891 resultados para Hyperthyroidism, body composition
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The metabolic equivalent (MET) is a widely used physiological concept that represents a simple procedure for expressing energy cost of physical activities as multiples of resting metabolic rate (RMR). The value equating 1 MET (3.5 ml O2 x kg(-1) x min(-1) or 1 kcal x kg(-1) x h(-1)) was first derived from the resting O2 consumption (VO2) of one person, a 70-kg, 40-yr-old man. Given the extensive use of MET levels to quantify physical activity level or work output, we investigated the adequacy of this scientific convention. Subjects consisted of 642 women and 127 men, 18-74 yr of age, 35-186 kg in weight, who were weight stable and healthy, albeit obese in some cases. RMR was measured by indirect calorimetry using a ventilated hood system, and the energy cost of walking on a treadmill at 5.6 km/h was measured in a subsample of 49 men and 49 women (26-45 kg/m2; 29-47 yr). Average VO2 and energy cost corresponding with rest (2.6 +/- 0.4 ml O2 x kg(-1) x min(-1) and 0.84 +/- 0.16 kcal x kg(-1) x h(-1), respectively) were significantly lower than the commonly accepted 1-MET values of 3.5 ml O2 x kg(-1) x min(-1) and 1 kcal x kg(-1) x h(-1), respectively. Body composition (fat mass and fat-free mass) accounted for 62% of the variance in resting VO2 compared with age, which accounted for only 14%. For a large heterogeneous sample, the 1-MET value of 3.5 ml O2 x kg(-1) x min(-1) overestimates the actual resting VO2 value on average by 35%, and the 1-MET of 1 kcal/h overestimates resting energy expenditure by 20%. Using measured or predicted RMR (ml O2 x kg(-1) x min(-1) or kcal x kg(-1) x h(-1)) as a correction factor can appropriately adjust for individual differences when estimating the energy cost of moderate intensity walking (5.6 km/h).
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ABSTRACT: Ultramarathons comprise any sporting event involving running longer than the traditional marathon length of 42.195 km (26.2 miles). Studies on ultramarathon participants can investigate the acute consequences of ultra-endurance exercise on inflammation and cardiovascular or renal consequences, as well as endocrine/energetic aspects, and examine the tissue recovery process over several days of extreme physical load. In a study published in BMC Medicine, Schütz et al. followed 44 ultramarathon runners over 4,487 km from South Italy to North Cape, Norway (the Trans Europe Foot Race 2009) and recorded daily sets of data from magnetic resonance imaging, psychometric, body composition and biological measurements. The findings will allow us to better understand the timecourse of degeneration/regeneration of some lower leg tissues such as knee joint cartilage, to differentiate running-induced from age-induced pathologies (for example, retropatelar arthritis) and finally to assess the interindividual susceptibility to injuries. Moreover, it will also provide new information about the complex interplay between cerebral adaptations/alterations and hormonal influences resulting from endurance exercise and provide data on the dose-response relationship between exercise and brain structure/function. Overall, this study represents a unique attempt to investigate the limits of the adaptive response of human bodies.Please see related article: http://www.biomedcentral.com/1741-7015/10/78.
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The peroxisome proliferator-activated receptor gamma (PPARgamma) plays a major role in fat tissue development and physiology. Mutations in the gene encoding this receptor have been associated to disorders in lipid metabolism. A thorough investigation of mice in which one PPARgamma allele has been mutated reveals that male PPARgamma heterozygous (PPARgamma +/-) mice exhibit a reduced body size associated with decreased body weight, reflecting lean mass reduction. This phenotype is reproduced when treating the mice with a PPARgamma- specific antagonist. Monosodium glutamate treatment, which induces weight gain and alters body growth in wild-type mice, further aggravates the growth defect of PPARgamma +/- mice. The levels of circulating GH and that of its downstream effector, IGF-I, are not altered in mutant mice. However, the IGF-I mRNA level is decreased in white adipose tissue (WAT) of PPARgamma +/- mice and is not changed by acute administration of recombinant human GH, suggesting an altered GH action in the mutant animals. Importantly, expression of the gene encoding the suppressor of cytokine signaling-2, which is an essential negative regulator of GH signaling, is strongly increased in the WAT of PPARgamma +/- mice. Although the relationship between the altered GH signaling in WAT and reduced body size remains unclear, our results suggest a novel role of PPARgamma in GH signaling, which might contribute to the metabolic disorder affecting insulin signaling in PPARgamma mutant mice.
