Serum uric acid (SUA) has been associated with an increased risk of cardiovascular disease (CVD) in both the general population and individuals with type 2 diabetes (T2DM). Identification of high-risk individuals is crucial for the primary prevention of CVD. A growing array of newly discovered biomarkers has been identified for predicting CVD. Galectin-3 (Gal-3) is linked to inflammatory and fibrotic processes and has been suggested as a biomarker in patients with heart failure.

Aims

Our aim is to investigate whether Gal-3 is a marker that may predict the risk of CVD caused by asymptomatic hyperuricemia in patients with uncomplicated T2DM.

Methods

Twenty patients (male/female: 10/10) with T2DM with high SUA levels and 20 controls (male/female: 10/10) matched for age and gender with T2DM with normal SUA levels were involved.

Results

SUA and Gal-3 levels exhibited a statistically significant correlation (r_s = 0.33, P = 0.03). Although the high SUA group had higher Gal-3 levels than the normal SUA group, the observed difference did not achieve statistical significance (mean: 18 (95% confidence interval (CI): 10.7–29)) vs 14.4 (95% CI: 10.4–30), P = 0.33)).

Conclusions

The present study is the first to show a correlation between the level of SUA and Gal-3 in patients with uncomplicated T2DM. This result suggests that Gal-3 could potentially serve as a marker to predict the risk of CVD in patients with uncomplicated T2DM with high SUA levels.

Introduction

Type 2 diabetes (T2DM) is one of the crucial causes of cardiovascular disease (CVD).¹ Endothelial dysfunction and atherosclerosis by oxidative damage and inflammation, which are the significant contributors to macrovascular and microvascular complications of T2DM, lead to an increase in CVD.^{2, 3} For years, the increased risk of cardiovascular morbidity and mortality has been acknowledged in patients with T2DM; early detection of this risk has been investigated for prevention in recent years.

Serum uric acid (SUA) leads to platelet aggregation by inhibiting nitric oxide production⁴ and inducing oxidative stress in cells,⁵ contributing to endothelial damage and atherogenic effects.⁶ Besides the atherogenic effects, SUA can also exhibit antioxidant properties, potentially serving to prevent atherosclerosis and promote endothelial function.⁷ Recent clinical and epidemiological studies indicate a potential etiological role of SUA in conditions such as obesity, insulin resistance, T2DM, hypertension and metabolic syndrome.^{5, 8} Several cohort studies revealed an independent association between SUA and CVD in the general population^{9, 10} and patients with T2DM.¹¹ As a result of these, SUA has been defined as a biomarker that may predict cardiovascular risk.¹² In addition, studies in patients with T2DM have shown that hyperuricemia increases the risk of CVD independently of DM and its complications.¹¹ Therefore, new diagnostic interventions must be developed to predict CVD risk in patients with hyperuricemia.

Galectin-3 (Gal-3) is a member of the lectin family of carbohydrate-binding proteins composed of 25–30 kDa, associated with inflammatory and fibrotic processes.¹³ Recent studies have proposed that Gal-3 may be a biomarker for CVDs and might be used as a prognostic biomarker in patients with heart failure.¹⁴ It has also been proposed as a marker of vascular remodelling and endothelial dysfunction with inflammation, proliferation and atherosclerosis in healthy and diabetic individuals.¹⁵ In the literature, only a limited number of studies have reported a positive correlation between Gal-3 and SUA in individuals with heart disease.^{16, 17} However, to our knowledge, no study has investigated the association between Gal-3 and SUA in patients with T2DM without CVD.

We hypothesise that Gal-3 may be a marker for forecasting the risk of developing CVD related to elevated SUA levels in patients with uncomplicated T2DM. Our aim is to investigate the association between SUA and Gal-3 in patients with uncomplicated T2DM.

