Anthropometric Models to Predict Appendicular Lean Soft Tissue in Adolescent Athletes

Body composition assessment in individuals with class II/III obesity: a narrative review

BACKGROUND

Individuals with class II/III obesity have a high percentage of body fat. Assessing body composition in cases of severe obesity can be difficult and controversial both in clinical practice and scientific research. Thus, it is essential to explore the different aspects of evaluating body composition and to discuss the available methods to assess it in this population.

AIMS

To summarise and discuss the methods used to measure body composition in adults with class II/III obesity and their potential in clinical practice and scientific research.

METHODS

This is a narrative review using data from PubMed, Scielo, and Lilacs databases. Original articles on body composition analysis in adults with class II/III obesity i.e., a BMI ≥ 35 kg/m2 were eligible. Body composition assessment methods were analysed and described.

RESULTS

Some imaging methods produced significantly accurate results. Dual-energy X-ray absorptiometry (DXA) significantly produces accurate results and has been used in clinical studies. However, due to its high cost, it is not applicable in clinical practice. Multifrequency bioelectrical impedance analysis (BIA) has good accuracy and is more appropriate for clinical practice than other methods. We have highlighted several aspects of the importance and applicability of performing body composition analysis in individuals with class II/III obesity.

CONCLUSION

DXA has been considered the most adequate method for clinical research. Multifrequency BIA may be a viable alternative to DXA for use in clinical practice. Assessing body composition and its components is important for people with class II/III obesity. It can help improve the effectiveness of interventions and clinical treatments, especially in reducing the risk of losing muscle mass. Muscle loss can cause sarcopenic obesity and other clinical complications, so understanding body composition is crucial. Assessing body composition can also help understand the impact of interventions on bones and avoid clinical complications.

Relationship between relative age measured through decimal age, physical variables and anthropometry in elite youth soccer players

INTRODUCTION

Age differences between athletes born in the same year, as well as an over-representation of older players, are known as the Relative Age Effect (RAE). Players born at the beginning of the selection year have a physical and anthropometric advantage over their younger peers. Experts keep looking for new prediction variables for talent identification.

OBJECTIVES

The aim of the study is to correlate anthropometric, strength and power variables with the relative age (RA) and the level of the teams in which players played in each age category.

METHODS

All players (N = 366) from an elite soccer academy of a Spanish club volunteered to participate in the study (U23-U10).

RESULTS

There was a significant correlation between the RA of the players and the level of the team in which they played in each age category but no correlation between trimester of birth and level of the team. We found significant correlations between the players' physical capacities, anthropometry, RA and the level of the team in which they played for the same age category, mainly from U16 to U10. U23 did not show any correlation between RA and physical or anthropometric variables.

CONCLUSION

Coaches should be cautious of choosing players based only on anthropometric or physical attributes.

Exploring the correlation of skin temperature and body composition in athletes undergoing exhaustive physical exercise

During strenuous exercise, skin temperature (Tsk) plays an essential role in thermoregulatory processes. As indicated in the literature, its response might be influenced by body composition, among other factors. Hence, the objectives of this investigation were to determine whether there is a correlation between selected body components, specifically fat tissue and muscle tissue, and Tsk during graded exercise and recovery in athletes, and to identify which body component exhibits the strongest correlation with Tsk. Participants were grouped according to their aerobic capacity (VO2max/kg). A significant main effect was observed for the test stages (p < .001, η2 = 0.71), with Tsk decreasing from the start of the exercise, significantly decreasing at 12 km/h-1 (p < .001), and then increasing after exercise, especially within the first 5 min of recovery. Weak and non-significant effect for group/stage interaction was detected (p = .374, η2 = 0.03). A significant negative correlation was found between Tsk and both total tissue fat [%] (-0.51 < r < -0.63, p < .001) and lower limb tissue fat [%] (-0.50 < r < -0.71, p < .001) across all test stages. The correlation between Tsk and BMI was inconsistent, appearing only during the first stage of exercise and throughout recovery. No correlation was observed between Tsk and skeletal muscle mass, appendicular lean soft tissue, or relative skeletal muscle index. Endurance running to exhaustion leads to a progressive decrease in the Tsk of the lower extremity, followed by rewarming during recovery. The observed inverse correlation between adipose tissue and Tsk, along with the distinct temperature trends in groups with varying levels of fat tissue, could imply that the skin and subcutaneous tissue complex may play a more intricate role in thermal energy exchange beyond its insulating function. This implies a multifaceted involvement of these tissues in thermoregulation.

