Fat-free mass in adolescent athletes: Accuracy of bioimpedance equations and identification of new predictive equations

Fat-free mass predictive equation using bioelectrical impedance and maturity offset in adolescent athletes: Development and cross-validation

OBJECTIVES

This is a cross-sectional study, aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using single-frequency bioelectrical impedance (BIA), considering the predicted age at peak height velocity (PHV) as a variable. Additionally, the study aims to test the FFM-BIA obtained using a previous predictive equation that used skeletal maturity as a variable.

METHOD

The participants (n = 169 male adolescent athletes) were randomly divided into two groups: development of a new predictive equation (n = 113), and cross-validation (n = 56). The concordance test between the FFM values obtained by Koury et al. predictive equation and DXA data was determined (n = 169). Bioelectrical data was obtained using a single-frequency analyzer.

RESULTS

Among the models tested, the new predictive equation has resistance index (height2/resistance) and predictive age at PHV as variables and presented R2 = 0.918. The frequency of maturity status using skeletal maturity and PHV diagnosis was inadequate (Kappa = 0.4257; 95%CI = 0.298-0.553). Bland-Altman plots and concordance correlation coefficient showed substantial concordance between the FFM-DXA values (48.8 ± 11.2 kg) and the new predictive equation (CCC = 0.960). The results showed that the new equation performed better than the equation developed by Koury et al. (CCC = 0.901).

CONCLUSIONS

Our results show that it is feasible to predict FFM in male adolescent athletes using predictive age at PHV, with moderate concordance. The calculation of FFM using more economical and less complex variables is viable and should be further explored.

High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review

The appropriate use of predictive equations in estimating body composition through bioelectrical impedance analysis (BIA) depends on the device used and the subject's age, geographical ancestry, healthy status, physical activity level and sex. However, the presence of many isolated predictive equations in the literature makes the correct choice challenging, since the user may not distinguish its appropriateness. Therefore, the present systematic review aimed to classify each predictive equation in accordance with the independent parameters used. Sixty-four studies published between 1988 and 2023 were identified through a systematic search of international electronic databases. We included studies providing predictive equations derived from criterion methods, such as multi-compartment models for fat, fat-free and lean soft mass, dilution techniques for total-body water and extracellular water, total-body potassium for body cell mass, and magnetic resonance imaging or computerized tomography for skeletal muscle mass. The studies were excluded if non-criterion methods were employed or if the developed predictive equations involved mixed populations without specific codes or variables in the regression model. A total of 106 predictive equations were retrieved; 86 predictive equations were based on foot-to-hand and 20 on segmental technology, with no equations used the hand-to-hand and leg-to-leg. Classifying the subject's characteristics, 19 were for underaged, 26 for adults, 19 for athletes, 26 for elderly and 16 for individuals with diseases, encompassing both sexes. Practitioners now have an updated list of predictive equations for assessing body composition using BIA. Researchers are encouraged to generate novel predictive equations for scenarios not covered by the current literature.Registration code in PROSPERO: CRD42023467894.

Fat-free mass predictive equation using multifrequency bioelectrical impedance data in adolescent soccer athletes: development and cross-validation

OBJECTIVES

This study aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using multifrequency bioelectrical impedance analysis (BIA) data in adolescent soccer athletes.

METHODS

Male adolescent soccer athletes (n = 149; 13-19 y old) were randomly sorted using Excel and independently selected for development group (n = 100) or cross-validation group (n = 49). The FFM reference values were determined using dual-energy X-ray absorptiometry. Single-frequency BIA was used to plot tolerance ellipses. Multifrequency-BIA raw data were used as independent variables in regression models. Student's independent t-test was used to compare development and cross-validation groups. Stepwise multiple regression was used to develop the FFM predictive equation. Bland-Altman plots, Lin's concordance correlation coefficient, according to McBride criteria, precision, accuracy, and standard error of estimate (SEE) were calculated to evaluate the concordance and reliability of estimates. Bioelectrical impedance vector analysis was plotted to assess hydration status.

RESULTS

No differences (P > 0.05) were observed between development and validation groups in chronological age, anthropometric data, bioelectrical impedance data, and FFM values obtained using dual-energy X-ray absorptiometry. Bioelectrical impedance vector analysis tolerance showed that all participants presented adequate hydration status compared to the reference population. The new FFM predictive equation developed and validated: FFM (kg) = -7.064 + 0.592 × chronological age (y) + 0.554 × weight (kg) + 0.365 × height²/resistance (cm²/Ω), presented R² = 0.95; SEE = 1.76 kg; concordance correlation coefficient = 0.95, accuracy = 0.98, and strength of concordance = 0.99.

