Lean body mass (LBM) accounts for most of the human body and is known to be one of the main drivers of energy expenditure. It plays an important role in many physiological and pathological processes and is a major predictor of body functions, morbidity, and mortality [
Common LBM evaluation techniques in clinical settings include bioelectrical impedance analysis (BIA), magnetic resonance imaging (MRI), and dual-energy X-ray absorptiometry (DXA). BIA has simple operation and high speed, but its accuracy is relatively lower than that of the other two. Though MRI and DXA are highly accurate, they are hardly used in large-scale epidemiologic studies and in remote areas because of complicated operation and high cost [
Differences in LBM between different races have been observed [
This study aimed to develop simple anthropometric equations which would make estimation of LBM in both clinical and epidemiological settings and monitoring of southern Chinese people’s LBM easier. Furthermore, we validated our equations and analyzed the effects of body mass index (BMI) and menopause on equation development.
This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinan University (
Retrospective analysis was conducted on the consecutive people who did total body measurement using DXA in the First Affiliated Hospital of Jinan University from July 2005 to October 2015. Their case files were reviewed. Those with diseases which might affect LBM were excluded. Overall 12,194 subjects were included. Information about the 10,683 subjects (2,987 males and 7,696 females) from July 2005 to November 2014 was used to establish the equations. The males were aged 18.0 to 97.9 and the average age was 53.9 years, while the females were aged 18.0 to 98.6 with an average age of 55.8 years. The subjects were regrouped into six subgroups according to their BMI: the male and female underweight subgroups (16kg/m2 ≤ BMI < 18.5kg/m2), the male and female normal weight subgroups (18.5kg/m2 ≤ BMI < 25 kg/m2), and the male and female overweight subgroups (25kg/m2 ≤ BMI < 40 kg/m2). Besides, the female subjects were reassigned into two subgroups: the premenopausal subgroup and the postmenopausal one. Information about the 1,511 subjects (395 males and 1,116 females) from December 2014 to October 2015 was used to verify the equations. The males were aged 18.4 to 91.9 and their average age was 57.8 years. The females were aged 18.0 to 94.4 with an average age of 58.7 years. These subjects were also regrouped and reassigned using the above method. Current standards of BMI formulated by WHO were used [
Inclusion criteria were as follows:
Exclusion criteria were as follows:
Height (cm) was measured to the nearest 0.1 cm without shoes using a wall-mounted stadiometer. Weight (kg) was measured to the nearest 0.1 kg with light clothing on. BMI was calculated with the equation: BMI (kg/m2) = weight/ (height/100)2.
Lunar Prodigy DXA bone densitometer (GE Healthcare, Madison, WI) was used. During total body measurement, the participants were asked to lie supine on the scanning bed with their arms at their sides straightly, palms down isolated from the body, feet neutral, and ankles strapped. The scanner was calibrated daily with quality control model provided by the manufacturer and the performance was monitored according to the quality assurance protocol. Scanning was not performed until all the assurance procedures were finished. LBM measured by DXA (LBM_DXA) was analyzed automatically by the built-in Prodigy enCORE software (v.10.50.086). The root-mean-square coefficient of variation (RMS-%CV), or the short-term precision of LBM, was 0.93% [
Categorical and measurement data were analyzed using descriptive statistics. Measurement data were expressed as mean ± standard deviation. A
Equations for each group were developed with LBM_DXA as the dependent variable and anthropometric measures (height, weight, BMI, and age) as the predictor variables. They were analyzed using stepwise multilinear regression with the inclusion criterion being
The equations were validated in two ways in each validation subgroup. Paired-sample
Descriptive statistics, paired-sample
There were no significant differences in anthropometric measures (height, weight, BMI, and age) and LBM_DXA between the prediction participants and validation participants in both the male and female groups. All subjects’ general information is presented in Table
Participants’ characteristics (n=12,194).