Smartphone prediction of skeletal muscle mass: model development and validation in adults

BACKGROUND

Skeletal muscle is a large and clinically relevant body component that has been difficult and impractical to quantify outside of specialized facilities. Advances in smartphone technology now provide the opportunity to quantify multiple body surface dimensions such as circumferences, lengths, surface areas, and volumes.

OBJECTIVES

This study aimed to test the hypothesis that anthropometric body measurements acquired with a smartphone application can be used to accurately estimate an adult's level of muscularity.

METHODS

Appendicular lean mass (ALM) measured by DXA served as the reference for muscularity in a sample of 322 adults. Participants also had digital anthropometric dimensions (circumferences, lengths, and regional and total body surface areas and volumes) quantified with a 20-camera 3D imaging system. Least absolute shrinkage and selection operator (LASSO) regression procedures were used to develop the ALM prediction equations in a portion of the sample, and these models were tested in the remainder of the sample. Then, the accuracy of the prediction models was cross-validated in a second independent sample of 53 adults who underwent ALM estimation by DXA and the same digital anthropometric estimates acquired with a smartphone application.

RESULTS

LASSO models included multiple significant demographic and 3D digital anthropometric predictor variables. Evaluation of the models in the testing sample indicated respective RMSEs in women and men of 1.56 kg and 1.53 kg and R2's of 0.74 and 0.90, respectively. Cross-validation of the LASSO models in the smartphone application group yielded RMSEs in women and men of 1.78 kg and 1.50 kg and R2's of 0.79 and 0.95; no significant differences or bias between measured and predicted ALM values were observed.

CONCLUSIONS

Smartphone image capture capabilities combined with device software applications can now provide accurate renditions of the adult muscularity phenotype outside of specialized laboratory facilities. Am J Clin Nutr 2023;x:xx. This trial was registered at clinicaltrials.gov as NCT03637855 (https://clinicaltrials.gov/ct2/show/NCT03637855), NCT05217524 (https://clinicaltrials.gov/ct2/show/NCT05217524), and NCT03771417 (https://clinicaltrials.gov/ct2/show/NCT03771417).

Anthropometric equations to estimate appendicular muscle mass from dual-energy X-ray absorptiometry (DXA): A scoping review

BACKGROUND

Appendicular skeletal muscle mass (ASM) obtained from dual-energy x-ray absorptiometry (DXA) is recommended to quantify sarcopenia, but has limited availability in disadvantaged-income countries, moreover in an epidemiological context. Predictive equations are easier and less costly to apply, but a review of all available models is still lacking in the scientific literature. The objective of this work is to map, with a scoping review, the different proposed anthropometric equations to predict ASM measured by DXA.

METHODS

Six databases were searched without restriction on publication date, idiom, and study type. A total of 2,958 studies were found, of which 39 were included. Eligibility criteria involved ASM measured by DXA, and equations proposed to predict ASM.

RESULTS

predictive equations (n = 122) were gathered for 18 countries. The development phase involves sample size, coefficient of determination (r²), and a standard error of estimative (SEE) varying between 15 and 15,239 persons, 0.39 and 0.98, 0.07 and 3.38 kg, respectively. The validation phase involves a sample size, accuracy, and a SEE between 15 and 3,003 persons, 0.61 and 0.98, 0.09 and 3.65 kg, respectively.