CONCLUSIONS

The present study developed and cross-validated an FFM predictive equation based on multifrequency bioelectrical data providing substantial FFM accuracy for male adolescent soccer athletes.

Prediction of fat-free mass from body surface area in young basketball players

BACKGROUND

Fat Free Mass (FFM) is an important and essential indicator in various sports populations, since greater muscle and bone mass generates greater strength, endurance and speed in athletes.

OBJECTIVE

The purpose of the study was to validate Body Surface Area (BSA) as an anthropometric indicator to estimate FFM in young basketball players.

METHODS

A descriptive cross-sectional study was carried out in 105 male basketball players of the Brazilian Basketball Confederation of Sao Paulo (Campinas), Brazil. The age range was 11 to 15 years. Weight and height were evaluated. BSA, body mass index (BMI) and maturity status (MS) were calculated. Total body scanning was performed by dual X-ray absorptiometry (DXA). The components were extracted: Fat mass (FM), Fat free mass (FFM), percentage of fat mass (%FM) and bone mass (BM). The data were analyzed using the correlation coefficient of concordance (CCC) in terms of precision and accuracy.

RESULTS

Three regression equations were generated: equation 1 had age and body weight as predictors [FFM= -30.059+(2.926*age)+(0.625*Weight)] (R2 = 92%, precision = 0.96 and accuracy = 0.99), equation 2 used age and BSA [FFM=-45.719+(1.934*age)+(39.388*BSA)] (R2 = 94%, precision = 0.97 and accuracy = 0.99) and equation 3 was based on APHV and BSA [FFM=-15.284+(1.765*APHV)+(37.610*(BSA)] (R2 = 94%, precision = 0.96 and accuracy = 0.99).

CONCLUSIONS

The results suggest the use of anthropometric equation using decimal age and BSA to estimate FFM in young basketball players. This new method developed can be used to design, evaluate and control training programs and monitor the weight status of athletes.

Localized hamstring bioimpedance in marathon runners is related to muscle high-energy enzyme serum levels and predicts race time

Introduction: The aim was to analyze the response of serum levels of inflammatory, high-energy muscle biomarkers and hamstring localized bioimpedance (L-BIA) measurements to marathon running and to ascertain whether they correlate with each other or with race time. Methods: Blood samples and hamstrings tetra-polar L-BIA measurements from 14 Caucasian male recreational athletes at the Barcelona Marathon 2019 were collected at base line, immediately after and 48 h post-race. Serum C reactive protein (sCRP), creatinine kinase (sCK) and lactate dehydrogenase (sLDH) were determined using an AU-5800 chemistry analyzer. L-BIA was obtained at 50 kHz with a Quantum V Segmental phase-sensitive bioimpedance analyzer. Results: Median sCRP increased (4-fold) after 48 h post-race. Median sCK and sLDH levels increased immediately post-race (3-fold, 2-fold) and 48h post-race (5-fold, 1-fold). Left, right and combined hamstring reactance (Xc) and phase angle (PhA) increased immediately post-race. Xc combined hamstring pre- and immediately post-race correlated with race-time and with sCK and sLDH median levels pre-race. Xc combined hamstring pre- and immediately post-race > 15.6 Ω and 15.8 Ω, respectively, predicted the race time of 3:00:00 h. Conclusion: L-BIA reactance (Xc) is an objective direct, real time, easy, noninvasive bioelectrical parameter that may predict muscle and marathon athlete performance.

