893 resultados para Percentage of Fat Mass
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Introduction: Osteoarthritis, osteoarthrosis or degenerative joint disease characterized by progressive loss of articular cartilage, pain, changes in subchondral bone, osteophyte formation and proliferation. Age, bone mineral density, joint instability, excess weight among others, are risk factors. Methods: To check the influence os physical exercise in patients with the disease were evaluated 39 patients over 50 years, both genders, with clinical and / or radiographic osteoarthritis were divided into experimental group (EG) and control group (CG). EG performed regular physical activity (aerobics) three times a week for four months, while CG was submitted to physical therapy painkiller in the same period. We analyzed demographics, BMI, basal metabolic rate and percentage of fat mass. Results: The results showed that regular physical activity reduced the body fat, but because of their characteristics and low-impact aerobics was not observed consistent benefits in muscle component. However, compared with the CG demonstrated a positive impact on other parameters of body composition.
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Introducción: el triatlón es un deporte de resistencia e individual que está formado por tres disciplinas diferentes: natación, ciclismo y carrera a pie. El objetivo del estudio es describir las características antropométricas en triatletas varones universitarios, además de analizar y describir la composición corporal y el somatotipo de dichos triatletas. Metodología: estudio observacional y descriptivo de las características antropométricas, la composición corporal y el somatotipo de 39 triatletas varones universitarios entre 24 ± 4,5 años, participantes en el campeonato de España universitario de triatlón, modalidad sprint (Alicante 2010), procedentes de diferentes universidades españolas. Según la técnicas de medición antropométrica adoptadas por la International Society for the Advancement of Kinanthropometry (ISAK) y el Grupo Español de Cineantropometría (GREC) por un evaluador acreditado ISAK de nivel II. Resultados: nos encontramos con deportistas de talla baja, en los que destacan valores inferiores a lo normal en los pliegues cutáneos subescapular, supraespinal, tricipital y bicipital, un porcentaje de masa muscular (45,27 ± 3,29%), de masa grasa (10,22 ± 2,92%) y de masa ósea (16,65 ± 1,34%) y un somatotipo en el que predomina la mesomorfia. Discusión: los triatletas y corredores presentan más baja talla que los ciclistas y nadadores. Los triatletas y ciclistas muestran un peso similar, siendo menor que el de los nadadores de fondo y mayor que el de los corredores de 10 km. Los pliegues cutáneos cresta ilíaca, abdominal y muslo frontal de los ciclistas son inferiores al de los triatletas. El porcentaje de masa grasa de triatletas corredores y nadadores son similares; sin embargo, el de la masa muscular de los triatletas suele ser inferior al de los ciclistas pero similar a las demás modalidades. El somatotipo del triatleta se asemeja al del ciclista (mesomorfo). El del corredor es mesomorfo-ectomorfo y el del nadador puede oscilar de mesomorfo a ectomorfo.
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Background Body mass index (BMI) is frequently related to percentage body fat. Nevertheless, the relationship between BMI and fat mass/height(2) (FM/H-2), theoretically, should be more appropriate. Aim: This study seeks to evaluate the relationship between BMI and both percentage body fat and FM/H-2 in a group of Chinese Australian females. Subjects and methods: Forty subjects took part in the study and all were Chinese females resident in Brisbane, Australia. Body mass index was calculated from height and weight. Percentage body fat and fat mass were calculated from measurements of total body water. Results: The use of BMI to predict FM/H-2 accounted for double the variance of that found when BMI was used to predict percentage body fat. Conclusions: As a consequence, it is possible that the use of BMI to predict FM/H-2 and not percentage body fat in the first instance may prove to be more useful in a number of adult populations. Nevertheless, with a relatively small sample size it is difficult, if not impossible, to test the developed equations on a validation group and further investigation into the findings described in this paper needs to be undertaken.
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We evaluated the accuracy of skinfold thicknesses, BMI and waist circumference for the prediction of percentage body fat (PBF) in a representative sample of 372 Swiss children aged 6-13 years. PBF was measured using dual-energy X-ray absorptiometry. On the basis of a preliminary bootstrap selection of predictors, seven regression models were evaluated. All models included sex, age and pubertal stage plus one of the following predictors: (1) log-transformed triceps skinfold (logTSF); (2) logTSF and waist circumference; (3) log-transformed sum of triceps and subscapular skinfolds (logSF2); (4) log-transformed sum of triceps, biceps, subscapular and supra-iliac skinfolds (logSF4); (5) BMI; (6) waist circumference; (7) BMI and waist circumference. The adjusted determination coefficient (R² adj) and the root mean squared error (RMSE; kg) were calculated for each model. LogSF4 (R² adj 0.85; RMSE 2.35) and logSF2 (R² adj 0.82; RMSE 2.54) were similarly accurate at predicting PBF and superior to logTSF (R² adj 0.75; RMSE 3.02), logTSF combined with waist circumference (R² adj 0.78; RMSE 2.85), BMI (R² adj 0.62; RMSE 3.73), waist circumference (R² adj 0.58; RMSE 3.89), and BMI combined with waist circumference (R² adj 0.63; RMSE 3.66) (P < 0.001 for all values of R² adj). The finding that logSF4 was only modestly superior to logSF2 and that logTSF was better than BMI and waist circumference at predicting PBF has important implications for paediatric epidemiological studies aimed at disentangling the effect of body fat on health outcomes.
