Reference Values for Body Composition and Anthropometric Measurements in Athletes

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.

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.

Skeletal muscle (SM), which is found mainly within the appendicular lean soft tissue (ALST) compartment, is a biological important body compartment. Simple and accurate methods to estimate both SM and ALST remain difficult to obtain. We aimed to develop and to cross-validate anthropometric models for ALST in athletes, using dual-energy x-ray absorptiometry (DXA) as the reference method. ALST equations were developed in 176 athletic boys (15.0 +/- 2.8 yr; 64.5 +/- 15.8 kg; 1.72 +/- 0.15 m) and 92 athletic girls (14.6 +/- 2.8 yr; 53.0 +/- 13.1 kg; 1.61 +/- 0.13 m). Skinfolds were measured at the triceps, the thigh, and medial calf, and circumferences were measured at the midupper arm, the midthigh, and the midcalf. ALST was assessed using DXA (QDR-4500; Hologic, Walthman, MA; fan-beam mode). Two models were developed: a body weight model (WHt model) and a corrected muscle girth model (CMG model, which included the parameters height x CAG, height x CTG, and height x CCG, where CAG is corrected arm girth, CTG is corrected thigh girth, and CCG is the corrected calf). Simple regression analysis was used to identify the best model fit. The equations were internally cross-validated using the predicted residual sum of squares method, and performance of new equations was analyzed by regression analysis and agreement between methods. The new WHt model generated the following equation: ALST = -20.338 + 0.199(W) + 3.294(gender) + 14.230(height) + 0.192(age), where gender = 1 for male and 0 for female. The CMG model produced the following equation: ALST = 3.260 + 0.002(height x CTG) + 0.007(height x CAG) + 0.003(height x CCG). WHt equation had an R = 0.91 and an SEE = 2.00 kg, whereas CMG equation presented an R = 0.93 and an SEE = 1.80 kg. In both equations, slopes and intercepts did not differ from the line of identity; no mean differences between predicted and measured values and no trend line were observed (P > 0.05). Both models accurately predict ALST in young athletes, affording a practical means to quantify this compartment.

The aim of this study was to examine the gender differences in postural balance, physical activity (PA), and body mass index (BMI) and body composition in visually impaired (VI) athletes. A total of 66 athletes took part in this study. After anthropometric measurements, the PA level was assessed by the International Physical Activity Questionnaire-Short Form. The modified Clinical Test of Sensory Integration and Balance tool was used to assess postural balance in four standing conditions: (1) eyes-open on a firm surface, (2) eyes-closed on a firm surface, (3) eyes-open on a compliant surface, and (4) eyes closed on a compliant surface. Findings revealed gender differences in anthropometric measurements ( p < .05). No statistically significant differences were found in fat mass and fat free mass between sighted male and VI male athletes and between sighted female and VI female athletes ( p > .05). Weekly, high and moderate PA levels as well as walking were found to be different across the four groups ( p < .05). Assessment of postural balance in four standing conditions revealed no gender differences in sighted athletes ( p > .05). Postural balance scores in conditions 1 and 3 showed significant gender differences ( p < .05), whereas conditions 2 and 4 showed no gender differences in athletes with VI ( p > .05). Gender-matched differences were also apparent in conditions 1 and 3, revealing that VI male and female athletes scored worse in these conditions than their sighted male and female counterparts ( p < .05). There is a delay in balance parameter in VI athletes even if their fitness levels are similar with their sighted counterparts. The present findings could also be useful for gender-specific training session preparations in VI sports.

