Galectin-3, a Marker of Cardiac Fibrosis, Predicts Incident Heart Failure in the Community

Abstract

Objectives

We sought to examine the relation of galectin-3 (Gal-3), a marker of cardiac fibrosis, with incident heart failure (HF) in the community.

Background

Gal-3 is an emerging prognostic biomarker in HF, and experimental studies suggest that Gal-3 is an important mediator of cardiac fibrosis. Whether elevated Gal-3 concentrations precede the development of HF is unknown.

Methods

Gal-3 concentrations were measured in 3,353 participants in the Framingham Offspring Cohort (mean age 59 years, 53% women). The relation of Gal-3 to incident HF was assessed using proportional hazards regression.

Results

Gal-3 was associated with increased left ventricular mass in age- and sex-adjusted analyses (P=0.001); this association was attenuated in multivariable analyses (P=0.06). A total of 166 participants developed incident HF and 468 died during a mean follow-up of 8.1 years. Gal-3 was associated with risk of incident HF (HR 1.28 per 1 standard deviation increase in log-Gal-3, 95% CI 1.14–1.43, P<0.0001), and remained significant after adjustment for clinical variables and B-type natriuretic peptide (HR 1.23, 95% CI 1.04–1.47, P=0.02). Gal-3 was also associated with risk of all-cause mortality (multivariable-adjusted HR 1.15, 95% CI 1.04–1.28, P=0.01). The addition of Gal-3 to clinical factors resulted in negligible changes to the c-statistic and minor improvements in the net reclassification index.

Conclusions

Higher concentration of Gal-3, a marker of cardiac fibrosis, is associated with increased risk of incident HF and mortality. Future studies evaluating the role of Gal-3 in cardiac remodeling may provide further insights into the role of Gal-3 in the pathophysiology of HF.

Heart failure (HF) accounts for more than 1 million hospital admissions per year, with an estimated cost exceeding $39 billion annually in the United States (1). The development of HF is often a clinically silent process, with progressive cardiac remodeling that eventually leads to symptomatic presentation late in the course of disease progression. After HF diagnosis, nearly 60% of men and 45% of women will die within 5 years (1). While most therapies are implemented during the symptomatic phase of HF, when extensive remodeling has already occurred, strategies that target individuals with cardiac remodeling prior to the onset of symptoms may prevent complications associated with HF (2,3). Cost-effective strategies to identify this subgroup of patients are of great interest, as outlined in the American College of Cardiology/American Heart Association guidelines (4). While cardiac imaging of the general population is not recommended, a biomarker strategy to screen and identify individuals to refer for diagnostic noninvasive cardiac imaging may be useful (5). This may facilitate early recognition of asymptomatic LV dysfunction and initiation of therapy to favorably alter the course of progression to HF.

Cardiac fibrosis is an important contributor to the pathophysiology of left ventricular (LV) systolic and diastolic dysfunction. It is also a pathologic phenomenon common to cardiac remodeling caused by hypertensive, ischemic, and other conditions affecting the myocardium. Galectin-3 (Gal-3) is a beta-galactoside binding lectin that appears to be a mediator of cardiac fibrosis in a number of recent experimental studies (6,7).

Gal-3 has been related to mortality in individuals with acute and chronic HF (8–11), as well as in the general population (12). The role of Gal-3 as a predictor of incident HF in apparently healthy individuals has not been studied. We sought to examine the clinical correlates of Gal-3 in order to explore mechanisms by with which Gal-3 may be associated with an adverse cardiovascular prognosis. We also examined the cross-sectional relations of Gal-3 to LV structure and function, to assess whether Gal-3 is associated with subclinical changes in cardiac function. Lastly, we sought to study the association of Gal-3 levels and incident HF events in the community. We hypothesized that Gal-3, a prognostic biomarker in patients with HF, would be associated with incident HF.

