Anthropometric Models to Predict Appendicular Lean Soft Tissue in Adolescent Athletes

Interest in evaluating the composition of specific anatomical regions has become commonplace in a variety of settings. Appendicular lean soft tissue (ALST) estimates are considered in the diagnosis of sarcopenia. While dual-energy x-ray absorptiometry (DXA) is viewed as a reference method for regional assessments, its availability is limited. Thus, explaining discrepancies in regional body composition estimates between DXA and the more accessible bioelectrical impedance analysis (BIA) is of utmost importance. PURPOSE: To assess the anthropometric and physiological predictors of variations between BIA and DXA segmental lean soft tissue (LST) estimates. METHODS: During a single visit, 179 participants (103 females, 76 males; Mean ± SD: 33.6 ± 15.3 years; 73.4 ± 16.2 kg; 171.2 ± 9.2 cm; 28.2 ± 8.9% DXA body fat %) underwent body composition assessments via DXA and 8-point single-frequency BIA. Potential predictors of discrepancies between DXA and BIA LST estimates were obtained from these methods and additional laboratory techniques. Specifically, air displacement plethysmography, 3-dimensional optical scanning, and bioimpedance spectroscopy were used to estimate body volume, anthropometrics, and hydration variables, respectively. Significant predictors (p ≤ 0.05) of the mean difference between DXA and BIA estimates of trunk LST (TLST) and ALST were established using ordinary least squares regression. Standardized model coefficients, p-values for coefficients, and R2 values were generated. RESULTS: For both TLST and ALST, extracellular fluid percentage, LST hydration, height, total LST mass, the male sex, and racial identification as Black significantly predicted discrepancies between DXA and BIA. Additional predictors for TLST discrepancies were DXA total fat mass (FM) to LST ratio and DXA TLST, while additional predictors of ALST discrepancies included DXA ALST, DXA FM to LST ratio of the legs, DXA appendicular FM, and DXA-derived volume of the arms and legs. Regression models including these significant predictor variables produced R2 values of 0.92 and 0.95 for TLST and ALST, respectively. CONCLUSIONS: Hydration variables, the quantity of LST in the region of interest, and height were the most influential predictor variables for discrepancies between DXA and BIA segmental LST estimates.

Lean body mass (LBM) and muscle mass remain difficult to quantify in large epidemiological studies due to the unavailability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual-energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n = 2,220; 36% women; age 18-79 yr), representing a wide range of body mass index (14–44 kg/m2), participated in this study. Their LBM, including ALST, was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA-measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height, and weight explained >90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of skinfold thicknesses) increased the explained variation by 5–8% in the fully adjusted models predicting LBM and ALST. More complex equations using all of the above anthropometric variables could predict the DXA-measured LBM and ALST accurately, as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively), as well as good agreement by Bland-Altman analyses (Bland JM, Altman D.Lancet 1: 307–310, 1986). These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition.