One-Year Changes in Bioelectrical Impedance Data in Adolescent Athletes

Raw bioelectrical impedance (BI) data and vector analysis (BIVA) have been used to evaluate fat-free mass (FFM) cross-sectionally in adolescent athletes; however, there have been no longitudinal studies about it. This study aimed to assess the magnitude of changes in raw BI data (resistance [R], reactance [Xc], and phase angle [PhA]), BIVA, and FFM in adolescent athletes (n = 137, 40% female). BI data were collected using a single-frequency device at baseline and after one year of sports practice. Baseline chronological age categorized the participants (11, 12, or 13 years [y]). In females, Xc/H increased (13 to 14 y, p = 0.04) while R/H decreased in all age groups (p = 0.001). PhA (11 to 12 y, p = 0.048) and FFM (11 to 12 y and 12 to 13 y groups p = 0.001) increased and showed the lowest magnitude of changes in the 13 to 14 y group (p = 0.05). In males, Xc/H decreased (11 to 12 and 12 to 13 y groups, p = 0.001) with a higher magnitude of changes in the 13 to 14 y group (p = 0.004); R/H decreased (p = 0.001); FFM increased in all groups (p = 0.001); however, no magnitude of changes was observed. PhA increased in the 13 to 14 y group (p = 0.004). BIVA showed no differences among ellipse distances in females. In males, a high distance was observed in the 11 to 12 y group. "Time interval" influenced PhA and Xc/H in the female group and R/H and Xc/H in the male group. "Initial age" and "time interval" influenced the increase in PhA in the male group. Raw BI data and BIVA patterns can detect the magnitude of the changes in a sex-dependent manner.

Utilization of bioelectrical impedance vector analysis (BIVA) in children and adolescents without diagnosed diseases: a systematic review

Introduction. Bioelectrical impedance vector analysis (BIVA) emerges as a technique that utilizes raw parameters of bioelectrical impedance analysis and assumes the use of a reference population for information analysis.Objective. To summarize the reference values, main studies objectives, approaches, pre-test recommendations and technical characteristics of the devices employed in studies utilizing BIVA among children and adolescents without diagnosed diseases.Methods. A systematic search was conducted in nine electronic databases (CINAHL, LILACS, PubMed, SciELO, Scopus, SPORTDiscus, Science Direct, MEDLINE, and Web of Science). Studies with different designs which allowed extracting information regarding reference values of BIVA in children and adolescents without diagnosed diseases, aged 19 years or younger, were included. The systematic review followed PRISMA procedures and was registered in PROSPERO (registration: CRD42023391069).Results. After applying the eligibility criteria, 36 studies were included. Twenty studies (55.6%) analyzed body composition using BIVA, thirteen studies (36.1%) aimed to establish reference values for BIVA, and three studies (8.3%) investigated the association of physical performance with BIVA. There was heterogeneity regarding the reference populations employed by the studies. Fifteen studies used their own sample as a reference (41.6%), four studies used the adult population as a reference (11.1%), and five studies used reference values from athletes (13.9%).Conclusion. Nutricional status and body composition were the main studies objectives. References values were not always adequate or specific for the sample and population. Furthermore, there was no pattern of pre-test recommendations among the studies.

Age-, sex-, and maturity-associated variation in the phase angle after adjusting for size in adolescents

Background

Applied research using the phase angle (PhA) in children and adolescents has increased notably. Using multilevel modeling in a fully Bayesian framework, we examined the relationships between PhA, age, sex, biological maturity status, and body size in 10–16-year-old adolescents.

Methods

The sample comprised 519 adolescents (women, n = 241; men, n = 278) from Campinas, São Paulo, Brazil. Biological maturity status was assessed with self-examination of pubertal development for sexual maturity and maturity offset protocol to estimate age at peak height velocity (PHV) for somatic maturity status. Stature and body mass were measured by anthropometry. Phase angle was calculated based on raw resistance and reactance values (50 kHz frequency) obtained by bioelectrical impedance with the foot-to-hand technology.

Results

The multilevel regression analysis revealed that boys had significantly higher values of phase angle than girls, adjusting for age group and sexual maturity status. Overall, older and more mature adolescents had higher values of phase angle. When considering aligning variation in the phase angle by distance to estimated PHV (maturity offset), there was a higher association between the phase angle and time before and after predicted age at PHV for boys (r = 0.31, 90% CI: 0.23 to 0.39) than girls (r = 0.2, 90% CI: 0.11 to 0.28). When including body mass in the multilevel models, corresponding changes in the overall body mass mediate most of the influence of the maturity status and age group on the phase angle.

Conclusion

The present study demonstrated that the variability in phase angle is related to inter-individual variation in sex, age, and maturity status, as well as differences in body size. Research with adolescents considering phase angle should use multilevel modeling with standardized parameters as default to adjust for the concurrent influence of sex, age, maturity status, and body size.