METHODS

Participants

The Framingham Heart Study is a longitudinal community-based cohort initiated in 1948 to prospectively study cardiovascular disease and associated risk factors. The Framingham offspring cohort includes children (and spouses children) of the original cohort participants, and participants have been examined approximately every 4 years since its inception in 1971 (13). Each examination includes routine questionnaires, physical examination, anthropometry, and blood testing. Gal-3 levels were measured at the sixth examination (1996–1998). Of 3,450 participants with sample available for Gal-3 measurement, a total of 97 participants were excluded due to prevalent HF (n=40), stage IV kidney disease (n=10), missing covariates (n=39), missing Gal-3 measurements (n=2), extreme Gal-3 outliers (> 5 log-standard deviations above or below the log-transformed mean, n=6, specified a priori), leaving 3,353 participants (97%) for analysis.

Biomarker measurement

Blood samples were collected after an overnight fast and immediately centrifuged and stored at −70°C until assayed. Plasma concentrations of Gal-3 were measured using an enzyme-linked immunosorbent assay (BG Medicine, Waltham, MA, USA) (14). The lower detection limit was 1.32 ng/ml with an upper detection limit of 96.6 ng/ml. Across this measurement range, the within run and total precision are reported between 2.1%–5.7% and 4.2%–12.0%, respectively (14). B-type natriuretic peptide (BNP) was previously measured (15).

Clinical Assessment

Participants underwent a comprehensive clinical assessment at the sixth offspring cohort examination (13). Hypertension was defined as a systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current antihypertensive drug treatment. Cardiovascular events were adjudicated by a 3-physician panel after review of medical records. History of coronary heart disease included prior myocardial infarction, acute coronary insufficiency (prolonged ischemic symptoms with new ECG abnormalities in the absence of biomarker elevations indicative of infarction), or angina pectoris. Atrial fibrillation was determined after examining all available ECGs. Valvular heart disease was defined as a systolic murmur ≥ grade 3/6 or any diastolic murmur. Total and high-density lipoprotein cholesterol levels were measured. Diabetes mellitus was defined as a fasting glucose ≥ 126 mg/dL, non-fasting glucose ≥ 200 mg/dL, or the use of insulin or oral hypoglycemic medications. Estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease equation (16).

Definition of HF

At each follow-up examination or health update, interim cardiovascular disease events were identified and medical records obtained. Initial HF was confirmed by a panel of 3 physicians after systematic review of outpatient and hospital records using established protocols and Framingham criteria (17). The present study included initial HF events occurring between the baseline examination (1996–1998) through the end of 2008 as incident events.

Echocardiographic Methods

A total of 2,425 participants with Gal-3 measurements also underwent routine M-mode and two-dimensional echocardiography (18), and had complete data for analysis. LV end-diastolic (LVDD), end-systolic dimensions (LVSD), left atrial end-systolic diameter (LAD), and end-diastolic LV septal and posterior wall thicknesses were measured according to American Society of Echocardiography guidelines (19). Fractional shortening was calculated as [(LVDD − LVSD)/LVDD] × 100, and LV systolic dysfunction defined as fractional shortening < 29%. LV mass was calculated as follows: LV mass = 0.8 [1.04 ((LVDD + LV posterior wall thickness + LV septal wall thickness)³ − (LVDD)³)] + 0.6 (20). LV mass was indexed to height^2.7 (21), and elevated LV mass was defined as an indexed value greater than or equal to the sex-specific 80^th percentile.

Statistical Analysis

Due to non-normality, Gal-3, eGFR, and BNP levels were log-transformed for subsequent analyses. Baseline clinical characteristics were summarized by sex-specific quartiles of log-Gal-3, and trends in means across quartiles examined. Multivariable regression analysis of Gal-3 correlates was performed using a stepwise selection model with inclusion and of variables at P<0.05. Due to sex differences in Gal-3 distribution, sex-standardized log-Gal-3 was used for correlation and regression analyses.