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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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OBJECTIVE: Low and high body mass index (BMI) values have been shown to increase health risks and mortality and result in variations in fat-free mass (FFM) and body fat mass (BF). Currently, there are no published ranges for a fat-free mass index (FFMI; kg/m(2)), a body fat mass index (BFMI; kg/m(2)), and percentage of body fat (%BF). The purpose of this population study was to determine predicted FFMI and BFMI values in subjects with low, normal, overweight, and obese BMI. METHODS: FFM and BF were determined in 2986 healthy white men and 2649 white women, age 15 to 98 y, by a previously validated 50-kHz bioelectrical impedance analysis equation. FFMI, BFMI, and %BF were calculated. RESULTS: FFMI values were 16.7 to 19.8 kg/m(2) for men and 14.6 to 16.8 kg/m(2) for women within the normal BMI ranges. BFMI values were 1.8 to 5.2 kg/m(2) for men and 3.9 to 8.2 kg/m(2) for women within the normal BMI ranges. BFMI values were 8.3 and 11.8 kg/m(2) in men and women, respectively, for obese BMI (>30 kg/m(2)). Normal ranges for %BF were 13.4 to 21.7 and 24.6 to 33.2 for men and women, respectively. CONCLUSION: BMI alone cannot provide information about the respective contribution of FFM or fat mass to body weight. This study presents FFMI and BFMI values that correspond to low, normal, overweight, and obese BMIs. FFMI and BFMI provide information about body compartments, regardless of height.
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Objective: Fat-free mass (FFM) reduction and the tendency for a reduction in surrounding fatty issue and increase in the middle are a natural consequence of growing old and should be studied in order to gain a better understanding of the aging process. This study set out to find the FFM differences between active elderly women in two age groups (60-69 and 70-80 years) and to determine which of the anthropometric measurements, body weight (BW), abdominal circumference (AC), or body mass index (BMI) are the best predictors of FFM variation within the group. Methods: Eighty-one (n = 81) active elderly women of the Third Age willingly signed up to participate in the research during the activities at the University of the Third Age (UTA) in Brazil. The research was approved by the Research Ethics Committee of the Faculty of Medical Sciences of the State University of Campinas (UNICAMP). Body weight (BW), height (H) and the BMI were measured according to the international standards. The AC was measured in centimetres at the H of the navel and body composition was ascertained using bioimpedance analysis. The SAS program was used to perform the statistical analysis of independent samples and parametric data. Results: The results showed FFM values with significant differences between the two groups, with the lowest values occurring among the women who were over 70 years of age. In the analysis, the Pearson`s Correlation Coefficient for each measured independent variable was ascertained, with the BW measurement showing the highest ratio (0.900). Conclusions: The BW measurement was regarded as reliable, low-cost and easy to use for monitoring FFM in elderly women who engage in physical activities. (C) 2011 Elsevier Ireland Ltd. All rights reserved.
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GH-binding protein (GHBP) corresponds to the extracellular domain of the GH receptor (GHR) and has been shown to be closely related to body fat. This study aimed to examine the inter-relationship between GHBP, leptin and body fat, and to test the hypothesis that GHBP is modified by GH replacement in GH-deficient adults and predicts IGF-I response. Twenty adults, mean age 47 years (range 20-69) with proven GH deficiency were randomly allocated to either GH (up to 0.25 U/kg/week in daily doses) or placebo for 3 months before cross-over to the opposite treatment. Plasma GHBP and leptin were measured at baseline and 2, 4, 8 and 12 weeks after each treatment. Whole body composition was measured at baseline by dual-energy X-ray absorptiometry (DEXA). There was a strong correlation between baseline leptin and GHBP (r = 0.88, P < 0.0001) and between baseline GHBP and percentage body fat, (r = 0.83, P < 0.0001). Mean GHBP levels were higher on GH compared with placebo, 1.53 +/- 0.28 vs 1.41 +/- 0.25 nM, P = 0.049. There was no correlation between baseline IGF-I and GHBP (r = -0.049, P = 0.84), and GHBP did not predict IGF-I response to GH replacement. The close inter-relationship between GHBP, leptin and body fat suggests a possible role for GHBP in the regulation of body composition. GHBP is increased by GH replacement in GH-deficient adults, but does not predict biochemical response to GH replacement. (C) 1999 Churchill Livingstone.
