Assessment of a low skeletal muscle mass (SM) is important for diagnosis of ageing and disease-associated sarcopenia and is hindered by heterogeneous methods and terminologies that lead to differences in diagnostic criteria among studies and even among consensus definitions. The aim of this review was to analyze and summarize previously published cut-offs for SM applied in clinical and research settings and to facilitate comparison of results between studies. Multiple published reference values for discrepant parameters of SM were identified from 64 studies and the underlying methodological assumptions and limitations are compared including different concepts for normalization of SM for body size and fat mass (FM). Single computed tomography or magnetic resonance imaging images and appendicular lean soft tissue by dual X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are taken as a valid substitute of total SM because they show a high correlation with results from whole body imaging in cross-sectional and longitudinal analyses. However, the random error of these methods limits the applicability of these substitutes in the assessment of individual cases and together with the systematic error limits the accurate detection of changes in SM. Adverse effects of obesity on muscle quality and function may lead to an underestimation of sarcopenia in obesity and may justify normalization of SM for FM. In conclusion, results for SM can only be compared with reference values using the same method, BIA- or DXA-device and an appropriate reference population. Limitations of proxies for total SM as well as normalization of SM for FM are important content-related issues that need to be considered in longitudinal studies, populations with obesity or older subjects.

Background It remains unknown why adiponectin levels are associated with poor physical functioning, skeletal muscle mass and increased mortality in older populations. Methods In 190 healthy adults (59-86 years, BMI 17-37 kg/m 2 , 56.8% female), whole body skeletal muscle mass (normalized by height, SMI, kg/m 2 ), muscle and liver fat were determined by magnetic resonance imaging. Bone mineral content (BMC) and density (BMD) were assessed by dual X-ray absorptiometry (n = 135). Levels of insulin-like growth factor 1 (IGF-1), insulin, inflammation markers, leptin and fibroblast growth factor 21 were measured as potential determinants of the relationship between adiponectin and body composition. Results Higher adiponectin levels were associated with a lower SMI (r = À0.23, P < 0.01), BMC (r = À0.17, P < 0.05) and liver fat (r = À0.20, P < 0.05) in the total population and with higher muscle fat in women (r = 0.27, P < 0.01). By contrast, IGF-1 showed positive correlations with SMI (r = 0.33), BMD (r = 0.37) and BMC (r = 0.33) (all P < 0.01) and a negative correlation with muscle fat (r = À0.17, P < 0.05). IGF-1 was negatively associated with age (r = À0.21, P < 0.01) and with adiponectin (r = À0.15, P < 0.05). Stepwise regression analyses revealed that IGF-1, insulin and leptin explained 18% of the variance in SMI, and IGF-1, leptin and age explained 16% of the variance in BMC, whereas adiponectin did not contribute to these models. Conclusions Associations between higher adiponectin levels and lower muscle or bone mass in healthy older adults may be explained by a decrease in IGF-1 with increasing adiponectin levels.

Lower bone mass in older adults may be mediated by the endocrine crosstalk between muscle, adipose tissue and bone. In 150 community-dwelling adults (59–86 years, BMI 17–37 kg/m2; 58.7% female), skeletal muscle mass index, adipose tissue and fat mass index (FMI) were determined. Levels of myokines, adipokines, osteokines, inflammation markers and insulin were measured as potential determinants of bone mineral content (BMC) and density (BMD). FMI was negatively associated with BMC and BMD after adjustment for mechanical loading effects of body weight (r-values between −0.37 and −0.71, all p < 0.05). Higher FMI was associated with higher leptin levels in both sexes, with higher hsCRP in women and with lower adiponectin levels in men. In addition to weight and FMI, sclerostin, osteocalcin, leptin × sex and adiponectin were independent predictors of BMC in a stepwise multiple regression analysis. Muscle mass, but not myokines, showed positive correlations with bone parameters that were weakened after adjusting for body weight (r-values between 0.27 and 0.58, all p < 0.01). Whereas the anabolic effect of muscle mass on bone in older adults may be partly explained by mechanical loading, the adverse effect of obesity on bone is possibly mediated by low-grade inflammation, higher leptin and lower adiponectin levels.

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