Methods

Study population

Twenty patients, all older than 18 years, diagnosed with T2DM with high SUA levels, and 20 controls with T2DM with normal SUA levels who presented to our outpatient clinic between July and September 2017 were included. Exclusion criteria were malignancy, pregnancy, lactation, chronic liver disease, systemic inflammatory disease, a recent history of infection or trauma within the preceding 2 weeks, high levels of CRP and current use of drugs affecting plasma SUA level (all diuretics, losartan). Additionally, the study did not include patients using SUA-lowering drugs such as xanthine oxidase inhibitors (allopurinol, febuxostat). The diagnosis of chronic kidney disease (CKD) was made according to the KDIGO (Kidney Disease: Improving Global Outcomes) 2012 guidelines.¹⁸ The estimated glomerular filtration rate (eGFR) was determined through the use of the Chronic Kidney Disease Epidemiology Collaboration equation formula (CKD-EPI).¹⁹ Patients with an eGFR <60 mL/min/1.73 m² were excluded from the study. All participants were examined for microvascular and macrovascular complications of T2DM. Medical records were reviewed for previous eye examinations to search for the presence of diabetic retinopathy. The presence of diabetic neuropathy was screened by conducting a detailed history of the relevant symptoms accompanied by a neurological examination. The development of microalbuminuria, which is calculated by urine albumin to creatinine ratio (ACR) >30 mg/g, had been equated with incipient nephropathy, and it was calculated based on a morning fasting spot urine sample using the urinary albumin to urinary creatinine ratio.²⁰ Diabetic nephropathy was excluded in patients with <30 mg/day of microalbuminuria.²¹ Patients with any macrovascular complications of diabetes, which were confirmed by clinical examination, laboratory evaluation or imaging techniques, including atherosclerotic cardiovascular and cerebrovascular disease, were excluded. The study was organised as a cross-sectional study at our university hospital.

Data collection and measurement

Data including age, gender, educational and marital status, weight, height, duration of T2DM and current use of anti-diabetics, anti-hypertensives and anti-dyslipidemics were collected. An anthropometric set was suitable for the Charder-type standard technique used for height (m) measurement. Electronic medical scales were used for weight (kg) measurement. Body mass index (BMI) was measured using the Quetelet Index (kg/m²).²² Blood pressure (mmHg) was measured using a sphygmomanometer from both arms while participants were seated, following a rest period of at least 10 min.

After 8 h of fasting, we obtained 10 mL of peripheral venous blood from each participant, and the serum samples were stored at −80°C until analysis. Serum obtained following the centrifugation of blood samples at 1200 g (g = relative centrifugal force) for 10 min was utilised for Gal-3 measurements. Laboratory assessments encompassed fasting blood glucose, fasting insulin, complete blood cell count, blood urea nitrogen (BUN), serum creatinine, SUA, C-reactive protein (CRP) and evaluation of the serum lipid profile. Glycosylated haemoglobin (HbA1c) was tested using high-performance liquid chromatographic analysis. Microalbuminuria was characterised by an ACR ranging from 30 to 299 mg/g. SUA and Gal-3 were calculated on an Abbott Architect c8000 device. It was based on colorimetric evaluation with enzymatic study.

Study outcomes

The patients were divided into two groups based on their levels of SUA; those with SUA levels >6.5 mg/dL were designated as the high SUA group, and those with SUA levels <6.5 mg/dL were described as the normal SUA group. Both units were matched for age, gender, the time since the onset of T2DM, BMI and HbA1c, and only differed concerning SUA level.

Ethical approval

This study received approval from the local ethics committee (protocol number: 29032017-5) and was conducted by the principles outlined in the Declaration of Helsinki. Written consent was obtained from each participant for their involvement.

Statistical analysis

Statistical analyses were conducted using the Statistical Package for the Social Sciences (spss) version 22.0 (IBM Corp., NY, USA). Categorical variables were expressed as numbers and percentages, normally distributed continuous variables as mean ± standard deviation (SD) and non-normally distributed as median (minimum–maximum). The independent samples t test was applied to compare numeric variables under parametric assumptions, while the Mann–Whitney U test was employed for non-parametrically distributed variables. The associations between variables were evaluated with Spearman correlation coefficient. For interpreting the Spearman rho (r) coefficient, the following benchmarks were employed: 0–0.20, indicating poor correlation; 0.21–0.40, suggesting fair correlation; 0.41–0.60, indicative of moderate correlation; 0.61–0.80, pointing to substantial or strong correlation; and 0.81–1.0, representing near-perfect correlation.²³ A P-value <0.05 was regarded as statistically significant.

Results

The clinical values and demographic features of the participants are presented in Table 1. The high SUA group had a mean age of 61.3 years, whereas the normal SUA group had a mean age of 60.5 years. No significant difference was observed between the two groups regarding age, gender, BMI, haemoglobin, HbA1c, total and low- and high-density lipoprotein cholesterol and ACR. There was also no significant difference between the two groups regarding Gal-3 (P = 0.34).

