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Article

High Levels of Galectin-3 and Uric Acid Are Independent Predictors of Renal Impairment in Patients with Stable Coronary Artery Disease

by
Nayleth Leal-Pérez
1,
Luis M. Blanco-Colio
2,3,
José Luis Martín-Ventura
2,3,4,
Carlos Gutiérrez-Landaluce
5,
Ignacio Mahíllo-Fernández
6,
María Luisa González-Casaus
7,
Óscar Lorenzo
4,8,9,
Jesús Egido
2,4,8,9,† and
José Tuñón
2,3,4,10,*,†
1
Department of Gynecology and Obstetrics, Príncipe de Asturias University Hospital, 28805 Madrid, Spain
2
Laboratory of Vascular Pathology, IIS-Fundación Jiménez Díaz, 28040 Madrid, Spain
3
Biomedical Research Networking Center on Cardiovascular Diseases (CIBERCV), 28029 Madrid, Spain
4
Department of Medicine, Medical School, Autónoma University, 28049 Madrid, Spain
5
Department of Cardiology, Hospital Universitario de Fuenlabrada, 28942 Madrid, Spain
6
Research Unit, IIS-Fundación Jiménez Díaz, 28040 Madrid, Spain
7
Laboratory of Nephrology and Mineral Metabolism, Hospital La Paz, 28046 Madrid, Spain
8
Renal, Vascular and Diabetes Research Laboratory, IIS-Fundación Jiménez Díaz, 28040 Madrid, Spain
9
Diabetes and Associated Metabolic Diseases Networking (CIBERDEM), 28029 Madrid, Spain
10
Department of Cardiology, IIS-Fundación Jiménez Díaz, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Clin. Med. 2025, 14(15), 5264; https://doi.org/10.3390/jcm14155264
Submission received: 22 May 2025 / Revised: 19 July 2025 / Accepted: 23 July 2025 / Published: 25 July 2025
(This article belongs to the Special Issue Chronic Kidney Disease Management: Integrating Pharmacotherapy, Biomarkers, and Lifestyle Interventions for Improved Outcomes)
Versions Notes

Abstract

Background: High plasma levels of Galectin-3 (Gal-3) and uric acid (UA) are associated with a decline in renal function in different populations. However, this association has not yet been studied in patients with coronary artery disease (CAD). Methods: We included 556 patients with stable CAD. Plasma levels of Gal-3, UA, N-Terminal probrain natriuretic peptide (NT-proBNP), calcidiol, fibroblast growth factor 23, phosphate, parathormone, and klotho were assessed at baseline. The primary outcome was the percentage decrease in eGFR; the secondary outcomes were the absolute decrease in eGFR and achieving a reduction of ≥20% in this parameter. Results: Age was 63.1 ± 12.2 years, and 73.9% of patients were male. The median eGFR was 86.77 (72.27, 97.85) mL/min/1.73 m2. After 3.47 (2.10–5.72) years of follow-up, eGFR declined by 3.62% [−2.07–13.82]. Baseline UA (0.012 [CI95% 0.003, 0.020]; p = 0.008), Gal-3 (0.0153 [CI95% 0.001, 0.029]; p = 0.037), and NT-proBNP (0.017 [CI95% 0.000–0.025]; p = 0.027) were independent positive predictors of the percentage decrease in eGFR, while calcidiol (−0.005 [CI95% −0.009, −0.002]; p = 0.005) was an inverse predictor of this outcome. Similarly, UA and Gal-3 were positive independent predictors of the absolute decline in eGFR (0.009 [0.003, 0.017]; p = 0.004 and 0.012 [0.001, 0.023]; p = 0.031, respectively), while calcidiol was inversely associated (−0.003 [−0.005]–[−0.001]; p = 0.020). Uric acid (1.237 [1.046–1.463]; p = 0.013) and NT-proBNP (1.000 [1.000–1.001]; p = 0.049) levels were positive independent predictors of a ≥20% decrease in eGFR. In patients with eGFR ≥ 60 mL/min/1.73 m2, UA was the only biomarker independently associated with renal function decline. Conclusions: In patients with CAD and normal or mildly reduced renal function, UA and Gal-3 plasma levels are independent positive predictors of a future decrease in eGFR. These findings could lead to a change in the approach to patients with CAD in the future.

