Visceral obesity is an independent risk factor for cardiovascular diseases (CVDs), type 2 diabetes mellitus (T2DM), dyslipidaemia, and renal dysfunction due to the development of obesity-associated glomerulopathy.^1,2 The adoption and widespread use of highly informative imaging methods to visualize fat depots made it possible to quantify individual differences in visceral fat distribution in the body^3,4 and thus to identify ectopic adipose tissue—e.g., perirenal fat (PRF) tissue, pericardial adipose tissue, perivascular adipose tissue—in addition to abdominal fat depot.

The cardiac ectopic fat depot is the most studied of the fat depots. Noticeably fewer studies are dedicated to PRF, the tissue of which promotes the development of renal dysfunction by mechanically compressing the blood vessels and activating the renin-angiotensin-aldosterone system,⁵ as well as through its direct lipotoxic action.⁶ The adipocytes of dysfunctional PRF actively secrete adipokines and cytokines that foster inflammation and fibrosis in the kidneys.^7,8 Thus, Hou et al.⁸ experimentally established a link between PRF and elevated urinary albumin excretion in obese rats. That finding is attributable to the influence of PRF on the development of endothelial dysfunction in renal arteries caused by oxidative stress and adipokine/cytokine imbalance in obese rats.⁸ Other clinical studies have established a link between PRF and renal dysfunction.^9-11 According to a study that involved T2DM patients, increased PRF thickness was also accompanied by renal dysfunction with a reduced glomerular filtration rate (GFR).⁹ In our previous study, we also revealed a link between PRF thickness and chronic kidney disease (CKD) stage as defined by GRF.¹⁰

Neutrophil gelatinase-associated lipocalin (NGAL),^11,12 monocyte chemoattractant protein-1 (MCP-1),^13,14 and other specific substances are used as highly informative biomarkers of early renal damage caused by injury of proximal tubuli and the tubulointerstitium. Gharishvandi et al.¹¹ showed serum NGAL level to be of greater diagnostic value for detecting early stages of CKD than serum cystatin C and creatinine. In a study by Kuzmin et al.,¹² NGAL level was a more sensitive biomarker than the urinary concentration of kidney injury molecule-1 (KIM-1) of early tubulointerstitial kidney injury in patients with arterial hypertension (HTN) without diabetes mellitus or renal diseases at an early stage of renal dysfunction. Furthermore, in a systematic review, Mansour et al.¹³ showed MCP-1 to be a significant marker of renal tissue fibrosis. Another study, by Satirapoj et al.,¹⁴ regarded MCP-1 as a prognostic marker of renal function decline in T2DM patients.

Despite numerous studies that confirm the adverse effect of obesity on renal function, the contribution of overall obesity or individual ectopic fat depots to kidney injury is uncertain. The literature lacks findings on the relationships of PRF to early biomarkers of kidney injury in HTN patients. In our study, we aimed to explore the links between PRF thickness and the urinary concentrations of MCP-1 and NGAL in patients with HTN.

Written informed consent was obtained from all study participants. We conducted a cohort study that adhered to the Declaration of Helsinki. The study was approved by the Institutional Review Board of Sechenov University (protocol no. 01-21) and involved 338 patients (139 male and 199 female) aged 63.5±12.3 years.

The inclusion criteria were age >18 years and signed informed agreement to participate. The exclusion criteria were symptomatic HTN, clinical manifestations of ischaemic heart disease and/or cerebrovascular disease, functional class II–IV chronic heart failure, cardiac arrhythmia, inflammatory diseases of any aetiology, type 1 diabetes mellitus or T2DM, hepatic failure, GFR <30 mL/min/1.73 m², oncological disease, pregnancy, and mental disease.

To assess the degree of visceral obesity, anthropometric measurements (height, weight, and waist circumference) were conducted, and body mass index (BMI) as weight (kg)/height (m)² was calculated.

The presence of excessive body mass and class were assessed according to the 2017 clinical guidelines on diagnosis, treatment, and prevention of obesity and associated diseases.¹⁵ Patients were divided into metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). Individuals with abdominal obesity (waist circumference >102 cm in male and >88 cm in female) without metabolic abnormalities, defined as fasting glucose, triglyceride (TG), and high-density lipoprotein levels within normal ranges and no history of CVD or no more than 1 of the above, were classified as MHO. Patients with abdominal obesity, HTN (blood pressure level ≥140/90 mmHg), and TG level ≥1.7 mmol/L and/or high-density lipoprotein cholesterol (HDL-C) <1.0 mmol/L in males and <1.2 mmol/L in females and/or low-density lipoprotein cholesterol (LDL-C) ≥3.0 mmol/L were classified as MUO.¹⁵

The HTN grade was assessed according to the 2018 European Society of Cardiology/European Society of Hypertension guidelines.¹⁶ Blood chemistry tests, including total cholesterol, HDL-C, LDL-C, and TGs concentrations, were performed. GFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula and its calculator:https://www.kidney.org/professionals/kdoqi/gfr_calculator. The CKD stage was assessed in accordance with the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guidelines as follows: CKD stage 1, GFR>90 mL/min/1.73 m²; CKD stage 2, 60≤GFR<90 mL/min/1.73 m²; and CKD stage 3, 30≤GFR<60 mL/min/1.73 m².¹⁷

Urine MCP-1 concentrations were evaluated using the Sigma Human MCP-1/CCL2 enzyme-linked immunosorbent assay (ELISA) kit test system (Merck); urine NGAL concentrations was measured using the Sigma Human Lipocalin-2/NGAL ELISA kit test system (Merck).