Table 1. Baseline characteristics and laboratory parameters of the participants

Characteristic	High SUA group	Normal SUA group	P-value
Number	20	20
Age (year)	61.3 ± 7.1†	60.5 ± 7.2†	0.722
Males/females, n (%)	10/10 (50/50)	10/10 (50/50)
BMI (kg/m²)	27.3 ± 2.5†	28.5 ± 3.2†	0.193
SUA (mg/dL)	7.78 ± 0.73†	4.8 ± 0.93†	<0.001
Hb (g/dL)	14.9 ± 1.8†	15.5 ± 1.5†	0.571
HbA1c (%)	7.16 ± 1.34†	7.65 ± 1.97†	0.365
TC (mg/dL)	214 (103–343)‡	186 (110–252)‡	0.521
LDL-C (mg/dL)	119 (48–237)‡	110.5 (61–155)‡	0.514
HDL-C (mg/dL)	37.8 ± 7†	42.4 ± 11.9†	0.143
Gal-3 (ng/mL)	18 (10.7–29)‡	14.4 (10.4–30)‡	0.342
Creatinine (mg/dL)	0.96 ± 0.18†	0.86 ± 0.17†	0.088
eGFR (mL/min/1.73 m²)	75.5 ± 15.3†	84.3 ± 12.3†	0.061
ACR (mg/g)	14.3 ± 7.9†	13.9 ± 6.4†	0.872

Data are given as numbers and percentages for categorical variables, mean ± SD for normally distributed continuous variables, and median (minimum–maximum) for non-normally distributed continuous variables. Values are presented as number (%) or mean (95% confidence interval).
† Shows mean ± SD results.
‡ Represents median (range) values.
ACR, albumin to creatinine ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; Gal-3, galectin-3; Hb, haemoglobin; HbA1c, glycosylated haemoglobin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; M/F, men/female; SD, standard deviation; SUA, serum uric acid; TC, total cholesterol.

A positive correlation was found between SUA level and Gal-3, one of the study's main objectives (r_s = 0.33, P = 0.04) (Table 2). This correlation was found to be stronger in men (r_s = 0.48, P = 0.03). A moderate positive correlation was found between SUA and BUN in all participants (r_s = 0.43, P = 0.005). A moderate positive correlation was found between SUA level and low-density lipoprotein in men (r_s = 0.51, P = 0.01). A weak negative correlation was found between Gal-3 and ACR (r_s = −0.33, P = 0.03).

Table 2. Correlation analysis results of the factors and Gal-3

Parameters	Rho coefficient	P-value
Age	0.220	0.172
BMI	−0.144	0.374
SUA	0.332	0.036
Hb	−0.297	0.062
HbA1c	−0.227	0.159
Creatinine	−0.022	0.893
eGFR	−0.232	0.149
ACR	−0.333	0.036

ACR, albumin to creatinine ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; Gal-3, galectin-3; Hb, haemoglobin; HbA1c, glycosylated haemoglobin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid; TC, total cholesterol. P values below 0.05, which are considered statistically significant, are marked in bold.

Discussion

The current study identified a positive correlation between SUA and Gal-3. Despite the higher Gal-3 levels observed in the high SUA group compared to the normal SUA group, this difference did not achieve statistical significance.

SUA, despite being an antioxidant in plasma, leads to insulin resistance, oxidative stress, endothelial dysfunction and inflammation.^{5, 24} Hyperuricemia has also been proposed as being associated with an increased risk of cardiovascular morbidity and mortality.^{10, 25} According to the Swedish AMORIS (Apolipoprotein-related Mortality Risk) study conducted in 417.734 patients, acute myocardial infarction, stroke and congestive heart failure incidence was associated with moderate levels of SUA.²⁶ On the other hand, Gal-3 is considered a potential biomarker for cardiovascular inflammation.^{16, 17} In addition, many studies have suggested that Gal-3 may be associated not only with CVDs but also with other diseases related to atherosclerosis.²⁷ In our study, it may be speculated that the correlation between SUA and Gal-3 may be associated with SUA-induced atherosclerosis and cardiovascular risk.

