29 resultados para Fat-free mass

em University of Queensland eSpace - Australia


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Purpose: To determine whether a significant relationship exists between fat mass (FM) development and physical activity (PA) and/or sugar-sweetened drink (SD) consumption in healthy boys and girls aged 8-19 yr. Methods: A total of 105 males and 103 females were assessed during childhood and adolescence for a maximum of 7 yr and a median of 5 yr. Height was measured biannually. Fat-free mass (FFM) and FM were assessed annually by dual x-ray absorptiometry (DXA). PA was evaluated two to three times annually using the PAQ-C/A. Energy intake and SD were assessed using a 24-h dietary intake questionnaire also completed two to three times per year. Years from peak height velocity were used as a biological maturity age indicator. Multilevel random effects models were used to test the relationship. Results: When controlling for maturation, FFM, and energy intake adjusted for SD, PA level was negatively related to FM development in males (P < 0.05) but not in females (P > 0.05). In contrast, there was no relationship between SD and FM development of males or females (P > 0.05). There was also no interaction effect between SD and PA (P > 0.05) with FM development. Conclusion: This finding tends support to the idea that increasing PA in male youths aids in the control of FM development. Models employed showed no relationship between SD and FM in either gender.

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Background: Body cell mass (BCM) may be estimated in clinical practice to assess functional nutritional status, eg, in patients with anorexia nervosa. Interpretation of the data, especially in younger patients who are still growing, requires appropriate adjustment for size. Previous investigations of this general issue have addressed chemical rather than functional components of body composition and have not considered patients at the extremes of nutritional status, in whom the ability to make longitudinal comparisons is of particular importance. Objective: Our objective was to determine the power by which height should be raised to adjust BCM for height in women of differing nutritional status. Design: BCM was estimated by K-40 counting in 58 healthy women, 33 healthy female adolescents, and 75 female adolescents with anorexia nervosa. The relation between BCM and height was explored in each group by using log-log regression analysis. Results: The powers by which height should be raised to adjust BCM,A,ere 1.73. 1.73, and 2.07 in the women, healthy female adolescents, and anorexic female adolescents, respectively. A simplified version of the index, BCM/height(2), was appropriate for all 3 categories and was negligibly correlated with height. Conclusions: In normal-weight women, the relation between height and BCM is consistent with that reported previously between height and fat-free mass. Although the consistency of the relation between BCM and fat-free mass decreases with increasing weight loss, the relation between height and BCM is not significantly different between normal-weight and underweight women. The index BCM/height(2) is easy to calculate and applicable to both healthy and underweight women. This information may be helpful in interpreting body-composition data in clinical practice.

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The prevalence of obesity in the western world is dramatically rising, with many of these individuals requiring therapeutic intervention for a variety of disease states. Despite the growing prevalence of obesity there is a paucity of information describing how doses should be adjusted, or indeed whether they need to be adjusted, in the clinical setting. This review is aimed at identifying which descriptors of body size provide the most information about the relationship between dose and concentration in the obese. The size descriptors, weight, lean body weight, ideal body weight, body surface area, body mass index, fat-free mass, percent ideal body weight, adjusted body weight and predicted normal body weight were considered as potential size descriptors. We conducted an extensive review of the literature to identify studies that have assessed the quantitative relationship between the parameters clearance (CL) and volume of distribution (V) and these descriptors of body size. Surprisingly few studies have addressed the relationship between obesity and CL or V in a quantitative manner. Despite the lack of studies there were consistent findings: (i) most studies found total body weight to be the best descriptor of V. A further analysis of the studies that have addressed V found that total body weight or another descriptor that incorporated fat mass was the preferred descriptor for drugs that have high lipophilicity; (ii) in contrast, CL was best described by lean body mass and no apparent relationship between lipophilicity or clearance mechanism and preference for body size descriptor was found. In conclusion, no single descriptor described the influence of body size on both CL and V equally well. For drugs that are dosed chronically, and therefore CL is of primary concern, dosing for obese patients should not be based on their total weight. If a weight-based dose individualization is required then we would suggest that chronic drug dosing in the obese subject should be based on lean body weight, at least until a more robust size descriptor becomes available.

