Body adiposity index assess body fat with high accuracy in nondialyzed chronic kidney disease patients.
ABSTRACT OBJECTIVE: High body fat (BF) is an alarming condition that also affects nondialyzed chronic kidney disease (CKD) patients. Distinct methods are used to evaluate BF; however, in CKD population it remains unclear which one is more reliable showing high accuracy. Dual-energy X-ray absorptiometry (DXA), used as reference method to estimate adiposity, is expensive and time consuming to be applied in clinical settings. Recently, a new body adiposity index (BAI), that estimates BF from easily accessible measures, was validated in the general population. The aim of this study was to evaluate which simple and practical method, routinely used to estimate BF, shows the highest accuracy compared with DXA, in nondialyzed CKD patients. DESIGN AND METHODS: In this cross-sectional study BF was estimated by DXA, bioelectrical impedance analysis (BIA), anthropometry (ANTHRO), and BAI. Serum leptin levels were determined. RESULTS: Studied patients (n = 134) were 55% males, 54% overweight/obese, and 64.9 ± 12.5 years old, with estimated glomerular filtration rate (eGFR) = 29.0 ± 12.7 ml/min. The correlation coefficient was higher between DXA vs. ANTHRO (r = 0.76) and BAI (r = 0.61) than with BIA (r = 0.57), after adjusting for gender, age, and eGFR (P < 0.0001). Therefore, the Lin's concordance correlation coefficient and Bland-Altman plots were performed to measure the accuracy (C_b) between DXA with both ANTHRO and BAI. A higher accuracy (C_b = 0.82) and lower mean difference (-3.4%) was observed for BAI than for ANTHRO (C_b = 0.61; -8.4%). Leptin levels correlated (P < 0.0001) with DXA (r = 0.56) and BAI (r = 0.59). CONCLUSIONS: These findings suggest that BAI estimates BF with high accuracy in nondialyzed CKD patients and may be helpful in the treatment of this population with increased BF.
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ABSTRACT: Obesity, the epidemic of the 21st century, carries a markedly increased risk for comorbid complications, such as type 2 diabetes, cancer, hypertension, dyslipidemia, cardiovascular disease, and sleep apnea. In addition, obesity increases the risk for CKD and its progression to ESRD. Paradoxically, even morbid obesity associates with better outcomes in studies of ESRD patients on maintenance dialysis. Because the number of obese CKD and maintenance dialysis patients is projected to increase markedly in developed as well as low- and middle-income countries, obesity is a rapidly emerging problem for the international renal community. Targeting the obesity epidemic represents an unprecedented opportunity for health officials to ameliorate the current worldwide increase in CKD prevalence. Nephrologists need more information about assessing and managing obesity in the setting of CKD. Specifically, more precise estimation of regional fat distribution and the amount of muscle mass should be introduced into regular clinical practice to complement more commonly used practical markers, such as body mass index. Studies examining the effects of obesity on kidney disease progression and other clinical outcomes along with weight management strategies are much needed in this orphan area of research.Journal of the American Society of Nephrology 10/2013; · 8.99 Impact Factor
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ABSTRACT: Obesity is a problem of serious concern among chronic kidney disease (CKD) patients; it is a risk factor for progression to end-stage renal disease and its incidence and prevalence in dialysis patients exceeds those of the general population. Obesity, typically assessed with the simple metric of body mass index (BMI), is considered a mainstay for nutritional assessment in guidelines on nutrition in CKD. While regular BMI assessment in connection with the dialysis session is a simple and easy-to-use monitoring tool, such ease of access can lead to excess-of-use, as the value of this metric to health care professionals is overestimated. This review examines BMI as a clinical monitoring tool in CKD practice and offers a critical appraisal as to what a high or a low BMI may signify in this patient population. Topics discussed include the utility of BMI as a reflection of body size, body composition and body fat distribution, diagnostic versus prognostic performance, and consideration of temporal trends over single assessments.Seminars in Dialysis 08/2014; · 2.25 Impact Factor
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ABSTRACT: Background The aim of this study was to estimate obesity parameters: waist circumference (WC), waist-to-hip ratio (WHR), weight-to-height ratio (WHtR), visceral adiposity index (VAI), body adiposity index (BAI), and serum adipokines (leptin, adiponectin, resistin) and their associations with estimated glomerular filtration rate (eGFR), serum creatinine, and microalbuminuria (MA) in patients with early stages of CKD and in non-CKD obese patients. Material and Methods 67 non-diabetic obese (BMI ≥30 mg/kg2) out-clinic patients (25 males, 42 females), aged from 36.5 to 64 years were divided into 2 groups: Group A (n=15) - patients with early stages of CKD (eGFR between 30 and 60 ml/min/1.73 m2 or with MA >20 mg/l in morning urine sample independently from GFR) and Group B - patients without chronic CKD (n=52). Results In Group A compared to Group B, BAI and leptin were higher (42.2±7.1 vs. 37.5±7.0; p<0.05 and 51.8±26.7 ng/mL vs. 35.3±24.9 ng/mL; p<0.05; respectively) and negative correlations occurred between eGFR and BAI (r=-0.709; p=0.003), leptin (r=-0.68; p=0.005), and resistin (r=-0.528; p<0.05). In Group B, negative correlations occurred between creatinine and VAI (r=-0.332; p<0.05), BAI (r=-0.619; p<0.0001), leptin (r=-0.676; p<0.0001), and adiponectin (r=-0.423; p=0.002), and between eGFR and resistin (r=-0.276; p<0.05). Conclusions BAI may be a valuable obesity parameter as a predictor of early stages of CKD in patients with obesity. Leptin may be an important pathogenic factor in obese patients with early stages of CKD. Resistin is associated with eGFR in obese patients, independently of CKD.Medical science monitor: international medical journal of experimental and clinical research 01/2013; 19:1063-72. · 1.22 Impact Factor