Relationship between proteinuria, plasma adropin levels, carotid intima-media thickness, and epicardial adipose tissue in type 2 diabetes mellitus

Abstract

AimThis study aimed to evaluate the relationship between proteinuria, plasma adropin levels, carotid intima-media thickness (CIMT), and epicardial adipose tissue (EAT) thickness in patients with type 2 diabetes mellitus (T2DM).[A1.1] MethodsThis single-center, observational, cross-sectional study included 50 proteinuric patients with T2DM, 50 non-proteinuric patients with T2DM, and 50 healthy controls. The proteinuric group was defined by 24-hour total urinary protein excretion >150 mg/day, whereas the non-proteinuric group was defined by protein excretion ≤150 mg/day. Plasma adropin levels were measured using a competitive enzyme immunoassay (EIA). CIMT and EAT thickness were assessed by the same cardiologist. Group comparisons and correlation analyses were performed using appropriate parametric or nonparametric tests. ResultsPlasma adropin levels were significantly lower in both diabetic groups than in controls (P < .001). Right and left CIMT and EAT thickness were significantly higher in the diabetic groups than in controls (P < .001). Adropin levels were negatively correlated with proteinuria, EAT thickness, body mass index (BMI), and waist-to-hip ratio; however, no significant correlation was found between adropin and CIMT. ConclusionThese findings suggest that reduced adropin levels in T2DM may be associated with proteinuria and increased cardiometabolic risk burden.

Keywords

Introduction

Type 2 diabetes mellitus is a metabolic disorder with increasing global prevalence and substantial morbidity and mortality due to microvascular and macrovascular complications. Individuals with diabetes are at increased risk of cardiovascular disease and chronic kidney disease, and this risk is influenced not only by hyperglycemia but also by interrelated mechanisms such as insulin resistance, obesity, dyslipidemia, inflammation, endothelial dysfunction, and renal involvement.^1,2 Therefore, practical biomarkers that can help identify renal and vascular injury at an early stage are clinically important in type 2 diabetes mellitus (T2DM).
Proteinuria is one of the clinically measurable indicators of renal involvement in diabetic patients. Structural and functional disruption of the glomerular filtration barrier, podocyte injury, oxidative stress, inflammation, and glomerular hemodynamic alterations may all contribute to the development of proteinuria.^3,4 In this respect, proteinuria may reflect not only kidney damage but also systemic metabolic and vascular impairment.
The relationship between proteinuria and cardiovascular risk in diabetic patients is clinically relevant.⁵ Beyond increased glomerular permeability, proteinuria can also be considered a systemic manifestation of endothelial dysfunction, inflammation, and microvascular damage. Therefore, although proteinuria-based grouping cannot fully replace albuminuria categories, it may provide a meaningful clinical framework for evaluating renal-vascular burden in diabetic patients.
Endothelial dysfunction is recognized as an early and central pathophysiological step in the development of atherosclerosis and vascular complications in T2DM. Hyperglycemia, insulin resistance, free fatty acids, dyslipidemia, and chronic low-grade inflammation accelerate endothelial dysfunction.^6,7 Over time, this process may lead to increased subclinical atherosclerosis and cardiovascular event risk.
Carotid intima-media thickness (CIMT) is a noninvasive structural marker of subclinical atherosclerosis and has been associated with cardiovascular risk.^8,9 Epicardial adipose tissue (EAT), located in close anatomical proximity to the myocardium and coronary arteries, is a visceral adipose tissue depot. Under physiological conditions, it has protective roles such as mechanical cushioning, free fatty acid buffering, and anti-inflammatory adipokine secretion. However, in the setting of obesity, insulin resistance, and diabetes, increased EAT thickness may be accompanied by a shift toward a pro-inflammatory phenotype. Recent reviews have reported associations between EAT and diabetes, insulin resistance, metabolic syndrome, and coronary atherosclerosis.^10,11
Adropin is a peptide hormone thought to be involved in energy homeostasis, glucose-lipid metabolism, insulin sensitivity, and endothelial function.¹² Recent studies indicate that adropin may be associated with diabetic kidney disease and cardiometabolic risk.¹³ However, data evaluating adropin levels together with proteinuria, CIMT, and EAT thickness in the same patient population remain limited. In this study, plasma adropin levels, CIMT, and EAT thickness were compared among proteinuric and non-proteinuric patients with T2DM and healthy controls. The associations between adropin levels and proteinuria, vascular markers, and metabolic parameters were also investigated.

