The perirenal fat thickness was independently associated with serum uric acid level in patients with type 2 diabetes mellitus

Thickened Perirenal Fat Predicts Poor Renal Outcome in Patients with Immunoglobulin A Nephropathy: A Population-Based Retrospective Cohort Study

Introduction

Perirenal fat is a pad that fills the retroperitoneal space outside the kidney, which affects kidney function in various ways. However, the association between perirenal fat and IgA nephropathy (IgAN) has not yet been elucidated. This study aimed to investigate the role of perirenal fat in predicting IgAN progression.

Methods

A total of 473 patients with biopsy-proven IgAN and follow-up information were recruited, and perirenal fat thickness (PFT) was measured using color Doppler ultrasonography at renal biopsy. Patients were divided into two groups according to the median PFT: the low-PFT group (PFT ≤1.34 cm, n = 239) and the high PFT group (PFT >1.35 cm, n = 234). A total of 473 healthy participants were included in the control group. Basic clinical characteristics were assessed at the time of renal biopsy, and the relationship between PFT and combined endpoints was analyzed. The renal composite endpoints were defined as a two-fold increase in blood creatinine level, end-stage renal disease (dialysis over 3 months). Kaplan-Meier survival analysis was used to explore the role of PFT in the progression of IgAN. Three clinicopathological models of multivariate Cox regression analysis were established to evaluate the association between PFT and renal prognosis in patients with IgAN.

Results

Compared to healthy subjects, patients with IgAN showed significantly higher PFT. After a median follow-up of 50 months, 75 of 473 patients (15.9%) with IgAN reached renal composite endpoints. Among those, 13 of 239 patients (5.4%) were in the low PFT group, and 62 of 234 patients (26.5%) were in the high PFT group (p < 0.001). The results of three Cox regression models (including demographics, pathological and clinical indicators, and PFT) demonstrated that a higher PFT was significantly associated with a higher risk of reaching renal composite endpoints in patients with IgAN.

Conclusion

This study indicated a positive relationship between PFT at renal biopsy and renal progression in patients with IgAN, suggesting that perirenal fat might act as a marker of poor prognosis in patients with IgAN.

The association between obesity indicators and metabolic risk factors in type-2 diabetic patients

Rationale and objectives

Obesity, accumulation of adipose tissue, is a global disease that can lead to cardiovascular and metabolic complications. The aim of this study was to investigate the relationship between obesity indicators and metabolic risk factors in type 2 diabetes mellitus (T2DM) patients.

Materials and methods

A total of 337 T2DM subjects were included in our study. The metabolic risk factors including diabetes duration, fast plasma glucose (FPG), height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), estimated average glucose (eAG), glycated hemoglobin (HbA1c), total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-c), low-density lipoprotein-cholesterol (LDL-c), triglyceride (TG), blood urea nitrogen (BUN), serum creatinine (Scr), free fatty acid (FFA), uric acid (UA), cystatin c (cysc), albumin (Alb), urinary albumin creatinine ratio (UACR) were recorded. The obesity indicators included body surface area (BSA), body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), para-perirenal fat thickness (PRFT), total abdominal fat (TAF), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT). The association between obesity indicators and metabolic risk factors was investigated by univariate and multivariate analysis.

Results

HDL-c was independently associated with WHR and PRFT (β = -0.126 vs. -0.214, both p < 0.05). TG and Scr were both independently associated with PRFT (β = 0.173 vs. 0.218, both p < 0.01, respectively). UA was independently associated with BSA (β = 0.172, p < 0.01) and PRFT (β = 0.151, p < 0.01). cysc, Alb and UACR were independently associated with WC (β = 0.274 vs. 0.204 vs. 0.182, all p < 0.01).

Conclusion

In T2DM patients, obesity indicators were significantly associated with metabolic risk factors.

Perirenal Fat Thickness is Associated with Metabolic Dysfunction-Associated Fatty Liver Disease in Type 2 Diabetes Mellitus

Objective

Recent advances in perirenal adipose tissue (PAT) highlighted that PAT might involve in the pathogenesis of chronic inflammatory and dysfunctional metabolic diseases. This study assessed the association between perirenal fat thickness (PrFT) and metabolic dysfunction-associated fatty liver disease (MALFD) in type 2 diabetes mellitus (T2DM).

Methods

This study comprised 867 eligible participants with T2DM. Trained reviewers collected anthropometric and biochemical measurements. The diagnosis of MAFLD was based on the latest international expert consensus statement. PrFT and fatty liver were evaluated by computed tomography. The visceral fat area (VFA) and subcutaneous fat area (SFA) were measured by bioelectrical impedance analysis. The non-alcoholic fatty liver disease fibrosis score (NFS) and fibrosis-4 (FIB-4) index were used to assess progressive liver fibrosis in MAFLD.

Results

Overall, the prevalence of MAFLD was 62.3% in T2DM. The PrFT in the MAFLD group was statistically increased than in the non-MAFLD group (P < 0.05). Correlation analysis showed that PrFT was significantly correlated with dysfunctional metabolic factors like body mass index, waist circumference, triglycerides, high-density lipoprotein cholesterol, systolic blood pressure, diastolic blood pressure, uric acid, and insulin resistance. Multiple regression analysis revealed that PrFT was positively correlated with NFS (β=0.146, P<0.001) and FIB-4 (β=0.082, P=0.025) in the MAFLD. In contrast, PrFT was negatively correlated with CT_L-S (β=-0.188, P<0.001). Furthermore, PrFT was also significantly associated with MAFLD independent of VFA and SFA, the OR (95% CI) was 1.279 (1.191-1.374). Meanwhile, PrFT also had a good identifying value for MAFLD as VFA. The area under the curve (95% CI) value of PrFT identifying MAFLD was 0.782 (0.751-0.812). The optimal cut-off value of PrFT was 12.6mm, with a sensitivity of 77.8% and specificity of 70.8%.

Conclusion

PrFT was independently associated with MAFLD, NFS, and FIB-4 and showed a similar identifying value for MAFLD as VFA, which suggested that PrFT can be used as an alternative index to VFA.