CONCLUSIONS

The different proposed predictive anthropometric equations of ASM DXA were mapped, including validated pre-existing equations, offering an easy-to-use referential article for clinical and research applications. It is necessary to propose more equations for other continents (Africa and Antarctica) and specific health-related conditions (e.g., diseases), once the equations can only have sufficient validity and accuracy to predict ASM generally when applied to the same population.

Body composition and grip strength constraints in elite male rink-hockey players of contrasting ethnicity

Rink hockey is a highly specialized and physiological demanding sport with sparse research regarding the game and athletes’ characteristics. A cross-sectional study was developed to characterize the body composition and grip strength of elite male rink hockey players and to establish the relationship between ethnicity on body composition and grip strength. A sample of 100 elite rink-hockey athletes aged 26.59 ± 6.02 participated in the study, comprised of 69 Caucasian male adults aged 27.58 ± 6.44 years and 31 Black African male adults aged 24.39 ± 4.27. Body composition was assessed by anthropometric measurements. Static grip strength was assessed with an adjustable dynamometer. Multiple regression analysis was applied to understand which variables constraints body fat percentage (BF) and grip strength. Body mass showed an average of 76.36 ± 9.18 kg for 175.80 ± 5.87 cm of height and BF% of 10.82 ± 5.07%. Maximal right grip strength was 50.91 ± 6.26 kg and 50.27 ± 6.23 kg for left grip strength. Four predictors accounted for 70.01% of the variance of BF%: abdominal circumference (p < 0.001), right thigh circumference (p < 0.001), right calf circumference (p = 0.001) and ethnicity (p = 0.016). Three predictors accounted for 13.1% of the variance of right grip strength: ethnicity (p = 0.013), chronological age (p = 0.024) and right distal thigh circumference (p = 0.014). Results suggest that elite rink hockey athletes have a specific anthropometric identity, which at the elite level may lead to reduced body fat and greater handgrip strength. Ethnicity seems to predict body fat and grip strength in elite rink hockey athletes.

Probabilistic prediction of segmental body composition in Iranian children and adolescents

Background

Adolescents' body composition is considered an important measure to evaluate health status. An examination of any of the segmental compartments by anthropometric indices is a more usable method than direct methods.

Objectives

To propose a method based on the network approach for predicting segmental body composition components in adolescent boys and girls using anthropometric measurements.

Methods

A dual-energy X-ray absorptiometry (DXA) dataset in the south of Iran, including 476 adolescents (235 girls and 241 boys) with a range of 9–18 years, was obtained. Several anthropometric prediction models based on the network approach were fitted to the training dataset (TRD 80%) using bnlearn, an R add-in package. The best fitted models were applied to the validation dataset (VAD 20%) to assess the prediction accuracy.

Results

Present equations consisting of age, weight, height, body mass index (BMI), and hip circumference accounted for 0.85 (P < 0.001) of the variability of DXA values in the corresponding age groups of boys. Similarly, reasonable estimates of DXA values could be obtained from age, weight, height, and BMI in girls over 13 years, and from age, weight, height, BMI, and waist circumference in girls under 13 years, respectively, of 0.77 and 0.83 (P < 0.001). Correlations between robust Gaussian Bayesian network (RGBN) predictions and DXA measurements were highly significant, averaging 0.87 for boys and 0.82 for girls (P < 0.001).

Conclusions

The results revealed that, based on the present study’s predictive models, adolescents' body composition might be estimated by input anthropometric information. Given the flexibility and modeling of the present method to test different motivated hypotheses, its application to body compositional data is highly appealing.

Association of phase angle and appendicular upper and lower body lean soft tissue with physical performance in young elite soccer players: a pilot study

BACKGROUND

In soccer, a better understanding of the bioimpedance parameters with physical performance may be useful to efficiently monitor and interpret players' performance variation throughout a certain period of the season. Therefore, this study aimed to examine the association between phase angle (PhA) and arms and legs lean soft tissue (ALST and LLST) with physical performance in young elite soccer players.