The associations of Gal-3 with measures of cardiac structure and function (including LV mass, LAD, and LV fractional shortening) were examined using linear regression models adjusting for a) age, sex, and height; b) age, sex, height, systolic blood pressure, anti-hypertensive medication use, diabetes mellitus, and previous myocardial infarction. Analyses of fractional shortening were not adjusted for height, and analyses of LAD were also adjusted for valvular heart disease. Participants with prevalent atrial fibrillation were excluded from echocardiographic analyses.

Crude HF incidence rates were estimated by sex-specific Gal-3 quartile. The cumulative incidence of HF across Gal-3 quartiles was examined using a Kaplan-Meier-like method while accounting for competing risk of death (22). Multivariable proportional hazards regression models were constructed to evaluate the association of Gal-3 with incident HF events and with all-cause mortality (23). Models were created adjusting for a) age and sex; b) age, sex, systolic blood pressure, antihypertensive medication use, body-mass index, diabetes mellitus, current smoking status, prevalent coronary heart disease, valvular heart disease, and atrial fibrillation; c) all covariates in model b, plus BNP. Proportional hazards assumptions were met. Analyses for mortality were further adjusted for eGFR, total and high-density lipoprotein cholesterol. In secondary analyses, we examined the association of Gal-3 and cardiovascular death (defined as death from coronary heart disease, cerebrovascular disease, or other cardiovascular cause), adjusting for the same covariates as analyses for all-cause mortality.

We adjusted HF analyses for eGFR in secondary analyses, since changes in kidney function may mediate Gal-3 effects on incident HF. We conducted sensitivity analyses, excluding 283 individuals with prevalent chronic kidney disease (CKD, defined as eGFR < 60 ml/min/1.73m²), and we used a time-dependent covariate to adjust for incident CKD events in analyses for both incident HF and mortality.

To assess the incremental benefit of Gal-3 in the prediction of HF and mortality risk, the c statistic were compared between models with traditional risk factors with and without Gal-3 (24). We estimated the Integrated Discrimination Improvement (IDI), and the category-free Net Reclassification Improvement (NRI) metric for the addition of Gal-3 in fully adjusted models (25,26). All statistical analyses were conducted with SAS version 9.2 for Windows.

RESULTS

Baseline clinical characteristics of 3,353 participants are displayed by Gal-3 quartiles in Table 1. The mean age was 59 years and 53% of participants were women. The distribution of Gal-3 levels in our sample is shown in Supplemental Figure 1. Gal-3 concentrations were higher in women compared with men (P<0.05), with median Gal-3 in women of 14.3 ng/ml (Quartile [Q] 1-Q3 12.0–16.8) versus 13.1 ng/ml (11.1–15.4) in men. Participants with higher Gal-3 levels were older, and had a higher prevalence of traditional cardiovascular risk factors, including hypertension, diabetes mellitus, previous coronary heart disease, higher body-mass index, and lower eGFR (P for trend <0.0001 for all).

Table 1.

Baseline characteristics of 3,353 participants by sex-specific galectin-3 quartile

