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The early identification of undernourished patients with CKD could help instating appropriate nutritional intervention before the full development of the threatening condition known as Protein Energy Wasting (PEW). Handgrip strength (HGS) and blood hemoglobin (Hb) concentration are two parameters considered representative of nutritional status but not included among the criteria for PEW diagnosis. In the present work we investigated whether they could help identifying CKD patients at risk of undernutrition. We performed a two-step cluster analysis to classify a cohort of 71 stage 3–5 CKD patients, none of which with PEW, according to their Hb concentration and dominant-hand HGS. Two clusters were finely separated using this method. When we compared the two groups for main body composition and nutritional variables by using t-test statistics or Mann–Whitney test, as appropriate, we found significant differences in PhA, ECW/TBW, ASMI, serum iron. Then we stratified our population by gender and performed cluster analysis as well. PhA, ECW/TBW were still significantly different in the two clusters both in M and in F, while serum iron concentration only in males and ASMI only in females. These results suggest that either in male than in female Hb concentration and HGS may distinguish two subgroups of CKD patients with different nutritional status and disease severity. Patient belonging to either of these cluster can be easily identified by using the HGS/Hb ratio which represents the HGS normalized per gr Hb. [Display omitted] • CKD patients often experience undernutrition to the extend of PEW or Sarcopenia • HGS and blood Hb are considered representative of nutritional status • 2-step cluster analysis using Hb and HGS to discriminate 3-5 CKD patients' nutritional status • Differences between the 2 clusters, stratified by gender, in PhA and ECW/TBW • Combined use of blood Hb and HGS discern patients with different nutritional status [ABSTRACT FROM AUTHOR]

Background and Aims: The most used indicator for the renal function is the glomerular filtration rate (GFR). Current used predictive GFR equations were calibrated on patients with chronic kidney disease. Thus, they are not very precise in healthy individuals. The estimation of skeletal muscle mass (SMM) allows the prediction of the daily urinary creatinine excretion (24hUCrE). This study proposes an equation for the estimation of GFR based on SMM (eGFRMuscle) and serum creatinine (SCr).Methods and Results: Four hundred sixty-six free-living men underwent a bioelectrical impedance analysis for the evaluation of SMM (kg), a blood withdrawal for the measurement of SCr (mg/dL), and a 24-h urinary collection for the assessment of 24hUCrE (g/24 h). The linear regression analysis between SMM and 24hUCrE and the measurement of SCr allowed developing a predictive equation of eGFRMuscle. The equation predicting eGFRMuscle (ml/min/1.73 m2) was SMM (kg) × 3.06/SCr (mg/dL). eGFRMuscle was statistically different from eGFR predicted by Cockroft-Gault, MDRD Study, and CKD-EPI equations (p = 0.017, p < 0.001, and p < 0.001, respectively). Pairwise comparison of standard error of the area under the ROC curve (AUC) of eGFRMuscle with all the other AUCs of ROC curves highlighted significant differences.Conclusions: The equation presented in this study results in age, weight, gender, and ethnicity independent because it arises directly from SMM estimation. Therefore, the proposed equation could allow evaluating the GFR also in healthy people with low, average, or high weight, and in older people, regardless of GFR and SCr levels. [ABSTRACT FROM AUTHOR]

Background and Aims: A progressive decrease in muscle mass until full-blown sarcopenia may occur in patients on peritoneal dialysis (PD) and worsen their life quality and expectancy. Here we investigate the prevalence of obesity and obesity-associated muscle wasting in PD patients.Patients and Methods: The study design was observational, cross sectional. Body composition was assessed with BIA and BIVA in 88 PD patients (53.4 ± 13.1 years; 67% male). Patients with obesity and/or with reduced muscle mass were identified using FMI and SM/BW cutoff values, respectively. Inflammatory status was assessed by measuring CRP and fibrinogen blood levels.Results: A total of 44.3% of the patients showed a reduced muscle mass (37.5% moderate and 6.8% severe). The prevalence of obesity was 6.1%, 81.8%, and 100% in patients with normal, moderately, and severely reduced muscle mass, respectively (p < 0.05). Of the total, 15.2% of the patients with normal muscle mass, 18.4% of those with moderately reduced muscle mass, and 66.7% of those with severely reduced muscle mass had diabetes. The prevalence of severe muscle mass loss was higher in those with diabetes than in those without diabetes (22.2% vs. 2.8%, p < 0.05). Patients with obesity-associated muscle wasting showed higher fibrinogen (613.9 ± 155.1 vs. 512.9 ± 159.5 mg/dL, p < 0.05) and CPR (1.4 ± 1.3 vs. 0.6 ± 0.8 mg/dL, p < 0.05) blood concentrations than those with normal body composition.Conclusion: Obesity and diabetes were strongly associated with muscle mass loss in our PD patients. It remains to be established whether prevention of obesity with nutritional interventions can halt the occurrence of muscle mass loss in patients on PD. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To describe bioelectrical impedance vector distribution in relation to BMI (body mass index; body weight/stature2) in a population of healthy children in order to detect possible changes in body composition status. Design: Observational study involving 464 healthy 8-year-old children. The subjects were divided into three groups based on their BMI: 218 normal weight (NW) children with BMI<18.4 for male and BMI<18.3 for female; 135 overweight (OW) children with BMI 18.4 to <21.6 for male and with BMI 18.3 to <21.6 for female; 111 obese (OB) children with BMI≥21.6. Skinfold thickness was measured at the triceps using a Holtain caliper. Bioelectrical impedance analysis (BIA) measurements were performed. Total body water (TBW), fat-mass (FM), fat-free mass (FFM), body cell mass (BCM) and extra-cellular water (ECW) were estimated using conventional BIA regression equations. The resistance–reactance graph (RXc graph) method was used for vector BIA using as reference population the set of 353 children with BMI 14.0–21.5kg/m2. Results: Mean vector displacement followed a definite pattern, with progressive vector shortening in groups with increasing BMI class, and along a fixed phase angle. This pattern indicates an increase in TBW due to an increase in soft tissue mass with an average, normal hydration. In NW children, vectors out of the right and upper half of the 75% tolerance ellipse indicating leanness, and vectors falling out of the right and lower half of the tolerance ellipse indicating undernutrition, show a significantly reduced value of BCM but no significant differences in FM or triceps skinfold thickness (TST), respectively, compared to vectors falling within the 75% tolerance ellipse. Conclusions: Although BMI is a reliable measure to grade overweight, it cannot differentiate whether weight change is due to variation of FM, FFM or water. In our study a different impedance vector pattern has been associated with normal weight to obesity, and we have established the trajectory followed by the impedance vector of standardized age, healthy children grouped by BMI. This BIVA may be useful for clinical purposes due to ability to detect changes in hydration or body composition in children. [Copyright &y& Elsevier]