Fat-Free Mass Using Bioelectrical Impedance Analysis as an Alternative to Dual-Energy X-Ray Absorptiometry in Calculating Energy Availability in Female Adolescent Athletes

Energy availability (EA) is calculated by subtracting exercise energy expenditure from energy intake, adjusted for fat-free mass (FFM) obtained using accurate methods, such as dual-energy X-ray absorptiometry (DXA). Unlike DXA, the bioelectrical impedance analysis (BIA) is low in cost, simple and easy to carry out. This study aimed to test the concordance between the calculation of EA using FFM values from four BIA predictive equations and FFM obtained using DXA in female adolescent athletes (n = 94), recruited via social media. Paired Student's t test, Wilcoxon test, Lin's concordance correlation coefficient, root mean square error, limits of agreement, and mean absolute percentage error were used to evaluate agreement between the FFM values obtained by the four SF-BIA predictive equations and DXA. Regression linear analysis was used to determine the relation between FFM values obtained using DXA and the BIA predictive equations. Standardized residuals of the FFM and EA were calculated considering DXA values as reference. The most appropriate model for the FFM (limits of agreement = 4.0/-2.6 kg, root mean square error = 1.9 kg, mean absolute percentage error = 4.34%, Lin's concordance correlation coefficient = .926) and EA (limits of agreement = 2.51/4.4 kcal·kg FFM-1·day-1, root mean square error = 1.8 kcal·kg FFM-1·day-1, mean absolute percentage error 4.24%, Lin's concordance correlation coefficient = .992) was the equation with sexual maturity as a variable, while the equation with the greatest age variability was the one with the lowest agreement. FFM-BIA predictive equations can be used to calculate EA of female adolescent athletes. However, the equation should be chosen considering sex, age, and maturation status. In the case of athletes, researchers should use equations developed for this group.

Development and Cross-Validation of a Predictive Equation for Fat-Free Mass in Brazilian Adolescents by Bioelectrical Impedance

The bioelectrical impedance analysis (BIA) is one of the most commonly used techniques for assessing body composition in a clinical setting and in field approaches, as it has the advantages of easy application, fast, and non-invasive, in addition to its relatively low cost. However, the available predictive equations need to be valid for the evaluated subjects. The aim of this study was to verify the validity of several published BIA equations in estimating fat-free mass (FFM) among Brazilian adolescents, in addition to developing and cross-validating a BIA equation to estimate FFM appropriate for Brazilian adolescents. This is a cross-sectional study with 257 adolescents (128 girls) aged 10-19 years, randomly divided into two groups, namely, development (n = 172) and cross-validation (n = 85). The standard technique for assessing FFM was dual X-ray absorptiometry (DXA). The paired t-test, multiple regression, and the Bland-Altman plots were used to test the validity of the proposed models and to perform cross-validation of the model. The equation derived in this study was as follows: FFM = -17.189 + 0.498 (Height²/Resistance) + 0.226 Weight + 0.071 Reactance - 2.378 Sex + 0.097 Height + 0.222 Age; r ² = 0.92; standard error of the estimate = 2.49 kg; the new equation for FFM showed better agreement when compared with that of the equations developed in other countries. In conclusion, the newly developed equations provide a valid FFM estimation and are recommended for Brazilian adolescents with similar characteristics.

Bioimpedance Vector Patterns according to Age and Handgrip Strength in Adolescent Male and Female Athletes

Bioelectric Impedance Vector Analysis (BIVA) can be used to qualitatively compare individuals’ hydration and cell mass independently of predictive equations. This study aimed to analyze the efficiency of BIVA considering chronological age and handgrip strength in adolescent athletes. A total of 273 adolescents (male; 59%) engaged in different sports were evaluated. Bioelectrical impedance (Z), resistance (R), reactance (Xc), and phase angle (PhA) were obtained using a single-frequency bioelectrical impedance analyzer. Fat-free mass (FFM) and total body water were estimated using bioimpedance-based equations specific for adolescents. Female showed higher values of R (5.5%, p = 0.001), R/height (3.8%, p = 0.041), Z (5.3%, p = 0.001), and fat mass (53.9%, p = 0.001) than male adolescents. Male adolescents showed higher values of FFM (5.3%, p = 0.021) and PhA (3.1%, p = 0.033) than female adolescents. In both stratifications, adolescents (older > 13 years or stronger > median value) shifted to the left on the R-Xc graph, showing patterns of higher hydration and cell mass. The discrimination of subjects older than 13 years and having higher median of handgrip strength values was possibly due to maturity differences. This study showed that BIVA identified age and strength influence in vector displacement, assessing qualitative information and offering patterns of vector distribution in adolescent athletes.