Materials and Methods

This single-center, observational, cross-sectional study included patients diagnosed with T2DM according to the American Diabetes Association criteria between 2014 and 2015.² Patients were divided into two groups according to 24-hour total urinary protein excretion. Patients with protein excretion >150 mg/day were classified as the proteinuric diabetes group, whereas those with protein excretion ≤150 mg/day were classified as the non-proteinuric diabetes group. A total of 150 participants were included: 50 proteinuric diabetic patients, 50 non-proteinuric diabetic patients, and 50 healthy controls. During the study period, patients with type 2 diabetes mellitus who attended the outpatient clinic were consecutively screened for eligibility. After confirmation of 24-hour urinary total protein excretion, eligible patients were stratified into proteinuric and non-proteinuric diabetic groups. Recruitment was continued until 50 patients were included in each diabetic group. Once the proteinuric group reached the target sample size, further proteinuric patients were not enrolled, whereas recruitment of eligible non-proteinuric patients continued until the comparison group size was completed. Healthy controls were recruited on a voluntary basis using convenience sampling from individuals without known chronic disease, diabetes mellitus, renal disease, cardiovascular disease, active infection, or inflammatory disease. Individual matching was not performed; however, the groups were statistically compared in terms of age and sex distribution.[A2.1] Volunteers aged 18-70 years were included. Patients were excluded if they had a history of malignancy, chronic inflammatory or autoimmune disease, chronic kidney or liver disease, peripheral vascular disease, coronary artery disease, previous myocardial infarction, cerebrovascular event, congestive heart failure, pregnancy, active infection, urinary stone disease, or renal disease unrelated to diabetes. Age, sex, duration of diabetes, smoking, medication and anthropometric measurements were recorded. Body mass index (BMI) was calculated as kg/m². Blood samples were obtained in the morning after a 12-hour fast. Routine biochemical parameters were analyzed using standard laboratory methods. Twenty-four-hour urinary protein measurements were performed twice at three-month intervals, and the mean values were used for analysis. The control group consisted of volunteers without known chronic disease. Clinical history and physical examination findings were recorded using the same protocol for all participants. For adropin measurement, blood samples were collected into tubes containing K3EDTA/aprotinin, and plasma samples were stored at -80 °C until the day of analysis. Plasma adropin levels were measured using a commercial Adropin EIA kit (Cat. No: EK-032-35; Phoenix Pharmaceuticals, Burlingame, CA, USA) with a competitive enzyme immunoassay method in accordance with the manufacturer’s instructions. The assay detection range was 0.01-100 ng/mL, and the assay sensitivity was 0.3 ng/mL. The intra-assay and inter-assay coefficients of variation were <10% and <15%, respectively. All samples were analyzed in duplicate.[A3.1] CIMT and EAT thickness measurements were performed by the same cardiologist who was blinded to clinical and laboratory data. EAT thickness was measured using a Vivid 7 echocardiography device (GE Healthcare) from the parasternal long-axis view over the free wall of the right ventricle at end-diastole. CIMT was measured approximately 10 mm proximal to the carotid bifurcation, taking into account the lumen-intima and media-adventitia interfaces. The mean of three measurements was used for analysis. Intra-observer and inter-observer variability analyses were not formally performed for CIMT and EAT measurements. However, all measurements were performed by the same experienced cardiologist who was blinded to clinical and laboratory data, and the mean of three repeated measurements was used for analysis to improve measurement reliability.[A4.1] Statistical analyses were performed using IBM SPSS Statistics version 22. The normality of continuous variables was assessed using the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots. Homogeneity of variances was evaluated using Levene’s test. Continuous variables were expressed as mean ± standard deviation, and categorical variables as number and percentage. Depending on data distribution and variable type, chi-square test, independent samples t-test, Mann-Whitney U test, one-way analysis of variance, or Kruskal-Wallis test was used for group comparisons. For significant three-group comparisons, Tukey’s post hoc test was used after one-way analysis of variance when assumptions were met, and Bonferroni-corrected pairwise comparisons were used after nonparametric analyses. Correlation analyses were performed using Pearson or Spearman correlation tests, as appropriate. A p value <0.05 was considered statistically significant.[A5.1] In the tables, the overall p value represents the three-group comparison; p1 represents proteinuric diabetes versus non-proteinuric diabetes; p2 represents proteinuric diabetes versus control; and p3 represents non-proteinuric diabetes versus control. An a priori sample size calculation was not performed because this study was conducted using data from a previously completed single-center institutional research project, and all eligible participants with complete clinical, laboratory, adropin, CIMT, and EAT measurements were included. However, a post hoc sensitivity analysis was performed for the available sample size. With three groups, a total sample size of 150 participants, an alpha level of 0.05, and 80% statistical power, the study was able to detect a medium effect size of approximately Cohen’s F = 0.26 for one-way analysis of variance. Based on the observed plasma adropin values among the three groups, the effect size was approximately Cohen’s F = 0.49.[A6.1] This observational cross-sectional study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.[A7.1] Ethical Approval This study was approved by the Clinical Research Ethics Committee of Canakkale Onsekiz Mart University Faculty of Medicine (Date: 25.06.2014, Decision No: 2014-12). Written informed consent was obtained from all participants.

