNP.[11]

Considering the promising prognostic potential of Gal-3 in heart failure, and the recent introduction of Gal-3 in Vietnam at the initiation of our study, our objective was to compare Gal-3 levels in heart failure patients upon admission and discharge, and to evaluate the predictive accuracy of Gal-3 in combination with BNP for in-hospital mortality in patients with heart failure.

2. Methods

2.1. Study design

A prospective observational study was conducted from February 2017 to June 2020 at the Department of Cardiology, Trung Vuong hospital, Vietnam. A research doctor was responsible for recruiting participants. All patients who met the 2016 European Society of Cardiology Guidelines for heart failure diagnosis[12] admitted to the hospital during the research period were included in the study. Patients with chronic kidney disease stage 4 (eGFR < 30 mL/min/1.73 m2), liver failure, cirrhosis, malignancy, autoimmune disease, severe infections, or pulmonary fibrosis were excluded from the study due to their potential higher risk of mortality. All patients underwent baseline evaluation including total cholesterol, triglyceride, HDL, LDL, echocardiographic and electrocardiogram examination.

Patients were considered eligible for discharge based on the following criteria: their clinical symptoms, such as dyspnea, edema, and palpitations, had been alleviated and their blood pressure was stable (systolic blood pressure > 90 mm Hg) with a SpO2 level of >95% and no signs of orthostatic hypotension; had not received any vasopressors for at least 24 hours prior to discharge, had stable renal function, and normal urine volume, with or without the use of oral diuretics; BNP reduced by at least 30% compared to admission; had received an optimized medical treatment plan, with appropriate drug dosages tailored to each patient based on the heart failure treatment protocol, including renin-angiotensin-aldosterone system inhibitors, beta-blockers, and diuretics (including loop diuretics and potassium-sparing diuretics) as well as SGLT2 inhibitors.[13]

2.2. Blood sampling and assays

On admission, a sample of 3 mL of venous blood will be taken from each patient. Samples were stored in 5 mL red top tubes without ethylene diamine-tetra acetic acid. The blood was left undisturbed for 1 hour to clot, then centrifuged at 3000 rpm for 15 minutes. The serum was transferred to white top tube and stored at −22°C to −25°C. All serum samples were thawed only once prior to measuring the Gal-3 and BNP. We measured Gal-3 and BNP levels using the chemiluminescent microparticle immunoassay (Architect Gal-3 and Architect BNP assay; Abbott Diagnostics) on an Architect i2000SR analyzer (Abbott). Upon discharge, venous blood was collected from all patients, as was done on admission, and Gal-3 and BNP levels were reassessed.

2.3. Statistical analysis

Continuous data are reported as either mean ± standard deviation (SD) or median with interquartile range (IQR 25th-75th), depending on the distribution. Categorical data are described using frequency and percentage. To compare admission and discharge data, we used either paired Student t test or paired Wilcoxon rank sum test, depending on the normality of the data. We calculated Pearson or Spearman correlation coefficients, as appropriate to the distribution, to determine the relationship between Gal-3 and other biomarkers. Statistical significance was defined as a P value of <.05. We used receiver operating characteristic analysis with Youden index to find optimal cutoff values for Gal-3 and BNP, and logistic regression to evaluate their predictive ability for in-hospital mortality. All statistical analyses were performed using R Statistical Software (v4.2.2; R Core Team 2021).

2.4. Ethical issues

The research has been approved by the Biomedical Research Ethics Committees at the Hue University of Medicine and Pharmacy, Vietnam (Approval number: 3129/QĐ-ĐHYD, Date: 9/11/2016) and Trung Vuong Hospital, Ho Chi Minh City, Vietnam (Approval number: 180/BVTV, Date: 1/3/2017). All patients included in the study provided written consent.

3. Results

A total of 111 patients were enrolled in the study, and there were no patients who declined to participate after being informed that the laboratory tests would be covered by the research team. Table 1 presents the baseline characteristics of the study participants. The sample was evenly distributed between male and female patients. The majority of patients were elderly, with an average age of 69.66 ± 13.94 years, and almost half of the participants (46.9%) were classified as overweight. The median hospital stay duration was 11 (IQR 9–14) days.