1. Introduction

Chronic kidney disease (CKD) is a major health concern, affecting 13.4% (11.7–15.1%) of the global population [1]. It is considered a risk factor for cardiovascular disease [2] and is directly associated with increased morbidity and mortality worldwide [3,4]. There is an association between CKD and coronary artery disease (CAD), given that atherosclerosis may affect both organs [5]. Identifying independent predictors of renal damage in CAD patients could improve patient management, enabling timely intervention strategies to mitigate disease progression.
Galectin-3 (Gal-3) has been suggested to play a role in renal damage, primarily by promoting fibrosis [6,7]. Consequently, Gal-3 plasma levels have been shown to predict a decrease in estimated glomerular filtration rate (eGFR) in various settings, including patients with CKD [8], diabetes [9], and the general population [10,11], among others. However, it remains unclear whether Gal-3 levels can predict an impairment of renal function in CAD patients.
Increased uric acid (UA) plasma levels have been associated with the development and progression of CKD and cardiovascular disease [12,13,14]. While plasma UA peaks have historically been considered a consequence of renal failure, new theories challenge this perspective, suggesting that UA may also be an active contributor to kidney damage [13]. Uric acid has been suggested to mediate renal damage by promoting inflammation [15] and activating the renin–angiotensin–aldosterone system [16,17], among other biological processes. The potential of UA as a marker of future renal function deterioration has been studied in different population subgroups, yielding controversial results in CKD patients [18,19,20,21,22,23,24] and positive results in the general population [25,26,27,28,29,30], patients with hypertension [31], and patients with type I [32] and type 2 diabetes mellitus [33]. However, no results have been reported on the potential predictive role of UA plasma levels in patients with CAD.
In this study, we investigate the potential role of Gal-3 and UA plasma levels in predicting a decrease in eGFR in patients with stable CAD. We also examine several components of mineral metabolism (fibroblast growth factor 23 [FGF-23], α-Klotho, 25-dihydroxyvitamin D [calcidiol], phosphate, and parathyroid hormone [PTH]), as abnormalities in these factors have been associated with a decline in renal function [34]. Furthermore, we determined N-terminal pro-brain natriuretic peptide (NT-proBNP), which is associated with heart failure and cancer [35].

2. Materials and Methods

2.1. Patients

The present study is a sub-study of the BACS and BAMI studies (biomarkers in acute coronary syndrome and biomarkers in acute myocardial infarction), which were conducted in five hospitals in Madrid, as previously described [36].
Six months after experiencing the index acute coronary syndrome, patients attended an outpatient consultation. At this time, clinical variables were recorded, and blood samples were taken.
Of the 1257 patients included during the acute event, 964 attended the outpatient appointment and completed the follow-up. The present study focuses on the 556 patients included in two of the five centeres (Fundación Jiménez Díaz and Puerta de Hierro), as these were the only institutions with analytical data available at the end of the follow-up period.

2.2. Ethics Statement

The research protocol follows the ethical guidelines outlined in the Declaration of Helsinki of 1975, having received prior approval from the ethics committees of the institutions participating in this research: the Fundación Jiménez Díaz and Puerta de Hierro Hospitals. All patients provided voluntarily informed consent and signed all relevant documents.

2.3. Study Design

Plasma extraction and baseline visits took place between January 2007 and December 2014. The last follow-up visits were carried out in June 2016. Creatinine values at the end of the follow-up were obtained from the electronic records of the patients. We used the chronic kidney disease epidemiology collaboration (CKD-EPI) equation to estimate eGFR. The primary outcome was the percentage decrease in eGFR from baseline to the end of follow-up. As secondary endpoints, we assessed (a) the decrease in eGFR in absolute numbers and (b) a reduction of 20% or more in eGFR from baseline to the end of follow-up. Traditional markers of renal impairment, such as doubling of creatinine levels or development of end-stage kidney disease, which are commonly used in populations with CKD, were not included as endpoints. This is because it was not expected that a significant number of patients with normal or mildly reduced renal function would develop these outcomes. Finally, we also analyzed the incidence of acute ischemic events (acute coronary syndrome, ischemic stroke, or TIA), heart failure, and death.