Results

There were no significant differences among the groups in terms of age or sex distribution. Duration of diabetes and glycated hemoglobin (HbA1c) were higher in the proteinuric diabetes group than in the non-proteinuric diabetes group. BMI and waist-to-hip ratio were higher in both diabetic groups than in the control group. Total cholesterol and low-density lipoprotein (LDL)-cholesterol levels were similar among the groups. The difference in triglyceride levels was mainly attributable to the difference between the proteinuric diabetes and control groups. High-density lipoprotein (HDL)-cholesterol levels were lower in the diabetic groups than in controls. Proteinuria was 482±240 mg/day in the proteinuric diabetes group, 111±24 mg/day in the non-proteinuric diabetes group, and 86±27 mg/day in the control group. Proteinuria was significantly higher in the proteinuric diabetes group than in both the non-proteinuric diabetes and control groups (P < .001) (Table 1). Right CIMT values in the proteinuric diabetes, non-proteinuric diabetes, and control groups were 0.77±0.19 mm, 0.75±0.19 mm, and 0.59±0.09 mm, respectively. Left CIMT values were 0.80±0.19 mm, 0.79±0.23 mm, and 0.60±0.08 mm, respectively. Both CIMT measurements were significantly higher in the diabetic groups than in the control group (P < .001), whereas no significant difference was found between the proteinuric and non-proteinuric diabetic groups. EAT thickness was 5.26±1.85 mm in the proteinuric diabetes group, 5.50±2.70 mm in the non-proteinuric diabetes group, and 3.55±1.04 mm in the control group. EAT thickness was significantly higher in both diabetic groups than in controls (P < .001), but it did not differ between the diabetic groups. Plasma adropin levels were 7.94±4.48 ng/mL in the proteinuric diabetes group, 9.46±7.16 ng/mL in the non-proteinuric diabetes group, and 14.46±5.00 ng/mL in the control group. Plasma adropin levels were significantly lower in both diabetic groups than in controls (P < .001). Although adropin levels were numerically lower in the proteinuric diabetes group than in the non-proteinuric group, this difference was not statistically significant (P = .612) (Table 2). No significant correlation was found between plasma adropin levels and right CIMT (r = -0.158; P = .053) or left CIMT (r = -0.140; P = .087). In contrast, adropin levels were negatively correlated with EAT thickness (r = -0.178; P = .029), proteinuria (r = -0.241; P = .003), BMI (r = -0.215; P = .008), and waist-to-hip ratio (r = -0.217; P = .008) (Figure 1). There was no significant association between adropin levels and age, duration of diabetes, HbA1c, smoking status, or lipid parameters. Proteinuria showed weak positive correlations with right CIMT (r = 0.198; P = .015) and left CIMT (r = 0.183; P = .025).

