However, many sport-performance trainings facilities do not have access to DXA equipment; therefore other simpler methods, such as anthropometry, are used to evaluate SMM [ 10 ]. It is easier to conduct anthropometric measurements in the field, a feasible alternative compared to expensive imaging methods [ 10 ]. The anthropometric measurements are usually employed to estimate SMM through several equations [ 11 — 18 ], but most of them were developed in nonathletic populations.
Some studies have analyzed the accuracy of these equations in college students [ 19 ] and in older adults [ 20 ]. To our knowledge, there are few studies regarding the accuracy of anthropometric equations in athletic populations, and among those that have been conducted, the results have been inconclusive [ 21 — 23 ].
Therefore, the purpose of this study was to compare anthropometric equations that estimate SMM with that derived from DXA in order to find which ones are more accurate in professional male soccer players. We obtained data in a cross-sectional way from professional male soccer players, aged 18 to 37 years.
Subjects were evaluated from to as part of their annual medical assessment which took place in our laboratory at a. They were instructed to avoid any exercise prior to their evaluations. These consisted of anthropometric measurements and a DXA whole-body scan performed on the same day. Only nonrepeated data were analyzed, and if multiple evaluations were registered, we kept the most recent. All subjects read and signed a written statement of consent prior to the evaluations.
Measurements consisted of the assessment of body mass to the nearest 0. Trained personnel assessed all measurements following standardized protocols [ 24 , 25 ]. To calculate bone mineral content and the fat and lean body mass, the scan was analyzed with a Hologic QDR v A certified DXA technician executed the whole procedure. The difference between DXA whole body weight and body scale weight was on average The calculated SMM was set as the reference. Nine mathematical equations that estimate SMM through anthropometric measurements were analyzed Table 1.
Negative results represented underestimation while positive results of the analyzed equation represented overestimation. The sample mean age was They showed The body composition components measured with DXA were on average The other equations showed higher mean differences and wider limits of agreement compared with DXA. The equations that overestimated the most were those reported by Martin et al.
Conversely, from the equations that differed significantly from DXA, those reported by Martin et al. In our study, we found that the SMM estimated with the equations from Heymsfield [ 12 ] and Lee [ 17 ] were statistically similar to values obtained with DXA. The equation published by Heymsfield et al. The SMM estimated with the equation published by Lee et al. This smaller variability could be attributable to the use of more variables three limb girths: relaxed arm, mid-thigh, and calf, corrected by skinfolds, Table 1 and the use of an anatomical cylindrical model [ 8 , 12 ].
Other studies have compared the accuracy of anthropometric equations to estimate SMM in different populations. For example, Berral de la Rosa et al. However, they compared the sum of several anthropometric equations to estimate different tissues i. Rodriguez-Rodriguez et al. They reported that the equations published by Heymsfield [ 12 ] and Drinkwater [ 13 ] were statistically similar to DXA-derived values; however, they did not perform a deeper statistical analysis for the agreement between these equations and DXA.
A few years earlier, Gobbo et al. Finally, Rech et al. The importance of SMM for exercise performance is well recognized [ 4 , 5 , 27 ]; however, it is difficult to find studies that report SMM, probably because it is difficult to assess it accurately, requiring expensive and complex methods such as magnetic resonance, computed axial tomography, or DXA. Some limitations in our study were as follows: 1 the ALST was measured and analyzed with different equipment and software Hologic than the one used by Kim et al.
These differences in the reference method account for the differences observed with the SMM obtained by DXA, because they estimate SMM at different levels of composition and with different assumptions [ 28 ]. We found that the SMM evaluated with DXA, in professional male soccer players, can be accurately estimated with the anthropometric equations published by Lee [ 17 ] and Heymsield [ 12 ].
Alongside these predictive equations, you can also determine BMR and percentage of fat mass with a few clicks, all of them evidence-based. Check here what other equations are available. With no need for a calculator or excel sheets, Nutrium can now quickly provide your clients with a more accurate anthropometric evaluation. Independent variables were organized into 2 separate formulas. One formula included mainly limb circumferences and skinfold thicknesses [model 1: height in m and skinfold-corrected upperarm, thigh, and calf girths CAG, CTG, and CCG, respectively; in cm ].
The other formula included mainly body weight in kg and height model 2. Conclusion: These 2 anthropometric prediction models, the first developed in vivo by using state-of-the-art body-composition methods, are likely to prove useful in clinical evaluations and field studies of SM mass in nonobese adults. Although skeletal muscle SM makes up the largest fraction of body mass in nonobese adults 1 , measurement methods that are suitable for field studies are lacking.
This is unfortunate, because SM is involved in many biological processes and quantification would likely provide new and important insights. However, CT remains impractical as a routine method for measuring SM because radiation exposure precludes studies in children and young women.