METHODS

Fifteen young male elite soccer players (age = 14.2±1.2 years, BMI = 20.51±1.38 kg/m2) participated in this investigation. Raw bioimpedance parameters (reactance, resistance, and phase angle) were obtained by a bioelectrical impedance analysis (BIA) device. Then, ALST and LLST were estimated. All players underwent a physical testing battery including countermovement jump (CMJ), 10-m and 20-m sprint, and Yo-Yo Intermittent Recovery Test level 1 (YoYo IRTL1) in the domains of anaerobic and aerobic performance, respectively.

RESULTS

The results showed that LST (total, arms and legs) positively correlated with CMJ (0.64 < r < 0.69; p < 0.001) and negatively correlated with 10-m (-0.59 < r < -0.63; p < 0.05) and 20-m sprint (-0.67 < r < -0.73; p < 0.001), while PhA positively correlated with CMJ (r = 0.57; p < 0.05) and negatively correlated (r = -0.54; p < 0.05) only with 20-m sprint. No significant association was found between the BIA-related parameters (PhA and LST) and Yo- Yo IRT level 1.

CONCLUSIONS

The present findings highlight the existing association of PhA and LST with jumping and sprinting performance in young elite soccer players. This result supports the use of BIA-related measures as a simple and practical approach to monitoring anaerobic performance changes, rather than aerobic, over time throughout the season.

Allometric Scaling of Force-velocity Test Output Among Pre-pubertal Basketball Players

Basketball is characterized by high-intensity episodes predominantly reliant on anaerobic metabolism. The force-velocity test enables individual determination of an optimal braking force and emerged as appropriate to estimate optimal peak power. It has rarely been used in youth basketball. This study aimed to examine the contribution of body size, composition, and biological maturation to interindividual variation in force-velocity test output among pre-pubertal basketball players. The sample consisted of 64 male participants (8.4-12.3 years). Stature, sitting height, body mass and two skinfolds were measured, and leg length estimated. Fat-free mass and lower limb volume were estimated from anthropometry. Age at peak height velocity was predicted from maturity offset. Optimal peak power was correlated with all body size descriptors (correlation: 0.541-0.700). Simple allometric models explained 30-47% of inter-individual variance, with fat-free mass being the best predictor of performance. Whole-body fat-free mass (as a surrogate for active muscle mass) plus the indicator of maturation emerged as the best proportional allometric model (53% explained variance). Even at pre-pubertal ages, the interpretation of the force-velocity test requires assessing the metabolically active component of body mass.

The Effects of 3-Month Skill-Based and Plyometric Conditioning on Fitness Parameters in Junior Female Volleyball Players

PURPOSE

This study compared the effects of skill-based and plyometric conditioning (both performed in addition to regular volleyball training twice a week for 12 wk) on fitness parameters in female junior volleyball players.

METHODS

The participants [n = 47; age: 16.6 (0.6) y; mass: 59.4 (8.1) kg; height: 175.1 (3.0) cm] were randomized into a plyometric (n = 13), a skill-based (n = 17), and a control (n = 17) groups. The variables included body height, body mass, calf girth, calf skinfold, corrected calf girth, countermovement jump, 20-m-sprint, medicine ball toss, and sit-and-reach test.

RESULTS

Two-way analysis of variance (time × group) effects for time were significant (P < .05) for all variables except body mass. Significant group × time interactions were observed for calf skinfold [η² = .14; medium effect size (ES)], 20-m sprint (η² = .09; small ES), countermovement jump (η² = .29; large ES), medicine ball (η² = .58; large ES), with greater gains (reduction of skinfold) for plyometric group, and sit-and-reach (η² = .35; large ES), with greater gains in plyometric and skill-based groups. The magnitude-based inference indicated positive changes in 1) medicine ball toss and countermovement jump for all groups; 2) sit-and-reach for the plyometric and skill-based groups; and 3) 20-m sprint, calf girth, calf skinfold, and corrected calf girth for plyometric group only.

CONCLUSION

Selected variables can be improved by adding 2 plyometric training sessions throughout the period of 12 weeks. Additional skill-based conditioning did not contribute to improvement in the studied variables compared with regular volleyball training.

