Using bioelectrical impedance analysis in children and adolescents: Pressing issues

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.

Nutritional and Body Composition Changes in Paediatric β-Thalassemia Patients Undergoing Hematopoietic Stem Cell Transplantation: A Retrospective Study Using Bioelectrical Impedance Analysis

Objective

This retrospective study evaluated nutritional status and body composition changes in paediatric β-thalassemia (β-TM) patients before and after hematopoietic stem cell transplantation (HSCT), using bioelectrical impedance analysis (BIA), and explored their relationship with HSCT outcomes.

Methods

A cohort of 40 paediatric β-TM patients undergoing allogeneic HSCT was assessed for their nutritional status, anthropometric parameters, including body mass index (BMI), weight, and height, and body composition parameters pre-and post-HSCT, focusing on BIA measurements, including intracellular water (ICW), extracellular water (ECW), fat mass (FAT), fat-free mass (FFM), Skeletal Muscle Mass (SMM), soft Lean Mass (SLM), percent body fat (PBF), Body Cell Mass (BCM), Phase angle (PA) and muscle balance pre- and post-HSCT. Post-HSCT clinical outcomes, including acute graft-vs-host disease (aGVHD), engraftment time, oral mucositis (OM), sinusoidal obstruction syndrome (SOS), and diarrhoea in relation to nutrition status after HSCT were analysed.

Results

After HSCT, 28.21% experienced diminished nutritional status, with 71.43% of those who were wasting before HSCT showing diminished nutritional status, significantly higher than the normal group (18.75%, P = 0.012). Anthropometric changes included significant weight reduction (87.5%, 22.15 ± 7.46 vs 20.74 ± 6.57, P < 0.001) and BMI decrease (90%, 15.19 ± 1.70 vs 14.05 ± 1.48, P < 0.001). Body composition parameters, which are FFM, SMM, SLM, ICW, ECW, BCM, and PA (18.26 ± 5.71 vs 17.27 ± 5.19, 8.68 ± 3.30 vs 7.93 ± 3.02, 17.11 ± 5.28 vs 16.06 ± 4.84, 8.19 ± 2.54 vs 7.62 ± 2.31, 5.15 ± 1.58 vs 4.94 ± 1.47, 11.74 ± 3.63 vs 10.92 ± 3.32, 4.42 ± 0.50 vs 3.90 ± 0.57, respectively, P < 0.001) analysis revealed significant decreases. No significant differences in clinical outcomes were observed based on nutritional status.

Conclusion

Paediatric β-TM patients undergoing HSCT exhibit significant changes in nutrition status and body composition, emphasizing the need for focused attention on malnourished children who are more prone to diminished nutritional status. Comprehensive BIA aids in understanding the impact, urging consideration for extended follow-up and larger cohorts in future research.

Determination of resistance at zero and infinite frequencies in bioimpedance spectroscopy for assessment of body composition in babies

Objective. Bioimpedance spectroscopy (BIS) is a popular technique for the assessment of body composition in children and adults but has not found extensive use in babies and infants. This due primarily to technical difficulties of measurement in these groups. Although improvements in data modelling have, in part, mitigated this issue, the problem continues to yield unacceptably high rates of poor quality data. This study investigated an alternative data modelling procedure obviating issues associated with BIS measurements in babies and infants.Approach.BIS data are conventionally analysed according to the Cole model describing the impedance response of body tissues to an appliedACcurrent. This approach is susceptible to errors due to capacitive leakage errors of measurement at high frequency. The alternative is to model BIS data based on the resistance-frequency spectrum rather than the reactance-resistance Cole model thereby avoiding capacitive error impacts upon reactance measurements.Main results.The resistance-frequency approach allowed analysis of 100% of data files obtained from BIS measurements in 72 babies compared to 87% successful analyses with the Cole model. Resistance-frequency modelling error (percentage standard error of the estimate) was half that of the Cole method. Estimated resistances at zero and infinite frequency were used to predict body composition. Resistance-based prediction of fat-free mass (FFM) exhibited a 30% improvement in the two-standard deviation limits of agreement with reference FFM measured by air displacement plethysmography when compared to Cole model-based predictions.Significance.This study has demonstrated improvement in the analysis of BIS data based on the resistance frequency response rather than conventional Cole modelling. This approach is recommended for use where BIS data are compromised by high frequency capacitive leakage errors such as those obtained in babies and infants.