Discussion

In this study, adropin levels were negatively associated with proteinuria, EAT thickness, BMI, and waist-to-hip ratio. Plasma adropin levels were lower, whereas CIMT and EAT thickness were higher, in diabetic patients than in controls. These findings suggest that reduced adropin in T2DM may reflect a broader renal, metabolic, and cardiovascular risk profile.
The main message of the study is that adropin levels are reduced in T2DM and that this reduction is associated with proteinuria and markers of adiposity and visceral cardiac fat accumulation. The lack of a significant adropin difference between the two diabetic groups suggests that adropin may not solely reflect proteinuric status, but may also be influenced by metabolic, vascular, and inflammatory burden. In patients with coronary artery disease, low serum adropin was associated with hyperhomocysteinemia and higher synergy between PCI with TAXUS and Cardiac Surgery (SYNTAX) scores, supporting a possible link between adropin and atherosclerotic vascular burden.¹⁴
Previous studies have reported lower adropin levels in patients with T2DM than in healthy individuals. Zang et al. found decreased serum adropin levels in T2DM, particularly in overweight and obese patients.¹⁵ A systematic review and meta-analysis also showed lower circulating adropin levels in diabetic patients than in controls.¹⁶ Our findings in both diabetic groups are consistent with these reports.
The negative correlations between adropin, BMI, and waist-to-hip ratio support a relationship with adiposity and visceral fat accumulation. Erman et al. reported lower serum adropin concentrations in obese patients than in healthy controls.¹⁷ Similarly, the higher BMI and waist-to-hip ratio observed in our diabetic groups suggest that reduced adropin may be related to the metabolic burden accompanying diabetes.
EAT is a visceral adipose tissue depot closely related to the coronary arteries and myocardium and has been associated with diabetes, insulin resistance, metabolic syndrome, and coronary atherosclerosis.^10,11 In our study, EAT thickness was significantly higher in diabetic patients than in controls and was negatively correlated with adropin levels. This finding suggests that reduced adropin may be associated with visceral cardiac fat accumulation and increased cardiometabolic risk.
Proteinuria is an important clinical indicator of renal involvement in diabetic patients and may reflect impairment of the glomerular filtration barrier.^4,18 The negative correlation between adropin and proteinuria suggests that low adropin levels may be associated with renal or renal-vascular dysfunction. Hu and Chen reported associations between adropin, diabetic nephropathy, and renal function parameters in T2DM.¹⁹ Es-Haghi et al. also found lower adropin levels in diabetic nephropathy than in diabetes without nephropathy and in controls.²⁰ Although our study did not classify nephropathy by albumin-to-creatinine ratio, the negative association between 24-hour proteinuria and adropin is compatible with the literature.
However, no significant difference in adropin levels was observed between the proteinuric and non-proteinuric diabetic groups. Several explanations may account for this finding. First, the reduction in adropin levels may occur earlier in the course of T2DM and may be driven not only by proteinuria, but also by obesity, visceral adiposity, insulin resistance, and overall systemic metabolic burden. Second, proteinuria may not be the sole determinant of circulating adropin levels, as glycemic control, inflammation, adiposity, medication use, and disease duration may also influence this relationship. Third, the cross-sectional design and limited sample size of the present study may have restricted the ability to detect smaller differences between the two diabetic groups. Therefore, although the negative correlation observed in this study suggests an association between adropin and proteinuria, it does not allow us to conclude that adropin is an independent marker capable of distinguishing proteinuric status on its own.
The weak positive correlation between proteinuria and CIMT suggests that renal involvement and subclinical vascular changes may share common mechanisms. However, the low correlation coefficients require cautious interpretation and do not establish an independent or causal relationship.
Endothelial dysfunction has a central role in subclinical atherosclerosis and cardiovascular complications in type 2 diabetes mellitus.^6,7 Adropin may have protective endothelial effects through mechanisms related to nitric oxide bioavailability and vascular homeostasis.²¹ The negative associations of adropin with proteinuria and EAT thickness support its potential role as an integrated marker of renal and vascular-metabolic impairment.
CIMT is an important noninvasive parameter for evaluating subclinical atherosclerosis.⁸ In this study, right and left CIMT were higher in diabetic patients than in controls, but CIMT did not correlate significantly with adropin. In contrast, proteinuria showed weak positive correlations with both CIMT measurements, suggesting that it may reflect not only renal damage but also vascular injury and subclinical atherosclerotic burden. The absence of a significant adropin-CIMT relationship may be explained by the multifactorial nature of CIMT, which is influenced by age, diabetes duration, blood pressure, lipid profile, smoking, and other risk factors. CIMT is a structural measure of long-term vascular risk accumulation, whereas adropin may be more closely related to dynamic metabolic and endothelial processes.
This study has several strengths, including the simultaneous evaluation of proteinuric diabetes, non-proteinuric diabetes, and healthy controls, and the assessment of adropin, proteinuria, CIMT, and EAT in the same cohort. In addition, CIMT and EAT measurements were performed by the same cardiologist blinded to clinical and laboratory data, supporting measurement standardization.
The clinical contribution of this study is that plasma adropin levels may provide additional information when interpreted together with renal and metabolic risk indicators rather than as a stand-alone marker of proteinuric status. However, because multivariable analysis was not performed, the observed associations should not be interpreted as evidence that adropin is an independent biomarker of renal or vascular risk. Larger prospective studies including albuminuria categories, eGFR change, carotid plaque burden, EAT volume, and multivariable models are needed to clarify whether adropin has independent clinical value in renal-vascular risk assessment.