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The aim of this study was to evaluate risk factors for low bone mineral density (BMD) and vertebral fractures, in juvenile systemic lupus (JSLE). Thirty-one consecutive patients with JSLE were compared with 31 gender- and age-matched healthy controls. BNID and body composition from all participants were measured using dual-energy X-ray absorptiometry. Vertebral fractures were defined as a reduction of >= 20% of the vertebral height for all patients. Lumbar spine and total femur BMD was significantly decreased in patients compared with controls (P = 0.021 and P = 0.023, respectively). A high frequency of vertebral fractures (22.58%) was found in patients with JSLE. Analysis of body composition revealed lower lean mass (P = 0.033) and higher fat mass percentage (P = 0.003) in patients than in controls. Interestingly, multiple linear regression using BMD as a dependent variable showed a significant association with lean mass in lumbar spine (R(2) = 0.262; P = 0.004) and total femur (R(2) = 0.419, P = 0.0001), whereas no association was observed with menarche age, SLE Disease Activity Index, Systemic Lupus International Collaborating Clinics/American College of Rheumatology, and glucocorticoid. This study indicates that low BMD and vertebral fractures are common in JSLE, and the former is associated with low lean mass, suggesting that muscle rehabilitation may be an additional target for bone therapeutic approach.
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Objective: Several limitations of published bioelectrical impedance analysis (BIA) equations have been reported. The aims were to develop in a multiethnic, elderly population a new prediction equation and cross-validate it along with some published BIA equations for estimating fat-free mass using deuterium oxide dilution as the reference method. Design and setting: Cross-sectional study of elderly from five developing countries. Methods: Total body water (TBW) measured by deuterium dilution was used to determine fat-free mass (FFM) in 383 subjects. Anthropometric and BIA variables were also measured. Only 377 subjects were included for the analysis, randomly divided into development and cross-validation groups after stratified by gender. Stepwise model selection was used to generate the model and Bland Altman analysis was used to test agreement. Results: FFM = 2.95 - 3.89 (Gender) + 0.514 (Ht(2)/Z) + 0.090 (Waist) + 0.156 (Body weight). The model fit parameters were an R(2), total F-Ratio, and the SEE of 0.88, 314.3, and 3.3, respectively. None of the published BIA equations met the criteria for agreement. The new BIA equation underestimated FFM by just 0.3 kg in the cross-validation sample. The mean of the difference between FFM by TBW and the new BIA equation were not significantly different; 95% of the differences were between the limits of agreement of -6.3 to 6.9 kg of FFM. There was no significant association between the mean of the differences and their averages (r = 0.008 and p = 0.2). Conclusions: This new BIA equation offers a valid option compared with some of the current published BIA equations to estimate FFM in elderly subjects from five developing countries.
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IMPORTANCE There is a high prevalence of obesity in psychiatric patients, possibly leading to metabolic complications and reducing life expectancy. The CREB-regulated transcription coactivator 1 (CRTC1) gene is involved in energy balance and obesity in animal models, but its role in human obesity is unknown. OBJECTIVE To determine whether polymorphisms within the CRTC1 gene are associated with adiposity markers in psychiatric patients and the general population. DESIGN, SETTING, AND PARTICIPANTS Retrospective and prospective data analysis and population-based samples at Lausanne and Geneva university hospitals in Switzerland and a private clinic in Lausanne, Switzerland. The effect of 3 CRTC1 polymorphisms on body mass index (BMI) and/or fat mass was investigated in a discovery cohort of psychiatric outpatients taking weight gain-inducing psychotropic drugs (sample 1, n = 152). The CRTC1 variant that was significantly associated with BMI and survived Bonferroni corrections for multiple comparison was then replicated in 2 independent psychiatric samples (sample 2, n = 174 and sample 3, n = 118) and 2 white population-based samples (sample 4, n = 5338 and sample 5, n = 123 865). INTERVENTION Noninterventional studies. MAIN OUTCOME AND MEASURE Difference in BMI and/or fat mass between CRTC1 genotype groups. RESULTS Among the CRTC1 variants tested in the first psychiatric sample, only rs3746266A>G was associated with BMI (Padjusted = .003). In the 3 psychiatric samples, carriers of the rs3746266 G allele had a lower BMI than noncarriers (AA genotype) (sample 1, P = .001; sample 2, P = .05; and sample 3, P = .0003). In the combined analysis, excluding patients taking other weight gain-inducing drugs, G allele carriers (n = 98) had a 1.81-kg/m2 lower BMI than noncarriers (n = 226; P < .0001). The strongest association was observed in women younger than 45 years, with a 3.87-kg/m2 lower BMI in G allele carriers (n = 25) compared with noncarriers (n = 48; P < .0001), explaining 9% of BMI variance. In the population-based samples, the T allele of rs6510997C>T (a proxy of the rs3746266 G allele; r2 = 0.7) was associated with lower BMI (sample 5, n = 123 865; P = .01) and fat mass (sample 4, n = 5338; P = .03). The strongest association with fat mass was observed in premenopausal women (n = 1192; P = .02). CONCLUSIONS AND RELEVANCE These findings suggest that CRTC1 contributes to the genetics of human obesity in psychiatric patients and the general population. Identification of high-risk subjects could contribute to a better individualization of the pharmacological treatment in psychiatry.
