840 resultados para Percentage of Fat Mass


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Detailed analysis of body composition in children has helped to understand changes that occur in growth and disease. Bioelectrical impedance analysis (BIA) has gained popularity as a simple, non-invasive and inexpensive tool of body composition assessment. Being an indirect technique, prediction equations have to be used in the assessment of body composition. There are many prediction equations available in the literature for the assessment of body composition from BIA. This study aims to cross-validate some of those prediction equations to determine the suitability of their use on Australian children of white Caucasian and Sri Lankan origins. Height, weight and BIA were measured. Total body water was measured using the isotope dilution method (D2O). Fat-mass (FM) and %FM were estimated from BIA using ten prediction equations described in the literature. Five to 14.99-year-old healthy, 96 white Caucasians and 42 Sri Lankan children were studied. The equation of Schaefer et al was the most suitable prediction equation for this group with the lowest mean bias for %FM assessment in both Caucasian (–1.0±9.6%) and Sri Lankan (1.6±5.2%) children and the fat content of the individuals did not influence the predictions by this equation. Impedance index (height2/impedance) explained for 80% of TBW in white Caucasians and 93% in Sri Lankans and figures were similar for the prediction of FFM. We conclude that BIA can be used effectively in the assessment of body composition in children. However, for the assessment of body composition using BIA, either prediction equations should be derived to suit the local populations or existing equations should be cross-validated to determine their suitability before their application.

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Background It is evident from previous research that the role of dietary composition in relation to the development of childhood obesity remains inconclusive. Several studies investigating the relationship between body mass index (BMI), waist circumference (WC) and/or skin fold measurements with energy intake have suggested that the macronutrient composition of the diet (protein, carbohydrate, fat) may play an important contributing role to obesity in childhood as it does in adults. This study investigated the possible relationship between BMI and WC with energy intake and percentage energy intake from macronutrients in Australian children and adolescents. Methods Height, weight and WC measurements, along with 24 h food and drink records (FDR) intake data were collected from 2460 boys and girls aged 5-17 years living in the state of Queensland, Australia. Results Statistically significant, yet weak correlations between BMI z-score and WC with total energy intake were observed in grades 1, 5 and 10, with only 55% of subjects having a physiologically plausible 24 hr FDR. Using Pearson correlations to examine the relationship between BMI and WC with energy intake and percentage macronutrient intake, no significant correlations were observed between BMI z-score or WC and percentage energy intake from protein, carbohydrate or fat. One way ANOVAs showed that although those with a higher BMI z-score or WC consumed significantly more energy than their lean counterparts. Conclusion No evidence of an association between percentage macronutrient intake and BMI or WC was found. Evidently, more robust longitudinal studies are needed to elucidate the relationship linking obesity and dietary intake.

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Context: High bone mass (HBM), detected in 0.2% of dual-energy x-ray absorptiometry (DXA) scans, is characterized by raised body mass index, the basis for which is unclear. Objective: To investigate why body mass index is elevated in individuals with HBM, we characterized body composition and examined whether differences could be explained by bone phenotypes, eg, bone mass and/or bone turnover. Design, Setting, and Participants: We conducted a case-control study of 153 cases with unexplained HBM recruited from 4 UK centers by screening 219 088 DXA scans. Atotal of 138 first-degree relatives (of whom 51 had HBM) and 39 spouses were also recruited. Unaffected individuals served as controls. Main Outcome Measures: We measured fat mass, by DXA, and bone turnover markers. Results: Amongwomen, fat mass was inversely related to age in controls (P<.01), but not in HBM cases (P<.96) in whom mean fat mass was 8.9 [95% CI 4.7, 13.0] kg higher compared with controls (fully adjusted mean difference, P<.001). Increased fat mass in male HBM cases was less marked (gender interaction P = .03). Compared with controls, lean mass was also increased in female HBM cases (by 3.3 [1.2, 5.4] kg; P<.002); however, lean mass increases wereless marked than fat mass increases, resulting in 4.5% lower percentage lean mass in HBM cases (P<.001). Osteocalcin was also lower in female HBM cases compared with controls (by 2.8 [0.1, 5.5]μg/L; P = .04). Differences in fat mass were fully attenuated after hip bone mineral density (BMD) adjustment (P = .52) but unchanged after adjustment for bone turnover (P < .001), whereas the greater hip BMD in female HBM cases was minimally attenuated by fat mass adjustment (P<.001). Conclusions: HBM is characterized by a marked increase in fat mass in females, statistically explained by their greater BMD, but not by markers of bone turnover. Copyright © 2013 by The Endocrine Society.