Body shape indices are predictors for estimating fat-free mass in male athletes

It is unknown whether body size and body shape parameters can be predictors for estimating whole body fat-free mass (FFM) in male athletes. This study aimed to investigate whether body size and shape variables can be predictors for FFM in male athletes. Using a whole-body dual-energy X-ray absorptiometry scanner, whole body fat mass (FM) and FFM were determined in 132 male athletes and 14 sedentary males. The sample was divided into two groups: validation (N = 98) and cross-validation (N = 48) groups. Body height (BH), body mass (BM), and waist circumference at immediately above the iliac crest (W) were measured. BM-to-W and W-to-BH ratios were calculated as indices of body shapes. Stepwise multiple regression analysis revealed that BM/W and W/BH were selected as explainable variables for predicting FFM. The equation developed in the validation group was FFM (kg) = 0.883 × BM/W (kg/m) + 43.674 × W/BH (cm/cm)- 41.480 [R2 = 0.900, SEE (%SEE) = 2.3 kg (3.8%)], which was validated in the cross-validation group. Thus, the current results demonstrate that an equation using BM/W and W/BH as independent variables is applicable for predicting FFM in male athletes.

Body mass-to-waist ratio strongly correlates with skeletal muscle volume in children

Purpose

We hypothesized that body mass-to-waist ratio is strongly associated with the total-body skeletal muscle volume (SMV) in children. The purpose of the present study was to examine this hypothesis.

Methods

By using magnetic resonance imaging, total-body SMV (SMV_MRI) was determined in 70 boys and 53 girls aged 6 to 12 years. Waist was measured at each of the level of umbilicus (Wumb) and the minimum circumference (Wmin), and the ratio of body mass to each of the two measured values was calculated (BM/Wumb and BM/Wmin, respectively). A single regression analysis was used to examine the relationships between SMV_MRI and either BM/Wumb or BM/Wmin. On the basis of the obtained regression equations, SMV_MRI was estimated and referred to as SMV_BM/Wumb or SMV_BM/Wmin.

Results

In both boys and girls, SMV_MRI was highly correlated to BM/Wumb (r = 0.937 for boys and r = 0.939 for girls, P < 0.0001) and BM/Wmin (r = 0.915 and 0.942, P < 0.0001). R² and the standard error of estimate for SMV_BM/Wumb were 0.878 and 706.2 cm³, respectively, in boys and 0.882 and 825.3 cm³, respectively, in girls, and those for SMV_BM/Wmin were 0.837 and 814.0 cm³, respectively, in boys and 0.888 and 804.1 cm³, respectively, in girls. In both boys and girls, there were no significant differences between SMV_MRI and either SMV_BM/Wumb or SMV_BM/Wmin, without systematic errors in Band-Altman plots. There was no significant effect of model on the absolute values of the residuals in both boys and girls.

Conclusion

The current results indicate that body mass-to-waist ratio can be a convenient outcome measure for assessing the total-body skeletal muscle volume in children.

Concurrent agreement between an anthropometric model to predict thigh volume and dual-energy X-Ray absorptiometry assessment in female volleyball players aged 14-18 years

Background

A variety of performance outputs are strongly determined by lower limbs volume and composition in children and adolescents. The current study aimed to examine the validity of thigh volume (TV) estimated by anthropometry in late adolescent female volleyball players. Dual-energy X-ray absorptiometry (DXA) measures were used as the reference method.

Methods

Total and regional body composition was assessed with a Lunar DPX NT/Pro/MD+/Duo/Bravo scanner in a cross-sectional sample of 42 Portuguese female volleyball players aged 14–18 years (165.2 ± 0.9 cm; 61.1 ± 1.4 kg). TV was estimated with the reference method (TV-DXA) and with the anthropometric method (TV-ANTH). Agreement between procedures was assessed with Deming regression. The analysis also considered a calibration of the anthropometric approach.