2.4. Biomarker and Analytical Studies

Plasma determinations were performed at the Vascular Pathology and Biochemistry laboratories at Fundación Jiménez Díaz and at Nephrology department of the Gómez-Ulla hospital for mineral metabolism. UA determinations were performed at the Biochemistry laboratories of the Fundación Jiménez Díaz and Puerta de Hierro hospitals. The investigators who performed the laboratory studies were unaware of the clinical data. Twelve-hour fasting venous blood samples were withdrawn and collected. Blood samples were centrifuged at 2500 g for 10 min, and plasma was stored at −80 °C. UA, lipids, glucose, and creatinine determinations were performed by standard methods (ADVIA 2400 Chemistry System, Siemens Healthineers, Munich, Germany). Plasma calcidiol levels were quantified by chemiluminescent immunoassay (CLIA) on the LIAISON XL analyzer (LIAISON 25OH-Vitamin D total Assay DiaSorin, Saluggia, Italy), FGF23 was measured by an enzyme-linked immunosorbent assay, which recognizes epitopes within the carboxyl-terminal portion of FGF-23 (Human FGF23, C-Term, Immutopics Inc., San Clemente, CA, USA), klotho levels were determined by ELISA (Human soluble alpha klotho assay kit, Immuno-Biological Laboratories Co., Gunma, Japan), the intact parathyroid hormone was analyzed by a second-generation automated chemiluminescent method (Elecsys 2010 platform, Roche Diagnostics, Mannheim, Germany), phosphate was determined by an enzymatic method (Integra 400 analyzer, Roche Diagnostics, Mannheim, Germany), high-sensitivity C-reactive (hs-CRP) protein was assessed by latex-enhanced immunoturbidimetry (ADVIA 2400 Chemistry System, Siemens Healthineers, Munich, Germany), and NTproBNP was determined by immunoassay (VITROS; Ortho Clinical Diagnostics Raritan, NJ, USA). Galectin-3 was determined using commercially available enzyme-linked immunoabsorbent assay kits (DCP00, R&D Systems, Minneapolis, MN, USA), and Hs-cTnI was assessed by direct chemiluminescence (ADVIA Centaur; Siemens Healthineers, Berlin, Germany). All experiments were performed in duplicate.

2.5. Statistical Analysis

Quantitative data following a normal distribution are presented as the mean ± standard deviation (SD); those not normally distributed are displayed as a median (interquartile range). Qualitative variables are presented as percentages.
The influence of baseline variables, as displayed in Table 1, on outcome variables was first assessed using simple linear regression analysis for numerical dependent variables and simple logistic regression for qualitative dependent variables. Subsequently, multiple linear regression and multiple logistic regression analyses were performed, entering the variables that achieved a “p”-value < 0.20 during univariate analyses. Furthermore, age, sex, Caucasian race, body mass index, and hypertension were included in multivariate analyses, independently of the “p”-value obtained during univariate analyses. Cox univariate and multivariate analyses were used to investigate the potential predictors of adverse cardiovascular events. A power analysis for the global F-test was conducted, using the effect size f2 derived from the obtained R2 [37].
Analyses were performed with R v3.5.1 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at p < 0.05 (two-tailed).

3. Results

3.1. Patients

The median follow-up period was 3.47 (2.10–5.72) years. The median age was 63.1 ± 12.2 years; the majority of patients were male (73.9%) and of Caucasian ethnicity (95.1%) (Table 1). Sixty-five percent had hypertension, and 22.7% had diabetes. In total, 4.1% were receiving hypouricemic agents (Alopurinol). The median eGFR was 86.77 (72.27, 97.85) mL/min/1.73 m2, and 7.6% of the patients had a left ventricular ejection fraction lower than 40%.
The index event, prior to the start of this study, was ST-elevation myocardial infarction in 326 and non-ST elevation acute coronary syndrome in 230. Seventy-five percent of all patients underwent complete revascularisation. The interval between the index event and the outpatient blood extraction for this substudy was 6.2 months (6.1–6.6).

3.2. eGFR Percentage Decline During Follow-Up

Only nine (1.6%) patients doubled their plasma creatinine levels, and one patient started haemodialysis at the end of the follow-up. The estimated glomerular filtration rate declined by 3.62% [−2.07–13.82]. To detect predictors of the percentage decrease in eGFR, a simple linear regression analysis was first performed (see Supplementary Table S1 online). Then, a multiple linear regression analysis was conducted, which showed that elevated plasma levels of UA, Galectin-3, and NT-proBNP, as well as age, the presence of hypertension, and the use of insulin, among others, were independently and directly related to the percentage decrease in eGFR (Table 2). On the other hand, baseline calcidiol, male gender, and the use of anticoagulants were inversely associated with this outcome.
A power analysis for the global F-test, using the effect size f2 derived from an R2 ≈ 0.13 showed that with the 13 predictors obtained, an n ≈ 500 and an α error = 0.05, the resulting power exceeds 99%.
When UA and Galectin-3 were removed from the model, R2 decreased to 0.105 (Supplementary Table S2 online).

3.3. Absolute Difference in the Whole Population Between eGFR at Baseline and at the End of Follow-Up

The glomerular filtration rate diminished from 86.77 (72.27, 97.85) to 82.15 (64.39, 95.00) mL/min/1.73 m2 (p < 0.001) during follow-up, resulting in a median decrease of 4.62 mL/min/1.73 m2 during 3.47 years of follow-up (1.33 mL/min/1.73 m2 per year). Table S3 in the Supplementary Material shows the variables predicting this outcome at univariate analysis.
During the multiple linear regression analysis, UA and Galectin-3 plasma levels, age, the existence of hypertension, and treatment with insulin were again directly related to the decrease in eGFR during follow-up (Table 3).
On the other hand, calcidiol, and the use of anticoagulants were inversely related to this outcome.