All patients underwent spiral computed tomography of the abdomen using the Toshiba Aquilion Prime tomograph under the standardized protocol with a peak voltage of 120 kV, automatic current selection in the 100 to 500 mA range, and slice thickness of 1 mm. PRF was measured as the vertical distance between the left posterior renal capsule and the junction of the abdominal wall and paraspinal musculature on a single slice at the left renal vein level. To adjust GFR calculations for muscle mass, axial L3 images were analysed using the specialized software QCT Pro Tissue Composition Module (Mindways Software) with measurement of muscle cross sectional area (CSA) and calculation of skeletal muscle index (SMI) defined as CSA/height².

Statistical analysis was performed with the Statistica version 10.0 software package (StatSoft Inc.). In a normal distribution of quantities, the average and standard deviation (М±σ) were calculated; in a non-parametric data distribution, the median and interquartile range (IQR) were calculated. For inter-group comparison among several independent samples of quantitative indicators, we used the Kruskal-Wallis test; for comparing two independent samples, we used the non-parametric Mann-Whitney U-test. To study correlation, we used the Spearman R non-parametric method. Multiple linear regression analysis was implemented to assess significant relationships between the indicators and the dependent variable, with significant defined as P<0.05 error probability.

The clinical and demographic profiles of the patients examined are shown in Table 1. The group was assessed as a whole without sex adjustment. Above-normal BMI was found in 78% of those examined. All patients with BMI >30 kg/m² had visceral-type obesity. The average SMI was 45.3 cm²/m² (IQR, 39.3 to 50.2).

The median PRF thickness in the examined cohort was 1.61 cm (IQR, 1.03 to 2.46). According to our previous study, PRF thickness was significantly higher in patients with stage 3 CKD compared with those with either stage 1 CKD or stage 2 CKD.¹⁰ The threshold of normal PRF thickness was calculated in that previous study, identifying the group of conditionally healthy patients (without CVD) and the 90th percentile of PRF thickness. The study group was relatively small (n=34), and we did not study sex differences in perirenal obesity using PRF thickness.¹⁸ PRF thickness ≥1.91 cm was considered the threshold for perirenal obesity. Further validation studies on larger populations of healthy adults are needed to support this specific cut-off.

Patients with PRF <1.91 cm had urinary MCP-1 and NGAL concentrations significantly lower than those of persons with PRF ≥1.91 cm: 0.98 pg/mL (IQR, 0.21 to 2.05) vs. 2.35 pg/mL (IQR, 0.37 to 5.22); 50.0 pg/mL (IQR, 48.9 to 67.8) vs. 98.3 pg/mL (IQR, 68.4 to 187.1), respectively (P<0.05) (Fig. 1).

Correlation analysis revealed associations between PRF thickness and urinary concentrations of MCP-1 (r=0.46, P<0.05) and NGAL (r=0.53, P<0.05).

The MCP-1 and NGAL concentrations varied depending on CKD stage (Fig. 2). At CKD stage 1, MCP-1 was (GFR>90 mL/min/1.73 m²) 0.33 pg/mL (IQR, 0.21 to 1.35) and NGAL was 50.0 pg/mL (IQR, 49.4 to 85.5); at CKD stage 2, MCP-1 was (60≤GFR<90 mL/min/1.73 m²) 0.61 pg/mL (IQR, 0.12 to 4.20) and NGAL was 62.2 pg/mL (IQR, 50.0 to 151.0); and at CKD stage 3, MCP-1 was (30≤GFR<60 mL/min/1.73 m²) 4.47 pg/mL (IQR, 0.23 to 10.81) and NGAL was 126.45 pg/mL (IQR, 57.5 to 205.15).

MCP-1 and NGAL concentrations were significantly different between patients with CKD stages 1 and 3: 0.33 pg/mL (IQR, 0.21 to 1.35) vs. 4.47 pg/mL (IQR, 0.23 to 10.81); 50.0 pg/mL (IQR, 49.4 to 85.5) vs. 126.45 pg/mL (IQR, 57.5 to 205.15), respectively (P=0.04) (Fig. 2).

Urine concentrations of MCP-1 in MHO were significantly lower than in MUO: 0.65 pg/mL (IQR, 0.21 to 2.15) vs. 3.28 pg/mL (IQR, 2.05 to 5.22) (P=0.014) (Fig. 3). The concentration of NGAL in MHO patient urine was also lower than in that of MUO: 50.0 pg/mL (IQR, 49.4 to 62.2) vs. 98.3 pg/mL (IQR, 50.0 to 174.8) (P=0.04) (Fig. 3).