Assessing the Body Composition of "Picky Eaters" Using Body Impedance Analysis: An Experience From a Tertiary Care Center

BACKGROUND

Picky eating might be associated with a higher risk of being underweight and poor growth. This study aimed to investigate if picky eaters aged between three and 12 years showed differences in height, weight, and body composition compared to their non-picky peers using a body impedance analysis (BIA).

METHODS

This cross-sectional study was conducted between March 1, 2022, and July 31, 2022, on children aged three to 12 years who presented to the outpatient pediatric clinics at Al Habib Ar Rayyan in Riyadh, Saudi Arabia. Body composition was measured using BIA after manually inserting the height, gender, and age into the machine, where body mass index (BMI), fat mass, and skeletal muscle mass were recorded. Participants were classified as under/normal/over for each body composition measurement.

RESULTS

A total of 2234 children were entered into the final data analysis. Our analysis showed that 1917 (85.8%) were Saudis and 1117 (50%) were males. The mean age of participants was 6.08±2.01 years and 1151 (51.5%) were in the pre-school age. The reported prevalence of picky eaters was 1684 (75.4%), of whom, 606 (27.0%) were selective eaters, 365 (16.2%) were low appetite eaters, and 723 (32.2%) were both selective and low appetite eaters. Being underweight was significantly more common among the picky eaters 487 (28.9%) compared to those non-picky eaters 55 (10.0%) (p<0.001). Significantly, 1280 (76%) picky eaters had below-average skeletal muscle mass compared to 151 non-picky eaters (27.5%) (p<0.001). The low appetite picky eater group had more under skeletal muscle mass children 277 (75.9%) compared to the selective picky eater group 412 (68.0%) (p=0.009). Additionally, the low appetite group possessed lower muscle ratios (p=0.012) and were more underweight than the selective group (p<0.001). Furthermore, the low appetite group showed a higher percentage of children below the 3rd percentile in the height for age category (p=0.003) compared to the selective group.

CONCLUSION

This study is the first of its kind in Saudi and globally to evaluate body composition using BIA among children. The study showed that picky eating is more associated with underweight children and low muscular mass. Despite the normal BMI, height, and weight of a picky eater, their skeletal muscle mass might be less than average, which could be associated with an increased risk of morbidity.

Body Composition and Metabolic Syndrome Components in Transgender/Gender Diverse Adolescents and Young Adults

Purpose: The objective of this study was to examine the association of designated sex at birth, body composition, and gender-affirming hormone treatment (GAHT) with the components of metabolic syndrome (MetS) (overweight/obesity, elevated blood pressure [BP], altered glucose metabolism, and dyslipidemia) in transgender/gender diverse (TGD) adolescents and young adults. Methods: TGD individuals underwent body composition studies by bioelectrical impedance analysis according to designated sex at birth, and their muscle-to-fat ratio (MFR) z-scores were calculated. Generalized estimating equations with binary logistic models (n = 326) were used to explore associations while adjusting for potential confounders. Results: A total of 55 TGD females and 111 TGD males, with mean age of 18 ± 1.9 years and median duration of GAHT of 1.4 years (interquartile range = 0.6-2.5), were enrolled. Overall, 118/166 (71%) of the TGD cohort showed evidence of at least one MetS component, with a significantly higher rate among TGD males compared with TGD females (91.1% vs. 50.9%, p < 0.001). TGD males were at increased odds for overweight/obesity, elevated/hypertensive BP, elevated triglycerides (TGs), and an atherogenic dyslipidemia index (TG/high-density lipoprotein cholesterol [HDL-c], TG:HDL-c). The odds of overweight/obesity increased by 44.9 for each standard deviation decrease in the MFR z-score, while the odds for an elevated TG:HDL-c index increased by 3.7. Psychiatric morbidity increased the odds for overweight/obesity by 2.89. Conclusions: After considering confounding variables, the TGD males on GAHT were found to be at an increased risk for cardiometabolic disease. Our observations support the importance of targeted medical nutrition intervention in this group of individuals.

