Validation of bioelectrical impedance analysis for body composition assessment in children with obesity aged 8-14y

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.

Development and validation of new bioelectrical impedance equations to accurately estimate fat mass percentage in a heterogeneous Caucasian population

Fat mass percentage (%FM) is frequently determined by nutritionists and personal trainers with bioelectrical impedance analysis (BIA) devices. The aims of the present study were: (1) to develop new regression equations using dual-energy X-ray absorptiometry (DXA) as the reference method for estimating %FM in a heterogeneous Caucasian population with a foot-to-hand device (BIA-101) and a hand-to-hand device (BIA-TELELAB) and (2) to compare the new equations with the manufacturers' equations. We hypothesized that the new equations would lead to more accurate estimations compared with DXA. A total of 218 healthy Caucasian participants aged 18 to 65 years were divided into a development group and a validation group. The accuracy of the different equations was assessed by mean differences, coefficient of determination, standard error of the estimate (SEE), intraclass correlation coefficients (ICC), and Bland-Altman plots. The proposed equation for BIA-101 explained 90.0% of the variance in the DXA-derived %FM, with a low random error (SEE = 2.98%), excellent agreement (ICC = 0.94), no fixed bias, and relatively low individual variability (5.86%). For BIA-TELELAB, the proposed equation explained 88.0% of the variance in the DXA-derived %FM, with a low random error (SEE = 3.27%), excellent agreement (ICC = 0.93), no fixed bias, and relatively low individual variability (6.37%). The results obtained for the manufacturers' equations confirm that these equations are not a good option for %FM assessment. As hypothesized, the new regression equations for BIA-101 and BIA-TELELAB devices can accurately estimate %FM in a heterogeneous Caucasian population with a broad age range.

BMI-based obesity classification misses children and adolescents with raised cardiometabolic risk due to increased adiposity

OBJECTIVE

To assess how inaccurately the body mass index (BMI) is used to diagnose obesity compared to body fat percentage (BF%) measurement and to compare the cardiometabolic risk in children and adolescents with or without obesity according to BMI but with a similar BF%.

METHODS

A retrospective cross-sectional investigation was conducted including 553 (378 females/175 males) white children and adolescents aged 6-17 years, 197 with normal weight (NW), 144 with overweight (OW) and 212 with obesity (OB) according to BMI. In addition to BMI, BF% measured by air displacement plethysmography, as well as markers of cardiometabolic risk had been determined in the existing cohort.

RESULTS

We found that 7% of subjects considered as NW and 62% of children and adolescents classified as OW according to BMI presented a BF% within the obesity range. Children and adolescents without obesity by the BMI criterion but with obesity by BF% exhibited higher blood pressure and C-reactive protein (CRP) in boys, and higher blood pressure, glucose, uric acid, CRP and white blood cells count, as well as reduced HDL-cholesterol, in girls, similar to those with obesity by BMI and BF%. Importantly, both groups of subjects with obesity by BF% showed a similarly altered glucose homeostasis after an OGTT as compared to their NW counterparts.

CONCLUSIONS

Results from the present study suggest increased cardiometabolic risk factors in children and adolescents without obesity according to BMI but with obesity based on BF%. Being aware of the difficulty in determining body composition in everyday clinical practice, our data show that its inclusion could yield clinically useful information both for the diagnosis and treatment of overweight and obesity.

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

Background

Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms.

Methods

A cross-sectional study was conducted and the subjects who came to the hospital to receive a health examination were enrolled. Body composition was assessed using bioelectrical impedance analyser. A model generator with a gradient-boosting tree algorithm (LightGBM) combined with the SHapley Additive exPlanations method was adapted to train and interpret the model. Receiver-operating characteristic curves were used to analyze the predictive value.