Prediction participants | Validation participants | |||
---|---|---|---|---|
Males | Females | Males | Females | |
n | 2,987 | 7,696 | 395 | 1,116 |
LBM_DXA (kg) | 50.0±6.7 |
36.5±4.4# | 50.8±7.5 | 37.0±4.8 |
Age (year) | 53.9±17.7 |
55.8±15.5# | 57.8±18.0 | 58.7±16.2 |
Height (cm) | 167.9±6.3 |
156.7±5.4# | 168.2±6.9 | 156.3±6.0 |
Weight (kg) | 65.3±12.5 |
55.2±9.4# | 68.1±15.4 | 56. 7±11.2 |
BMI (kg/m2) | 23.1±3.7 |
22.4±3.4# | 23.9±4.3 | 23.2±4.0 |
16–18.49 | 328 (11.0%) | 885 (11.5%) | 25 (6.3%) | 112 (10.0%) |
18.5–24.99 | 1,823 (61.0%) | 5,238 (68.1%) | 245 (62.0%) | 718 (64.3%) |
25–39.99 | 836 (28.0%) | 1,573 (20.4%) | 125 (31.6%) | 286 (25.6%) |
Grouping variables were expressed as frequency (rate), while numerical values were expressed as mean ± standard deviation.
Abbreviations: LBM_DXA, lean body mass measured by dual-energy X-ray absorptiometry.
In the male group, variance analysis results showed that F and
Equation for all males (PEM) had the highest predictive ability (R2 = 0.782, SEE = 3.14kg). Weight, height, and BMI were positively correlated with LBM, while age was negatively correlated with it. BMI-subgrouping did not increase but slightly reduced the prediction accuracy of the equations (R2= 0.724 to 0.776, SEE = 2.77kg to 3.33kg).
There were statistically significant differences between LBM_PEM and LBM_PEM-under, LBM_PEM-normal, and LBM_PEM-over while PEM was used to predict LBM of each BMI-subgroup; however, the differences were very small (mean differences: 0.04kg to 0.13kg,
Comparison between LBM_PE and LBM_DXA in the validation subjects (n = 1,511).
Groups | n | LBM_P |
LBM by equations for subgroups (kg) | LBM_DXA (kg) | Bias |
|
95% |
R2 | SEE | |
---|---|---|---|---|---|---|---|---|---|---|
Males | All | 395 | 50.87±7.46 | / | 50.82±7.53 | 0.05±3.42 | 0.756 | -6.6, 6.8 | 0.803 | 3.35 |
Underweight | 25 | 42.99±2.87 |
42.88±2.72 | 43.59±3.33 | -0.59±2.16 | 0.183 | -4.8, 3.6 | 0.790 | 2.18 | |
Normal weight | 245 | 48.06±3.97 |
48.02±4.02 | 48.24±5.22 | -0.17±3.16 | 0.387 | -6.4, 6.0 | 0.734 | 3.16 | |
Overweight | 125 | 58.06±7.91 |
57.93±7.48 | 57.33±7.61 | 0.73±3.97 | 0.062 | -7.0, 8.5 | 0.757 | 3.77 | |
|
||||||||||
Females | All | 1,116 | 36.92±4.38 | / | 36.95±4.83 | -0.03±2.50 | 0.669 | -4.9, 4.9 | 0.734 | 2.49 |
Underweight | 112 | 32.51±2.17 |
31.28±2.45 | 32.61±3.21 | -0.10±2.21 | 0.633 | -4.4, 4.2 | 0.727 | 2.21 | |
Normal weight | 718 | 35.97±2.94 |
36.00±2.94 | 36.08±3.67 | -0.10±2.42 | 0.259 | -4.8, 4.6 | 0.748 | 2.42 | |
Overweight | 286 | 41.02±4.95# | 41.03±4.94 | 40.85±5.36 | 0.17±2.81 | 0.308 | -5.3, 5.7 | 0.731 | 2.79 | |
Premenopausal | 339 | 38.30±5.26 |
38.39±5.25 | 38.38±5.85 | -0.08±2.67 | 0.559 | -5.3,5.2 | 0.791 | 2.68 | |
Postmenopausal | 777 | 36.32±3.78 |
36.22±3.70 | 36.33±4.16 | -0.01±2.43 | 0.917 | -4.8, 4.7 | 0.668 | 2.40 |
LBM, lean body mass;
In the female group, variance analysis results showed that F and
Equation for all females (PEF) had higher predictability (R2 = 0.698, SEE = 2.43kg), though when compared with PEM its R2 was slightly lower. Weight and height were positively correlated with LBM, which was similar to the situation in the male group, while BMI was negatively correlated with LBM and age was not introduced into PEF. Neither BMI-subgrouping nor menopause-subgrouping significantly improved the prediction accuracy of the equations (R2 = 0.662 to 0.733, SEE = 2.22kg to 2.68kg).