Results

The equation that best predicted TV-DXA was: -0.899 + 0.876 × log₁₀ (body mass) + 0.113 × log₁₀ (TV-ANTH). This new model (NM) was validated using the predicted residual sum of squares (PRESS) method (R²_PRESS = 0.838). Correlation between the reference method and the NM was 0.934 (95%CI: 0.880–0.964, S_y∙x = 0.325 L).

Conclusions

A new and accurate anthropometric method to estimate TV in adolescent female volleyball players was obtained from the equation of Jones and Pearson alongside with adjustments for body mass.

Electronic supplementary material

The online version of this article (doi:10.1186/s12887-016-0730-7) contains supplementary material, which is available to authorized users.

Prediction equation for lower limbs lean soft tissue in circumpubertal boys using anthropometry and biological maturation

Lean soft tissue (LST), a surrogate of skeletal muscle mass, is largely limited to appendicular body regions. Simple and accurate methods to estimate lower limbs LST are often used in attempts to partition out the influence of body size on performance outputs. The aim of the current study was to develop and cross-validate a new model to predict lower limbs LST in boys aged 10-13 years, using dual-energy X-ray absorptiometry (DXA) as the reference method. Total body and segmental (lower limbs) composition were assessed with a Hologic Explorer-W QDR DXA scanner in a cross-sectional sample of 75 Portuguese boys (144.8±6.4 cm; 40.2±9.0 kg). Skinfolds were measured at the anterior and posterior mid-thigh, and medial calf. Circumferences were measured at the proximal, mid and distal thigh. Leg length was estimated as stature minus sitting height. Current stature expressed as a percentage of attained predicted mature stature (PMS) was used as an estimate of biological maturity status. Backward proportional allometric models were used to identify the model with the best statistical fit: ln (lower limbs LST)  = 0.838× ln (body mass) +0.476× ln (leg length) - 0.135× ln (mid-thigh circumference) - 0.053× ln (anterior mid-thigh skinfold) - 0.098× ln (medial calf skinfold) - 2.680+0.010× (percentage of attained PMS) (R = 0.95). The obtained equation was cross-validated using the predicted residuals sum of squares statistics (PRESS) method (R2PRESS = 0.90). Deming repression analysis between predicted and current lower limbs LST showed a standard error of estimation of 0.52 kg (95% limits of agreement: 0.77 to -1.27 kg). The new model accurately predicts lower limbs LST in circumpubertal boys.

Contribution of body composition components and soft-tissue biochemical factors to genetic variation of body mass index (BMI) in an ethnically homogeneous population

OBJECTIVES

Elevated BMI results from an excess of not only fat mass (FM) but also fat-free soft tissue mass (FFM). Both components of body soft tissue, FM, and FFM, are now considered as active endocrine organs. The major aim of this study was to explore the genetic architecture of BMI, considering genetic variations of its major soft tissue components, and the main biochemical factors associated with their corresponding metabolism: leptin, adiponectin, E-selectin, and insulin-like growth factor binding protein, IGFBP-1.

METHODS

A total of 1,502 apparently healthy individuals (783 men, 719 women) from 359 ethnically homogeneous families were assessed anthropometrically for body composition. Model-based quantitative genetic analyses were implemented to reveal genetic and shared environmental factors affecting the variation and covariation of the studied phenotypes.

RESULTS

We found that inter-individual variation in BMI is strongly correlated with both body composition components (r > 0.92, P < 0.001). These correlations are caused by shared genetic and environmental factors that were interpreted to be a direct result of the intimate genetic and environmental correlations between FM and FFM. The latter were also significantly correlated with leptin, E-selectin, and IGFBP-1. However, whereas leptin displayed both genetic and environmental correlations with both FM and FFM, their correlations with E-selectin were caused only by common genes, and with IGFBP-1-only by a shared environment.

CONCLUSIONS

This study clearly suggests that FM and FFM contributed almost equally to BMI variation, and provides evidence that this contribution is caused by common genetic as well as shared environmental and metabolic factors.