At the time of admission, blood samples were collected into tubes containing ethylenediaminetetraacetic acid and then transported to the Biochemistry Department of Cho Ray ‎Hospital to be processed and frozen at -80ºC for eventual measurements of galectin-3, BNP, troponin I, and albumin. ‎Galectin-3 was analyzed using ARCHITECT galectin-3 assay, which is a two-step immunoassay ‎procedure (ARCHITECT galectin-3, Abbott, Park, IL, USA) on the Architect i2000SR analyzer (Abbott Laboratories, Park, IL, USA), according to the manufacturer's instructions22. BNP was analyzed using ARCHITECT BNP (ARCHITECT BNP, Abbott) assay ‎on the Architect i2000SR analyzer (Abbott Laboratories).‎ Troponin I was analyzed using chemiluminescent microparticle immunoassay on the ADVIA Centaur XP analyzer (Siemens Diagnostics, Tarrytown, NY, USA). Albumin was analyzed using the colorimetric method on the ADVIA 1800 analyzer (Siemens Diagnostics, Tarrytown, NY, USA).

Continuous variables were presented as mean and standard deviation (SD) for the quantitative variables or as median and interquartile range (IQR) for those without a normal distribution, and as absolute values and percentages for the qualitative variables. Pairwise comparisons between patients with acute heart failure achieving or not achieving the endpoint were performed using the Student’s T-test or non-parametric Mann-Whitney U test for continuous variables,‎ or chi-square test or Fisher’s exact test for categorical variables, where appropriate‎. Univariable logistic regression was used to evaluate how well galectin-3 and BNP predicted the outcomes.‎ We then used the multivariable logistic regression for the covariates of galectin-3, BNP, and ‎other factors in predicting the outcomes. The variable selection was performed with Bayesian ‎model averaging (BMA) method23. Receiver-operating characteristic (ROC) curves were also derived to evaluate the performance of galectin-3, BNP, and troponin I for predicting the endpoint in patients with acute heart failure. Differences were considered as statistically significant with a p-value less than 0.05, or when the 95% CI of the HR excluded the value of 1. All analyses were conducted using the Rstudio version 1.2 (PBC, Boston, MA).

A total of 117 patients diagnosed with acute heart failure were included in the study. Six patients (5%) were lost to follow-up, and fifty-nine patients (53.2%) died within one year after hospital admission. ‎The clinical and analytical characteristics of patients with acute heart failure are presented in Table 1. ‎The median (IQR) plasma level of galectin-3 in patients with acute heart failure was 34.6 ng/mL ‎‎(26.7 – 44.1). Approximately 74% of patients with acute heart failure had a prior history of chronic ‎heart failure and coronary artery disease.

Figure 1

A total 59 AHF patients died within one year after ‎hospital admission. In predicting one-year mortality, it was found that galectin-3 ‎level in the deceased group was significantly higher than that in the alive group (Figure 1). Differences in demographic, medical history, and biomarker characteristics between the alive and deceased groups are presented in Table 2. In ‎addition to the difference in galectin-3 levels, patients reaching the endpoint within one year after hospitalization showed a statistically significant difference in age (p = 0.018), eGFR (p = 0.001), plasma troponin I (p = 0.016), albumin (p = 0.010) ‎and BNP (p = 0.001). We use Cox regression to evaluate the effect of some characteristics, including plasma galectin-3, on one-year mortality as seen in Table 3. Univariate cox regression analysis showed that increased plasma ‎galectin-3 levels had an HR of 3.47 ‎ (95% CI, ‎2.07 – 5.83; p < 0.001) for one-year ‎mortality. After adjustment for age‎, eGFR, ‎plasma troponin I, albumin, and BNP, the increased plasma galectin-3 levels had an HR of ‎2.60‎ (95% CI, ‎1.47 – 4.60‎‎; p <0.001‎‎), implying the independent prognostic ability of galectin-3.