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Objectives: Obesity is a disease with excess body fat where health is adversely affected. Therefore it is prudent to make the diagnosis of obesity based on the measure of percentage body fat. Body composition of a group of Australian children of Sri Lankan origin were studied to evaluate the applicability of some bedside techniques in the measurement of percentage body fat. Methods: Height (H) and weight (W) was measured and BMI (W/H-2) calculated. Bioelectrical impedance analysis (BIA) was measured using tetra polar technique with an 800 mu A current of 50 Hz frequency. Total body water was used as a reference method and was determined by deuterium dilution and fat free mass and hence fat mass (FM) derived using age and gender specific constants. Percentage FM was estimated using four predictive equations, which used BIA and anthropometric measurements. Results: Twenty-seven boys and 15 girls were studied with mean ages being 9.1 years and 9.6 years, respectively. Girls had a significantly higher FM compared to boys. The mean percentage FM of boys (22.9 +/- 8.7%) was higher than the limit for obesity and for girls (29.0 +/- 6.0%) it was just below the cut-off. BMI was comparatively low. All but BIA equation in boys under estimated the percentage FM. The impedance index and weight showed a strong association with total body water (r(2)= 0.96, P < 0.001). Except for BIA in boys all other techniques under diagnosed obesity. Conclusions: Sri Lankan Australian children appear to have a high percentage of fat with a low BMI and some of the available indirect techniques are not helpful in the assessment of body composition. Therefore ethnic and/or population specific predictive equations have to be developed for the assessment of body composition, especially in a multicultural society using indirect methods such as BIA or anthropometry.

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Background: Body mass index ( BMI) is used to diagnose obesity. However, its ability to predict the percentage fat mass (% FM) reliably is doubtful. Therefore validity of BMI as a diagnostic tool of obesity is questioned. Aim: This study is focused on determining the ability of BMI- based cut- off values in diagnosing obesity among Australian children of white Caucasian and Sri Lankan origin. Subjects and methods: Height and weight was measured and BMI ( W/H-2) calculated. Total body water was determined by deuterium dilution technique and fat free mass and hence fat mass derived using age- and gender- specific constants. A % FM of 30% for girls and 20% for boys was considered as the criterion cut- off level for obesity. BMI- based obesity cut- offs described by the International Obesity Task Force ( IOTF), CDC/ NCHS centile charts and BMI- Z were validated against the criterion method. Results: There were 96 white Caucasian and 42 Sri Lankan children. Of the white Caucasians, 19 ( 36%) girls and 29 ( 66%) boys, and of the Sri Lankans 7 ( 46%) girls and 16 ( 63%) boys, were obese based on % FM. The FM and BMI were closely associated in both Caucasians ( r = 0.81, P < 0.001) and Sri Lankans ( r = 0.92, P< 0.001). Percentage FM and BMI also had a lower but significant association. Obesity cut- off values recommended by IOTF failed to detect a single case of obesity in either group. However, NCHS and BMI- Z cut- offs detected cases of obesity with low sensitivity. Conclusions: BMI is a poor indicator of percentage fat and the commonly used cut- off values were not sensitive enough to detect cases of childhood obesity in this study. In order to improve the diagnosis of obesity, either BMI cut- off values should be revised to increase the sensitivity or the possibility of using other indirect methods of estimating the % FM should be explored.

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Background: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA - a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18-82 years), bodyweight (40.7-216.5kg) and BMI values (17.1-69.9 kg/m(2)). Patients in population B had BMI values of 18.7-38.4 kg/m(2). A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r(2) = 0.78, mean error [ME] = 2.30 x 10(-3), root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r(2) = 0.93, ME = -0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r(2) = 0.85, ME -0.04, RMSE = 4.39 [approximately 7% of mean]). Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.