BackgroundBody mass is the primary metabolic compartment related to a vast number of clinical indices and predictions. The extent to which skeletal muscle (SM), a major body mass component, varies between people of the same sex, weight, height, and age is largely unknown. The current study aimed to explore the magnitude of muscularity variation present in adults and to examine if variation in muscularity associates with other body composition and metabolic measures.MethodsMuscularity was defined as the difference (residual) between a person's actual and model‐predicted SM mass after controlling for their weight, height, and age. SM prediction models were developed using data from a convenience sample of 492 healthy non‐Hispanic (NH) White adults (ages 18–80 years) who had total body SM and SM surrogate, appendicular lean soft tissue (ALST), measured with magnetic resonance imaging and dual‐energy X‐ray absorptiometry, respectively; residual SM (SMR) and ALST were expressed in kilograms and kilograms per square meter. ALST mass was also evaluated in a population sample of 8623 NH‐White adults in the 1999–2006 National Health and Nutrition Examination Survey. Associations between muscularity and variation in the residual mass of other major organs and tissues and resting energy expenditure were evaluated in the convenience sample.ResultsThe SM, on average, constituted the largest fraction of body weight in men and women up to respective BMIs of 35 and 25 kg/m2. SM in the convenience sample varied widely with a median of 31.2 kg and an SMR inter‐quartile range/min/max of 3.35 kg/−10.1 kg/9.0 kg in men and 21.1 kg and 2.59 kg/−7.2 kg/7.5 kg in women; per cent of body weight as SM at 25th and 75th percentiles for men were 33.1% and 39.6%; corresponding values in women were 24.2% and 30.8%; results were similar for SMR indices and for ALST measures in the convenience and population samples. Greater muscularity in the convenience sample was accompanied by a smaller waist circumference (men/women: P < 0.001/=0.085) and visceral adipose tissue (P = 0.014/0.599), larger liver (P = 0.065/<0.001), kidneys (P = 0.051/<0.009), and bone mineral (P < 0.001/<0.001), and larger magnitude resting energy expenditure (P < 0.001/<0.001) than predicted for the same sex, age, weight, and height.ConclusionsMuscle mass is the largest body compartment in most adults without obesity and is widely variable in mass across people of similar body size and age; and high muscularity is accompanied by distinct body composition and metabolic characteristics. This previously unrecognized heterogeneity in muscularity in the general population has important clinical and research implications.

BackgroundDespite the importance of body composition in athletes, reference sex- and sport-specific body composition data are lacking. We aim to develop reference values for body composition and anthropometric measurements in athletes.MethodsBody weight and height were measured in 898 athletes (264 female, 634 male), anthropometric variables were assessed in 798 athletes (240 female and 558 male), and in 481 athletes (142 female and 339 male) with dual-energy X-ray absorptiometry (DXA). A total of 21 different sports were represented. Reference percentiles (5th, 25th, 50th, 75th, and 95th) were calculated for each measured value, stratified by sex and sport. Because sample sizes within a sport were often very low for some outcomes, the percentiles were estimated using a parametric, empirical Bayesian framework that allowed sharing information across sports.ResultsWe derived sex- and sport-specific reference percentiles for the following DXA outcomes: total (whole body scan) and regional (subtotal, trunk, and appendicular) bone mineral content, bone mineral density, absolute and percentage fat mass, fat-free mass, and lean soft tissue. Additionally, we derived reference percentiles for height-normalized indexes by dividing fat mass, fat-free mass, and appendicular lean soft tissue by height squared. We also derived sex- and sport-specific reference percentiles for the following anthropometry outcomes: weight, height, body mass index, sum of skinfold thicknesses (7 skinfolds, appendicular skinfolds, trunk skinfolds, arm skinfolds, and leg skinfolds), circumferences (hip, arm, midthigh, calf, and abdominal circumferences), and muscle circumferences (arm, thigh, and calf muscle circumferences).ConclusionsThese reference percentiles will be a helpful tool for sports professionals, in both clinical and field settings, for body composition assessment in athletes.

ABSTRACTThe lean body mass (LBM) components have been suggested as important predictors of anaerobic performance, which is highly involved in basketball. We explored with descriptive cross-sectional design the relationship between anaerobic performance and full molecular and cellular body composition profile in young male basketball players. Twenty-one players (age = 16.8 ± 1.6 years; body mass = 76.3 ± 15.7 kg, height = 189.3 ± 12.6 cm) were recruited, 11 elite and 10 local level. Participants were evaluated on multicomponent body composition [LBM, appendicular lean soft tissue (ALST), bone mineral content (BMC), total body water (TBW), intracellular water (ICW) and extracellular water (ECW)] and field-based anaerobic performance (vertical jump, linear sprint, and handgrip strength). The stepwise regression analyses adjusted for confounders showed significant relationships of whole-body and regional body composition components with handgrip and jump performance (P ≤ 0.03). Prediction models combining body composition variables assessed by bioimpedance analysis (BIA) and double-energy X-ray absorptiometry (DXA) revealed that lean mass and hydration ratios (ICW/ECW and ECW/TBW) were strongly associated with jump performance (CMJ and CMJ25kg), independently of the competition level (P < 0.01). The novel finding in this study was that water quality (ICW/ECW) and water distribution (ECW/TBW, ICW) of total and regional LBM were the main predictors of vertical jump capacity in young basketball players.