Table 1 - Baseline characteristics of participants (n = 111).
Characteristics Frequency (%)
Male 60 (54.1%)
Age (yr-old) 69.66 ± 13.94
BMI ≥ 23 (kg/m2) 52 (46.9%)
Hospitalization duration (d) 11 (9–14)
Hb (g/dL) 12.58 ± 2.02
eGFR (mL/ph/1.73 m2) ≥90 11 (9.9%)
60-<90 47 (42.4%)
45-<60 28 (25.2%)
30-<45 25 (22.5%)
LVEF (%) 44.69 ± 13.73
LVDd (mm) 54.69 ± 10.49
LVEDV 152.60 ± 65.02
LVM (g) 242.57 ± 79.84
LVMI (g/m2) 150.73 ± 52.16
Stage II 12 (10.8%)
III 91 (82.0%)
IV 8 (7.2%)
Hypertension 91 (82.0%)
Diabetes 38 (34.2%)
Dyslipidemia 68 (61.3%)
Coronary artery disease 85 (76.6%)
Prior myocardial infarction 22 (19.8%)
Smoking 8 (7.2%)
Symptoms at admission
 Tachycardia (>100 b/m) 31 (27.9%)
 Tachypnea (> 20 b/m) 44 (28.6%)
 Hypertension 51 (46.0%)

The Gal-3 circulating levels at discharge (24.08 ± 9.55) were significantly lower than those at admission (30.71 ± 11.22) (Fig. 1A). The majority of participants (Fig. 1B) experienced a decrease in Gal-3 levels, with a median reduction of 19.9% (IQR 8.7–29.8). The decrease in Gal-3 level was seen in patients of any heart failure stage (Fig. 1C). Gal-3 levels showed a weak correlation with BNP levels both at admission and discharge (R = 0.24, P = .011 and R = 0.27, P = .006, respectively, Fig. 1D and E). The decrease in Gal-3 level at discharge were observed in both male and female patients, as well as in both young and elderly patients (Fig. 2). Patients with a history of myocardial infarction had significantly higher Gal-3 levels both at admission (P = .0075) and discharge (P = .014). There was no significant association observed between Gal-3 secretion and other cardiovascular risk factors, such as hypertension, diabetes, dyslipidemia, or coronary artery disease (Fig. 3).

Figure 1.:
Galectin-3 level at admission and discharge and the correlation with BNP. (A) Gal-3 at admission and discharge. (B) The distribution of Gal-3 reduction. (C) Gal-3 at admission and discharge categorized by the stage of heart failure. (D) Correlation between Gal-3 and BNP at admission. (E) Correlation between Gal-3 and BNP at discharge. BNP = B-type natriuretic peptide, Gal-3 = galectin-3.
Figure 2.:
Gal-3 level at admission and discharge categorized by sex and age. Gal-3 = galectin-3.
Figure 3.:
The association between cardiovascular risk factors and Gal-3 secretion. Gal-3 = galectin-3.

BNP levels and the percentage of patients in different heart failure stages were significantly different between patients who died during hospitalization and those who did not (Table 2). When compared to using BNP alone, combining BNP and Gal-3 significantly improved the ability to predict in-hospital mortality, as reflected by the increase in the area under the curve from 0.768 to 0.783. Including heart failure stage as a third predictor along with BNP and Gal-3 further improved the predictive accuracy, with the area under the curve increasing to 0.810 (Fig. 4). The optimal cutoff value for Gal-3 to predict in-hospital mortality was 28.1 ng/mL, with a sensitivity of 72.7% and specificity of 46.0%. The optimal cutoff value for BNP was 1782.6 pg/mL, with a sensitivity of 80.0% and specificity of 74.0%.

Table 2 - Factors related to in-hospital death.
Characteristics No death (n = 100) Death (n = 11) P value
Gal-3 at admission 30.6 ± 11.3 35.2 ± 14.5 .404
BNP at admission 1324.8 ± 1106.2 2903.2 ± 1686.1 .005
Heart failure stage .046
 II 12 (12.0%) 0 (0.0%)
 III 83 (83.0%) 8 (72.7%)
 IV 5 (5.0%) 3 (27.3%)
BNP = B-type natriuretic peptide, Gal-3 = galectin-3.

