POTENTIAL ROLE OF WAIST CIRCUMFERENCE AND RENAL SINUS FAT AS NON-INVASIVE PREDICTORS FOR EARLY DIAGNOSIS OF RENAL DYSFUNCTION IN OVERWEIGHT AND OBESE PATIENTS

Introduction:

Obesity, particularly central obesity, is a critical global health issue linked to an increased risk of chronic kidney disease (CKD). Waist circumference (WC) is an important indicator of central obesity, associated with ectopic fat deposition, including renal sinus fat. This study aimed to investigate the relationship between WC, renal sinus fat, and renal function (eGFR and urine albumin-creatinine ratio [ACR]) in overweight and obese individuals, assessing their potential as non-invasive predictors for early detection of renal dysfunction.  

Methods:

This cross-sectional observational study was conducted at IPGMER & SSKM Hospital, Kolkata, from January 2020 to July 2021. A total of 50 overweight and obese patients aged 18 to 60 years were included, with overweight and obesity defined as BMI ≥23 kg/m² and ≥25 kg/m², respectively, according to Indian criteria. Waist circumference was measured to the nearest 0.1 cm at the mid-point of the costal margin and the highest point of the iliac crest after normal expiration, with WC >90 cm in men and >80 cm in women as the cut-off for central obesity. Renal sinus fat was quantified using a single MDCT slice within the right kidney, identified by MDCT pixel density in Hounsfield Units (HU) centered on -120 HU. Normal renal sinus fat cut-offs were defined as 0.445 cm² in women and 0.71 cm² in men. Renal function was assessed through eGFR (calculated using the CKD-EPI equation) and urine ACR. Proteinuria is graded based on the urine albumin-to-creatinine ratio (ACR) as follows: Normo-albuminuria: Urine ACR < 30 μg/mg ,Microalbuminuria: Urine ACR between 30-300 μg/mg ,Macroalbuminuria: Urine ACR > 300 μg/mg.

The study population had a mean age of 38.88±9.226 years, with a female-to-male ratio of 1.38:1 (Female 29,Male 21). The mean BMI was 32.21±3.80 kg/m², mean renal sinus fat content was 1.19±0.29 cm², mean eGFR was 73.86±24 ml/min/1.73m², mean WC was 93.1±7.4 cm, and mean urine ACR was 222±183 μg/mg. Hypertension was present in some patients, but none were on ACE inhibitors or ARBs, and blood pressure was well-controlled during the study period. Exclusion criteria included pregnancy, secondary causes of obesity (e.g., Cushing’s syndrome), CKD unrelated to obesity (e.g., SLE, drug-induced glomerulonephritis, vasculitis), and abnormal renal ultrasound findings. Statistical analysis was performed using SPSSv22, with significance set at p<0.05.

Results:

Waist Circumference and Renal Sinus Fat: A significant positive correlation was found between WC and renal sinus fat (r = 0.87, p < 0.001), indicating that higher WC is associated with increased ectopic fat in the renal sinus.

Renal Sinus Fat and Renal Function: Renal sinus fat was inversely correlated with eGFR (r = -0.50, p < 0.01) and positively correlated with urine ACR (r = 0.52, p < 0.05), suggesting that higher ectopic fat in the renal sinus is linked to decreased renal function and increased albuminuria.

Waist Circumference and Renal Function: Higher WC was significantly associated with lower eGFR and higher urine ACR. Patients with WC >100 cm had a mean eGFR of 62 ml/min/1.73m², compared to 89 ml/min/1.73m² in those with WC <90 cm (p < 0.05). Additionally, 84% of patients with WC >100 cm exhibited albuminuria compared to 16% in those with WC <90 cm (p < 0.001).

Conclusions:

This study highlights the significant relationships between waist circumference, renal sinus fat, and renal function in overweight and obese patients. WC and renal sinus fat may serve as early, non-invasive predictors for CKD, particularly in younger populations. Monitoring central obesity through WC measurements could facilitate early diagnosis and intervention, potentially preventing the progression of CKD by identifying at-risk individuals early.

I have no potential conflict of interest to disclose.

I used generative AI and AI-assisted technologies in the writing process.
During the preparation of this work, the authors used OpenAI's ChatGPT to assist in drafting and refining the language and structure of the abstract, and SPSS software for statistical analysis. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.