Limitations

This study has limitations. First, its single-center, cross-sectional design precludes causal interpretation. Second, proteinuria was assessed using total protein excretion in 24-hour urine samples; therefore, the absence of albumin-to-creatinine ratio or microalbuminuria measurements may limit the detailed staging of renal involvement. Third, intra-observer and inter-observer variability analyses were not formally performed for CIMT and EAT measurements. Although all measurements were performed by the same experienced cardiologist blinded to clinical and laboratory data and the mean of three repeated measurements was used, the absence of formal reproducibility analysis should be considered a limitation. Fourth,[A9.1] the relatively limited diabetes duration in both diabetic groups and the single time-point nature of HbA1c may have restricted assessment of cumulative glycemic and metabolic burden. Because renal and atherosclerotic changes are influenced by long-term exposure to hyperglycemia, hypertension, dyslipidemia, and inflammation, this may have attenuated associations between adropin and proteinuria, CIMT, or EAT. Finally, the data were collected in previous years, so the effects of contemporary antidiabetic and cardiorenal protective therapies could not be evaluated. These findings therefore require confirmation under current treatment conditions.

Conclusion

In conclusion, plasma adropin levels were significantly lower in patients with T2DM than in healthy controls. The negative correlations of adropin with proteinuria, EAT thickness, BMI, and waist-to-hip ratio suggest that adropin may be associated with renal, metabolic, and cardiovascular risk burden. However, because of the observational cross-sectional design and the absence of multivariable analysis, these findings should be interpreted as associations rather than evidence of an independent biomarker role. Larger prospective multicenter studies including multivariable models are needed to clarify whether adropin has independent clinical value in renal-vascular risk assessment in T2DM.

Declarations

Abbreviations

References

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