Multiple linear regression analysis was applied to assess the statistical significance of the associations between urinary MCP-1 and NGAL levels and confounding variables. During construction of the model, indicators of age, PRF thickness, BMI, presence of dyslipidemia, and smoking were included. The model for urinary MCP-1 level (r=0.726, r²=0.638, P<0.001) revealed significant correlations with PRF thickness (β±standard error [SE], 0.41±0.15; P<0.001) and smoking (β±SE, 0.26±0.13; P=0.01). The second model was constructed for the urinary NGAL level (r=0.836, r²=0.724, P<0.001). Of all the factors included, a significant correlation of urinary NGAL level was revealed for age (β±SE, 0.45±0.16; P<0.01) and PRF thickness (β±SE, 0.49±0.15; P<0.001).

Like any other visceral adipose tissue, PRF is highly active. To assess the secretory activity of PRF in this study, we examined the urinary concentrations of NGAL and the pro-inflammatory cytokine MCP-1. Relationships of the urinary concentrations of NGAL^12,19,20 and MCP-1^13,14 with the development of renal dysfunction have been described in literature. MCP-1 is mainly produced by monocytes/macrophages,²¹ and its expression is stimulated by pro-inflammatory cytokines such as interleukin 1β (IL-1β), tumor necrosis factor-α (TNF-α), interferon-γ, lipopolysaccharide, and granulocyte-macrophage colony-stimulating factor.²² As a result, MCP-1 is widely used as a marker of inflammatory kidney diseases, particularly chronic glomerulonephritis²³ and lupus nephritis.²⁴ Urinary MCP-1 concentration has been established to be a highly sensitive and specific marker of interstitial fibrosis with prognostic significance in chronic glomerulonephritis.²³

HTN patients often develop non-immune glomerulopathy, with manifestations of intra-glomerular HTN and hyper-filtration. Our study cohort largely consisted of persons who were overweight or obese. Any visceral adipose tissue, particularly PRF, actively secretes adipokines and cytokines including adiponectin, leptin, resistin, TNF-α, visfatin, IL-6, and IL-1β.²⁵ Therefore, we tested the hypothesis that HTN and obesity involve an inflammatory component of injury to the kidney’s glomerular apparatus by assessing urinary excretion of NGAL. In the kidneys, NGAL is expressed in the loop of Henle and in collecting tubule cells.²⁶ Literary sources describe the use of NGAL for early diagnosis of CKD.¹² According to Gharishvandi et al.,¹¹ the level of NGAL in blood serum was more valuable for diagnosis of early stages of CKD than serum levels of cystatin C and creatinine. Depending on CKD stage, we found significant differences in the urinary concentrations of MCP-1 (P=0.04) and NGAL (P=0.04) between patients with first- and third-stage CKD, which corresponds to the literary data on the links of MCP-1 and NGAL with injury to the kidney’s glomerular apparatus. A study by Xiang et al.²⁷ involving 240 patients yielded similar findings: urinary NGAL level was closely correlated with CKD of various severity. The absolute values of MCP-1 and NGAL in our cohort were considerably lower than those previously found in patients with chronic glomerulonephritis²³ but generally matched those found in groups with tubulointerstitial injury in HTN.¹⁴

In our study, PRF thickness positively correlated with urinary concentrations of NGAL and MCP-1 both in correlation analysis and multiple linear regression analysis. These findings agree with findings of PRF as a source of pro-inflammatory cytokines that plays a role in the development of renal dysfunction. Further, we previously compared the levels of MCP-1 (P=0.028) and NGAL (P=0.002) in patients with normal PRF thickness and perirenal obesity and found significant differences that point to an association between perirenal obesity and reduced renal function.10 Additionally, we found the concentrations of MCP-1 (P=0.014) and NGAL (P=0.04) to differ depending on obesity phenotype, namely MHO and MUO. Chang et al.²⁸ demonstrated reduced renal function in obese patients irrespective of obesity phenotype; however, MUO was more closely related to reduced renal function.

Our data confirm the concept of kidney function impairment and CKD development due to humoral injury to the glomeruli in HTN and perirenal obesity, which manifests with higher urinary excretion of inflammation and kidney injury markers such as MCP-1 and NGAL. Excessive PRF thickness should be regarded not only as a component of overall obesity, but also as an isolated form of obesity, accompanied by metabolic and humoral consequences typical for visceral obesity. Detection of perirenal obesity may help specify a patient’s metabolic status and assist in developing a more personalized approach to preventing obesity complications.

The authors declare no conflict of interest.

Study concept and design: AB, YR, and VP; acquisition of data: YR, DB, and MKV; analysis and interpretation of data: AB, YR, DB, MKV, and AF; drafting of the manuscript: YR, DB, and MKV; critical revision of the manuscript: AB, KO, and VP; statistical analysis: YR and KO; administrative, technical, or material support: AB, KO, and VP; and study supervision: AB and VP.