Phase Angle from Bioelectric Impedance and Maturity-Related Factors in Adolescent Athletes: A Systematic Review

Phase angle (PhA) is a body composition (BC) parameter from bioelectrical impedance analysis that is suggested as a marker of cell integrity and general health. In adolescent athletes, PhA values are used to monitor the effects of training and competitions and seem to vary according to age, having an upward trajectory during puberty. Since adolescence is a period composed of maturation stages, the aim of this systematic review was to investigate the relationship between PhA and maturation stage in adolescent athletes. A search was conducted in PubMed/MEDLINE, BVS, Web of Science (WOS), Scopus, Science Direct, and Academic Search Complete (EBSCO) databases up to May 2020. Eligibility criteria followed the PICOS strategy and studies with healthy athletes aged 10–19 years of any level of competition were included. Six cross-sectional studies and two longitudinal studies met the inclusion criteria. Results showed that early and mature athletes have higher PhA than those who are non-matured or maturated late. Thus, PhA is influenced by the pubertal status. Considering BC is one of the factors influencing sports performance together with its growth-related changes, practitioners may use PhA values in BC analysis of adolescent athletes.

Identifying Athlete Body Fluid Changes During a Competitive Season With Bioelectrical Impedance Vector Analysis

PURPOSE

To analyze the association between body fluid changes evaluated by bioelectrical impedance vector analysis and dilution techniques over a competitive season in athletes.

METHODS

A total of 58 athletes of both sexes (men: age 18.7 [4.0] y and women: age 19.2 [6.0] y) engaging in different sports were evaluated at the beginning (pre) and 6 months after (post) the competitive season. Deuterium dilution and bromide dilution were used as the criterion methods to assess total body water (TBW) and extracellular water (ECW), respectively; intracellular water (ICW) was calculated as TBW-ECW. Bioelectrical resistance and reactance were obtained with a phase-sensitive 50-kHz bioelectrical impedance analysis device; bioelectrical impedance vector analysis was applied. Dual-energy X-ray absorptiometry was used to assess fat mass and fat-free mass. The athletes were empirically classified considering TBW change (pre-post, increase or decrease) according to sex.

RESULTS

Significant mean vector displacements in the postgroups were observed in both sexes. Specifically, reductions in vector length (Z/H) were associated with increases in TBW and ICW (r = -.718, P < .01; r = -.630, P < .01, respectively) and decreases in ECW:ICW ratio (r = .344, P < .05), even after adjusting for age, height, and sex. Phase-angle variations were positively associated with TBW and ICW (r = .458, P < .01; r = .564, P < .01, respectively) and negatively associated with ECW:ICW (r = -.436, P < .01). Phase angle significantly increased in all the postgroups except in women in whom TBW decreased.

CONCLUSIONS

The results suggest that bioelectrical impedance vector analysis is a suitable method to obtain a qualitative indication of body fluid changes during a competitive season in athletes.

Maturity Related Differences in Body Composition Assessed by Classic and Specific Bioimpedance Vector Analysis among Male Elite Youth Soccer Players

The aim of this study was to analyze the efficiency of classic and specific bioelectrical impedance vector analysis (BIVA) in the assessment of maturity related differences in body composition among male elite youth soccer players, and to provide bioelectrical impedance reference data for this category. A group of 178 players (aged 12.1 ± 1.6 years) were registered in a professional Italian soccer team participating in the first division (Serie A). They were divided into three groups according to their maturity status while bioelectrical resistance and reactance were obtained. The classic and specific BIVA procedures were applied, which correct bioelectrical values for body height and body geometry, respectively. Percentage of fat mass (FM%) and total body water (TBW (L)) were estimated from bioelectrical values. Age-specific z-scores of the predicted age at peak height velocity identified 29 players as earlier-, 126 as on time-, and 23 as later-maturing. TBW was higher (p < 0.01) in adolescents classified as “early” maturity status compared to the other two groups and classic BIVA confirmed these results. Conversely, no differences in FM% were found among the groups. Specific vector length showed a higher correlation (r = 0.748) with FM% compared with the classic approach (r = 0.493). Classic vector length showed a stronger association (r = −0.955) with TBW compared with specific (r = −0.263). Specific BIVA turns out to be accurate for the analysis of FM% in athletes, while classic BIVA shows to be a valid approach to evaluate TBW. An original data set of bioelectric impedance reference values of male elite youth soccer players was provided.