The metabolic load-capacity model and cardiometabolic health in children and youth with obesity

BACKGROUND

The metabolic load-capacity index (LCI), which represents the ratio of adipose to skeletal muscle tissue-containing compartments, is potentially associated with cardiometabolic diseases.

OBJECTIVES

To examine the associations between the LCI and cardiometabolic risk factors in children and youth with obesity.

METHODS

This is a cross-sectional study including 10-18 years-old participants with a BMI of ≥95th . LCI by air-displacement plethysmography (ADP) was calculated as fat mass divided by fat-free mass, and LCI by ultrasound (US) as subcutaneous adipose tissue divided by skeletal muscle thickness. Sex-specific medians stratified participants into high versus low LCI. Single (inflammation, insulin resistance, dyslipidemia and hypertension) and clustered cardiometabolic risk factors were evaluated. Linear and logistic regression models tested the associations between these variables, adjusted for sexual maturation.

RESULTS

Thirty-nine participants (43.6% males; 59% mid-late puberty) aged 12.5 (IQR: 11.1-13.5) years were included. LCI by ADP was positively associated with markers of inflammation and dyslipidemia; having a higher LCI predicted dyslipidemia in logistic regression. Similarly, LCI by US was positively associated with markers of dyslipidemia and blood pressure. In mid-late pubertal participants, LCI by US was positively associated with markers of insulin resistance and inflammation.

CONCLUSIONS

Participants with unfavourable cardiometabolic profile had higher LCI, suggesting its potential use for predicting and monitoring cardiometabolic health in clinical settings.

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.

Body Composition Evaluation and Clinical Markers of Cardiometabolic Risk in Patients with Phenylketonuria

Cardiovascular diseases are the main cause of mortality worldwide. Patients with phenylketonuria (PKU) may be at increased cardiovascular risk. This review provides an overview of clinical and metabolic cardiovascular risk factors, explores the connections between body composition (including fat mass and ectopic fat) and cardiovascular risk, and examines various methods for evaluating body composition. It particularly focuses on nutritional ultrasound, given its emerging availability and practical utility in clinical settings. Possible causes of increased cardiometabolic risk in PKU are also explored, including an increased intake of carbohydrates, chronic exposure to amino acids, and characteristics of microbiota. It is important to evaluate cardiovascular risk factors and body composition in patients with PKU. We suggest systematic monitoring of body composition to develop nutritional management and hydration strategies to optimize performance within the limits of nutritional therapy.

Body composition in pediatric patients

Undernutrition is highly prevalent in children who are critically ill and is associated with increased morbidity and mortality, including a higher risk of infection due to transitory immunological disorders, inadequate wound healing, reduced gut function, longer dependency on mechanical ventilation, and longer hospital stays compared with eutrophic children who are critically ill. Nutrition care studies have proposed that early interventions targeting nutrition assessment can prevent or minimize the complications of undernutrition. Stress promotes an acute inflammatory response mediated by cytokines, resulting in increased basal metabolism and nitrogen excretion and leading to muscle loss and changes in body composition. Therefore, the inclusion of body composition assessment is important in the evaluation of these patients because, in addition to the nutrition aspect, body composition seems to predict clinical prognosis. Several techniques can be used to assess body composition, such as arm measurements, calf circumference, grip strength, bioelectrical impedance analysis, and imaging examinations, including computed tomography and dual-energy x-ray absorptiometry. This review of available evidence suggests that arm measurements seem to be well-established in assessing body composition in children who are critically ill, and that bioelectrical impedance analysis with phase angle, handgrip strength, calf circumference and ultrasound seem to be promising in this evaluation. However, further robust studies based on scientific evidence are necessary.

Prediction of fat-free mass in young children using bioelectrical impedance spectroscopy

BACKGROUND

Bioimpedance devices are practical for measuring body composition in preschool children, but their application is limited by the lack of validated equations.

OBJECTIVES

To develop and validate fat-free mass (FFM) bioimpedance prediction equations among New Zealand 3.5-year olds, with dual-energy X-ray absorptiometry (DXA) as the reference method.

METHODS

Bioelectrical impedance spectroscopy (SFB7, ImpediMed) and DXA (iDXA, GE Lunar) measurements were conducted on 65 children. An equation incorporating weight, sex, ethnicity, and impedance was developed and validated. Performance was compared with published equations and mixture theory prediction.