	Quartile of Gal-3*
Characteristic	1 n=835	2 n=842	3 n=842	4 n=834	P (trend)
Clinical
Age, years	55 (9)	58 (9)	60 (9)	64 (10)	<0.0001
Female	454 (54)	431 (51)	436 (52)	461 (55)	0.98
Systolic blood pressure, mmHg	124 (18)	127 (18)	130 (19)	132 (20)	<0.0001
Diastolic blood pressure, mmHg	75 (9)	76 (9)	76 (9)	75 (10)	0.34
Antihypertensive medication use	134 (16)	193 (23)	250 (30)	355 (43)	<0.0001
Diabetes mellitus	56 (7)	66 (8)	81 (10)	115 (14)	<0.0001
Coronary heart disease	32 (4)	40 (5)	66 (8)	106 (13)	<0.0001
Body mass index, kg/m2	26.9 (4.7)	27.6 (5.1)	28.5 (5.2)	28.6 (5.3)	<0.0001
Smoking	121 (14)	132 (16)	149 (18)	109 (13)	0.63
Laboratory
Total cholesterol, mg/dl	200 (36)	205 (37)	209 (37)	208 (42)	<0.0001
HDL cholesterol, mg/dl	54 (17)	52 (16)	51 (16)	48 (15)	<0.0001
eGFR, ml/min/1.73m2	94 (22)	90 (24)	88 (25)	80 (25)	<0.0001
B-type natriuretic peptide, pg/ml	12.8 (15.6)	13.7 (17.7)	14.7 (18.3)	22.4 (30.6)	<0.0001
Echocardiographic (n=2,425)*
Left ventricular mass, g/m2	156 (42)	159 (44)	163 (45)	166 (45)	<0.0001
Fractional shortening, %	37.1 (5.2)	37.1 (5.7)	37.1 (5.4)	37.3 (6.2)	0.59
Left atrial dimension, mm	39.0 (5.0)	39.3 (5.2)	39.3 (5.1)	40.0 (5.4)	0.001

Data shown are means (SD) or individuals (%).

Abbreviations: HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate

^*Lower and upper limits for Gal-3 quartiles for men and women were as follows: men Q13.9–11.1 ng/ml; Q2, 11.1–13.1 ng/ml; Q3, 13.1–15.4 ng/ml; Q4, 15.4–47.7 ng/ml; women Q1, 5.0–12.0 ng/ml; Q2 12.0–14.3 ng/ml; Q3 14.3–16.8 ng/ml; Q4 16.8–52.1 ng/ml.

Clinical Correlates of Gal-3

In multivariable analyses, Gal-3 was positively associated with age, hypertension, body-mass index, prevalent coronary heart disease, BNP, and negatively associated with eGFR (Table 2). The R² of this model was 0.15. There was a weak correlation between Gal-3 and BNP (age- and sex-adjusted Pearson partial correlation r=0.05, P=0.002),

Table 2.

Clinical correlates of galectin-3 in 3,353 participants

	Coefficient	s.e.	P
Age	0.222	0.019	<0.0001
Antihypertensive treatment	0.180	0.039	<0.0001
Body-mass index	0.113	0.016	<0.0001
Coronary heart disease	0.168	0.065	0.009
Estimated glomerular filtration rate	−0.141	0.017	<0.0001
B-type natriuretic peptide	0.041	0.018	0.02

Log-gal-3 was standardized by sex. The regression coefficients indicate the increase in log-Gal-3 in the presence vs absence of the trait for dichotomous variables, and per 1 standard deviation increase in continuous variables (per 10 year increase in age, per 5.1 kg/m² increase in body-mass index, per 0.26 increase in log-eGFR, per 0.90 increase in log-B-type natriuretic peptide). The following variables were not significant in the stepwise selection model (P > 0.05): systolic blood pressure, diabetes, smoking, total and high-density lipoprotein cholesterol, valvular heart disease, and atrial fibrillation.

Among 2,425 participants who had usable echocardiographic data, a 1 standard deviation increase in log-Gal-3 was associated with a 2-fold increased odds of having elevated LV mass in age- and sex-adjusted analyses (95% CI 1.33–3.08, P= 0.001). When LV mass was used as a continuous variable, higher Gal-3 remained positively associated with higher LV mass (P=0.03). This association was attenuated after adjustment for clinical covariates (Table 3). Gal-3 was not associated with fractional shortening, LV systolic dysfunction, or left atrial size.

Table 3.