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Objectives: To compare measures of fat-free mass (FFM) by three different bioelectrical impedance analysis (BIA) devices and to assess the agreement between three different equations validated in older adult and/or overweight populations. Design: Cross-sectional study. Setting: Orthopaedics ward of Brisbane public hospital, Australia. Participants: Twenty-two overweight, older Australians (72 yr ± 6.4, BMI 34 kg/m2 ± 5.5) with knee osteoarthritis. Measurements: Body composition was measured using three BIA devices: Tanita 300-GS (foot-to-foot), Impedimed DF50 (hand-to-foot) and Impedimed SFB7 (bioelectrical impedance spectroscopy (BIS)). Three equations for predicting FFM were selected based on their ability to be applied to an older adult and/ or overweight population. Impedance values were extracted from the hand-to-foot BIA device and included in the equations to estimate FFM. Results: The mean FFM measured by BIS (57.6 kg ± 9.1) differed significantly from those measured by foot-to-foot (54.6 kg ± 8.7) and hand-to-foot BIA (53.2 kg ± 10.5) (P < 0.001). The mean ± SD FFM predicted by three equations using raw data from hand-to-foot BIA were 54.7 kg ± 8.9, 54.7 kg ± 7.9 and 52.9 kg ± 11.05 respectively. These results did not differ from the FFM predicted by the hand-to-foot device (F = 2.66, P = 0.118). Conclusions: Our results suggest that foot-to-foot and hand-to-foot BIA may be used interchangeably in overweight older adults at the group level but due to the large limits of agreement may lead to unacceptable error in individuals. There was no difference between the three prediction equations however these results should be confirmed within a larger sample and against a reference standard.

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The aim of the study was to determine the reliability of body mass index based (BMI) cutoff values in diagnosing obesity among Sri Lankan children. Height, weight, waist circumference (WC) and hip circumference (HC) in 282 children were measured. Total body water was determined by deuterium dilution and fat mass (FM) derived using age and gender specific constants. A percentage FM of 30% for girls and 25% for boys were considered as cutoff levels for obesity. Two hundred and eighty two children (M/F: 158/124) were studied and 99 (80%) girls and 72 (45.5%) boys were obese based on % body fat. Eight (6.4%) girls and nine (5.7%) boys were obese based on International Obesity Task Force (IOTF) cutoff values. Percentage FM and WC centile charts were able to diagnose a significant proportion of children as true obese children. The FM and BMI were closely associated in both girls (r = 0.82, p < 0.001) and boys (r = 0.87, p < 0.001). Percentage FM and BMI had a very low but significant association; girls (r = 0.32, p < 0.001) and boys (r = 0.68, p < 0.001). FM had a significant association with WC and HC. BMI based cutoff values had a specificity of 100% but a very low sensitivity, varying between 8% and 23.6%. BMI is a poor indicator of the percentage fat and the commonly used cutoff values were not sensitive to detect cases of childhood obesity in Sri Lankan children.

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To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits.

