Body composition describes the amount of the various components of the human body1,2 and plays an important role in multiple aspects of physical health. For instance, fat mass is related to health status due to its association with various pathologies, such as cardiovascular and metabolic disease,3,4 while inadequate levels of skeletal muscle mass (SMM) or bone mineral content are associated with an increased injury risk in older adults.5,6
SMM is essential for human function and health status.7,8 It is one of the most dynamic tissues and, in healthy humans, represents approximately 40% of total body mass.9 There is considerable interindividual variation in SMM amount due to genetic factors and training effects, but it usually reaches a peak in the early adult life and declines with ageing.10
Decreased levels of SMM and the reduced muscle strength are commonly referred to as sarcopenia.11 Due to the ageing process in combination with sedentary lifestyles, sarcopenia is a major health issue in elderly populations. It has a prevalence of 8 to 40% of adults aged over 60 years and over 50% of adults aged 80 years or older.12,13 Sarcopenia is associated with poor health status and decreased quality of life in the elderly.14 In older adults, lower and upper limb strength has even been associated with higher all-cause mortality risk.15
However, reductions in SMM and its functional capacity are not limited to older adults and can also occur through local injury, denervation, systemic disease, and chronic inflammation.16 Low SMM is related to obesity, diabetes, cognitive impairment and many other health issues.17,18 In addition, the odds of presenting metabolic syndrome increase with lower SMM, and increases in SMM and strength might prevent a substantial proportion of metabolic syndrome cases.19 In sports, SMM is associated with athletic performance and has even been used to distinguish between athletes at different competitive levels.20,21
Assessment of SMM is, therefore, essential to monitor physical performance and health status. However, as the direct assessment of body composition, including SMM, ‘in vivo’ is not possible,1 several indirect or double indirect methods have been developed over time. Indirect methods use assumptions or algorithms to estimate a certain body composition component while double indirect methods use regression equations that have been validated from indirect methods,22 with the exception of a few formulae that have been validated with cadaveric dissection.23
Imaging methods such as magnetic resonance imaging or computerized axial tomography have been reported to have excellent accuracy in the measurement of SMM and those methods have also been validated with cadaveric dissection.24 However, dual-energy X-ray absorptiometry (DXA) has recently emerged as an alternative method of providing a precise and reliable measurement of SMM. DXA does not assess SMM directly, but different equations to estimate SMM from appendicular lean soft tissue have been validated.25,26 As a result, these three methods are now considered the gold standard to measure SMM but they require generally expensive instruments which limits their application.22,27
Therefore, despite limitations with regard to accuracy, other methods including bioelectrical impedance and surface anthropometry have been used in many studies to estimate body composition.22,28–30 Surface anthropometry is one of the most widely used methods because it is inexpensive, easy to use and transport, and results are obtained relatively quickly.31,32 Furthermore, it has demonstrated to have sufficient validity and reliability in the assessment of body composition.33–35
Many different predictive equations have been developed to estimate SMM from anthropometric measurements in adults. Three of the most widely used36–38 require the measurement of several skinfold thicknesses as well as several limb circumferences, which most equations to determine other body compartments do not use.
In addition, Martin and Doupe equations36,38 have repeatedly demonstrated to overestimate or underestimate SMM in different populations.16,27,39,40 The two equations with better results, those proposed by Lee et al.,37 have also sometimes overestimated SMM.16,40 Lee et al.37 developed two anthropometric equations: the first, hereafter referred to as Lee-1, used height, sex, age, race, three skinfold thicknesses, and three limb circumferences while the second, hereafter referred to as Lee-2, used body mass, height, sex, age, and race, a more widely available group of measures. This latter equation is as follows:37
$$SMM \left(kg\right)=0.244·body mass+7.80·stature-0.098·age+6.6·sex+race-3.3$$
where sex = 1 for males and 0 for females, race = -1.2 for Asian, 1.4 for African American and 0 for white or Hispanic. Body mass is in kg, stature in m, and age in years.
Because it would be useful for nutritionists and other practitioners to have a simple valid equation for the assessment of SMM, the aim of this study was to develop a new regression equation, validated with DXA as the “criterion standard”, for estimating SMM from anthropometric measures in a general population, avoiding the use of limb circumferences. In addition, we aim to compare the new equation with the Lee-2 equation, which also does not rely on limb circumferences.