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

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.

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.

Fat-Free Mass Index for body composition analysis in pediatric sport: a cross-sectional study

BACKGROUND

Analyzing fat free mass (FFM) helps sport professionals during the prescription of sport training for children and adolescents in a sport initiation program. In this way, it is possible to determine fat mass (FM) (FFM subtracted from total body weight) and design interventions to increase FFM and reduce %F, making it possible to maximize performance in relation to the physical demands of sport. However, there is still no reliable anthropometric index to analyze FFM in this population. The aim the present study was to develop the Fat-Free Mass Index (FFMI) for pediatrics of both sexes.

METHODS

Cross-sectional study with a sample composed of 254 pediatrics (139 males [age: 13.0±2.3] and 115 females [age: 12.5±2.2]), from a sports initiation school. We divided the sample into the groups: 1) development (N.=169); and 2) cross-validation (N.=85). The body composition was analyzed by dual-energy X-ray absorptiometry (DXA), in addition we acquired anthropometric data (height, body weight and hip circumference) for the development of the FFMI - Pediatric (FFMIp). By means of linear regression we tested the predictive power of FFM using DXA as a reference method, then we developed FFMIp and tested its reliability and validity in relation to DXA.

RESULTS

FFMIp consisted of: -16.679 + (0.615 × body mass (kg)) - (2.601 × sex) + (0.618 × age(years)) - (0.332 × hip(cm)) + (0.278 × stature(cm)), where for sex 0 = male and 1 = female. For the FFM analysis, FFMIp showed no significant difference from DXA (P>0.05). It also showed significant accuracy (Cb>0.960), precision (ρ>0.990) and agreement (CCC>0.960) for both groups (development and cross-validation).

CONCLUSIONS

Pediatric FFMI proposed by this study proved to be valid for the analysis of fat-free mass in pediatric athletes of sports initiation of both sexes.

The bioelectrical impedance analysis (BIA) international database: aims, scope, and call for data

BACKGROUND

Bioelectrical impedance analysis (BIA) is a technique widely used for estimating body composition and health-related parameters. The technology is relatively simple, quick, and non-invasive, and is currently used globally in diverse settings, including private clinicians' offices, sports and health clubs, and hospitals, and across a spectrum of age, body weight, and disease states. BIA parameters can be used to estimate body composition (fat, fat-free mass, total-body water and its compartments). Moreover, raw measurements including resistance, reactance, phase angle, and impedance vector length can also be used to track health-related markers, including hydration and malnutrition, and disease-prognostic, athletic and general health status. Body composition shows profound variability in association with age, sex, race and ethnicity, geographic ancestry, lifestyle, and health status. To advance understanding of this variability, we propose to develop a large and diverse multi-country dataset of BIA raw measures and derived body components. The aim of this paper is to describe the 'BIA International Database' project and encourage researchers to join the consortium.

METHODS

The Exercise and Health Laboratory of the Faculty of Human Kinetics, University of Lisbon has agreed to host the database using an online portal. At present, the database contains 277,922 measures from individuals ranging from 11 months to 102 years, along with additional data on these participants.

CONCLUSION

The BIA International Database represents a key resource for research on body composition.

Evidence of whole-body vibration exercises on body composition changes in older individuals: a systematic review and meta-analysis

Introduction: The aging process is associated with changes in body composition, including fat gain and skeletal muscle loss from middle age onward. Moreover, increased risk of functional decline and the development of chronic diseases are also related to aging. Objective: This systematic review and meta-analysis aimed to evaluate the effects of whole-body vibration exercise (WBVE), as a physical exercise, on body composition in people over 60 years of age. Methods: Searches were performed on PubMed, Scopus, Web of Science, and Embase. Only randomized clinical trials evaluating the effects of WBVE on body composition in older individuals were considered. The methodological quality of the studies involved was assessed using the Physiotherapy Evidence Database (PEDro) scale, recommendations from the Cochrane Collaboration were used to assess risk of bias, and quality of evidence was assessed using the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) methodology. RevMan 5.4 was used to calculate standardized mean differences and confidence intervals of 95% (CIs). Results: Eight studies were included in this review with a mean methodological quality score of 7.5, which is considered high quality on the PEDro scale. The included studies suggest that more robust research with protocols and well-designed comparison groups is required to better assess changes in the body composition of older individuals through WBVE. Quantitative results were calculated, with differences in weighted means, differences in standardized means, and 95% confidence intervals (CIs). Conclusion: WBVE evaluated by the studies included in this review did not demonstrate improvements in body composition, and no significant effect of WBVE was found on fat mass with standardized differences (SD = -1.92; 95% CI: -4.81 to -0.98; p = 0.19), lean mass with standardized mean differences (SMD = 0.06 CI 95% [-0.21; -0.33]; p = 0.67), or skeletal muscle mass with standardized differences (SD = 0.10; CI 95% [-1.62; 1.83]; p = 0.91). Therefore, to date, there is lack of adequate evidence to state that WBVE can benefit the body composition of men and women over 60 years of age. However, further studies are required to better understand the physiological impacts of WBVE on body composition. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/#myprosperoCRD42021248871, identifier CRD42021248871.

Is there a difference in the parameters of the bioelectrical impedance obtained from devices from different manufacturers? A cross-sectional study in hospitalized cancer patients

BACKGROUND

Cancer is a disease with high and increasing incidence rates in the world and its course tends to harm the body composition. Monitoring these body changes is very important. Therefore, it is essential to have reliable, accessible, and practical methods for evaluating body compartments. This study aims to evaluate the correlation and agreement of results for the bioelectrical impedance analysis (BIA) obtained from devices from different manufacturers.

