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Updated: May 7, 2025

Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
The relationship between kidney function and cardiometabolic risk factors, anthropometric indices, and dietary
Samaneh Madani1, Seyed Jalil Masoumi2,3,4, Afsane Ahmadi5
1School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Background:
The prevalence of chronic kidney disease (CKD) is estimated to be about 13.4% worldwide. Studies have shown that CKD accounts for up to 2% of the health cost burden. Various factors, such as genetic polymorphisms, metabolic disorders, and unhealthy lifestyles, can contribute to the occurrence of CKD. Therefore, the present study aimed to investigate the relationship between renal function and cardiometabolic risk factors, anthropometric characteristics, and the dietary inflammatory index (DII) in an Iranian population.
Methods:
This study was conducted on 2472 male and female employees of Shiraz University of Medical Sciences (SUMS), selected through census between 2018 and 2019. In this cross-sectional study, renal function was evaluated using serum creatinine (sCr), blood urea nitrogen (BUN), and estimated glomerular filtration rate (eGFR). Biochemical indices including sCr, BUN, fasting blood sugar (FBS), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride (TG) were measured using standard laboratory methods. eGFR was calculated using the Modification of Diet in Renal Disease formula. Systolic (SBP) and diastolic (DBP) blood pressure as well as anthropometric indices such as height (Ht), weight (Wt), hip circumference (HC), waist circumference (WC), body mass index (BMI), conicity index (C-Index), visceral adiposity index (VAI), abdominal volume index (AVI), body adiposity index (BAI), and body shape index (ABSI) were measured and calculated using standard methods and formulas. Diet was evaluated through a 113-item food frequency questionnaire, and the DII was calculated according to its specific instructions. To predict the factors influencing renal function and to remove the impact of confounders, multivariable linear regression was employed using the backward elimination method.
Results:
There was a significant direct relationship between sCr and FBS, TG, HDL, DBP, Wt, and BAI as well as between BUN and age, TG, HDL, and BAI. In addition, there was a significant inverse relationship between eGFR and SBP, Wt, BAI, and VAI. In women compared to men, sCr and BUN values were significantly lower, while eGFR levels were significantly higher.
Conclusions:
It seems that age, gender, lipid profile, glycemic status, BP, BAI, VAI, and Wt can be considered factors associated with renal function.
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