RESULTS

The equation developed in ~70% (n = 45) of the population (FFM [kg] = 1.39 + 0.30 weight [kg] + 0.39 length²/resistance at 50 kHz [cm²/Ω] + 0.30 sex [M = 1/F = 0] + 0.28 ethnicity [1 = Asian/0 = non-Asian]) explained 88% of the variance in FFM and predicted FFM with a root mean squared error of 0.39 kg (3.4% of mean FFM). When internally validated (n = 20), bias was small (40 g, 0.3% of mean FFM), with limits of agreement (LOA) ±7.6% of mean FFM (95% LOA: -0.82, 0.90 kg). Published equations evaluated had similar LOA, but with marked bias (>12.5% of mean FFM) when validated in our cohort, likely due to DXA differences. Of mixture theory methods assessed, the SFB7 inbuilt equation with personalized body geometry values performed best. However, bias and LOA were larger than with the empirical equations (-0.43 kg [95% LOA: -1.65, 0.79], p < 0.001).

CONCLUSIONS

We developed and validated a bioimpedance equation that can accurately predict FFM. Further external validation of the equation is required.

Bioelectrical Impedance Analysis-Derived Phase Angle and Body Composition Are Predictors of Health-Related Fitness in Children and Adolescents with Obesity

There is little evidence in children and adolescents with obesity of the relationships between muscle strength/cardiorespiratory fitness (both components of health-related fitness = HRF) and body composition. Body composition and HRF were studied in 281 children and adolescents with obesity to explore their mutual relationship and to identify the predictors of HRF. By performing a bioelectrical impedance analysis (BIA), the fat-free mass (FFM) and percentage of body fat (%BF) were calculated, and the phase angle (PhA) was recorded. Handgrip strength (HGS), the standard broad jump (SBJ), and five broad jumps (FIVEBJ) were considered for the assessment of muscle strength, and the six-minute walking distance (SIXMWD) for cardiorespiratory fitness. The BMI Z-score was slightly higher in boys, and the %BF was higher in girls, with no difference in the FFM. HGS, the SBJ, and FIVEBJ were greater in the male sex. After controlling for sex, HGS was associated with the FFM, and with height, weight, and absolute BMI. On the contrary, the SBJ and FIVEBJ were negatively associated with adiposity, with a weak relationship with the FFM. The SIXMWD was only poorly related to height, the BMI Z-score, and the waist-to-height ratio. These results were confirmed with a multiple regression analysis. HGS, the SBJ, and FIVEBJ were higher in the first compared to the third tertile of the PhA in both sexes. The PhA also remained a consistent predictor of HGS, the SBJ, and FIVEBJ in a multiple regression analysis. In conclusion, the following predictors have been identified for HRF: the FFM for the isometric strength of the upper limbs and adiposity indicators for the SBJ and FIVEBJ. The PhA emerged as a proxy index of muscle strength.

The Impact of Physical Activity at School on Body Fat Content in School-Aged Children

(1) Background: Excessive amounts of adipose tissue is a health risk. The aim of this study was to assess the impact of increased physical activity (PA) at school on body fat content in children aged 10 to 12 years over a 2-year follow-up. (2) Methods: Children born in 2007 (n = 245) in two groups, (1) standard PA and (2) increased PA at school, 4 and 10 h of physical education lessons per week, respectively. BIA measurements of body fat content were taken twice a year. Results were interpreted based on children's fat content reference curves. (3) Results: During 2 years of observation, the percentage of children with excessive fat mass (overweight and obese) increased by one-third (from 28.11% to 39.67%) in the group of standard PA, while decreased by one-third in the increased PA one (from 28.92% to 21.00%); with normal fat content increased by one-quarter in the increased PA group (from 59.86% to 76.26%) and decreased by one-tenth in the standard PA one (from 61.61% to 56.29%). (4) Conclusions: An increase in PA at school has a positive impact on children's body fat content. It is recommended to increase the number of physical education lessons at school, which has a positive effect on children's health, reducing the risk of obesity.