To investigate the associations of breastfeeding with overall and regional body composition at 11 years of age, and to assess whether ultra-processed food (UPF) consumption mediates these associations. This prospective cohort study included participants from the 2004 Pelotas Birth Cohort, Brazil. Breastfeeding data were collected in early childhood, and overall and regional body composition were assessed at 11 years of age. Multiple linear regression models were used to examine the associations of exclusive and total breastfeeding duration, as well as breastfeeding patterns at 3 and 12 months, with overall (BMI z-score, fat mass index [FMI], and fat-free mass index [FFMI]) and regional body composition (android fat mass [AFM], gynoid fat mass [GFM], and appendicular lean soft tissue [ALST]). Mediation analysis was performed using the G-computation formula, considering UPF consumption at 6 years of age. Associations between breastfeeding and body composition were sex-specific and varied according to the breastfeeding indicator. Among boys, longer exclusive breastfeeding was associated with lower AFM, whereas receiving any breast milk at 3 months and longer total breastfeeding duration were associated with higher BMI z-score, FFMI, and regional lean and gynoid mass. Among girls, longer exclusive breastfeeding was associated with higher BMI z-score, FMI, GFM, and ALST, while continued breastfeeding at 12 months was associated with higher BMI z-score, FMI, FFMI, GFM, and ALST. UPF consumption did not mediate the associations between breastfeeding and body composition. Breastfeeding was associated with body composition at 11 years of age, particularly with regional fat distribution and lean mass, although the direction and magnitude of associations differed by sex and breastfeeding indicator. The findings suggest that breastfeeding may influence not only adiposity but also fat distribution and lean mass development.

There is a high prevalence of diabetes mellitus associated with insulin resistance in Indian adults, probably due to an inappropriately high accumulation of body fat at lower body mass indices (BMIs, kg/m(2)), as well as to a lower skeletal muscle mass. Although skeletal muscle is an important site of glucose disposal, the strength of its association with insulin sensitivity (IS) is unknown in the Indian population. This was a cross-sectional study performed to assess the association of body fat, muscle and muscle function with IS in 51 young, healthy, nonobese Indian men with BMI ranging from 15 to 25 kg/m(2), using hyperinsulinemic euglycemic clamp method. The median IS was 5.32 (mg/(kg min))/(microU/ml), lower, upper quartile, 4.03, 6.56); (SI units: 4.25, lower, upper quartile, 3.22, 5.24 (micromol/(kg min))/(pmol/l)). A multiple linear regression of the natural log transformed value of IS on BMI and body muscle mass measured as the appendicular lean soft tissue (expressed as a percentage of body weight) explained 49% of the variance in IS. Independently, body fat percent and muscle (handgrip) strength showed significant bivariate correlations with IS (rho=-0.61, P<0.001; rho=0.32, P=0.027, respectively), although these variables did not enter into the multiple regression. BMI and body fat have been shown to be inversely related to IS at higher BMIs. This study indicates that a relative increase in BMI and reduced muscle mass and possibly function are also associated with reduced IS in lean Indian men.