Figure 4.:
Receiver operating characteristic (ROC) curves for the predictive ability of BNP alone, Gal-3 alone, and a combination of BNP, Gal-3, and heart failure stage in predicting in-hospital mortality in patients with heart failure. The area under the curve (AUC) is presented for each ROC curve. BNP = B-type natriuretic peptide, Gal-3 = galectin-3.

4. Discussion

Our study found a significant decrease in Gal-3 levels among hospitalized heart failure patients from admission to discharge, regardless of the stage of heart failure. We investigated the potential role of Gal-3 as a criterion for discharge and as a predictor for in-hospital mortality among hospitalized heart failure patients.

The results showed that the circulating levels of Gal-3 significantly decreased from admission to discharge, and at discharge the patients experienced a median decrease of 19.9% in Gal-3 levels. The decrease in Gal-3 levels could be due to the treatment administered during hospitalization, which may have led to a reduction in the inflammatory response and cardiac fibrosis. Similar to how BNP reduction has been recommended as a potential discharge marker,[14] we suggest further studies to investigate the ability of Gal-3 reduction with a randomized trial design.

The present study found a weak correlation between Gal-3 and BNP in hospitalized patients with heart failure. Gal-3 and BNP are 2 biomarkers that have been extensively studied in heart failure.[15,16] The weak correlation between Gal-3 and BNP that we found may be reflecting different aspects of the pathophysiology of heart failure. BNP is primarily secreted by the ventricular myocardium in response to pressure or volume overload, and is widely recognized as the most established biomarker in heart failure due to its diagnostic, prognostic, and therapeutic guidance.[17] Natriuretic peptides are considered “loading markers” as they respond readily and robustly to ventricular stress, while Gal-3 levels reflect interstitial fibrosis, which may be less responsive to changes in loading conditions.[18] Our study results suggest that the combination of Gal-3 and BNP may provide complementary information regarding heart failure prognosis, rather than being interchangeable. Considering that BNP has been proposed as a valuable tool for predicting all-cause mortality,[19] our findings suggest that Gal-3 provides additional value in enhancing the predictive ability.

In the PREVEND (Prevention of Renal and Vascular End-stage Disease) study, de Boer et al have shown that a higher level of Gal-3 is associated to an increased risk of all-cause mortality in the general population, which independent of cardiovascular risk factors.[20] A meta-analysis conducted by Chen et al demonstrated that for every 1% increase in Gal-3 levels, there was a corresponding 28% increased risk of all-cause mortality.[21] Our study showed that combining Gal-3 with BNP improved the ability to predict in-hospital mortality compared to using BNP alone. This finding is consistent with a study by de Boer et al, which suggested similar results in heart failure patients with both reduced and preserved ejection fraction.[18] The combination of the 2 biomarkers, along with heart failure stage, further improved the predictive accuracy. Gal-3 is a promising biomarker for predicting in-hospital mortality among acute heart failure patients, and its combination with other biomarkers may enhance the risk stratification and clinical management of heart failure patients.

In conclusion, the present study demonstrates the potential utility of Gal-3 as a biomarker for predicting in-hospital mortality among chronic heart failure patients. The combination of Gal-3 with other biomarkers, such as BNP, may enhance the risk stratification and clinical management of heart failure patients. Further studies are needed to validate these findings in larger patient cohorts and to investigate the potential mechanisms underlying the association between Gal-3 and heart failure.

There are some limitations that should be noted. Firstly, the study design was observational, which means that we cannot establish a causal relationship between Gal-3 and BNP levels and in-hospital mortality. Secondly, the sample size was relatively small, although data was collected over a 4-year period, which may limit the generalizability of the results. Additionally, it should be noted that the study population consisted only of acute heart failure patients with the majority of whom were in stages III and IV of heart failure. Future studies with larger and more diverse populations are needed to validate our results and better understand the utility of these biomarkers in predicting mortality in different populations of heart failure patients.

Author contributions

Conceptualization: Thanh-Hien Thi Bui, Nhan Hieu Dinh.

Formal analysis: Thanh-Hien Thi Bui, Nhan Hieu Dinh.

Investigation: Thanh-Hien Thi Bui.

Methodology: Thanh-Hien Thi Bui, Nhan Hieu Dinh.