Physical assessments are of increasing value in the development of peak conditioning in athletes. However, time constraints may often limit which assessment is performed. Measuring thoracic gas volume is an option provided during air displacement plethysmography (ADP), but adds time to the body composition (BC) assessment. Predicting lung volume may save time as less testing is required. However, what remains in question is the accuracy of using predicted lung volume in determining BC in athletes. PURPOSE: The aim of this study was to assess the validity of using predicted thoracic gas volume in determining BC during ADP in collegiate athletes. METHODS: Thirty-one subjects (5 males, 26 females) from 3 collegiate teams underwent BC procedures using ADP with the subject characteristics being height (m): 1.78 ± 0.08, body mass (kg): 78.66 ± 14.27, and BMI (kg/m2): 24.85 ± 3.70. Percent fat free mass (FFM) and percent fat mass (FM) were calculated from the assessment using predicted and measured lung volumes. Paired t-tests were used to compare the means of FFM and FM using predicted and measured measurements. A Pearson correlation coefficient calculated the relationship between subjects' predicted and measured body composition. RESULTS: A paired t-test revealed no significant difference between the mean measurements, p > 0.05. A Pearson correlation coefficient revealed a strong correlation between predicted and measured body composition in all subjects with r = 0.981, p = 0.001. CONCLUSION: Using the predicted lung volume provides a valid FFM and FM during BC testing during ADP in collegiate athletes. Time and effort may be saved by not measuring lung volume directly during ADP.Body Composition Values determined by Predicted and Measured Thoracic Lung Volumes.

Higher-protein intake and physical activity are associated with healthier body composition and cardiometabolic health in Hispanic adults

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.

This study aims to investigate body fluids and muscle changes evoked by different trail races using anthropometric, bioelectrical, and creatine kinase (CK) measurements. A total of 92 subjects (55 men, 37 women) participating in three different races of 14, 35, and 52 km were evaluated before (PRE) and after (POST) the races. Classic bioelectrical impedance vector analysis was applied at the whole-body level (WB-BIVA). Additionally, muscle-localized bioelectrical assessments (ML-BIVA) were performed in a subgroup of 11 men (in the quadriceps, hamstrings, and calves). PRE-POST differences and correlations between bioelectrical values and CK, running time and race distance were tested. Changes in whole-body vectors and phase angles disclosed an inclination towards dehydration among men in the 14, 35, and 52 km groups (p < 0.001), as well as among women in the 35 and 52 km groups (p < 0.001). PRE Z/H was negatively correlated with running time in the 35 km men group and 14 km women group (r = -0.377, p = 0.048; r = -0.751, p = 0.001; respectively). POST Z/H was negatively correlated with running time in the 14 km women group (r = -0.593, p = 0.02). CK was positively correlated with distance in men and women (p < 0.001) and negatively correlated with reactance and vector length in the 14 km men group (p < 0.05). ML-BIVA echoed the same tendency as the WB-BIVA in the 35 and 52 km runners, with the most notable changes occurring in the calves (p < 0.001). WB-BIVA and CK measurements underscored a conspicuous trend towards post-race dehydration and muscle damage, displaying a weak association with performance. Notably, ML-BIVA detected substantial alterations primarily in the calves. The study underscores the utility of BIVA as a technique to assess athlete's body composition changes.

Introduction: Currently, there are no formulas to estimate the percentage of fat by segments of the body from anthropometric measurements. The objective of this work was to correlate the percentage of arm fat mass (obtained through segmental bioimpedance) with anthropometric measurements, to generate a prediction formula valid for both genders. Methods: A sample of 100 individuals (50 women and 50 men) from 18 to 70 years old was analyzed in this observational study. A bioimpedance analysis was performed along with anthropometric determinations according to ISAK standards. Results: The percentage of arm fat mass estimated by bioimpedance strongly and positively correlated with the triceps and biceps skin folds, the arm fat area, and its percentage of fat area, in both sexes. In women, the percentage of arm fat mass also correlated with body mass index, arm circumference, and arm muscle area. Conclusion: Through a linear regression formula applicable to both sexes, the percentage of arm fat can be estimated from three anthropometric measurements.