3.4. Individual Falls of 20% or More in the Glomerular Filtration Rate During Follow-Up

The eGFR decreased by 20% or more in 82 patients. This represents 14.7% of the whole sample and 22.8% of the 360 patients who experienced a fall in eGFR. To detect probable predictors of this reduction, we performed a simple logistic regression analysis. Table S4 of the Supplementary Material shows the predictors of this outcome. Multiple logistic regression analysis showed that UA levels, as well as NT-proBNP, the existence of hypertensionand the use of nitrates and anticoagulant therapy were directly and independently associated with this outcome (Table 4).

3.5. Absolute Differences Between eGFR at Baseline and the End of Follow-Up According to the Presence of Significant CKD

We estimated the determinants associated with the absolute decrease in eGFR at follow-up in patients with and without eGFR < 60 mL/min/1.73 m2 at baseline. In total, 72 patients (12.9%) had an eGFR < 60 mL/min/1.73 m2, and 484 patients (87.1%) had an eGFR ≥ 60 mL/min/1.73 m2. The results of the simple logistic regression analysis are displayed in Tables S5 and S6 of the Supplementary Material. During the multiple linear regression analysis, in patients with eGFR ≥ 60 mL/min/1.73 m2, UA, but not Gal-3, was independently and positively associated with a decrease in eGFR, along with age, the existence of hypertension and the prescription of insulin, while caucasian race and male gender were inversely related to this outcome (Table 5). In patients with eGFR < 60 mL/min/1.73 m2, calcidiol was the only biomarker that was an independent predictor of the absolute decrease in eGFR, showing an inverse relationship.

3.6. Influence of Baseline Variables on the Appearance of Cardiovascular Events

During follow-up, 91 patients (16.4%) developed a major cardiovascular event (acute ischemic events (i.e., acute coronary syndrome, ischemic stroke, or TIA), heart failure, or death). From them, 15 patients had two events, 13 had three events, and 4 developed four events. In total, there were 20 episodes of unstable angina, 7 acute myocardial infarctions with ST elevation, 20 acute myocardial infarctions without ST elevation, 36 episodes of heart failure, 9 ischemic strokes, 12 transient ischemic attacks, and 40 deaths. The cause of death was cardiovascular in 18 cases (3.2%), oncological in 8 (1.4%), infection in 3 (0.5%), renal failure in 1 (0.2%), pancreatitis in 2 (0.4%), gastrointestinal bleeding in 1 (0.2%, other in 3 (0.5%), and unknown in 4 (0.7%).
Several variables were related to the prediction of events during the univariate analysis (Supplementary Table S7). However, during the multivariate analysis, only NT-ProBNP and PTH plasma levels were independent predictors of major cardiovascular events in our cohort, along with different clinical variables (Table 6). Neither UA levels nor Gal-3 or calcidiol were independently associated with the occurrence of this outcome.