The relationship among the age at menarche, anthropometric characteristics, and socio-economic factors in Bengali girls from Kolkata, India

OBJECTIVES

The aim of the study was to measure the causal effect of selected socio-economic factors and anthropometrical characteristics on the menarche occurrence.

METHODS

The sample consisted of 2195 Bengali girls (aged 7-21) from middle-class families, from Kolkata city, India. The age at menarche was recorded from the retrospective data and status quo method. The causal effect of anthropometric and socio-economic variables on menarche occurrence was estimated by the nonparametrical analysis of survival probability (survival random forest).

RESULTS

In the examined cohort menarche occurred, on average, at 11.8 years of age. The probability of menarche occurrence amplified with the increasing values of factors such as body mass index, height-for-age z-scores, number of family members, household rooms, and toilets, but decreased when expenditures increased. The relation maintained a similar pattern of causal effect with girls' age.

CONCLUSIONS

A complex pattern of relationship among sexual development, physique, and socio-economic characteristics was defined. The tendency toward early menarche, along with the observed causal relationships indicate that the analyzed sample is nearing the characteristics and standards of living noted in other middle and even high-income countries in the world.

Features of physical development and somatotype of girls and women involved in fitness

Purpose : Comparative analysis of the physical development features and somatotype of girls and women involved in fitness. Material : The experiment involved 95 girls and women, divided into 2 groups. Group 1 - 48 girls (36.04 ± 0.19) years, Group 2 - 47 women (43.26 ± 0.22) years, (p <0.01). Participants practiced fitness 2-3 times a week. The duration of the training was 60 minutes. It was determined the body mass and body length, wrist circumference, waist and hips circumferences. The TANITA BC 587 analyzer was applied to determine muscle and fat mass, the specific weight of fat tissue, water content, percentage of visceral fat, basal metabolism value, bone mass mineralization. It was calculated the body mass index and the waist-to-hip ratio. The results were evaluated applying the Student's t-test (t) and Rosenbaum (Q) and Wilcoxon - Mann - Whitney (U) criteria. Results : There were no significant differences in body length and body mass. The girls confirmed a less value of hip circumference (t = 2.04, p <0.05) and a tendency to less waist circumference (t = 1.97, p <0.1) in comparison with group 2. The participants of group 2 had a larger wrist circumference (U = 891, p <0.05). According to this parameter, persons with asthenic body type prevailed among participants. Their specific weight was (56.25 ± 7.16)% of group 1 and (53.19 ± 7.28)% of group 2. It was confirmed less absolute body fat content in group 1 (t = 2.09, p <0.05). All participants were characterized by larger muscle mass. There were no differences in this parameter between groups. It was determined the high concentration of specific weight of fat tissue in comparison with age standards. The content of visceral fat was within the age norm. Its content was higher in group 2 (Q = 18, p <0.01). The basal metabolic value was larger in group 2 (Q = 17, p <0.01). The values of bone mass mineralization reflect the correspondence to the body mass of the participants. This parameter did not have significant differences in the groups. The body mass index value in group 1 was within the average interval. In group 2, this index was higher than the standard one. The body mass index in group 1 was significantly lower (t = 2.18, p <0.05). The participants with a body mass index above the norm were (35.42 ± 6.90)% of group 1 and (59.57 ± 7.16)% of group 2. The prevalence of waist-to-hip ratio above the norm was (54.17 ± 7.19)% 1 group and (59.57 ± 7.17)% 2 group, (p> 0.05). Conclusions : it was determined the proximity of the main anthropometric parameters (mass and body length). The girls have lower values of the hip circumference. Persons with asthenic body type prevailed among the participants. Analysis of the indices confirmed the high prevalence of overweight and high risk of developing metabolic syndrome. The application of bio-impedance method confirmed and clarified the results of anthropometric research. The participants demonstrated the increased content of subcutaneous and visceral fat, increasing with age. The water level in the body gradually decreases with age. This is considered as a reflection of age-related physiological changes in metabolism and should be considered in fitness training organizing. Studies have confirmed the informativeness and adequacy of the bio-impedance method.