Gal-3 associations with echocardiographic traits in 2,425 participants

	Model 1*		Model 2†
Dichotomous variables	Odds ratio (95% CI)	P	Odds ratio (95% CI)	P
Increased LV mass	2.02 (1.33–3.08)	0.001	1.54 (0.99–2.38)	0.06
LV systolic dysfunction	2.15 (0.99–4.65)	0.05	1.72 (0.75–3.93)	0.20
Continuous variables	Coefficient (s.e.)	P	Coefficient (s.e.)	P
LV mass, g/m2	6.440 (2.880)	0.03	1.700 (2.804)	0.54
Fractional shortening, %	−0.007 (0.005)	0.14	−0.007 (0.004)	0.14
Left atrial dimension, mm	−0.004 (0.038)	0.91	−0.062 (0.037)	0.09

^*Adjusted for age and sex. Analyses for LV mass and left atrial dimension also adjusted for height

^†Adjusted for age, sex, diabetes, systolic BP, anti-hypertensive medication, previous MI. Additionally, analyses for LV mass and left atrial dimension adjusted for height, and left atrial dimension analyses adjusted for valvular heart disease.

Odds ratios and regression coefficients denote change associated with a one standard deviation increase in log-Gal-3 levels

Gal-3 and Incident HF Events

During a mean follow-up of 8.1 years, 166 (5.1%) individuals experienced a first HF event. The crude HF incidence rate increased over Gal-3 quartiles, with rates of 2.8, 3.8, 5.2, and 12.4 events/1,000 person-years in quartiles 1 through 4, respectively. Figure 1 demonstrates higher cumulative incidence of HF with increasing Gal-3 quartiles (log-rank test P<0.0001). In age- and sex-adjusted analyses, a one standard deviation (SD) increase in log-Gal-3 was associated with a 28% increased risk of incident HF (95% CI 1.14–1.43, P<0.0001) (Table 4). After multivariable adjustment and addition of BNP, Gal-3 remained predictive of HF risk (HR 1.23 per SD increment in log-Gal-3, 95% CI 1.04–1.47, P=0.02). BNP in the same model was associated with a 46% increased risk of HF (95% CI 1,23–1.75, P<0.0001). In analyses examining the association of Gal-3 quartile and incident HF, there was a significant increase in HF risk across quartiles in age- and sex-adjusted analyses (P=0.004); however, this did not reach statistical significance after multivariable-adjustment (P=0.11) (Supplemental Table 1).

Figure 1. Heart failure and galectin-3 quartiles.

The cumulative incidence of heart failure increases with higher galectin-3 quartiles.

Table 4.

Association of plasma galectin-3 levels, incident heart failure and mortality

Outcome	Model	HR (95% CI)	P
Incident heart failure	Age- and sex-adjusted	1.39 (1.17–1.65)	0.0002
	Multivariable-adjusted*	1.27 (1.06–1.52)	0.01
	Multivariable-adjusted + BNP	1.23 (1.04–1.47)	0.02
All-cause mortality	Age- and sex-adjusted	1.25 (1.12–1.39)	<0.0001
	Multivariable-adjusted*	1.15 (1.04–1.29)	0.01
	Multivariable-adjusted + BNP	1.14 (1.03–1.28)	0.02

Hazard ratio and 95% confidence intervals denote hazard associated with 1 standard deviation increase in log-Gal-3 levels. This increase is equivalent to comparing a Gal-3 level of 14.0 ng/ml (sample mean) to 18.1 ng/ml.

^*Adjusted for age, sex, systolic blood pressure, anti-hypertensive treatment, body-mass index, diabetes mellitus, smoking, prevalent coronary heart disease, atrial fibrillation, valvular heart disease. Mortality analyses were additionally adjusted for estimated glomerular filtration rate, total and high-density lipoprotein cholesterol.

In secondary analyses, adjusting for eGFR had modest impact (multivariable-adjusted HR 1.22, 95% CI 1.01–1.46, P=0.04; multivariable + BNP adjusted HR 1.19, 95% CI 0.99–1.42, P=0.06). Sensitivity analyses excluding individuals with prevalent CKD, or adjusting for the development of incident CKD attenuated the association of Gal-3 and incident HF (P>0.05 for both).

