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.

Summary: Traditional diagnosis and understanding of the pathophysiology of obesity are based on excessive fat storage due to a chronically positive energy balance characterized by body mass index (BMI). Quantitative and qualitative analysis of lean and adipose tissue compartments by body composition analysis reveals that characterization of obesity as “overfat” does not facilitate a comprehensive understanding of obesity‐associated health risk. Instead of being related to fat mass, body composition characteristics underlying BMI‐associated prognosis may depend (i) on accelerated growth by a gain in lean mass or fat‐free mass (FFM) in children with early BMI rebound or adolescents with early puberty; (ii) on a low muscle mass in aging, associated chronic disease, or severe illness; and (iii) on impaired adipose tissue expandability with respect to cardiometabolic risk. It is therefore time to call the adipocentric paradigm of obesity into question and to avoid the use of BMI and body fat percentage. By contrast, obesity should be seen in face of a limited FFM/muscle mass together with a limited capacity of fat storage.

Background: A high amount of adipose tissue limits the accuracy of methods for body composition analysis in obesity. Objectives: The aim was to quantify and explain differences in fat-free mass (FFM) (as an index of skeletal muscle mass, SMM) measured with bioelectrical impedance analysis (BIA), dual energy X-ray absorptiometry (DXA), air displacement plethysmography (ADP), and deuterium dilution in comparison to multicompartment models, and to improve the results of BIA for obese subjects. Methods: In 175 healthy subjects (87 men and 88 women, BMI 20–43.3 kg/m², 18–65 years), FFM measured by these methods was compared with results from a 3- (3C) and a 4-compartment (4C) model. FFM_4C was compared with SMM measured by magnetic resonance imaging. Results: BIA and DXA overestimated and ADP underestimated FFM in comparison to 3C and 4C models with increasing BMI (all p < 0.001). ­Differences were largest for DXA. In obesity, BIA results were improved: value_corrected = ­value_uncorrected – a(BMI – 30 kg/m²), a = 0.256 for FFM and a = 0.298 for SMM. SMM accounts for 45% of FFM in women and 49% in men. Conclusions: In obesity, the use of FFM is limited by a systematic error of reference methods. In addition, SMM accounts for about 50% of FFM only. Corrected measurement of SMM by BIA can overcome these drawbacks.

Summary: Humans expend energy at rest (REE), and this major energy exchange component is now usually estimated using statistical equations that include weight and other predictor variables. While these formulas are useful in evaluating an individual's or group's REE, an important gap remains: available statistical models are inadequate for explaining underlying organ‐specific and tissue‐specific mechanisms accounting for resting heat production. The lack of such systems level REE prediction models leaves many research questions unanswered. A potential approach that can fill this gap began with investigators who first showed in animals and later in humans that REE reflects the summated heat production rates of individual organs and tissues. Today, using advanced imaging technologies, REE can be accurately estimated from the measured in vivo mass of 10 organ‐tissue mass components combined with their respective mass‐specific metabolic rates. This review examines the next frontier of energy expenditure models and discusses how organ‐tissue models have the potential not only to better predict REE but also to provide insights into how perturbations in organ mass lead to structure–function changes across other interacting organ systems. The introductory ideas advanced in this review provide a framework for future human energy expenditure modelling research.