There were statistically significant differences between LBM_PEF and LBM_PEF-under, LBM_PEF-normal, LBM_PEF-pre, and LBM_PEF-post while PEF was used to predict LBM of each subgroup. However, the differences were very small (mean differences: −0.09kg to 0.63kg,
Sex-specific anthropometric equations of LBM are developed in this study with a large sample of healthy southern Chinese adults. LBM measured by DXA are considered as the standard ones. Validation results show that the equations boast high accuracy. We found that there was a correlation between BMI and LBM; however, the results demonstrate that there is no need for BMI-subgrouping and menopause-subgrouping while developing LBM prediction equations. These equations could be valuable tools to estimate LBM in large-scale epidemiologic studies and in remote areas where DXA or MRI are not available.
In addition to ethnicity, height, weight, and age are also important influencing factors of LBM. Studies have also found that LBM is also associated with BMI [
Several anthropometric prediction equations of LBM have been developed, showing high predictability with high R2 ranging from 0.78 to 0.94 and low SEE ranging from 0.82kg to 3.61kg [
LBM prediction equations for BMI-based subgroups are designed to analyze the effect of BMI on LBM. However, BMI-subgrouping does not increase the accuracy of the equations but slightly decreases R2 in both male and female groups, which is believed to be the result of the narrow BMI ranges. This belief is confirmed by the fact that BMI cannot serve as a variable of both PEM-under and PEF-under. Statistically significant differences are observed between LBM predicted by PEM/PEF and LBM predicted by subgroup’s equations in each BMI-subgroup, except the overweight female group. However, the differences are very small (mean difference: 0.04kg to 0.13kg in males and -0.09kg to 0.63kg in females,
Compared with PEF’s prediction accuracy (R2 = 0.698), PE F-pre’s is slightly higher (R2 = 0.733), while PEF-post’s is relatively lower (R2 = 0.677). This may be because estrogen levels decrease after menopause and there are differences in body composition between postmenopausal and premenopausal women. Some studies have suggested that decrease in the levels of estrogen and testosterone in postmenopausal women’s serum may be a key cause of declining of LBM [
Weight and height are positively correlated with LBM in both males (PEM) and females (PEF). However, BMI is positively correlated with LBM in males, while it is negatively correlated with LBM in females. Yu et al. [
Another interesting finding is that age is not an influencing factor of females’ LBM, while it is negatively correlated with males’ LBM. Some studies reported that both males and females experienced age-related decease in LBM and that age had more impacts on males’ LBM than on females’ [
Our equations are developed using simplest anthropometric measurements, which can be made quickly in epidemiologic settings. The large sample and a broad range of age and BMI guarantee their high accuracy. It should be noted that, although they have been validated and have high accuracy in epidemiological settings, they are not accurate enough for clinical or individual use. The 95% LoA is (-6.6kg, 6.8kg) in males and (-4.9kg, 4.9kg) in females in this study, which is similar to the study by Lee et al. [
This study has several limitations. The data about body composition were collected only from the First Affiliated Hospital of Jinan University. The study should have included people from other research centers in order to make the conclusions suitable for each southern Chinese adult. Secondly, differences in scanning pattern, software version, and calibration method among different DXA by different manufacturers might result in measurement errors [
Gender-specific prediction equations for southern Chinese people’s LBM are developed and verified with a large sample in this study. They can be used in epidemiological settings to evaluate body composition. BMI was related to LBM content; however, there is no need for further group based on BMI or menopause while developing LBM questions.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors had full access to the data, contributed to the study, and approved the final version for publication. The authors will take responsibility for the accuracy and integrity of this study.
All authors have declared no conflicts of interest.
The study was supported by the Science and Technology Program of Guangzhou (201804010440).