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OBJECTIVES: The validity of equations for the calculation of resting metabolic rate (RMR) were studied and new predictive equations were developed. STUDY DESIGN: The RMR was measured in a sample of 371 10- to 16-year-old prepubertal and postpubertal children. The study group included 193 male (116 nonobese and 77 obese) and 178 female (119 nonobese and 59 obese) subjects; for each group the RMRs predicted from five equations recommended for this age group were compared. The RMR was assessed by indirect calorimetry with a ventilated hood system for 45 minutes after an overnight fast. Body composition was estimated from skin-fold measurements. RESULTS: The mean +/- SD RMR was found to be 5600 +/- 972 kJ/24 hr and 7223 +/- 1220 kJ/24 hr in nonobese and obese boys, and 5112 +/- 632 kJ/24 hr and 6665 +/- 1106 kJ/24 hr in nonobese and obese girls, respectively. All five equations applicable to 10- to 16-year-old children overestimated RMR by 7.5% to 18.1% (p < 0.001 for each equation). Stepwise regression analysis, with independent variables such as age, weight, height, and gender, allowed development of new predictive equations for the calculation of RMR in 10- to 16-year-old boys (RMR = 50.9 Weight (kg) + 25.3 Height (cm) -50.3 Age (yr) + 26.9; R2 = 0.884, p < 0.0001) and girls (RMR = 51.2 Weight (kg) + 24.5 Height (cm) - 207.5 Age (yr) + 1629.8; R2 = 0.824, p < 0.0001). These predictive equations were tested in a second, independent cohort of children (80 male and 61 female subject) and were found to give a reliable estimate of RMR in 10- to 16-year-old obese and nonobese adolescents. CONCLUSIONS: The currently used predictive equations overestimate RMR in 10- to 16-year-old children. The use of the newly developed equations is recommended.
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A selection gradient was recently suggested as one possible cause for a clinal distribution of mitochondrial DNA (mtDNA) haplotypes along an altitudinal transect in the greater white-toothed shrew, Crocidura russula (Ehinger et al. 2002). One mtDNA haplotype (H1) rare in lowland, became widespread when approaching the altitudinal margin of the distribution. As H1 differs from the main lowland haplotype by several nonsynonymous mutations (including on ATP6), and as mitochondria play a crucial role in metabolism and thermogenesis, distribution patterns might stem from differences in the thermogenic capacity of different mtDNA haplotypes. In order to test this hypothesis, we measured the nonshivering thermogenesis (NST) associated with different mtDNA haplotypes. Sixty-two shrews, half of which had the H1 haplotype, were acclimated in November at semioutdoor conditions and measured for NST throughout winter. Our results showed the crucial role of NST for winter survival in C. russula. The individuals that survived winter displayed a higher significant increase in NST during acclimation, associated with a significant gain in body mass, presumably from brown fat accumulation. The NST capacity (ratio of NST to basal metabolic rate) was exceptionally high for such a small species. NST was significantly affected by a gender x haplotype interaction after winter-acclimation: females bearing the H1 haplotype displayed a better thermogenesis at the onset of the breeding season, while the reverse was true for males. Altogether, our results suggest a sexually antagonistic cyto-nuclear selection on thermogenesis.