Gal-3 and Mortality

There were 468 deaths during the follow-up period. Increasing Gal-3 quartiles were associated with higher all-cause mortality, as displayed in the cumulative incidence graphs in Figure 2. In age- and sex-adjusted analyses, a one SD increase in log-Gal-3 was associated with 24% increased risk of mortality (95% CI 1.12–1.38, P<0.0001) (Table 4). This association remained significant after accounting for clinical covariates and BNP (HR 1.14, 95% CI 1.02–1.27, P=0.02). BNP was associated with a similar risk of mortality in the same model (HR 1.12, 95% CI 1.01–1.24, P=0.03). The risk of mortality increased across Gal-3 quartiles (P for trend 0.0007), and the fourth quartile was associated with > 60% increased hazards of mortality compared with the first quartile (multivariable + BNP adjusted HR 1.62, 95% CI 1.18–2.22, P=0.003) (Supplemental Table 1).

Figure 2. Mortality and galectin-3 quartiles.

The cumulative incidence of all-cause mortality increases with higher galectin-3 quartiles.

In secondary analyses, Gal-3 was associated with cardiovascular death (98 events) in age- and sex-adjusted analyses (HR 1.48, 95% CI 1.21–1.82, P=0.0002). This association was partly attenuated after adjustment for clinical covariates (multivariable-adjusted HR 1.25, 95% CI 1.01–1.54, P=0.045; multivariable + BNP adjusted HR 1.21, 95% CI 0.98–1.49, P=0.08).

The exclusion of individuals with prevalent CKD did not attenuate the association of Gal-3 with mortality (multivariable-adjusted HR 1.21, 95% CI 1.08–1.36, P=0.0009), and the association persisted after further adjusting for incident CKD (HR 1.25, 95% CI 1.10–1.43, P=0.0008).

Performance of Gal-3 as a biomarker

When added to the clinical model for HF, Gal-3 did not substantially increase the c-statistic (0.855 to 0.859), with similar findings in the prediction of all-cause mortality (Table 5). Improvements in the IDI and relative IDI were small, and comparable to the addition of BNP alone into the clinical model for mortality. The category-free NRI for the addition of Gal-3 in predicting HF was 0.20 (95% CI 0.02–0.40), representing a weak effect size. Similar magnitudes were observed for the category-free NRI in the prediction of all-cause mortality.

Table 5.

Performance metrics of galectin-3 in risk prediction models

	C-statistic (95% CI)	IDI (95% CI)	Relative IDI (95% CI)	Category-free NRI (95% CI)
Incident heart failure
Clinical model*	0.855 (0.823, 0.887)
Clinical model + Gal-3	0.859 (0.828, 0.890)	0.001 (−0.002, 0.005)	0.014 (−0.022, 0.052)	0.203 (0.018, 0.397)
Clinical model + BNP	0.869 (0.839, 0.898)	0.007 (−0.002, 0.017)	0.070 (−0.021, 0.164)	0.290 (0.110, 0.473)
Clinical model + BNP + Gal-3†	0.871 (0.842, 0.900)	0.001 (−0.002, 0.005)	0.011 (−0.023, 0.044)	0.162 (−0.028, 0.360)
All-cause mortality
Clinical model*	0.785 (0.762, 0.808)
Clinical model + Gal-3	0.786 (0.763, 0.809)	0.001 (−0.001, 0.004)	0.007 (−0.008, 0.021)	0.184 (0.066, 0.297)
Clinical model + BNP	0.785 (0.762, 0.808)	0.002 (−0.001, 0.004)	0.009 (−0.004, 0.022)	0.108 (−0.011, 0.226)
Clinical model + BNP + Gal-3†	0.786 (0.763, 0.809)	0.001 (−0.002, 0.003)	0.004 (−0.010, 0.018)	0.178 (0.066, 0.291)

^*Clinical model includes: age, sex, systolic blood pressure, anti-hypertensive treatment, body-mass index, diabetes mellitus, smoking, prevalent coronary heart disease, atrial fibrillation, valvular heart disease. Mortality analyses were additionally adjusted for prevalent heart failure, estimated glomerular filtration rate, total and high-density lipoprotein cholesterol.