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Background: Changes in body composition are commonly reported in pediatric survivors of acute lymphoblastic leukemia (ALL). However, the effect of ALL and of its treatment on body composition in children in remission from ALL has not been fully examined with the use of a reference method. Objectives: We aimed to determine the body composition and composition of fat-free mass (FFM) in children in remission from ALL. We also aimed to compare the effects that prednisolone and dexamethasone had on the body composition of an ALL survivor population. Design: This cross-sectional study measured height, weight, body volume, total body water, and bone mineral content in 24 children in remission from ALL and 24 age-matched, healthy control subjects. Body composition and FFM composition were evaluated by using the 4-component model. Results: The mean body mass index and fat mass index were significantly (P = 0.05 for both) higher in the ALL survivors than in age-matched control subjects. The composition of the FFM in the 2 treatment groups was not observed to differ significantly. Examination of the composition of FFM made it evident that children in remission from ALL had both significantly greater hydration (P = 0.001) and lower density (P = 0.0001) of FFM than did the control children. Conclusions: Children in remission from ALL may develop excess body fat. To measure body composition accurately in an ALL population, the high hydration and low density of FFM in this population should be taken into consideration.

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Objective: The objective of this study was to investigate changes in body weight, BMI, body composition, and fat distribution among freshman women during their 1st year of college. Research Methods and Procedures: Freshman women during the 2004 to 2005 academic year were recruited to participate. The initial baseline visit occurred within the first 6 weeks of the fall 2004 semester, with the follow-up visit occurring during the last 6 weeks of the spring 2005 semester. At each visit, height, weight, BMI, waist and hip circumferences, and body composition (by DXA) were obtained. Results: One hundred thirty-seven participants completed both the fall and spring visits. Significant (p < 0.0001) increases between the fall and spring visits were observed for body weight (58.6 vs. 59.6 kg), BMI (21.9 vs. 22.3), percentage body fat (28.9 vs. 29.7), total fat mass (16.9 vs. 17.7 kg), fat-free mass (38.1 vs. 38.4 kg), waist circumference (69.4 vs. 70.3 cm), and hip circumference (97.4 vs. 98.6 cm), with no significant difference observed in the waist-to-hip ratio (0.71 vs. 0.71; p = 0.78). Discussion: Although statistically significant, changes in body weight, body composition, and fat mass were modest for women during their freshman year of college. These results do not support the purported freshman 15 weight gain publicized in the popular media.

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Our objective was to assess the contribution of lean body mass (LBM) and fat body mass (FBM) to areal bone mineral density (aBMD) in women during the years surrounding menopause. We used a 12-year observational design. Participants included 75 Caucasian women who were premenopausal, 53 of whom were available for follow-up. There were two measurement periods: baseline and 12-year follow-up. At both measurement periods, bone mineral content and aBMD of the proximal femur, posterior-anterior lumbar spine, and total body was assessed using dual-energy X-ray absorptiometry (DXA). LBM and FBM were derived from the total-body scans. General health, including current menopausal status, hormone replace therapy use, medication use, and physical activity, was assessed by questionnaires. At the end of the study, 44% of the women were postmenopausal. After controlling for baseline aBMD, current menopausal status, and current hormone replacement therapy, we found that change in LBM was independently associated with change in aBMD of the proximal femur (P = 0.001). The cross-sectional analyses also indicated that LBM was a significant determinant of aBMD of all three DXA-scanned sites at both baseline and follow-up. These novel longitudinal data highlight the important contribution of LBM to the maintenance of proximal femur bone mass at a key time in women's life span, the years surrounding menopause.

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Objective: To develop a standard weight descriptor that can be used for estimation of patient size for obese patients. Patients and methods: Data were available from 3849 patients: 2839 from oncology patients (index data set) and 1010 from general medical patients (validation data set). The patients had a wide range of age (16-100 years), weight (25-165kg) and body mass index (BMI) [12-52 kg/m(2)] in both data sets. From the normal-weight patients in the oncology data set, an equation for male and female patients was developed to predict their normal weight as the sum of the lean body mass and normal fat body mass. The equations were evaluated by predicting the weight of patients in the general medical data set who had a normal BMI (30 kg/m(2)).