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IMPORTANCE: Depression and obesity are 2 prevalent disorders that have been repeatedly shown to be associated. However, the mechanisms and temporal sequence underlying this association are poorly understood. OBJECTIVE: To determine whether the subtypes of major depressive disorder (MDD; melancholic, atypical, combined, or unspecified) are predictive of adiposity in terms of the incidence of obesity and changes in body mass index (calculated as weight in kilograms divided by height in meters squared), waist circumference, and fat mass. DESIGN, SETTING, AND PARTICIPANTS: This prospective population-based cohort study, CoLaus (Cohorte Lausannoise)/PsyCoLaus (Psychiatric arm of the CoLaus Study), with 5.5 years of follow-up included 3054 randomly selected residents (mean age, 49.7 years; 53.1% were women) of the city of Lausanne, Switzerland (according to the civil register), aged 35 to 66 years in 2003, who accepted the physical and psychiatric baseline and physical follow-up evaluations. EXPOSURES: Depression subtypes according to the DSM-IV. Diagnostic criteria at baseline and follow-up, as well as sociodemographic characteristics, lifestyle (alcohol and tobacco use and physical activity), and medication, were elicited using the semistructured Diagnostic Interview for Genetic Studies. MAIN OUTCOMES AND MEASURES: Changes in body mass index, waist circumference, and fat mass during the follow-up period, in percentage of the baseline value, and the incidence of obesity during the follow-up period among nonobese participants at baseline. Weight, height, waist circumference, and body fat (bioimpedance) were measured at baseline and follow-up by trained field interviewers. RESULTS: Only participants with the atypical subtype of MDD at baseline revealed a higher increase in adiposity during follow-up than participants without MDD. The associations between this MDD subtype and body mass index (β = 3.19; 95% CI, 1.50-4.88), incidence of obesity (odds ratio, 3.75; 95% CI, 1.24-11.35), waist circumference in both sexes (β = 2.44; 95% CI, 0.21-4.66), and fat mass in men (β = 16.36; 95% CI, 4.81-27.92) remained significant after adjustments for a wide range of possible cofounding. CONCLUSIONS AND RELEVANCE: The atypical subtype of MDD is a strong predictor of obesity. This emphasizes the need to identify individuals with this subtype of MDD in both clinical and research settings. Therapeutic measures to diminish the consequences of increased appetite during depressive episodes with atypical features are advocated.
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FNDC5/irisin has been recently postulated as beneficial in the treatment of obesity and diabetes because it is induced in muscle by exercise, increasing energy expenditure. However, recent reports have shown that WAT also secretes irisin and that circulating irisin is elevated in obese subjects. The aim of this study was to evaluate irisin levels in conditions of extreme BMI and its correlation with basal metabolism and daily activity. The study involved 145 female patients, including 96 with extreme BMIs (30 anorexic (AN) and 66 obese (OB)) and 49 healthy normal weight (NW). The plasma irisin levels were significantly elevated in the OB patients compared with the AN and NW patients. Irisin also correlated positively with body weight, BMI, and fat mass. The OB patients exhibited the highest REE and higher daily physical activity compared with the AN patients but lower activity compared with the NW patients. The irisin levels were inversely correlated with daily physical activity and directly correlated with REE. Fat mass contributed to most of the variability of the irisin plasma levels independently of the other studied parameters. Conclusion. Irisin levels are influenced by energy expenditure independently of daily physical activity but fat mass is the main contributing factor.