4. Discussion

Galectin-3 levels have been associated with the progression of CKD in different cohorts, including the general population [10,11], patients with CKD [38,39], and diabetes [9], among others. However, no studies have evaluated this in patients with CAD. This is therefore the first report to show that Gal-3 is an independent predictor of a decline in renal function in this population. This is relevant because atherosclerosis is the main cause of CAD, and it may also contribute to CKD [40]. Furthermore, the extensive adjustment for many clinical and analytical variables, including the components of the mineral metabolism system, reinforces the value of Gal-3 as an independent predictor of renal function.
The main pathway through which Gal-3 may be involved in kidney damage is the promotion of fibrosis, as evidenced at an experimental level [6,7,41]. However, Gal-3 inhibition has not been demonstrated to decrease collagen markers in patients with hypertension [41], and further research is required to confirm whether this molecule may be a potential target to prevent renal damage in humans.
The role of UA plasma levels as a predictor of worsening renal function and cardiovascular events has been controversial. While several studies conducted primarily on CKD patients have failed to establish an association between UA levels and renal function decline [18,19,20,21,22,23], other research involving a healthy population [25,26,27,28,29,30], CKD stage 3–4 patients [23,24], individuals with hypertension [30], IgA nephropathy [42], and diabetes mellitus [32,33] has revealed a positive association. Finally, the results in patients receiving a kidney transplant are diverse [43].
To our knowledge, no previous studies have investigated the potential association between UA levels and worsening renal function in patients with CAD and normal or mildly reduced renal function. In the present study, we demonstrate that high plasma UA levels are independent predictors of a worsening in the eGFR in this population. This result was evident when eGFR was analyzed in different ways and corrected for an extensive set of variables, including demographic data, cardiovascular risk factors, previous cardiovascular history, drug treatment, and a wide range of analytical variables, such as NT-proBNP, mineral metabolism components, hs-CRP, and lipids. Interestingly, the adjustments performed in most of the above-described studies were less extensive than those performed in the present work. In our study, the median eGFR decreased from 86.77 to 82.15. This equates to a reduction in eGFR of 4.62 mL/min/1.73 m2 over 3.47 years of follow-up, which amounts to a yearly decrease of 1.33 mL/min/1.73 m2. Interestingly, the annual decline in renal function in the general population is approximately 1 mL/min/1.73 m2 [44], indicating that the decline in eGFR in our CAD patients is comparable to that observed in the general population. Of interest, most of our patients had single-vessel disease and a low prevalence of previous peripheral arterial disease. Therefore, we cannot exclude the possibility that eGFR decline may be faster in populations with more extensive atherosclerosis.
Finally, UA remained a powerful independent predictor of renal function decline in the subgroup of patients with eGFR ≥ 60 mL/min/1.73 m2. The absence of significant predictive power in patients with eGFR < 60 mL/min/1.73 m2 is probably related to the small size of this subgroup (72 patients).
Among the mechanisms through which UA could affect renal function, the overactivation of the renin–angiotensin–aldosterone system [16,17] and vascular inflammation [15] have been proposed.
In contrast to the observed worsening of renal function, UA and Gal-3 plasma levels were not independent predictors of the incidence of cardiovascular events per se. Previous studies have shown that UA plasma levels are independent predictors of cardiovascular events, particularly in patients with heart failure [43], myocardial infarction [45], and even in the general population [46,47]. However, adjustments were not made for mineral metabolism components [48]. Regarding Gal-3, we obtained conflicting results [36,49]. In this study, only PTH and NT-proBNP levels were independent predictors of the incidence of cardiovascular events. Beyond the well-known prognostic value of NT-proBNP as a prognostic biomarker [50], PTH also has predictive ability [51,52]. Furthermore, PTH has been linked to the pathophysiology of heart failure and left ventricular hypertrophy [53,54], although not all the studies confirm this relationship [55,56]. Currently, there is no consensus on the relationship between PTH and CAD. Thus, PTH levels have been linked to the incidence and progression of CAD [57,58] and the incidence of cardiovascular events and mortality [59,60]. Conversely, other studies have failed to confirm this association [61].
Some limitations of this study must be acknowledged. Firstly, urinary albumin excretion, which is related to renal function, was not assessed because urine samples were not collected. While a comprehensive assessment of renal function includes both eGFR and the urine albumin-to-creatinine ratio, the latter was not available at the time of the patient’s recruitment. Without these data, the results cannot be fully interpreted or compared with those of other studies. Furthermore, our work is based on a single biomarker assessment, and further determinations during the follow-up period could have provided additional relevant information. Secondly, the fact that 95.1% of patients were Caucasian limits the applicability of our findings to more ethnically diverse populations. Thirdly, the existence of renal artery stenosis was not studied, which could have influenced the evolution of renal function in our patients. Finally, the low R2 values in the models presented in Table 2 and Table 3 of the results (0.126 and 0.115, respectively) indicate that the models only explain 12.6% and 11.5%, respectively, of the obtained outcomes. Clarifying this point is essential for a better understanding of these results. Nevertheless, the strength of our work lies in adjusting for multiple clinical and analytical variables to correct for confounding bias. Due to these limitations, our results should be considered preliminary and require further validation in future studies.

5. Conclusions

In conclusion, this study shows that high UA and Galectin-3 levels are independent predictors of eGFR decline in patients with CAD and normal or mildly reduced baseline renal function. These results may represent a step forward in the prediction of renal function decline in CAD patients in clinical practice. Further studies are needed to ascertain whether management strategies that consider UA and Gal-3 levels could prevent renal function decline in this population.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm14155264/s1. Table S1. Univariate Analysis. Association of variables with eGFR percentage decline during follow-up. Table S2. Independent predictors of the percentage decrease in eGFR after excluding uric acid and galectin-3 levels from the model. Table S3. Univariate Analysis. Absolute difference in the whole population between eGFR at baseline and at the end of follow-up. Table S4. Univariate Analysis. Association of variables with Individual falls of 20% or more in glomerular filtration rate during follow-up. Table S5. Univariate Analysis. Absolute differences between eGFR at baseline and at the end of follow-up according to the presence of significant CKD: Analysis stratified by glomerular filtration rate more or equal to 60. Table S6. Univariate Analysis. Absolute differences between eGFR at baseline and at the end of follow-up according to the presence of significant CKD: Analysis stratified by glomerular filtration rate less than 60. Table S7. Univariate Analysis. Association of variables as event predictors (Cox regression).