Abstract: Background and aim: Significant changes in body composition that have important health related effects may occur in the elderly. In this study, we evaluated the bioelectrical characteristics in a large group of apparently healthy Caucasian men in the age range 50–80years, as a function of age and body mass index. Methods: We studied 315 men with ages ranging from 50 to 80years. They were divided into three groups according to body mass index (kg/m2): 18.5–24.9 normoweight (NW); 25.0–29.9 overweight (OW); ≥30 obese (OB), and they were classified in nine age subgroups: 50–59 (young-old, YO); 60–69 (old, O); 70–80 (oldest, OS). Fat-free mass, fat mass and body cell mass were investigated using conventional bioelectrical impedance analysis. Body composition was also assessed by bioelectrical impedance vector analysis and the RXc graph method. Results: Body cell mass decreased significantly with age particularly in subgroups of the OW and OB groups (p <0.05). Mean vector displacement followed a definite pattern, with downward migration of the ellipses in the OW and OB groups, after 70years of age. Conclusions: Ageing was associated with a pattern of vector bioelectrical impedance analysis indicating decreased soft tissue mass (fat-free mass and body cell mass), particularly in OW and OB-OS healthy men. We suggest 70years of age as a cut-off for significant quantitative and qualitative (tissue electrical properties) body composition modifications. This bioelectrical impedance vector analysis pattern associated with ageing and across the different body mass index categories, may be useful for clinical purposes and can be used in geriatric routine to accurately assess the body composition modifications occurring in the elderly. [Copyright &y& Elsevier]

Summary: Background and aim: Gastric banding induced considerable and rapid weight loss in morbid obesity. Nevertheless data on changes in body composition following gastric banding are scanty. In this study, we evaluated the 2-year changes in body composition in a small group of morbidly obese women treated by laparoscopic adjustable gastric banding (LAGB) associated with a well balanced low-calorie diet. Methods and results: We studied 20 premenopausal morbid obese women with BMI ranging from 35 to 57 (kg/m2) before, and 6, 12 and 24 months after laparoscopic adjustable gastric banding (LAGB). A well balanced 5.4 MJ/day hypocaloric diet was prescribed after surgery. Total body water (TBW), fat-free mass (FFM) and fat mass (FM) were investigated using conventional bioelectrical impedance analysis (BIA). Tissue hydration was also assessed by impedance vector analysis and the RXc graph method. The subjects showed a total weight loss of 28% of baseline body weight. In the first 6 months after surgery, patients lost 18.5±5.9 kg of body weight (17.6±6.2 kg of FM and 0.7±1.4 kg of FFM). From 6 to 12 months, a further 12.5±7.5 kg of body weight was lost (10.5±8.2 kg of FM and 2.2±3.8 kg of FFM). During the last 12 months, weight loss was 3.0±2.3 kg (1.9±3.7 kg of FM and 1.1±2.9 kg of FFM). The weight loss observed after LAGB was mainly due to a decrease in FM, whereas TBW, FFM and BCM were only slightly and non-significantly reduced. No changes in body hydration status were observed after surgery. Conclusions: LAGB associated with a well balanced low-calorie diet achieved a satisfactory 2-year weight loss, while sparing FFM and not causing body fluid alterations. [Copyright &y& Elsevier]