^†IDI, relative IDI, and category-free NRI represented are for the addition of Gal-3 to the clinical model, or to the clinical model + BNP.

Gal-3 in HF with preserved versus reduced ejection fraction

Of 166 participants with incident HF events, 140 (84%) underwent assessment of LV function at or around the time of HF onset. Of these, 63 were classified as HF with preserved ejection fraction and 77 as HF with reduced ejection fraction. There was no difference in baseline Gal-3 levels in participants who developed HF with preserved versus reduced ejection fraction (16.3 ± 4.5 ng/ml versus 15.8 ± 4.2 ng/ml, respectively, P=0.54).

DISCUSSION

Our findings demonstrate that higher levels of Gal-3, a marker of cardiac fibrosis, are associated with an increased risk of incident HF and all-cause mortality in the community. Previous studies have examined the prognostic value of Gal-3 in individuals with existing HF. To our knowledge, our study is the first to report the association of Gal-3 with risk of new-onset HF in apparently healthy individuals. Our data also suggest that the association of Gal-3 with incident HF may be influenced by kidney function. Further studies on the link between Gal-3, kidney function, and myocardial injury and fibrosis will help elucidate the potential role of Gal-3 in the pathophysiology of HF.

Gal-3 is emerging as a prognostic biomarker in individuals with HF (8–11,27,28). More recently, higher Gal-3 levels were found to be associated with all-cause mortality in a community-based cohort (12). Our findings substantiate the prognostic role of Gal-3 with respect to all-cause mortality. Gal-3 is an indicator not only of myocardial fibrosis, but also other fibrotic conditions, including liver cirrhosis (29,30) and pulmonary fibrosis (31), all of which could increase risk of overall mortality. Beyond the association with all-cause mortality, a recent case-control study demonstrated an association of Gal-3 with HF risk following acute coronary syndrome (32). Ours is the first study to extend these findings to a longitudinal cohort of ostensibly healthy individuals, and to demonstrate the role of Gal-3 as a predictor of new-onset HF in the community.

Experimental evidence suggests that Gal-3 may be a mediator of fibrosis (33). Gal-3 is upregulated in a number of human fibrotic disease entities, including liver cirrhosis (29,30) and pulmonary fibrosis (31). Gal-3^−/− mice are protected against hepatic and renal fibrosis, and Gal-3 appears to be required for transforming growth factor (TGF)-β mediated myofibroblast activation and matrix production (30,34). Gal-3 is the most overexpressed gene in transgenic Ren-2 rats that rapidly progress to HF (6). Gal-3 is expressed in activated macrophages, with binding sites localized to the myocardial extracellular matrix and cardiac fibroblasts, where it induces fibroblast proliferation, collagen deposition, and ventricular dysfunction (6). Infusion of Gal-3 into the pericardial space leads to cardiac dysfunction in rats, a process that appears to be mediated via the TGF-β/Smad3 signaling pathway (7). In clinical studies, Gal-3 is correlated with markers of extracellular matrix turnover, supporting its role in collagen metabolism (35).

This collective experimental evidence suggests that Gal-3 may play a causal in cardiac remodeling. Although the incremental prognostic value of adding Gal-3 to existing clinical risk factors – particularly above and beyond BNP – was marginal, the potential clinical role of Gal-3 may be pathobiological, rather than prognostic in nature. Our findings are consistent with the notion that active fibrosis may precede clinical manifestations of HF by many years. While most HF therapies are initiated late in the course of the disease, the identification of fibrosis prior to impairment of LV function may offer a window of opportunity to initiate targeted preventive treatment early in the course of the disease. Due to its putative role as a mediator of fibrosis, directly modulating the Gal-3 pathway may be beneficial. Gal-3 levels can be modulated with modified citrus pectin, a soluble dietary fiber found in citrus fruit (36). This pectin derivative can bind to the carbohydrate recognition domain of Gal-3, altering its bioactivity (37). Treatment with modified citrus pectin decreased Gal-3 expression, and significantly attenuated renal fibrosis and inflammation in an animal model of acute kidney injury (38), and future studies examining the effect of Gal-3 inhibition on cardiovascular endpoints would be of high interest.