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To estimate the prevalence of metabolically healthy obesity (MHO) according to different definitions. Population-based sample of 2803 women and 2557 men participated in the study. Metabolic abnormalities were defined using six sets of criteria, which included different combinations of the following: waist; blood pressure; total, high-density lipoprotein or low-density lipoprotein-cholesterol; triglycerides; fasting glucose; homeostasis model assessment; high-sensitivity C-reactive protein; personal history of cardiovascular, respiratory or metabolic diseases. For each set, prevalence of MHO was assessed for body mass index (BMI); waist or percent body fat. Among obese (BMI 30 kg/m(2)) participants, prevalence of MHO ranged between 3.3 and 32.1% in men and between 11.4 and 43.3% in women according to the criteria used. Using abdominal obesity, prevalence of MHO ranged between 5.7 and 36.7% (men) and 12.2 and 57.5% (women). Using percent body fat led to a prevalence of MHO ranging between 6.4 and 43.1% (men) and 12.0 and 55.5% (women). MHO participants had a lower odd of presenting a family history of type 2 diabetes. After multivariate adjustment, the odds of presenting with MHO decreased with increasing age, whereas no relationship was found with gender, alcohol consumption or tobacco smoking using most sets of criteria. Physical activity was positively related, whereas increased waist was negatively related with BMI-defined MHO. MHO prevalence varies considerably according to the criteria used, underscoring the need for a standard definition of this metabolic entity. Physical activity increases the likelihood of presenting with MHO, and MHO is associated with a lower prevalence of family history of type 2 diabetes.
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Obesity prevalence is generally higher in women than in men, and there is also a sex difference in body fat distribution. Sex differences in obesity can be explained in part by the influence of gonadal steroids on body composition and appetite; however, behavioural, socio-cultural and chromosomal factors may also play a role. This review, which evolved from the 2008 Stock Conference on sex differences in obesity, summarizes current research and recommendations related to hormonal and neuroendocrine influences on energy balance and fat distribution. A number of important gaps in the research are identified, including a need for more studies on chromosomal sex effects on energy balance, the role of socio-cultural (i.e. gender) factors in obesity and the potential deleterious effects of high-fat diets during pregnancy on the foetus. Furthermore, there is a paucity of clinical trials examining sex-specific approaches and outcomes of obesity treatment (lifestyle-based or pharmacological), and research is urgently needed to determine whether current weight loss programmes, largely developed and tested on women, are appropriate for men. Last, it is important that both animal and clinical research on obesity be designed and analysed in such a way that data can be separately examined in both men and women.
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Objective: To identify the adherence rate of a statin treatment and possible related factors in female users from the Unified Health System. Method: Seventy-one women were evaluated (64.2 ± 11.0 years) regarding the socio-economic level, comorbidities, current medications, level of physical activity, self-report of muscular pain, adherence to the medical prescription, body composition and biochemical profile. The data were analyzed as frequencies, Chi-Squared test, and Mann Whitney test (p<0.05). Results: 15.5% of women did not adhere to the medical prescription for the statin treatment, whose had less comorbidities (p=0.01), consumed less quantities of medications (p=0.00), and tended to be younger (p=0.06). Those patients also presented higher values of lipid profile (CT: p=0.01; LDL-c: p=0.02). Musculoskeletal complains were not associated to the adherence rate to the medication. Conclusion: The associated factors to adherence of dyslipidemic women to statin medical prescription were age, quantity of comorbidities and quantity of current medication.
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The overall thermogenic response to food intake measured over a whole day in 20 young nondiabetic obese women (body fat mean +/- SEM: 38.6 +/- 0.7%), was compared with that obtained in eight nonobese control women (body fat: 24.7 +/- 0.9%). The energy expenditure of the subjects was continuously measured over 24 h with a respiration chamber, and the spontaneous activity was assessed by a radar system. A new approach was used to obtain the integrated thermogenic response to the three meals ingested over the day (from 8:30 AM to 10:30 PM). This method allows to subtract the energy expended for physical activity from total energy expenditure and to calculate the integrated dietary-induced thermogenesis as the difference between the energy expended without physical activity and basal metabolic rate. The thermogenic response to the three meals (expressed in percentage of the total energy ingested) was found to be blunted in obese women (8.7 +/- 0.8%) as compared with that of controls (14.8 +/- 1.1%). There was an inverse correlation between the percentage body fat and the diet-induced thermogenesis (r = -0.61, p less than 0.001). In addition, the relative increase in diurnal urinary norepinephrine excretion was lower in obese than in the control subjects. It is concluded that a low overall thermogenic response to feeding may be a contributing factor for energy storage in some obese subjects; a blunted response of the sympathetic nervous system could explain this low thermogenic response.