We appreciate Dr Safer et al.1 letter regarding our article. Although we agree with their point of view regarding the methodological limitations of our study, such as the basis of the exclusion criteria and the use of dual-energy X-ray absorptiometry (DXA) as a reference method, we have presented these issues as the study limitations in our article.2

Association between calf circumference and appendicular lean soft tissue in patients with colorectal cancer

To compare volumetric CT with DL-based fully automated segmentation and dual-energy X-ray absorptiometry (DXA) in the measurement of thigh tissue composition. This prospective study was performed from January 2019 to December 2020. The participants underwent DXA to determine the body composition of the whole body and thigh. CT was performed in the thigh region; the images were automatically segmented into three muscle groups and adipose tissue by custom-developed DL-based automated segmentation software. Subsequently, the program reported the tissue composition of the thigh. The correlation and agreement between variables measured by DXA and CT were assessed. Then, CT thigh tissue volume prediction equations based on DXA-derived thigh tissue mass were developed using a general linear model. In total, 100 patients (mean age, 44.9 years; 60 women) were evaluated. There was a strong correlation between the CT and DXA measurements (R = 0.813~0.98, p < 0.001). There was no significant difference in total soft tissue mass between DXA and CT measurement (p = 0.183). However, DXA overestimated thigh lean (muscle) mass and underestimated thigh total fat mass (p < 0.001). The DXA-derived lean mass was an average of 10% higher than the CT-derived lean mass and 47% higher than the CT-derived lean muscle mass. The DXA-derived total fat mass was approximately 20% lower than the CT-derived total fat mass. The predicted CT tissue volume using DXA-derived data was highly correlated with actual CT-measured tissue volume in the validation group (R2 = 0.96~0.97, p < 0.001). Volumetric CT measurements with DL-based fully automated segmentation are a rapid and more accurate method for measuring thigh tissue composition. • There was a positive correlation between CT and DXA measurements in both the whole body and thigh. • DXA overestimated thigh lean mass by 10%, lean muscle mass by 47%, but underestimated total fat mass by 20% compared to the CT method. • The equations for predicting CT volume (cm3) were developed using DXA data (g), age, height (cm), and body weight (kg) and good model performance was proven in the validation study.

Background: Data on reference values for lean mass (LM) and fat mass (FM) in the Southeast Asian populations are currently lacking. Therefore, we aimed to estimate the normative values and generate anthropometric prediction models for LM and FM in the Thai population. Methods: Consecutive community-dwelling individuals aged 20-90 years were recruited from Srinagarind Hospital, Khon Kaen, Thailand, between 2010 and 2015. LM and FM were measured using dual energy X-ray absorptiometry. Age and sex stratified percentile of LM and FM were presented. Anthropometric prediction models for LM and FM were developed by using linear regression to generate competing models. Results: A total of 832 individuals (334 males and 498 females) were included in the study. The mean ± SD age, LM, and FM were 50.0 ± 16.2 years, 38.9 ± 8.0 kg, and 15.5 ± 7.7 kg, respectively. LM decreased with age from 49.4 kg in 20-29 years group to 42.3 kg in ≥70 years group in male and 34.6 kg in 30-39 years group to 30.8 kg in ≥70 years group in females. FM has an inverse U-shaped association with age, which peaked at 11.9 kg in 60-69 years group in males and 20.7 kg in 50-59 years group in females. Among the various anthropometric models, the models incorporating age, sex, weight, and height were considered the best fit for predicting both LM and FM. Conclusion: In the Thai population, peak LM was reached during early adulthood and decline with age, whereas FM showed an inverse U-shaped association with age. The prediction models incorporating age, sex, weight, and height were proposed as practical tools for assessing LM and FM in clinical practice.