Table 2

Characteristics	One-year all-cause mortality
	No (N=52)	Yes (N=59)	p value
Age (year), mean ± SD	63.1 ± 18.4	70.7 ± 14.9	0.018
Gender male, n (%)	30 (57.7%)	30 (50.8%)	0.470
Smoking, n (%)	25 (48.1%)	29 (49.2%)	0.910
Hypertension, n (%)	31 (59.6%)	39 (66.1%)	0.480
Diabetes, n (%)	12 (23.1%)	21 (35.6%)	0.150
Atrial fibrillation, n (%)	8 (15.4%)	3 (5.1%)	0.070
Coronary artery disease, n (%)	39 (75%)	42 (71.2%)	0.652
Chronic heart failure, n (%)	37 (71.2%)	44 (74.6%)	0.685
Prior acute heart failure, n (%)	27 (51.9%)	33 (55.9%)	0.672
Systolic blood pressure, median (IQR)	120 (100, 120)	110 (100, 120)	0.513
Diastolic blood pressure, median (IQR)	70 (60, 70)	70 (60, 80)	0.884
Heart rate (beats per minute), median (IQR)	90 (80, 100)	90 (80, 109)	0.319
eGFR (ml/min/1.73 m2), median (IQR)	56.9 (36.5, 63.9)	41.8 (30.3, 55.7)	0.001
Ejection fraction (%), median (IQR)	27.5 (20, 34)	26 (20, 33)	0.703
Plasma albumin level (g/dL), median (IQR)	3.4 (3.0, 3.8)	3.2 (3.0, 3.5)	0.010
Plasma troponin I level (ng/mL), median (IQR)	0.2 (0.1, 2.4)	1.2 (0.2, 10.4)	0.016
Plasma BNP level (pg/mL), mean ± SD	2267.6 ± 1131.3	3069 ± 1264.3	0.001
Plasma Galectin-3 level (ng/mL), median (IQR)	31.2 (24.1, 38)	41.1 (31.9, 53.5)	< 0.001

Table 3

	Univariable analysis			Multivariable analysis
	HR	95% CI	P value	HR	95% CI	P value
Galectin-3 levels (ng/ml)
- Galectin-3 ≤ 40.75	1	-	Ref	1	-	Ref
- Galectin-3 > 40.75	3.47	2.07 – 5.83	<0.001	2.60	1.47 – 4.60	0.001
Age (year)	1.02	1 – 1.04	0.027	1.006	0.99 – 1.03	0.524
eGFR (ml/min/1.73m2)	0.98	0.96 – 0.99	0.006	0.99	0.98 – 1.01	0.318
Albumin (g/dL)	0.45	0.27 – 0.76	0.003	0.51	0.27 – 0.96	0.037
Troponin I (ng/mL)	1	1 – 1.005	0.090	1	0.99 – 1.005	0.108
BNP (pg/mL)	1	1 – 1.001	0.002	1	1 – 1	0.185

Figure 2

Our ROC curve analysis showed that ‎plasma galectin-3 alone was a good prognostic factor for one-year mortality with the AUC ‎‎(95% CI) of 0.71 (0.62 – 0.81) (Figure 2A). BNP and troponin I alone were not strong prognostic ‎factors, with AUCs (95% CI) of 0.69 (0.59 – 0.79) and 0.63 (0.53 – 0.74), respectively (Figure 2B, C). The optimal cut-off value for galectin-3 for predicting one-year all-cause mortality was 40.75 ng/mL, with a sensitivity of 50.1% and a specificity of 88.5%. Furthermore, the AUC (95% CI) ‎for the combined biomarkers (galectin-3 ‎+ BNP) was 0.78 (0.70 – 0.86), which was higher than the AUC of galectin-3 alone (Figure 2D). Based on the Kaplan-Meier analyses, patients with elevated galectin-3 and BNP had the highest rates of death, while patients with no elevated markers had the lowest rates (Figure 3).

Figure 3

Our study showed that galectin-3 could predict one-year all-cause mortality with an area under the ROC curve of ‎0.71 (95% CI, 0.62 – 0.81)‎, whereas BNP and troponin I were inferior to galectin-3 with AUCs (95% CI) of 0.69 (0.59 – 0.79) and 0.63 (0.53 – 0.74), respectively. Moreover, the combination of galectin-3 and BNP ‎resulted in an improved prognostic value for one-year mortality with AUC of ‎0.78 (95% CI, 0.70 – 0.86).

Previous studies have investigated the contribution of galectin-3 in the pathogenic process‎ of heart failure. Nguyen et al. suggested that the major factors contributing to the cardiac release of galectin-3 and elevated circulating levels of galectin-3 are cardiac inflammation and enhanced activation of β-adrenergic receptors24. Several studies have assessed the diagnostic capacity of galectin-3 and compared ‎it to natriuretic peptides (BNP and NT-proBNP) in identifying acute heart failure patients. For example, van Kimmenade et al. revealed that AUC of NT-proBNP was 0.94 for HF diagnosis in dyspneic patients, whereas the AUC of galectin-3 was 0.7416. Recently, studies have focused on discovering biomarkers of diagnostic capability in HF to biomarkers of prognostic value.