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Objective: In chronic renal failure patients under hemodialysis (HD) treatment, the availability of simple, safe, and effective tools to assess body composition enables evaluation of body composition accurately, in spite of changes in body fluids that occur in dialysis therapy, thus contributing to planning and monitoring of nutritional treatment. We evaluated the performance of bioelectrical impedance analysis (BIA) and the skinfold thickness sum (SKF) to assess fat mass (FM) in chronic renal failure patients before (BHD) and after (AHD) HD, using air displacement plethysmography (ADP) as the standard method. Design: This single-center cross-sectional trial involved comparing the FM of 60 HD patients estimated BHD and AHD by BIA (multifrequential; 29 women, 31 men) and by SKF with those estimated by the reference method, ADP. Body fat-free mass (FFM) was also obtained by subtracting the total body fat from the individual total weight. Results: Mean estimated FM (kg [%]) observed by ADP BHD was 17.95 +/- 0.99 kg (30.11% +/- 1.30%), with a 95% confidence interval (CI) of 16.00 to 19.90 (27.56 to 32.66); mean estimated FM observed AHD was 17.92 +/- 1.11 kg (30.04% +/- 1.40%), with a 95% CI of 15.74 to 20.10 (27.28 to 32.79). Neither study period showed a difference in FM and FFM (for both kg and %) estimates by the SKF method when compared with ADP; however, the BIA underestimated the FM and overestimated the FFM (for both kg and %) when compared with ADP. Conclusion: The SKF, but not the BIA, method showed results similar to ADP and can be considered adequate for FM evaluation in HD patients. (C) 2012 by the National Kidney Foundation, Inc. All rights reserved.

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The aim of this study was to investigate the methods adopted to reduce body mass (BM) in competitive athletes from the grappling (judo, jujitsu) and striking (karate and tae kwon do) combat sports in the state of Minas Gerais, Brazil. An exploratory methodology was employed through descriptive research, using a standardized questionnaire with objective questions self-administered to 580 athletes (25.0 +/- 3.7 yr, 74.5 +/- 9.7 kg, and 16.4% +/- 5.1% body fat). Regardless of the sport, 60% of the athletes reported using a method of rapid weight loss (RWL) through increased energy expenditure. Strikers tend to begin reducing BM during adolescence. Furthermore, 50% of the sample used saunas and plastic clothing, and only 26.1% received advice from a nutritionist. The authors conclude that a high percentage of athletes uses RWL methods. In addition, a high percentage of athletes uses unapproved or prohibited methods such as diuretics, saunas, and plastic clothing. The age at which combat sport athletes reduce BM for the first time is also worrying, especially among strikers.

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Background: Few equations have been developed in veterinary medicine compared to human medicine to predict body composition. The present study was done to evaluate the influence of weight loss on biometry (BIO), bioimpedance analysis (BIA) and ultrasonography (US) in cats, proposing equations to estimate fat (FM) and lean (LM) body mass, as compared to dual energy x-ray absorptiometry (DXA) as the referenced method. For this were used 16 gonadectomized obese cats (8 males and 8 females) in a weight loss program. DXA, BIO, BIA and US were performed in the obese state (T0; obese animals), after 10% of weight loss (T1) and after 20% of weight loss (T2). Stepwise regression was used to analyze the relationship between the dependent variables (FM, LM) determined by DXA and the independent variables obtained by BIO, BIA and US. The better models chosen were evaluated by a simple regression analysis and means predicted vs. determined by DXA were compared to verify the accuracy of the equations. Results: The independent variables determined by BIO, BIA and US that best correlated (p < 0.005) with the dependent variables (FM and LM) were BW (body weight), TC (thoracic circumference), PC (pelvic circumference), R (resistance) and SFLT (subcutaneous fat layer thickness). Using Mallows'Cp statistics, p value and r(2), 19 equations were selected (12 for FM, 7 for LM); however, only 7 equations accurately predicted FM and one LM of cats. Conclusions: The equations with two variables are better to use because they are effective and will be an alternative method to estimate body composition in the clinical routine. For estimated lean mass the equations using body weight associated with biometrics measures can be proposed. For estimated fat mass the equations using body weight associated with bioimpedance analysis can be proposed.