Author Contributions

Conceptualization, N.L.-P., J.E. and J.T.; Data curation, N.L.-P. and C.G.-L.; Formal analysis, I.M.-F.; Funding acquisition, J.E. and J.T.; Investigation, L.M.B.-C., J.L.M.-V., M.L.G.-C. and Ó.L.; Methodology, J.E. and J.T.; Project administration, J.E. and J.T.; Supervision, J.E. and J.T.; Validation, J.E. and J.T.; Visualization, N.L.-P.; Writing—original draft, N.L.-P.; Writing—review and editing, N.L.-P., J.E. and J.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from Fondo de Investigaciones Sanitarias (Projects PI14/01567, PI17/01495, PI20/00923, PI20/00487, PI23/00119, PI24/00978 and PI22/00233) funded by Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union (http://www.isciii.es/), the Ministry of Science and innovation (RTC2019-006826-1), the Spanish Society of Cardiology, and the Institute of heart de Salud Carlos III FEDER (FJD biobank: RD09/0076/00101). All ideas and opinions expressed remain entirely those of the authors.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Fundación Jiménez Díaz Hospital, Capio (protocol code: 25/2007; date of approval: 24 April 2007).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All the datasets used and/or analyzed in the study are available from the corresponding author upon reasonable request.

Acknowledgments

The DeepL Write application (version 1.45.0) was used to improve the English edition of the text.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
ACEIAngiotensin-Converting Enzyme Inhibitors
AUCArea Under the Curve
BACS and BAMIBiomarkers in Acute Coronary Syndrome and Biomarkers in Acute Myocardial Infarction
BMIBody Mass Index
CABGCoronary Artery Bypass Grafting
CIConfidence Interval
CKD-EPIChronic Kidney Disease Epidemiology Collaboration equation
CKDChronic Kidney Disease
eGFREstimated Glomerular Filtration Rate
FGF-23Fibroblast Growth Factor-23
HDLHigh-Density Lipoprotein
Hs-CRPHigh-Sensitivity C Reactive Protein
Hs-Tn IHigh-Sensitivity Troponin I
LDLLow-Density Lipoprotein
No-HDLNo-High-Density Lipoprotein
NSTEACSNon-ST Elevation Acute Coronary Syndrome
NT-ProBNPN-Terminal Pro-Brain Natriuretic Peptide
OROdds Ratio
PTHParathyroid Hormone
R2Determination Coefficient
SCADStable Coronary Artery Disease
STEMIST-Elevation Myocardial Infarction