Body Composition and Anthropometric Indicators in Children and Adolescents 6-15 Years Old

The problem of overweight and obesity among children and adolescents has now become a major public health challenge worldwide. The aim of this study was to assess the effect of age and gender on body composition components and anthropometric indices of children and adolescents aged 6-15 years; in addition, the study aimed to assess body composition indices in relation to BMI (Body Mass Index) levels. The study was conducted at the end of 2019 and in the first quarter of 2020 among 181 pupils attending primary schools. Waist circumference, hip circumference, body weight, and body height were measured. The collected data were used to calculate and then to analyse BMI, WHR (Waist-Hip Ratio), and WHtR (Waist to Height Ratio) indices. Body composition was determined with the use of the Bioelectrical Impedance Analysis (BIA). The analyses' statistics were performed using IBM SPSS Statistics 24 and Excel. The statistical methods used included Chi² tests of independence, one-factor analysis of variance, and two-factor analysis of variance taking into account the level of α = 0.05. Based on the results, there were no statistically significant differences in the gender and age distributions of the BMI groups. However, the analysis of interaction effects confirmed that there were statistically significant differences according to pubertal age and gender in body fat, muscle mass, hydration, and WHR. Body composition in boys and girls before the age of 12 is similar while, after the age of 12, there are differences between boys and girls in terms of body composition so there is a need to deepen the assessment of body mass, especially in adolescents at the age of puberty, by body composition analysis using the BIA method.

Proposal of a New Parameter for Evaluating Muscle Mass in Footballers through Bioimpedance Analysis

The evaluation of muscle mass in athletes correlates with sports performance directly. Bioimpedance vector analysis is a growing method of assessing body composition in athletes because it is independent of predictive formulas containing variables such as body weight, ethnicity, age, and sex. The study aims to propose a new parameter (Levi’s Muscle Index, LMI) that evaluates muscle mass through raw bioelectrical data. A total of 664 male footballers underwent bioimpedance assessment during the regular season. LMI was correlated with body cell mass (BCM) and phase angle (PA) to establish efficacy. The footballers were 24.5 ± 5.8 years old, 180.7 ± 5.9 cm tall and weighed 76.3 ± 7.1 kg. The relationships were: LMI-BMI: r = 0.908, r2 = 0.824, p < 0.001; LMI-PA: r = 0.704, r2 = 0.495, p = 0.009 and PA-BCM: r = 0.491, r2 = 0.241, p < 0.001. The results obtained confirm that LMI could be considered a new parameter that provides reliable information to evaluate the muscle mass of athletes. Furthermore, the higher LMI-BCM relationship than PA-BCM demonstrates specificity for muscle mass evaluation in athletes regardless of body weight, ethnicity, age, and sex.

Body fat assessment in youth with overweight or obesity by an automated bioelectrical impedance analysis device, in comparison with the dual-energy x-ray absorptiometry: a cross sectional study

BACKGROUND

Bioelectrical impedance analysis (BIA) is a widely used method to assess total body fat (TBF) depots characterising obesity. Automated BIA devices provide an inexpensive and easy assessment of TBF, making them widely available to the general public and healthcare providers without specific qualification to assess body composition. The equations included in the automated BIA devices have been developed in very few specific populations, which means that they are not suitable to assess TBF for everyone and need to be validated before use in other populations. The aim of the present work is to evaluate the accuracy of the automated BIA device Tanita® BC-532 in youth of White European ethnicity, compared with the dual-energy x-ray absorptiometry (DEXA), gold standard measurement of TBF.

METHODS

Total body fat percentage (TBF%) was measured with the BIA device Tanita® BC-532 and DEXA (Hologic® QDR4500W) in 197 youth of White European ethnicity (N = 104 girls), 7-17 years old, and visiting the Diabetes & Endocrinology Care Paediatrics Clinic, Centre Hospitalier de Luxembourg, for overweight or obesity management.

RESULTS

TBF% evaluated with BIA was significantly correlated with TBF% measured with DEXA in both boys (r _Pearson = 0.617) and girls (r _Pearson = 0.648) (p <  10^- 4). However, the residual mean between the assessment of TBF% by BIA and by DEXA [TBF _BIA (%)-TBF _DEXA (%)] is extremely high (mean ± standard deviation = 10.52% ± 5.22% in boys, respectively 9.96% ± 4.40% in girls). The maximal absolute residual value is also very high, about 24% in both genders.