Notably, we observed a strong association of Gal-3 with kidney dysfunction, a finding that has been corroborated by previous studies in both individuals with and without existing HF.(10,12,28,39) We also found that adjusting for kidney function attenuated the association of Gal-3 with incident HF events. This is consistent with prior studies in HF, where adjustment for kidney function appears to attenuate in part the prognostic impact of Gal-3 (39), and where the association of Gal-3 with kidney function appears to overshadow associations with cardiac structure and function (28). It may be that worsening kidney function mediates Gal-3 effects on cardiac remodeling and HF. Alternatively, elevated levels of Gal-3 in both kidney and heart may lead to HF, and consequently adjustment for kidney function may obscure the association of Gal-3 with HF risk.

We found that Gal-3 was associated with elevated LV mass in age- and sex-adjusted analysis, although this relation was only marginally significant in multivariable analyses. Gal-3 may be a stronger prognostic marker in those with preserved compared with reduced ejection fraction (39), and Gal-3 levels have been associated with measures of diastolic function in patients presenting with acute decompensated HF (11). Biologically, it is plausible that a matrix and fibrosis marker such as Gal-3 may play a more prominent role in those with preserved ejection fraction HF. However, in exploratory analyses, we found no difference in baseline Gal-3 concentrations in participants who eventually developed HF with preserved versus reduced ejection fraction.

There are several limitations worth noting. While the addition of Gal-3 to clinical factors resulted in improved classification as assessed by the category-free NRI, changes in the c-statistic and IDI were negligible. It may be that Gal-3 in combination with other novel biomarkers in a multi-marker approach might be useful, and the comparison of Gal-3 to other emerging biomarkers of HF, such as high sensitivity troponin, N-terminal pro-BNP, or soluble ST2 will need to be explored in future studies. The number of HF events was modest, and likely limited our power to conduct quartile analyses or other more complex analyses examining the role of kidney function in the association of Gal-3 and HF. In addition to its role in fibrosis in several organ systems, Gal-3 has also been associated with tumorigenesis in thyroid cancer and other malignancies (40). Circulating Gal-3 levels have not been elevated in these conditions (41), but we cannot exclude the possibility that Gal-3 might act in several pathophysiologic pathways to increase mortality risk. While secondary analyses demonstrate a suggestive association with cardiovascular death, Gal-3 may still reflect non-cardiac processes. Further elucidation of Gal-3 in relation to cardiac remodeling, including more sensitive measures of diastolic function or direct measures of cardiac fibrosis would be of great interest in future studies. Lastly, our study was limited to a predominantly white study sample, limiting generalization to other populations.

In summary, higher circulating Gal-3 concentrations are associated with increased risk of new-onset HF and all-cause mortality in the community. Future potential clinical uses of Gal-3 measurement might include the identification of asymptomatic individuals with early evidence of cardiac fibrosis, in whom targeted therapies may be useful to delay the onset of HF. Animal data suggest that Gal-3 is a mediator of fibrosis, and directly targeting the Gal-3 pathway may represent a future preventive treatment strategy.

Abbreviations List

BNP

B-type natriuretic peptide

eGFR

estimated glomerular filtration rate

Gal-3

galectin-3

HF

heart failure

IDI

integrated discrimination improvement

LAD

left atrial dimension

LVDD

left ventricular end-diastolic dimension

LVSD

left ventricular end-systolic dimension

NRI

net reclassification improvement

TGF-β

transforming growth factor-β