Wang et al. recently showed a notable relationship between Gal-3, serum creatinine and SUA in a cohort of 277 patients with heart failure.¹⁷ Nevertheless, the elevated levels of Gal-3 in their study are not solely linked to SUA, as the patients also had a diagnosis of heart failure. Our study demonstrates the correlation between hyperuricemia and Gal-3 more clearly because patients with uncomplicated diabetes were included.

Hyperuricemia is also known to cause intrarenal oxidative stress, renal vasoconstriction and afferent arteriolopathy²⁸ and histopathologically demonstrated that hyperuricemia causes transforming growth factor beta (TGF-β) increase by activating the smad pathway.²⁹ TGF-β is one of the primary mediators of tubulointerstitial fibrosis in proximal tubular cells.³⁰ Hyperuricemia indirectly causes tubulointerstitial fibrosis.²⁹ In recent studies, Gal-3 has also been linked to renal fibrosis and nephropathy.^{30, 31} In a community-based population study conducted in 9148 patients without pre-existing heart failure, higher serum levels of Gal-3 were correlated with an increased risk of developing new-onset CKD over a span of approximately 16 years of follow-up.³² According to an analysis of two extensive studies (Ludwigshafen Risk and Cardiovascular Health and 4D (German Diabetes and Dialysis) study) in patients with end-stage renal disease, it has been demonstrated that levels of Gal-3 increased concurrently with declining kidney function. However, this relationship is independent of the level of clinical decompensation or the presence of heart failure.^{33, 34} However, the increase in Gal-3 level starts mildly when eGFR is <90 mL/min/1.73 m² and is pronounced especially in cases with eGFR <60 mL/min/1.73 m².³³ Therefore, in our study, the correlation between SUA and Gal-3 was thought to be due to the atherosclerotic and cardiovascular effects of hyperuricemia rather than its renal effects, probably because patients with eGFR >60 mL/min/1.73 m² were included.

There are some limitations of this study. The sample may not be reflective of the broader patient population. While there was no statistical difference between Gal-3 and SUA in the specific groups, a significant correlation was observed between Gal-3 and SUA in all participants. This could potentially be attributed to the small sample size in the study. We might have found a statistical difference besides correlation if we had more patients.

In a study investigating the relationship between plasma Gal-3 and eGFR, patients with T2DM were divided into two groups based on the ACR: patients without albuminuria (ACR < 30 mg/g) and those with albuminuria (ACR ≥ 30 mg/g).³⁵ Gal-3 was higher in the group with albuminuria, and a statistically significant difference was detected.³⁵ On the other hand, our study found a weak negative correlation between Gal-3 and ACR. Since Gal-3 is known to increase as eGFR decreases,^{33, 34} the increase in Gal-3 in the albuminuric group in their study may be related to the decrease in eGFR. Additionally, we included patients with eGFR >60 mL/min/1.73 m² and ACR <30 mg/g in our study. Therefore, further investigations are needed to establish the exact relationship between Gal-3 and ACR.

Conclusion

To our knowledge, the present study is the first to demonstrate the correlation between SUA and Gal-3 in patients with uncomplicated T2DM. Gal-3 may be a marker for predicting the risk of developing CVD associated with asymptomatic hyperuricemia in uncomplicated T2DM. Future studies should determine whether there is a relationship between Gal-3 levels and the long-term development of CVD in those with asymptomatic hyperuricemia in uncomplicated T2DM. If CVD development is higher in those with increased levels of Gal-3, necessary precautions should be taken.

Acknowledgements

The authors thank all of the participants who agreed to participate in the study.

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Volume55, Issue7

July 2025

Pages 1169-1173

Association between serum uric acid levels and galectin-3 in patients with uncomplicated type 2 diabetes

Abstract

Background

Aims

Methods

Results

Conclusions

Introduction

Methods

Study population

Data collection and measurement

Study outcomes

Ethical approval

Statistical analysis

Results

Discussion

Conclusion

Acknowledgements

References

References

Information

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Association between serum uric acid levels and galectin-3 in patients with uncomplicated type 2 diabetes

Abstract

Background

Aims

Methods

Results

Conclusions

Introduction

Methods

Study population

Data collection and measurement

Study outcomes

Ethical approval

Statistical analysis

Results

Discussion

Conclusion

Acknowledgements

References

References

Related

Information