CONCLUSIONS

The automated BIA device Tanita® BC-532 appears to be not accurate to assess total body fat in youth with overweight or obesity. There is a need to calibrate the BIA device before its use in the populations where it was not previously validated.

Bioelectrical Impedance Analysis (BIA)- Derived Phase Angle in Children and Adolescents: A Systematic Review

Phase angle (PhA), a directly-measured bioelectrical impedance analysis variable, is suggested to be a proxy of body cell mass as well as extracellular/intracellular water ratio, and is related to cellular integrity and functions. The aim of this systematic review was to evaluate PhA in healthy youths in relation to sex, age, weight status, physical fitness, and sports activities. A systematic literature search (preferred reporting items for systematic reviews and meta-analyses criteria) until January 2022 was performed using PubMed, Embase, Scopus, and Web of Science regarding studies on PhA in healthy children and adolescents 4-18 years of age. Quality was assessed according to the National Institute of Health. After removing duplicates and studies not fulfilling the inclusion criteria, 22 cross-sectional and 1 longitudinal were considered appropriate. As for quality, 14 articles were rated fair and 9 good. Ten studies found that PhA increases with age: the increase was more marked after puberty, whereas changes in younger subjects are by far less defined. A clear sex difference was found in adolescents, likely due to pubertal development. Limited evidence suggests that PhA increases in participants with very high BMI. Limited data were reported on physically active youths without convincing findings. Positive associations of PhA with physical fitness and fat-free mass were found in few studies. In conclusion, partial and limited evidence suggests that changes in PhA over the first 2 decades of life reflect modification in body composition and fat-free mass composition. Further studies are needed for confirming PhA as a relevant marker of nutritional status in youths.

Body composition in children and adolescents with non-classic congenital adrenal hyperplasia and the risk for components of metabolic syndrome: An observational study

BACKGROUND

Treated or untreated non-classic congenital adrenal hyperplasia (NCCAH) diagnosed in childhood could pose an increased risk of obesity and metabolic derangements in adolescence and early adulthood. We aimed to explore the interaction between muscle-to-fat ratio (MFR) and components of metabolic syndrome in pediatric subjects with NCCAH.

METHODS

This retrospective observational study was conducted in the Tel Aviv Medical Center from January 2018 to January 2022. The study group comprised 75 subjects (26 males) with NCCAH (61 hydrocortisone-treated [21 males] and 14 untreated [5 males]) and 134 healthy sex- and age-matched subjects (41 males) with normal puberty served as controls. Body composition was measured by bioelectrical impedance analysis (BIA) and muscle-to-fat ratio (MFR) z-scores were calculated. Stepwise linear regression models were applied to evaluate explanatory variables for MFR z-scores, blood pressure percentiles, lipid profiles, and glucose metabolism.

RESULTS

The median age [interquartile range] was 7.5 years [5.3, 8.8] at NCCAH diagnosis and 12.3 years [8.9, 15.4] at BIA. The median cumulative hydrocortisone dose was 7620 mg/m² [2547, 12903]. Subjects with NCCAH had higher mean BMI z-scores and lower median MFR z-scores compared to controls [(0.47 ± 0.97 vs. -0.19 ± 1.04, p<0.001) and (-0.74 [-1.06, -0.14] vs.-0.37 [-0.99, 0.15], p=0.045), respectively]. The linear regression models dependent variables and their explanatory variables were: MFR z-score (R²= 0.253, p<0.001) - socioeconomic position index (β=0.348, p=0.003), birthweight z-score (β=-0.258, p=0.013), and duration of hydrocortisone treatment in years (β=0.048, p=0.023); systolic blood pressure percentile (R² = 0.166, p<0.001) - MFR z-score (β=-9.75, p<0.001); TG/HDL ratio (R² = 0.116, p=0.024) - MFR z-score (β=-0.300, p=0.024). No significant variables were found for glucose.

CONCLUSIONS

Children and adolescents with NCCAH have a body composition characterized by an imbalance between muscle and fat tissues, which may place them at increased risk for early-onset cardiometabolic derangements. It is reassuring that glucocorticoid therapy aimed to alleviate androgen overproduction does not appear to adversely affect their body composition.