The benefits of eccentric (ECC) training on fat mass (FM) remain underexplored. We hypothesized that in obese adolescents, ECC cycling training is more efficient for decreasing whole‐body FM percentage compared to concentric (CON) performed at the same oxygen consumption (VO2). Twenty‐four adolescents aged 13.4 ± 1.3 years (BMI > 90th percentile) were randomized to ECC or CON. They performed three cyclo‐ergometer sessions per week (30 min per session) for 12 weeks: two habituation, 5 at 50% VO2peak, and 5 at 70% VO2peak. Anthropometric measurements, body composition, maximal incremental CON tests, strength tests, and blood samples were assessed pre‐ and post‐training. Whole‐body FM percentage decreased significantly after compared to pretraining in both groups, though to a larger extent in the ECC group (ECC: −10% vs CON: −4.2%, P < 0.05). Whole‐body lean mass (LM) percentage increased significantly in both groups after compared to pretraining, with a greater increase in the ECC group (ECC: 3.8% vs CON: 1.5%, P <0.05). The improvements in leg FM and LM percentages were greater in the ECC group (−6.5% and 3.0%, P = 0.01 and P < 0.01). Quadriceps isometric and isokinetic ECC strength increased significantly more in the ECC group (28.3% and 21.3%, P < 0.05). Both groups showed similar significant VO2peak improvement (ECC: 15.4% vs CON: 10.3%). The decrease in homeostasis model assessment of insulin resistance index was significant in the ECC group (−19.9%). In conclusion, although both ECC and CON cycling trainings are efficient to decrease FM, ECC induces greater FM reduction, strength gains, and insulin resistance improvements and represents an optimal modality to recommend for obese adolescents.

Anthropometry is simple, cheap, portable and non-invasive method for the assessment of body composition. While the Nagamine and Suzuki body density prediction equation has been frequently used to estimate %BF of Japanese, the equation was developed more than 40 years ago and its applicability to the current Japanese population has not been studied. This study aimed to compare %BF results estimated from anthropometry and dual energy X-ray absorptiometry (DXA) in order to examine applicability of the Nagamine and Suzuki equation. Body composition of 45 Japanese males (age: 24.3+/-5.5 years, stature: 171.6+/-5.8 cm, body mass: 62.6+/-7.1 kg, %BF: 15.7+/-5.6%) were assessed using whole-body DXA (Hologic QDR-2000) scan and anthropometry using the protocol of the International Society for the Advancement of Kinanthropometry (ISAK). From anthropometric measurements %BF was calculated using the Nagamine and Suzuki equation. The results showed that the Nagamine and Suzuki equation significantly (p<0.05) underestimated %BF of Japanese males compared to the DXA results. There was a trend towards greater underestimation as the estimated %BF values using DXA increased. New %BF prediction equations were proposed from the DXA and anthropometry results. Application of the proposed equations may assist in more accurate assessment of body fatness in Japanese males living today.

To study the early markers of Metabolic syndrome in a cohort of low birth weight (LBW) children followed up since birth, at the age of 22 years. Prospective cohort study. Tertiary-care hospital. Neonates weighing less than 2000 g discharged from a neonatal special care unit were followed up prospectively; 153 cases and 77 controls were assessed at 22 years of age. Fasting, 30 minute and 120 minute glucose and insulin after a bolus of 75g of glucose was determined. Insulin resistance was calculated. A lipid profile was also done. Anthropometric measurements were taken and abdominal fat was determined by magnetic resonance imaging. Prevalence of the five components of Metabolic Syndrome as described by the International Diabetic Federation (IDF). 65.1% of the cohort was born small for gestational age. All three components of Metabolic syndrome were present in only three cases and none of the controls. However, two components were present in 25 (16.4%) cases and 5 (6%) controls (P=0.039). Cases in the lowest quartile of birthweight who became big at 22 years had significantly higher fasting insulin (P=0.001), Homeostatic Model Assessment - Insulin Resistance (Homa-IR) (P=0.001) and higher systolic blood pressure. Sum of skinfold thickness at 4 sites correlated significantly with fasting insulin and HOMA-IR, and was a stronger correlate compared to BMI, waist circumference and MRI fat. There was no difference in the biochemical parameters between appropriate for gestational age and small for gestational age infants. Prevelence of three or more components of Metabolic syndrome was low in LBW children at 22 years, but of two components was high. Those 'Small at birth and big at 22 years' had high insulin resistance.

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.

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

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.