Prediction equations to estimate muscle mass using anthropometric data: a systematic review

Abstract

Abstract Context Reduced muscle mass is linked to poor outcomes in both inpatients and outpatients, highlighting the importance of muscle mass assessment in clinical practice. However, laboratory methods to assess muscle mass are not yet feasible for routine use in clinical practice because of limited availability and high costs. Objective This work aims to review the literature on muscle mass prediction by anthropometric equations in adults or older people. Data Sources The following databases were searched for observational studies published until June 2022: MEDLINE, Embase, Scopus, SPORTDiscus, and Web of Science. Data Extraction Of 6437 articles initially identified, 63 met the inclusion criteria for this review. Four independent reviewers, working in pairs, selected and extracted data from those articles. Data Analysis Two studies reported new equations for prediction of skeletal muscle mass: 10 equations for free-fat mass and lean soft tissue, 22 for appendicular lean mass, 7 for upper-body muscle mass, and 7 for lower-body muscle mass. Twenty-one studies validated previously proposed equations. This systematic review shows there are numerous equations in the literature for muscle mass prediction, and most are validated for healthy adults. However, many equations were not always accurate and validated in all groups, especially people with obesity, undernourished people, and older people. Moreover, in some studies, it was unclear if fat-free mass or lean soft tissue had been assessed because of an imprecise description of muscle mass terminology. Conclusion This systematic review identified several feasible, practical, and low-cost equations for muscle mass prediction, some of which have excellent accuracy in healthy adults, older people, women, and athletes. Malnourished individuals and people with obesity were understudied in the literature, as were older people, for whom there are only equations for appendicular lean mass. Systematic Review Registration PROSPERO registration number CRD42021257200.