Born Large for Gestational Age: Bigger Is Not Always Better

Body composition can substantially impact elite swimming performance. In practice, changes in fat and lean mass of elite swimmers are estimated using body mass, sum of seven skinfolds (∑7) and lean mass index (LMI). However, LMI may be insufficiently accurate to detect small changes in body composition which could meaningfully impact swimming performance. This study developed equations which estimate dual-energy x-ray absorptiometry (DXA)-derived lean and fat mass using body mass and ∑7 data. Elite Australian swimmers (n = 44; 18 male, 26 female) completed a DXA scan and standardised body mass and ∑7 measurements. Equations to estimate DXA-derived lean and fat mass based on body mass, ∑7 and sex were developed. The relationships between ∑7, body mass and DXA-derived lean and fat mass were non-linear. Fat mass (Adjusted R2 = 0.91; standard error = 1.0 kg) and lean mass (Adjusted R2 = 0.99; standard error = 1.0 kg) equations were considered sufficiently accurate. Lean mass estimates outperformed the LMI in identifying the correct direction of change in lean mass (82% correct; LMI 71%). Using the accurate estimations produced by these equations will enhance the prescription and evaluation of programmes to optimise the body composition and subsequent performance in swimmers.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT? * Lean mass decreases with age while total body fat increases, leading to changes in the pharmacokinetics and pharmacodynamics of drugs. * Dual-energy X-ray absorptiometry (DXA) is a useful reference method to measure lean body weight but may not be readily utilized clinically. * Limited methods for measurement of lean body weight using easily obtainable participant characteristics, such as height and weight, have been validated for use in people aged > or =65 years. WHAT THIS STUDY ADDS * This study has shown that lean body weight can be estimated using three different equations from easily obtainable participant characteristics with results comparable to DXA-derived estimates. * These equations may be useful tools to estimate lean body weight in situations where DXA is not practical. AIMS Lean body weight (LBW) decreases with age while total body fat increases, altering drug pharmacokinetics. The aim of this study was to evaluate the ability of the LBW equation to predict dual-energy X-ray absorptiometry (DXA)-derived fat free mass (FFM(DXA)) in older community-dwelling males compared with that of two existing FFM equations: the Heitmann and Deurenberg equations. METHODS Data were obtained from 1655 older men enrolled in the Concord Health and Ageing in Men Project. The predictive performance of the LBW and FFM equations to predict FFM(DXA) accurately was assessed graphically using Bland-Altman plots and quantitatively for precision and bias using the method of Sheiner and Beal in all participants and in frailty and body mass index (BMI) subgroups. RESULTS The LBW and Heitmann equations consistently overestimated FFM(DXA) for all frailty and BMI subgroups with a mean difference [95% confidence interval (CI)] of 5.5 kg (-0.65, 11.63 kg) and 3.34 kg (-2.84, 9.64 kg), respectively. The Deurenberg equation overestimated FFM(DXA) for overweight participants but underestimated FFM(DXA) for not-frail participants, with a mean difference (95% CI) of 1 kg (-7.23, 5.25 kg) for all participants. CONCLUSION LBW and FFM estimated using these equations give results comparable to DXA-derived FFM. The LBW and Heitmann equations provide a more consistent estimate of FFM(DXA) in all frailty and BMI groups despite the Deurenberg equation having the smallest mean difference. Further studies to determine whether the LBW equation is a clinically useful substitute for weight when determining drug dose in older people appear warranted.

The monitoring of body composition is common in sports given the association with performance. Surface anthropometry is often preferred when monitoring changes for its convenience, practicality, and portability. However, anthropometry does not provide valid estimates of absolute lean tissue in elite athletes. The aim of this investigation was to develop anthropometric models for estimating fat-free mass (FFM) and skeletal muscle mass (SMM) using an accepted reference physique assessment technique. Sixty-four athletes across 18 sports underwent surface anthropometry and dual-energy X-ray absorptiometry (DXA) assessment. Anthropometric models for estimating FFM and SMM were developed using forward selection multiple linear regression analysis and contrasted against previously developed equations. Most anthropometric models under review performed poorly compared with DXA. However, models derived from athletic populations such as the Withers equation demonstrated a stronger correlation with DXA estimates of FFM (r = .98). Equations that incorporated skinfolds with limb girths were more effective at explaining the variance in DXA estimates of lean tissue (Sesbreno FFM [R2 = .94] and Lee SMM [R2 = .94] models). The Sesbreno equation could be useful for estimating absolute indices of lean tissue across a range of physiques if an accepted option like DXA is inaccessible. Future work should explore the validity of the Sesbreno model across a broader range of physiques common to athletic populations.