Our findings were supported by several studies that reported the usefulness of galectin-3 as a risk predictor of mortality in AHF patients. A large cohort study that included 1140 hospitalized heart failure patients showed that galectin-3 had a good prognostic value for in-hospital mortality with AUC of 0.71 (95% CI, 0.65 – 0.77)18. Besides, combining galectin-3 and another biomarker (NT-proBNP) resulted in an improved AUC of 0.81 (95% CI, 0.76 – 0.86). Another study by van Kimmenade et al.16 showed that galectin-3 represents a remarkable biomarker for predicting 60-day all-cause death in 209 AHF patients admitted to the emergency setting. This study also displayed that increased levels of galectin-3 had an OR of 14.3 (95% CI, 5.6 – 45.1; p < 0.001) after adjustment for age, glomerular filtration rate, and New York Heart Association (NYHA) functional classification. Therefore, the authors concluded that ‎galectin-3 was an independent prognostic factor for 60-day mortality and rehospitalization.

Similarly, Miró et al. 25 showed that galectin-3 has a superior prognostic value (AUC = 0.732, p = 0.041) for predicting the 30-day mortality in 115 AHF patients compared to NT-proBNP (AUC = 0.586, p = 0.446) and troponin I (AUC = 0.639, p = 0.222). Miró et al. also showed that the concentrations of galectin-3 could be applicable for risk stratification of short-term mortality in the emergency setting. For example, patients with galectin-3 levels between 30–42 μg/L had an OR of 5.25 (95% CI, 0.53 – 51.63), whereas those with galectin-3 levels > 42 μg/L had an OR of 8.88 (95% CI, 1.49 – 52.73).

Intriguingly, Demissei et al. 26 showed that the AUCs of galectin-3 measured at admission, day 3, day 7, and day 14 did not have any statistical difference in predicting the 180-day mortality in 2023 AHF patients. The serial measurement of galectin-3 during a hospital stay is not recommended. Accordingly, galectin-3 was measured only once for each patient in the study and at the time of admission in our study‎. To our knowledge, this is the first study assessing the prognostic ability of galectin-3 in predicting the long-term mortality in AHF patients in the Vietnamese population and the second in the Asian population. However, one of the limitations is the small sample size of AHF patients included in our study, which could have introduced a type II error. Besides, there was no matching control group to compare the circulatory levels of galectin-3 with the AHF group. Our study also did not assess the diagnostic capacity of galectin-3 in patients with AHF. Therefore, further studies with a more significant number of patients enrolled are warranted to evaluate the diagnostic and long-term prognostic utility of combining galactin-3 with other biomarkers, i.e., soluble ST2 and high-sensitivity (hs) troponin.

Galectin-3 measured at the time of admission showed an excellent prognostic capacity to predict one-year all-cause mortality in patients with acute heart failure. The results of our study support the utility of a combined galectin-3 and BNP model as a risk ‎predictor of long-term mortality in AHF patients.

AHF: acute heart failure

AUC: area under the curve

BMA: Bayesian ‎model averaging

BNP: brain natriuretic peptide

CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration

eGFR: Estimated glomerular filtration rate

HF: heart failure

IQR: inter quartile range

LVEF: left ventricular ejection fraction

NT-proBNP: N-terminal pro-brain natriuretic peptide

NYHA: New York Heart Association

ROC: Receiver-operating characteristic

SD: standard deviation

We thank the Ethical Committees of University of Medicine and Pharmacy at Ho Chi Minh city and Cho Ray hospital for approving this study; we thank Dr. Tran Thanh Vinh, head of Biochemistry Department of Cho Ray hospital, for performing galectin-3 and other biomarker assays; we thank Ms. Huynh Thi Lien Nga and Mr Dang Quang Loi for supporting this study.

Conceptualization and Methodology: H.V.S., D.Q.T, C.N.H; Data collection: H.V.S., D.Q.T., T.T.T.H.; Analysis: H.V.S., D.Q.T.; Writing original draft preparation: H.V.S., D.Q.T., T.T.T.H., C.N.H., T.T.T.; Writing, review, editing: H.V.S, D.Q.T. All authors read and approved the final manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data and materials used and/or analyzed during the current study are available from the corresponding author on reasonable request.

This study was conducted in accordance with the amended Declaration of Helsinki. The institutional review board of Cho Ray hospital approved the study with the number of approval letter being 63/DHYD-HD, and all participants provided written informed consent.

Not applicable.

The authors declare that they have no competing interests.

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