Supervision: Nhan Hieu Dinh.

Visualization: Nhan Hieu Dinh.

Writing – original draft: Thanh-Hien Thi Bui, Nhan Hieu Dinh.

Writing – review & editing: Nhan Hieu Dinh.


B-type natriuretic peptide
interquartile range


[1]. Savarese G, Becher PM, Lund LH, et al. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2022;118:3272–87.
[2]. Ambrosy AP, Fonarow GC, Butler J, et al. The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. J Am Coll Cardiol. 2014;63:1123–33.
[3]. Ho JE, Liu C, Lyass A, et al. Galectin-3, a marker of cardiac fibrosis, predicts incident heart failure in the community. J Am Coll Cardiol. 2012;60:1249–56.
[4]. Mann DL, Chakinala M. Heart failure: pathophysiology and diagnosis. In: Jameson JL, Fauci AS, Kasper DL, et al, (eds). Harrison’s Principles of Internal Medicine. 20th ed. New York, NYMcGraw-Hill Education; 2018. Available at: [Access date January 7, 2023].
[5]. Lloyd-Jones D, Adams RJ, Brown TM, et al. Heart disease and stroke statistics–2010 update. Circulation. 2010;121:e46–e215.
[6]. Zipes DP. Braunwald’s heart disease: a textbook of cardiovascular medicine, 11th edition. BMH Med J - ISSN 2348–392X. 2018;5:63–63.
[7]. Wu C, Lv Z, Li X, et al. Galectin-3 in predicting mortality of heart failure: a systematic review and meta-analysis. Heart Surg Forum. 2021;24:E327–32.
[8]. Díaz-Alvarez L, Ortega E. The many roles of galectin-3, a multifaceted molecule, in innate immune responses against pathogens. Mediators Inflamm. 2017;2017:9247574.
[9]. Suthahar N, Meijers WC, Silljé HHW, et al. Galectin-3 activation and inhibition in heart failure and cardiovascular disease: an update. Theranostics. 2018;8:593–609.
[10]. Jagodzinski A, Havulinna AS, Appelbaum S, et al. Predictive value of galectin-3 for incident cardiovascular disease and heart failure in the population-based FINRISK 1997 cohort. Int J Cardiol. 2015;192:33–9.
[11]. Hrynchyshyn N, Jourdain P, Desnos M, et al. Galectin-3: a new biomarker for the diagnosis, analysis and prognosis of acute and chronic heart failure. Arch Cardiovasc Dis. 2013;106:541–6.
[12]. Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2016;37:2129–200.
[13]. Bakosis G, Christofilis I, Karavidas A. Treatment goals and discharge criteria for hospitalized patients with acute heart failure. Contin Cardiol Educ. 2017;3:100–6.
[14]. Caldwell MA, Howie JN, Dracup K. BNP as discharge criteria for heart failure. J Card Fail. 2003;9:416–22.
[15]. de Boer RA, Yu L, van Veldhuisen DJ. Galectin-3 in cardiac remodeling and heart failure. Curr Heart Fail Rep. 2010;7:1–8.
[16]. Doust J, Lehman R, Glasziou P. The role of BNP testing in heart failure. AFP. 2006;74:1893–900.
[17]. Friões F, Lourenço P, Laszczynska O, et al. Prognostic value of sST2 added to BNP in acute heart failure with preserved or reduced ejection fraction. Clin Res Cardiol. 2015;104:491–9.
[18]. de Boer RA, Lok DJA, Jaarsma T, et al. Predictive value of plasma galectin-3 levels in heart failure with reduced and preserved ejection fraction. Ann Med. 2011;43:60–8.
[19]. Khanam SS, Son JW, Lee JW, et al. Prognostic value of short-term follow-up BNP in hospitalized patients with heart failure. BMC Cardiovasc Disord. 2017;17:215.
[20]. de Boer RA, van Veldhuisen DJ, Gansevoort RT, et al. The fibrosis marker galectin-3 and outcome in the general population. J Intern Med. 2012;272:55–64.
[21]. Chen A, Hou W, Zhang Y, et al. Prognostic value of serum galectin-3 in patients with heart failure: a meta-analysis. Int J Cardiol. 2015;182:168–70.

galectin-3; heart failure; mortality; prediction

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