METHODS

This is a single-center cross-sectional study including hospitalized patients with cancer. Two devices from different brands used for obtaining the BIA were used; both with a tetrapolar model and a single frequency (50 kHz). The results were evaluated for resistance (R) and reactance (Xc) and used to calculate the phase angle (PhA) and fat-free mass (FFM) indicators. Pearson and Spearman correlation tests and Bland-Altman plots were performed, with results expressed as bias and limits of agreement at 95% confidence intervals (95%CI).

RESULTS

We have included 116 patients, with a mean age of 60.8 ± 14.8, 51.7% were women. We have found very strong correlations between the measurements of R (rho = 0.971) and FFM (r = 0.979), and strong correlations for Xc (rho = 0.784) and PhA (rho = 0.768). However, the measurements did not agree between the methods.

CONCLUSIONS

Commercial brands of devices used for the BIA influence the results generated, a factor that must be considered when choosing the most appropriate method for this analysis.

Validity of the relative fat mass pediatric index (RFMp) for the analysis of body composition in physically active youths at different stages of biological maturation

BACKGROUND

The paediatric relative fat mass (RFMp) index was valid for analysis of percent body fat (BF%). However, the validation did not consider biological maturation (BM) stages. The present study aimed to verify the validity of the RFMp index in the estimation of BF% in children and adolescents of both sexes at different stages of BM.

METHODS

A cross-sectional study was conducted with a sample of 146 young (males: 64.5%. females: 35.5%. age: 13.0 ± 2.2 years) practising sports modalities. We tested the validity of four RFMp equations (1: for boys aged 8-14 years; 2: for girls aged 8-14 years; 3: for both sexes aged 8-14 years; and 4: for both sexes aged 15-19 years) to analyse BF% using dual-energy X-ray absorptiometry as a reference method. BM was analysed by peak height velocity (PHV). Thus, we created subgroups by BM stage (pre-PHV, circum-PHV and post-PHV).

RESULTS

Analyses of agreement between methods showed that only the RFMp-3 equation was reliable to analyse BF% in subjects of both sexes aged 8-14 years at the circum-PHV BM stage (proportion bias 95% confidence interval = -0.3 to 0.5, p = 0.7. concordance correlation coefficient = 0.3; validity = 0.9).

CONCLUSIONS

The RFMp equation developed for the paediatric population of both sexes aged 8-14 years was valid for predicting BF% in children and adolescents of both sexes at the Circum-PHV stage of the BM.

Triponderal mass index is as strong as body mass index in the determination of obesity and adiposity

OBJECTIVE

In determining obesity and body adiposity, triponderal mass index (TMI) is as strong an anthropometric measurement as body mass index (BMI). The aim of this study was to develop TMI reference values for Turkish children and adolescents and compare TMI with BMI according to body adiposity and obesity indices.

METHODS

Data from the DAMTCA-II (Determination of Anthropometric Measurements of Turkish Children and Adolescents II) study were used in this cross-sectional study. Data from 4330 children (1931 boys, 2399 girls) ages 6 to 17 y were evaluated, and the TMI percentile values were produced. The predictive power of TMI and BMI for obesity and overweight were done for waist circumference, waist/height ratio, body fat percentage, and upper arm fat area, which are different parameters used to determine body adiposity.

RESULTS

The 3rd, 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th, and 97th TMI percentiles and mean values were calculated for all children's age and sex. TMI cutoff values were calculated by receiver operating characteristic analysis regarding waist/height ratio 0.5, waist circumference ≥90 percentile, arm fat area ≥85 percentile, and body fat percentage ≥85. TMI and BMI area under the curve values were similar for each of these four measurements. TMI was as robust an index as BMI in demonstrating obesity and adiposity for all age groups in boys and girls. It was concluded that the values >90th percentile (median 15.8 kg/m³) in girls aged ≤10 y, 95th percentile (median 16.2 kg/m³) in girls aged >10 y, >85th percentile (median 14.9 kg/m³) in boys aged ≤12 y and 75th percentile (median value 14.5 kg/m³) in boys aged >12 y are critical values for TMI when evaluating adiposity and obesity.

CONCLUSIONS

We considered that TMI is as effective as BMI in terms of waist/height ratio, waist circumference, arm fat area, and body fat percentage in determining overweight and obesity in children. The ages at which TMI showed distinct variation were determined for both sexes.

Clinical Correlates of Measured and Predicted Resting Energy Expenditure in Patients with Anorexia Nervosa: A Retrospective Cohort Study

Resting energy expenditure (REE; i.e., the calorie amount required for 24 h during a non-active period) is an important parameter in nutritional rehabilitation of patients with anorexia nervosa (AN). This study determined whether age, body mass index, AN duration/subtype/specific symptoms/clinical severity, cognitive function alterations, and psychiatric comorbidities influenced REE or the difference between the calculated and estimated REE. Patients with AN who were followed at a daycare treatment facility between May 2017 and January 2020 (n = 138) underwent a complete assessment that included the MINI, Eating Disorder Examination Questionnaire, d2 test of attention, body fat composition by bioelectrical impedance analysis (BIA) and REE measurement by indirect calorimetry (REEIC). AN subtype (N = 66 for restrictive subtype and N = 69 for non-restrictive subtype; p = 0.005), free-fat mass (<0.001), and fat mass (<0.001) were associated with REEIC. Age (p < 0.001), height (p = 0.003), and AN duration (N = 46 for <3 years and N = 82 for ≥3 years; p = 0.012) were associated with the difference between estimated REE (using the Schebendach equation) and measured REEIC. Therefore, the Schebendach equation was adjusted differently in the two patients’ subgroups (AN duration ≤ or >3 years). Overall, REE was higher in patients with restrictive than non-restrictive AN. In the absence of BIA measures, REE-estimating equations should take into account AN duration.