RESEARCH DESIGN AND METHODS:School-based, cross-sectional study in 259 girls (9.8 ± 0.3 years; 18.0 ± 3.3 kg/m²; 27.8 ± 9.3 % body fat) and 274 boys (9.8 ± 0.3 years;18.24± 3.5 kg/m²; 22.0 ± 9.2 % body fat) randomly selected. Trunk fat mass (FM) and central FM were measured with DXA. Free-living physical activity (PA) was measured with accelerometry for 4 days. We created a standardised continuously distributed clustered metabolic risk variable using the following subcomponents:; blood pressure, fasting glucose, insulin, triglycerides, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. We examined the associations between PA variables (total volume, time spent at sedentary, moderate and vigorous intensity of PA) with clustered metabolic risk using general linear models. RESULTS:All PA variables were significantly associated with clustered metabolic risk, independent of gender and sexual maturation (p<0.003). These associations remained statistically significant (p<0.05) but were attenuated after further CONCLUSIONS:Physical activity is associated with clustered metabolic risk independently of precisely measured trunk and central FM in children. Our results emphasises the importance of promoting physical activity in children, which may have beneficial effects on metabolic risk factors regardless of the degree of adiposity.Keywords: accelerometry, energy expenditure, metabolic syndrome risk factors, children, Evidence suggests that children and adolescents may exhibit a cluster of cardiovascular disease risk factors (1,2), characterized by the coexistence of obesity, dyslipidemia, hypertension and glucose intolerance (2-6). Individual risk factors and clustered risk track from childhood and adolescence through adulthood (7,8).Additionally, abdominal obesity is closely associated with this pathological condition (9-13). Intra-abdominal adipose tissue has been linked to insulin resistance (14,15) with visceral adiposity being strongly related to both insulin and lipid risk factors in children and adolescents, contributing to the early stages of metabolic syndrome (15).We have recently shown that more inactive children are likely to be fatter than children that accumulate higher amounts of moderate and vigorous physical activity objectively measured by accelerometry (16). Objectively measured physical activity is also inversely and independently associated with insulin sensitivity (17), and clustered metabolic risk in Danish 9 to 10 year old children (18). Similarly, objectively measured physical activity is inversely associated with clustering of cardiovascular risk factors (19), and that this association is independent of amount of TV viewing (20). However, data on precisely measured abdominal adiposity was not available in any of these studies. Dual-energy X-ray absorptiometry (DXA) can be used to assess regional adiposity, by analysing segments of the body (21). Trunk fat mass is measured by subdividing the body into the trunk and other areas. Fat mass in the abdomi...

Both models accurately predict ALST in young athletes, affording a practical means to quantify this compartment.

Dias Quiterio, AL, Canero, EA, Baptista, FM, and Sardinha, LB. Skeletal mass in adolescent male athletes and nonathletes: relationships with high-impact sports. J Strength Cond Res 25(12): 3439-3447, 2011-This study examined the relationships between the practice of different categories of sports (high-impact vs. nonimpact) and bone status in adolescent male athletes and investigated differences from an age-matched control group. A total of 54 adolescent male athletes and 26 adolescent nonathletes were evaluated. Bone mineral density, bone mineral content (BMC), and bone area at the whole-body, limbs, and lumbar spine were determined by dual-energy x-ray absorptiometry, along with total and regional fat-free mass and body fat. The high-impact group included 34 athletes: 9 gymnasts, 18 basketball players, and 7 handball players (age: 15.7 ± 1.6 years; weight: 72.0 ± 15.0 kg; height: 178.5 ± 12.5 cm). The nonimpact group consisted of 20 swimmers (age: 16.4 ± 2.5 years; weight: 66.9 ± 10.4 kg; height: 173.7 ± 10.9 cm). The nonathletic control group included 26 male adolescents (age: 15.9 ± 2.8 years; weight: 64.7 ± 16.3 kg; height: 168.6 ± 15.1 cm). No differences were observed between the nonimpact and the control group in all bone variables, before and after adjustments for maturation level, body weight, and height (p > 0.05). After adjustments for these variables, the high-impact group displayed greater bone mass in most of the measured sites when compared to the other 2 groups (p < 0.001). Subjects in the nonimpact group showed lower values of BMC, particularly in the lower limbs, than both the high-impact and the nonathletic control groups (p < 0.05) after adjustments for maturation, high, and fat-free mass. This study reinforces the positive associations between high-impact physical activities and skeletal health in adolescent boys.

This study was designed to assess the usefulness of skinfold (SKF) equations developed by Jackson and Pollock (JP) and by Evans (Ev) in tracking body composition changes (relative fat mass [%FM], absolute fat mass [FM], and fat-free mass [FFM]) of elite male judo athletes before a competition using a 4-compartment (4C) model as the reference method. A total of 18 male, top-level (age: 22.6 +/- 2.9 yr) athletes were evaluated at baseline (weight: 73.4 +/- 7.9 kg; %FM4C: 7.0 +/- 3.3%; FM4C: 5.1 +/- 2.6 kg; and FFM4C: 68.3 +/- 7.3 kg) and before a competition (weight: 72.7 +/- 7.5 kg; %FM4C: 6.5 +/- 3.4%; FM4C: 4.8 +/- 2.6 kg; and FFM4C: 67.9 +/- 7.1 kg). Measures of body density assessed by air displacement plethysmography, bone mineral content by dual energy X-ray absorptiometry, and total-body water by bioelectrical impedance spectroscopy were used to estimate 4C model %FM, FM, and FFM. Seven SKF site models using both JP and Ev were used to estimate %FM, FM, and FFM along with the simplified Ev3SKF site. Changes in %FM, FM, and FFM were not significantly different from the 4C model. The regression model for the SKF in question and the reference method did not differ from the line of identity in estimating changes in %FM, FM, and FFM. The limits of agreement were similar, ranging from -3.4 to 3.6 for %FM, -2.7 to 2.5 kg for FM, and -2.5 to 2.7 kg for FFM. Considering the similar performance of both 7SKF- and 3SKF-based equations compared with the criterion method, these data indicate that either the 7- or 3-site SFK models are not valid to detect %FM, FM, and FFM changes of highly trained athletes. These results highlighted the inaccuracy of anthropometric models in tracking desired changes in body composition of elite male judo athletes before a competition.