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Table 1. Baseline characteristics of the population.
Table 1. Baseline characteristics of the population.
VariableDescription
Age (y)63.1 ± 12.2
Gender (Male)411 (73.9%)
Caucasian529 (95.1%)
Body Mass Index (kg/m2)27.7 (25.4, 30.5)
Smoker82 (14.7%)
Hypertension362 (65.1%)
Diabetes Mellitus126 (22.7%)
Dyslipidemia315 (56.7%)
Previous Stroke21 (3.8%)
Peripheral Artery Disease15 (2.7%)
Heart Failure60 (10.8%)
Atrial Fibrillation35 (6.3%)
Left Ventricular Ejection Fraction < 40%42 (7.6%)
PREVIOUS ACUTE CORONARY SYNDROME
STEMI/NSTEACS
326 (58.6%)/230 (41.4%)
Complete Revascularization:417 (75.0%)
Number of vessels diseased
027 (4.9%)
1327 (59.3%)
2142 (25.8%)
355 (10.0%)
Revascularization method (%)
0 No Revascularization52 (9.4%)
1 Coated Stent311 (55.9%)
2 Conventional Stent153 (27.5%)
3 Simple angioplasty21 (3.8%)
4 CABG19 (3.4%)
TREATMENT
Aspirin525 (94.4%)
P2Y12 Antagonist468 (84.2%)
Anticoagulant29 (5.2%)
Statin535 (96.2%)
Ezetimibe24 (4.3%)
Fibrates21 (3.8%)
Insulin33 (5.9%)
Oral Antidiabetics88 (15.8%)
ACEI416 (74.8%)
Angiotensin Receptor Blockers93 (16.7%)
Aldosterone Antagonist52 (9.4%)
Betablocker433 (77.9%)
Nitrates45 (8.1%)
Diltiazem11 (2.0%)
Verapamil0 (0.0%)
Dihydropyridines65 (11.7%)
Diuretic130 (23.4%)
Proton Pump Inhibitors436 (78.4%)
Digoxin1 (0.2%)
Amiodarone6 (1.1%)
ANALYTICS
Glucose (mmol/L)5.91 ± 1.69
Total Cholesterol (mmol/L)3.61 ± 0.87
LDL Cholesterol (mmol/L)1.99 ± 0.64
HDL Cholesterol (mmol/L)1.07 ± 0.30
Non-HDL Cholesterol (mmol/L)2.54 ± 0.75
Triglycerides (mmol/L)1.20 ± 0.63
eGFR (mL/min/1.73 m2)86.77 (72.27, 97.85)
Uric Acid (µmol/L)341.0 ± 91.0
Hs-Tn I (ng/L)4.0 (0.0, 11.0)
Hs-CRP (mg/L)0.620 (0.080, 2.277)
NT-ProBNP (pmol/L)23.6 (11.8, 49.3)
Galectin-3 (pmol/L)337.6 ± 129.3
Phosphorus (mmol/L)1.03 ± 0.18
Calcidiol (nmol/L)51.1 ± 22.1
FGF23 (ng/L)78.70 (60.50, 99.22)
Klotho (µg/L)561.6 (465.8, 681.8)
PTH (pmol/L)6.31 (4.88–8.05)
ACEI: angiotensin-converting enzyme inhibitors; CABG: coronary artery bypass graft; eGFR: estimated glomerular filtration rate; FGF23: fibroblast growth factor-23; HDL: high-density lipoprotein; Hs-CRP: high-sensitivity; Hs-TnI: high-sensitivity troponin I; LDL: low-density lipoprotein; NON-STEACS: non-ST elevation acute coronary syndrome; NT-proBNP: N-terminal pro-brain natriuretic peptide; PTH: parathyroid hormone; STEMI: ST-elevation myocardial infarction. Results for normally distributed quantitative variables are expressed as the mean ± standard deviation, and results for variables without a normal distribution are expressed as a median (interquartile range).
Table 2. Independent predictors of the percentage decrease in eGFR.
Table 2. Independent predictors of the percentage decrease in eGFR.
VariableCoef.(95% CI)pR2
Age (y)0.181(0.041, 0.321)0.0110.126
BMI (kg/m2)0.000(−0.000, 0.000)0.621
Caucasian−3.135(−8.763, 2.492)0.274
Gender (Male)−3.072(−5.951, −0.193)0.037
Hypertension4.023(1.335, 6.711)0.003
Diabetes Mellitus0.426(−2.797, 3.649)0.795
Calcidiol (nmol/L)−0.005(−0.009, −0.002)0.005
Uric Acid (µmol/L)0.0117(0.0031, 0.0200)0.008
eGFR (mL/min/1.73)0.118(0.030, 0.206)0.009
NT-ProBNP (pmol/L)0.017(0.000, 0.025)0.027
Anticoagulant−6.350(−11.69, −1.005)0.020
Insulin5.967(0.396, 11.54)0.036
Galectin-3 (nmol/L)0.0153(0.00088, 0.0298)0.037
BMI: body mass index. Other abbreviations are as for Table 1.
Table 3. Independent predictors of the difference in eGFR between baseline and final follow-up.
Table 3. Independent predictors of the difference in eGFR between baseline and final follow-up.
VariableCoefficient(95% CI)pR2
Age (y)0.162(0.056, 0.268)0.0030.115
BMI (kg/m2)0.000(−0.000, 0.000)0.552
Caucasian−3.437(−7.701, 0.828)0.114
Gender (Male)−2.072(−4.253, 0.109)0.063
Hypertension3.420(1.389, 5.451)0.001
Diabetes Mellitus0.532(−1.908, 2.973)0.668
eGFR (mL/min/1.73)0.161(0.095, 0.227)<0.001
Uric Acid (µmol/L)0.009(0.003, 0.017)0.004
Calcidiol (nmol/L)−0.003(−0.005, −0.001)0.020
Insulin4.598(0.388, 8.809)0.032
Galectin-3 (nmol/L)0.0121(0.0011, 0.0230)0.031
Anticoagulant−4.182(−8.217, −0.147)0.042
Abbreviations are as for Table 1 and Table 2.
Table 4. Variables independently associated with an eGFR decrease of ≥20%.
Table 4. Variables independently associated with an eGFR decrease of ≥20%.
VariableOR(95% CI)pAUC
Age (y)1.021(0.996, 1.047)0.0990.71
BMI (kg/m2)1.000(0.999, 1.001)0.949
Caucasian0.477(0.144, 1.575)0.224
Gender (Male)0.699(0.393, 1.243)0.222
Hypertension2.889(1.387, 6.017)0.005
Diabetes Mellitus1.685(0.967, 2.937)0.065
Uric Acid (µmol/L)1.237(1.046–1.463)0.013
Nitrates2.964(1.362, 6.452)0.006
Anticoagulant0.058(0.006, 0.553)0.013
NT-ProBNP (pmol/L)1.000(1.000, 1.001)0.049
AUC: area under the curve. Other abbreviations are the same as for Table 1 and Table 2.
Table 5. Variables independently associated with absolute eGFR decrease during follow-up in patients with and without eGFR < 60 mL/min/1.73 m2 at baseline.
Table 5. Variables independently associated with absolute eGFR decrease during follow-up in patients with and without eGFR < 60 mL/min/1.73 m2 at baseline.
GroupVariableCoef.(95% CI)pR2
eGFR ≥ 60Age (y)0.182(0.068, 0.296)0.0020.100
BMI (kg/m2)0.000(−0.000, 0.000)0.538
Caucasian−4.986(−9.408, −0.565)0.027
Gender (Male)−3.444(−5.887, −1.001)0.006
Hypertension3.573(1.402, 5.744)0.001
Diabetes Mellitus1.152(−1.550, 3.853)0.403
eGFR (mL/min/1.73 m2)0.176(0.083, 0.269)<0.001
Uric Acid (µmol/L)0.010(0.004, 0.017)0.001
Insulin5.283(0.018, 10.55)0.049
eGFR < 60Age (y)0.084(−0.260, 0.427)0.6280.155
BMI (kg/m2)−0.009(−0.524, 0.507)0.973
Caucasian8.625(−11.42, 28.67)0.393
Gender (Male)1.208(−3.577, 5.993)0.615
Hypertension4.601(−1.936, 11.14)0.164
Diabetes Mellitus1.465(−3.325, 6.254)0.543
Calcidiol (nmol/L)−0.008(−0.015, −0.002)0.014
Abbreviations: As for Table 1 and Table 2.
Table 6. Predictor variables of major cardiovascular events during follow-up.
Table 6. Predictor variables of major cardiovascular events during follow-up.
PredictorHR (IC95%)pC-Stat
NT-ProBNP (pmol/L)1.173 (1.034, 1.338)0.0140.77
Nitrates2.986 (1.774, 5.024)0.000
Heart failure2.668 (1.559, 4.566)0.000
ACEI0.589 (0.381, 0.909)0.017
Proton Pump Inhibitors3.049 (1.602, 5.802)0.001
BMI (kg/m2)1.083 (1.033, 1.135)0.001
Age (y)1.030 (1.010, 1.051)0.004
Statins0.370 (0.179, 0.763)0.007
Dyslipidemia1.713 (1.067, 2.752)0.026
PTH (pmol/L)1.944 (1.019, 3.707)0.043
Abbreviations are the same as for Table 1 and Table 2.
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Leal-Pérez, N.; Blanco-Colio, L.M.; Martín-Ventura, J.L.; Gutiérrez-Landaluce, C.; Mahíllo-Fernández, I.; González-Casaus, M.L.; Lorenzo, Ó.; Egido, J.; Tuñón, J. High Levels of Galectin-3 and Uric Acid Are Independent Predictors of Renal Impairment in Patients with Stable Coronary Artery Disease. J. Clin. Med. 2025, 14, 5264. https://doi.org/10.3390/jcm14155264