Despite these limitations, the cadaver studies showed the potential of predicting total-body SM from appendicular circumferences and skinfold thicknesses. The general concept is that about three-quarters of total-body SM exists in the extremities and that appendicular lean tissue is primarily SM 1 , that skinfold-corrected limb circumferences provide a measure of corresponding appendicular lean tissue circumferences, that squaring the appendicular lean tissue circumferences creates a lean tissue area estimate, and that taking the product of summed estimated appendicular lean tissue areas and height provides a measure of total-body SM in appropriate volume units.
The purpose of the present prospective study was to develop and cross-validate, in a large subject group, anthropometric prediction models for total-body SM by using MRI as the reference method. Healthy adults were recruited for study and completed anthropometric and MRI evaluations. The obese subjects were evaluated separately because there is some concern about the accuracy of anthropometric measurements in obese populations The nonobese subjects were randomly assigned to 1 of 2 groups: a model-development group group A and a cross-validation group group B.
Two prediction equations were developed by using data from the model development group, one based mainly on appendicular skinfold thicknesses and circumferences and the other based mainly on body weight and height. The equations developed were then cross-validated on the second nonobese group with the aim of pooling the data for all the nonobese subjects, if the models were successfully cross-validated, to develop final SM prediction equations.
The last stage of analysis was to cross-validate the equations in obese subjects. The subjects evaluated at the New York site nonobese and 24 obese subjects were recruited from among Hospital employees and students at local universities.
The subjects evaluated at the Kingston site 55 nonobese and 56 obese subjects were recruited from Queen's University and Hospital and from the general public through local media. All participants at both sites completed informed consent statements approved by the respective institutional review boards. Anthropometric measurements were all made by a highly trained observer at each study site using standardized procedures as reported by Lohman et al Body weight was measured to the nearest 0.
Height was measured with a stadiometer to the nearest 0. Skinfold thickness was measured on the right side of the body at appropriately marked sites and recorded to the nearest 0. Skinfold thickness was measured at the triceps, thigh, and medial calf according the standardized anatomic locations and methods reported by Lohman et al 12 and as summarized in Table 1. Anthropometric measurement sites 1. Adapted from reference Additional measurement details and technical errors are presented in that reference.
Circumference measurements were made in the plane orthogonal to the long axis of the body segment being measured. Circumferences of the midupper arm, midthigh, and midcalf were evaluated with a flexible standard measuring tape as reported in the Anthropometric Standardization Reference Manual 12 Table 1.
All circumference measurements were recorded to the nearest 1 mm. A series of 3 skinfold-thickness and 3 circumference measurements were made and the mean of all measurements was used for the analysis. Intrameasurer technical errors for skinfold-thickness and circumference measurements were consistent with those reported earlier The limb circumferences C limb were corrected for subcutaneous adipose tissue thickness 7 , 8.
The skinfold caliper measurement S was assumed to be twice the subcutaneous adipose tissue thickness. For dimensional consistency, corrected muscle circumferences were squared and multiplied by height to obtain a 3-dimensional SM measure 7 , 8.
Whole-body MRI scans were prepared by using 1. A T1-weighted spin-echo sequence with ms repetition time and a ms echo time was used to obtain the MRI data.
The MRI protocol was described in detail previously 13 , Briefly, the subjects lay in the magnet in a prone position with their arms placed straight overhead. The model used to segment the various tissues was described and illustrated previously 4 , 13 , Briefly, a multiple-step procedure was used to identify tissue area in cm 2 for a given MRI image.
In the first step, 1 of 2 equivalent techniques was used. Either a threshold was selected for adipose and lean tissues on the basis of the gray-level image pixel histograms or a filter-based watershed algorithm was used to identify tissue boundaries. Next, the observer labeled different tissues by assigning each one a specific code.
The original gray level was superimposed on the binary-segmented image by using a transparency mode to facilitate the corrections. The areas in cm 2 of the respective tissues in each image were computed automatically by summing the given tissue pixels and multiplying by the individual pixel surface area. The volume in cm 3 of each tissue in each slice was calculated by multiplying tissue area cm 2 by slice thickness 1.
The volume of each tissue was calculated by using a mathematical algorithm 13 , Volume units L were converted to mass units kg by multiplying the volumes by the assumed constant density for SM 1. We determined recently the reproducibility of MRI-SM measurements by comparing the intra- and interobserver estimates of MRI measurements one series of 7 images taken in the legs obtained in 3 male and 3 female subjects 4.
The intraobserver difference was calculated by comparing the analysis of 2 separate MRI acquisitions in a single observer, and the interobserver difference was determined by comparing 2 observers' analyses of the same images.
The interobserver difference was 1. In addition, we determined the reproducibility of whole-body MRI-SM measurements across the laboratories by comparing the 2 laboratories' analyses of the same images for 5 subjects. The interlaboratory difference was 2.
One experienced technician read all of the MRI scans at each site. The chi-square test was used for testing between-sex racial distribution differences.
The data sets from the 2 laboratories were combined because initial analyses did not detect between-center differences in developed models. Combining subjects creates a laboratory-independent prediction model and increases statistical power. Prediction models were prepared for the development sample with and without added skinfold-circumference measurements by using multiple regression analysis. We then explored the addition of other baseline variables and selected the highest adjusted R 2 model.
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