The main method of evaluating skeletal muscle is to detect skeletal muscle mass and muscle strength. Skeletal muscle mass can be measured by anthropometry, endogenous muscle metabolism, computed tomography (CT) [21], magnetic resonance imaging (MRI) [22], dual-energy X-ray absorptiometry (DXA) [23,24], and bioelectrical impedance analysis (BIA). Among these methods, BIA-derived phase angle measurement is an objective, noninvasive and very convenient method to assess muscle quality [25].
Currently, the percentage of skeletal muscle mass to total body weight (SMM) has been recommended in different consensuses [26,27]. At the same time, there are some applications of skeletal muscle mass divided by the square of height in some studies [28]. There are even other methods of calculation [29]. These methods are all defined as skeletal muscle mass index (SMI) by international study groups on sarcopenia [30]. So far, there is no universal consensus on assessment methods for research in this area or routine clinical practice. This undoubtedly leads to the confusion of SMI concept. Thus, the determination of which operational algorithms are most appropriate to assess skeletal muscle mass remains inconclusive [31,32]. Therefore, it is necessary to compare characteristics of different algorithms. Few available studies had compared SMM with SMH in the same group of subjects. Large-scale study in the Chinese population to address this issue are urgently warranted.
Theoretical analysis of algorithm comparison
The measurement of a person's skeletal muscle condition, mainly depends on the quality of skeletal muscle. When weight gains, it may be the increase of skeletal muscle mass, but it also can be the increase of body fat. The change of skeletal muscle can be well reflected by SMM. In theory, given a constant body weight, skeletal muscle mass should be negatively correlated with body fat mass. In the case of an adults' height unchanged, if the total weight is on the increase, which is mainly the increase of body fat, accompanied by a small increase in skeletal muscle mass, SMI will increase. Therefore, it is considered that SMM can better objectively reflect a person's skeletal muscle mass.
Main findings and comparison with other studies
From the results of this study, there was a significant difference between SMM and SMH. First of all, the sample data was large enough for normal distribution graphics. However, as can be seen by Fig. 1, Fig. 2 and Fig. 3, the normality, concentration, and stability of the distribution are expressed as SMM > BMI > BFP > SMH, which suggests that SMM is a superior assessment of body composition measurement.
Secondly, age and gender have been confirmed as important covariates in most body composition studies [33]. In our study, SMM and SMH showed the significant difference between male and female genders. Compared with different age groups, BMI, BFP, SMM and SMH had different trends with age in different populations. For example, among different age groups, SMM was the highest in the young group, followed by the middle-aged group, but SMM in the elderly group was higher than that in the middle-aged group. The living habits of people of different ages and genders varied, resulting in different changes in body composition. For example, in the Chinese population, elderly men began to exercise after retirement, and so their SMM would increase. The weight changed with the change of muscle mass, but the height could not change after adulthood, so the trend of SMH was different in males and females. Other authors reported high correlations between skeletal muscle mass and age [34]. According to previous research results, the change in SMM with age is more realistic than that of SMH.
The correlation between SMM and age, BMI, and BFP remained constant throughout, regardless of group. However, there was an inconsistent relationship between age, BMI, BFP, and SMH. These findings suggest that SMM is not only significantly correlated with age, BMI and BFP, but also remained relatively stable in the population with different genders.
As shown previously, SMM was negatively correlated with BMI or BFP, respectively. SMH was inversely correlated with BMI, but negatively correlated with BFP. Generally, as muscle increases, BFP or BMI should decrease. In our study, the higher the body fat, the higher the BFP. Similar correlations of BFP with BMI have been found in other studies [35,36]. SMM is more reasonable than SMH in this regard. Changes in SMM with different BMI groups and BFP is not only in line with the law of nature, but also which is consistent with the existing research and theory in sarcopenia. However, changes in SMH with different BMI groups is completely different from the ones in the existing researches [37,38]. Numerous studies have shown that the incidence of chronic diseases is different with different BMI [39]. Therefore, using different methods to calculate skeletal muscle mass parameters has different clinical guidance significance.
Strengths and Limitations
This study had several strengths. First, we included data from a large general population study in China covering all stages of the adult life. Secondly, the biological characteristics of the subjects were relatively comprehensive, including age, gender, and BMI. Thirdly, the number of study subjects was relatively larger than that of previous studies.
Our study also had some limitations. Firstly, the study was limited to Chinese people and did not include other ethnic groups. Secondly, this study was limited to healthy people, excluding patients with serious heart or brain diseases. Thirdly, BFP, SMM, and SMH were based only on BIA measurements, not on more precise methods available to directly measure body composition such as CT or MRI. Finally, this study was a cross-sectional study, selection and loss-to-follow up biases might have influenced accuracy of our conclusion. Ideally, however, future prospective studies should consider optimum sampling strategies when prediction formulas for SMI are developed based on BMI.