AMA Style

Leal-Pérez N, Blanco-Colio LM, Martín-Ventura JL, Gutiérrez-Landaluce C, Mahíllo-Fernández I, González-Casaus ML, Lorenzo Ó, Egido J, Tuñón J. High Levels of Galectin-3 and Uric Acid Are Independent Predictors of Renal Impairment in Patients with Stable Coronary Artery Disease. Journal of Clinical Medicine. 2025; 14(15):5264. https://doi.org/10.3390/jcm14155264

Chicago/Turabian Style

Leal-Pérez, Nayleth, Luis M. Blanco-Colio, José Luis Martín-Ventura, Carlos Gutiérrez-Landaluce, Ignacio Mahíllo-Fernández, María Luisa González-Casaus, Óscar Lorenzo, Jesús Egido, and José Tuñón. 2025. "High Levels of Galectin-3 and Uric Acid Are Independent Predictors of Renal Impairment in Patients with Stable Coronary Artery Disease" Journal of Clinical Medicine 14, no. 15: 5264. https://doi.org/10.3390/jcm14155264

APA Style

Leal-Pérez, N., Blanco-Colio, L. M., Martín-Ventura, J. L., Gutiérrez-Landaluce, C., Mahíllo-Fernández, I., González-Casaus, M. L., Lorenzo, Ó., Egido, J., & Tuñón, J. (2025). High Levels of Galectin-3 and Uric Acid Are Independent Predictors of Renal Impairment in Patients with Stable Coronary Artery Disease. Journal of Clinical Medicine, 14(15), 5264. https://doi.org/10.3390/jcm14155264

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