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Small daily positive energy imbalances of 200 to 800 kJ (about 50 to 200 kcal) due to reduced resting energy expenditure (REE), reduced diet-induced thermogenesis, or physical inactivity are believed to predispose to obesity. However, estimates of the magnitude of the weight gain often fail to account for concurrent changes in body composition and increases in maintenance energy requirements as weight increases and energy equilibrium is re-established. Using previously reported data on body composition and REE in women and the energy cost of tissue deposition, we used mathematical models to predict the theoretical effect of a persistent reduction in energy expenditure on long-term weight gain, assuming no adaptation in energy intake. The analyses indicate the following effects of a reduced level of energy expenditure in lean and obese women: (i) REE rises more slowly with increasing degrees of obesity due to a declining proportion of the more metabolically active fat-free mass; so, for the same positive energy balance, a significantly greater weight gain is expected for obese than for lean women before energy equilibrium is re-established; (ii) due to the greater energy density of adipose tissue, the time course of weight gain to achieve energy balance is longer for obese subjects: in general, this is approximately five years for lean and ten years for obese women; (iii) the magnitude of weight gain of lean women in response to a reduced energy expenditure of 200 to 800 kJ/day is only about 3 to 15 kg, amounts insufficient to explain severe obesity.
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Background and Aims: normal weight obesity (NWO) has been defined as an excessive body fat (BF) associated with a normal body mass index (BMI). Little is known regarding its prevalence in the general population or which cut-offs for BF should be used. Methods: convenience sample of 1,523 Portuguese adults. BF was measured by validated hand-held bioimpedance. NWO was defined as a BMI<25 kg/m2 and a %BF mass>30%, along other published criteria. Results: prevalence of NWO was 10.1% in women and 3.2% in men. In women, prevalence of NWO increased considerably with age, and virtually all women aged over 55 with a BMI<25 kg/m2 were actually considered as NWO. Using gender specific cut-offs for BF (29.1% in men and 37.2% in women) led to moderately lower of NWO in women. Using gender- and age-specific cut-points for %BF considerably decreased the prevalence of NWO in women (0.5 to 2.5% depending on the criterion) but not in men (1.9 to 3.4%). Conclusions: gender- and age- specific or at least gender-specific, instead of single cut-offs for %BF, should be used to characterize and study NWO.
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To investigate the effect of age and change in body composition on the increase in energy expenditure consecutive to the ingestion of a 75-g glucose load, respiratory exchange measurements were performed on 24 subjects, 12 elderly (mean +/- SEM, 73 +/- 1 yr) and 12 young (25 +/- 1 yr). The body weight was comparable, 62 +/- 2 kg in the elderly group vs 61 +/- 3 in the young, but the body fat content of the elderly group was significantly greater than that of the young (29 +/- 2% vs 19 +/- 2%, p less than 0.001). The elderly group presented a slight glucose intolerance according to the World Health Organization (WHO) criteria, with a 120-min plasma glucose of 149 +/- 9 mg/dl (p less than 0.005 vs young). The postabsorptive resting energy expenditure (REE) was 0.83 +/- 0.03 kcal/min in the elderly group vs 0.98 +/- 0.04 in the young (p less than 0.02); this decrease of 15% was mainly related to the decrease in fat free mass (FFM) in the elderly group, which averaged 14%. The difference was not significant when REE was expressed per kg FFM. The glucose-induced thermogenesis (GIT) expressed as percent of energy content of the load was 6.2 +/- 0.6% in the elderly group and 8.9 +/- 0.9% in the young (p less than 0.05). It is concluded that the glucose-induced thermogenesis is decreased in elderly subjects. However, when expressed per kg FFM, the increment in energy expenditure (EE), in response to the glucose load, is not different in elderly subjects, suggesting that the decrease of thermogenesis may be attributed to the age-related decrease in FFM.
