Background: The loss of skeletal muscle mass (MM) or muscle function (MF) alone increases the risk for losing physical independence in older adults. We aimed to examine the independent and synergic associations of low MM and low MF, both criteria of sarcopenia, with the risk for losing projected physical independence in later life (+90 years old).Methods: Cross‐sectional analyses were conducted in 3493 non‐institutionalized older adults (1166 males). Physical independence was assessed with a 12‐item composite physical function scale. Logistic regression was used to estimate the odds‐ratio (OR) for being at risk for losing physical independence.Results: Approximately 30% of the participants were at risk for losing physical independence at 90 years of age. Independent analysis demonstrated that participants with low MM had 1.65 (95%CI: 1.27–2.31) increased odds for being at risk for losing physical independence and participants with low MF had 6.19 (95%CI 5.08–7.53) increased odds for being at risk. Jointly, having a low MM and a low MF increased the risk for losing physical independence to 12.28 (95%CI 7.95 to 18.96).Conclusions: Although low MM represents a risk factor for losing physical independence, low MF seems to play a more dominant role in this relationship, with the presence of both sarcopenia criteria representing a substantial risk for losing physical independence in later life.

Despite the importance of body composition in athletes, reference sex- and sport-specific body composition data are lacking. We aim to develop reference values for body composition and anthropometric measurements in athletes. Body weight and height were measured in 898 athletes (264 female, 634 male), anthropometric variables were assessed in 798 athletes (240 female and 558 male), and in 481 athletes (142 female and 339 male) with dual-energy X-ray absorptiometry (DXA). A total of 21 different sports were represented. Reference percentiles (5^th, 25^th, 50^th, 75^th, and 95^th) were calculated for each measured value, stratified by sex and sport. Because sample sizes within a sport were often very low for some outcomes, the percentiles were estimated using a parametric, empirical Bayesian framework that allowed sharing information across sports. We derived sex- and sport-specific reference percentiles for the following DXA outcomes: total (whole body scan) and regional (subtotal, trunk, and appendicular) bone mineral content, bone mineral density, absolute and percentage fat mass, fat-free mass, and lean soft tissue. Additionally, we derived reference percentiles for height-normalized indexes by dividing fat mass, fat-free mass, and appendicular lean soft tissue by height squared. We also derived sex- and sport-specific reference percentiles for the following anthropometry outcomes: weight, height, body mass index, sum of skinfold thicknesses (7 skinfolds, appendicular skinfolds, trunk skinfolds, arm skinfolds, and leg skinfolds), circumferences (hip, arm, midthigh, calf, and abdominal circumferences), and muscle circumferences (arm, thigh, and calf muscle circumferences). These reference percentiles will be a helpful tool for sports professionals, in both clinical and field settings, for body composition assessment in athletes.

Lation aged 10 yr and older using accelerometry. Methods The accelerometers were worn for four consecutive days, and a valid record was defined as at least 3 d with 10 h of wear time, including one weekend day. Considering these conditions, 4696 participants were included. Results According to the recommendations of 60 min·d−1 of physical activity, 36% of participants age 10–11 yr (boys = 51.6%, girls = 22.5%) and 4% age 16–17 yr (boys = 7.9%, girls = 1.2%) were considered sufficiently active. In adulthood, ∼70% of participants age 18–64 yr (men = 76.6%, women = 65.2%) and 35% age older than 64 yr (men = 46%, women = 29%) reached the recommendation of 30 min·d−1 of physical activity, when counting every minute of moderate or greater intensity. Considering bouts of 10 min or more of moderate or greater intensity in participants older than 17 yr, the prevalence was 4%–6% at 18–39 yr, 7%–9% at 40–64 yr, and ∼3% in persons age 65 yr or older. Conclusions The design and implementation of strategies to promote physical activity for health among children and adolescents and older adults, particularly girls/women, should be encouraged....

Physical function is a key determinant that corresponds to the physiological capacity of older adults to perform normal everyday activities, safely and independently, without undue fatigue. We examined the associations of sedentary behavior (SB), breaks in sedentary time (BST), and moderate-to-vigorous physical activity (MVPA) with physical function in older adults. Physical activity and SB were assessed with accelerometers (ActiGraph, GT1M) and physical function with the Senior Fitness Test battery, among 87 males and 128 females aged between 65 and 94 years. A composite Z-score was created based on the individual scores for each Senior Fitness Test battery item. Associations of SB, BST, and MVPA with physical function were examined, adjusting for demographic attributes, physical independence, and medical status. A significant positive association was found between BST and the composite physical function Z-score, after adjusting for total SB, MVPA, and potential confounders. MVPA was also positively associated with physical function, after adjusting for SB, BST, and potential confounders. Those with low BST in conjunction with performing less than 30min/d of MVPA had lower physical function. Breaking-up sedentary time is associated with better physical function in older adults; and, it may have an important place in future guidelines on preserving older adults’ physical function to support activities of daily living.

There is controversial evidence on the associations between anthropometric measures with clustering of cardiovascular disease risk factors in pediatric ages. We aimed to examine the associations between body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) with clustered cardiometabolic risk factors and to determine whether these anthropometric variables can be used to discriminate individuals with increased cardiometabolic risk (increased clustered triglycerides, HDL-cholesterol, systolic and diastolic blood pressure, and HOMA-IR). The study sample of 4255 (2191 girls and 2064 boys) participants (8–17 years) was derived from pooled cross-sectional data comprising five studies. Outcomes included a continuous cardiometabolic risk factor z-score [corresponding to the sum of z-scores for triglycerides, HDL-cholesterol, systolic and diastolic blood pressure (mean arterial pressure), and HOMA-IR] and children with ≥1.0 SD in this score were defined as being at risk for clustering cardiometabolic risk factors.. Exposure variables were BMI, WC, WHtR. Statistics included mixed-effect regression and ROC analysis. All anthropometric variables were associated with clustered risk and the magnitudes of associations were similar for BMI, WC, and WHtR. Models including anthropometric variables were similar in discriminating children and adolescents at increased risk with areas under the ROC curve between 0.70 and 0.74. The sensitivity (boys: 80.5–86.4%; girls: 76.6–82.3%) was markedly higher than specificity (boys: 51.85–59.4%; girls: 60.8%). The magnitude of associations for BMI, WC, and WHtR are similar in relation to clustered cardiometabolic risk factors, and perform better at higher levels of BMI. However, the precision of these anthropometric variables to classify increased risk is low.