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Although both inflammatory and atherosclerosis markers have been associated with coronary heart disease (CHD) risk, data directly comparing their predictive value are limited. The authors compared the value of 2 atherosclerosis markers (ankle-arm index (AAI) and aortic pulse wave velocity (aPWV)) and 3 inflammatory markers (C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-alpha)) in predicting CHD events. Among 2,191 adults aged 70-79 years at baseline (1997-1998) from the Health, Aging, and Body Composition Study cohort, the authors examined adjudicated incident myocardial infarction or CHD death ("hard" events) and "hard" events plus hospitalization for angina or coronary revascularization (total CHD events). During 8 years of follow-up between 1997-1998 and June 2007, 351 participants developed total CHD events (197 "hard" events). IL-6 (highest quartile vs. lowest: hazard ratio = 1.82, 95% confidence interval: 1.33, 2.49; P-trend < 0.001) and AAI (AAI </= 0.9 vs. AAI 1.01-1.30: hazard ratio = 1.57, 95% confidence interval: 1.14, 2.18) predicted CHD events above traditional risk factors and modestly improved global measures of predictive accuracy. CRP, TNF-alpha, and aPWV had weaker associations. IL-6 and AAI accurately reclassified 6.6% and 3.3% of participants, respectively (P's </= 0.05). Results were similar for "hard" CHD, with higher reclassification rates for AAI. IL-6 and AAI are associated with future CHD events beyond traditional risk factors and modestly improve risk prediction in older adults.
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Fat balance plays an important role in fat mass regulation. The mechanisms by which fat intake and fat oxidation are controlled are poorly understood. In particular, no data are available on the origin, i.e. exogenous (meal intake) or endogenous (adipose tissue lipolysis), of fat oxidized during the postprandial period in children and the proportion between these two components. In this study we tested the hypothesis that there is a relationship between adiposity and the oxidative fate of fat taken with a mixed meal in a group of 15 children with a wide range of fat mass (9-64%). The combination of stable isotope analysis ([13C] enriched fatty acids added to a mixed meal) and indirect calorimetry allowed us to differentiate between the exogenous and endogenous resting fat oxidation rate over the 9-h postprandial period. During the 9 hours of the postprandial period, the children oxidized an amount of fat comparable to that ingested with the meal [26.8 (+/-2.31) g vs. 26.4 (+/-2.3) g, respectively, P = ns]. On average, exogenous fat oxidation [2.99 (+/-3.0) g/9 h] represented 10.8% (+/-0.9) of total fat oxidation. Endogenous fat oxidation, calculated as the difference between total fat oxidation and exogenous fat oxidation, averaged 23.4 (+/-1.9) g/9 h and represented 88.2% (+/-0.9) of total fat oxidation. Endogenous fat oxidation as well as exogenous fat oxidation were highly correlated to total fat oxidation (r = 0.83, P < 0.001; r = 0.84, P < 0.001, respectively). Exogenous fat oxidation expressed as a proportion of total fat oxidation was directly related to fat mass (r = 0.56, P < 0.03), while endogenous fat oxidation expressed as a proportion of total fat oxidation was inversely related (r = -0.57, P < 0.03) to the degree of adiposity. The enhanced exogenous fat oxidation observed when adiposity increases in the dynamic phase of obesity may be viewed as a protective mechanism to prevent further increase in fat mass and hence to maintain fat oxidation at a sufficient rate when the body is exposed to a high amount of dietary fat, as typically encountered in obese children.
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The resting metabolic rate (RMR) and body composition of 130 obese and nonobese prepubertal children, aged 6 to 10 years, were assessed by indirect calorimetry and skin-fold thickness, respectively. The mean (+/- SD) RMR was 4619 +/- 449 kJ.day-1 (164 +/- 31 kJ.kg body weight-1 x day-1) in the 62 boys and 4449 +/- 520 kJ.day-1 (147 +/- 32 kJ.kg body weight-1 x day-1) in the 68 girls. Fat-free mass was the best single predictor of RMR (R2 = 0.64; p < 0.001). Step-down multiple regression analysis, with independent variables such as age, gender, weight, and height, allowed several RMR predictive equations to be developed. An equation for boys is as follows: RMR (kJ.day-1) = 1287 + 28.6 x Weight(kg) + 23.6 x Height(cm) - 69.1 x Age(yr) (R2 = 0.58; p < 0.001). An equation for girls is as follows: RMR (kJ.day-1 = 1552 + 35.8 x Weight (kg) + 15.6 x Height (cm) - 36.3 x Age (yr) (R2 = 0.69; p < 0.001). Comparison between the measured RMR and that predicted by currently used formulas showed that most of these equations tended to overestimate the RMR of both genders, especially in overweight children.