Results

We found the significant difference in body composition parameters between the metabolic healthy normal weight (MHNW), metabolic healthy obesity (MHO), metabolic unhealthy normal weight (MUNW) and metabolic unhealthy obesity (MUO) individuals, especially among the MHNW, MUNW and MUO phenotype. MHNW phenotype had significantly lower whole fat mass (FM), trunk FM and trunk free fat mass (FFM), and had significantly lower visceral fat areas compared to MUNW and MUO phenotype, respectively. The bioimpedance phase angle, waist-hip ratio (WHR) and free fat mass index (FFMI) were found to be remarkably lower in MHNW than in MUNW and MUO groups, and lower in MHO than in MUO group. For predictive analysis, the LightGBM-based model identified 32 status-predicting features for MUNW with MHNW group as the reference, MUO with MHO as the reference and MUO with MHNW as the reference, achieved high discriminative power, with area under the curve (AUC) values of 0.842 [0.658, 1.000] for MUNW vs. MHNW, 0.746 [0.599, 0.893] for MUO vs. MHO and 0.968 [0.968, 1.000] for MUO and MHNW, respectively. A 2-variable model was developed for more practical clinical applications. WHR > 0.92 and FFMI > 18.5 kg/m2 predict the increased risk of MU.

Conclusion

Body composition measurement and validation of this model could be a valuable approach for the early management and prevention of MU, whether in obese or normal population.

A comparison of bioelectrical impedance analysis and air displacement plethysmography to assess body composition in children

Background

Accurate assessment of body composition (BC) is important to investigate the development of childhood obesity. A bioelectrical impedance analysis (BIA) device is portable and inexpensive compared with air displacement plethysmography (ADP) for the assessment of BC and is widely used in children. However, studies of the effectiveness of BIA are few and present different results, especially in pediatric populations. The aim of this study was to evaluate the agreement between BIA and ADP for estimating BC.

Methods

The BC of 981 Chinese children (3-5 years) was measured using the BIA device (SeeHigher BAS-H, China) and ADP (BOD POD).

Results

Our results showed that BIA underestimated fat mass (FM) and overestimated fat-free mass (FFM) in normal weight children (P < 0.05), but the opposite trend was shown in children with obesity (P < 0.05). The agreement between FM and FFM measured by the two methods was strong (CCC > 0.80). The linear regression equation of 5-year-old children was constructed.

Conclusion

The SeeHigher BAS-H multi-frequency BIA device is a valid device to evaluate BC in Chinese preschool children compared with ADP (BOD POD), especially in 5-year-old children or children with obesity. Further research is needed to standardize the assessment of BC in children.

Body Composition in Fussy-Eating Children, with and without Neurodevelopmental Disorders, and Their Parents, Following a Taste Education Intervention

Fussy eaters may have an increased risk of becoming overweight or obese as adolescents, with fussy eating and weight status also correlating with neurodevelopmental disorders (NDs) such as autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Further, maternal and children's weight status relationships are well-established. In this study, we analyzed the body composition of parent-child dyads using bioelectrical impedance analysis (BIA). Fifty-one children aged 8-12 years, with an ND (n = 18) and without (n = 33), and their parents, participated in a 7-week food-based Taste Education intervention with 6-month follow-up. The paired t-test was used to compare differences in body composition based on children's ND status. In logistic regression analysis, odds of children being in the overweight/obese or overfat/obese categories increased by a factor of 9.1 and 10.6, respectively, when having NDs, adjusting for parents' BMI (body mass index) or fat percentage (FAT%). Children with NDs and their parents had significantly higher mean BMI-SDS (BMI standard deviation score) and FAT% at pre-intervention than children without NDs and their parents. Mean BMI-SDS and FAT% lowered significantly between time points for children with NDs and their parents but not for children without NDs or their parents. The findings underline the need for additional exploration into the relationships between children's and parents' body composition based on children's ND status.

Whole-body phase angle correlates with pre-operative markers in total joint arthroplasty

Background

Bioimpedance derived whole body phase angle (ϕ), a measure of cellular integrity, has been identified as an independent marker of morbidity and mortality in many medical and surgical specialties. While similar measures of water homeostasis like extracellular edema (EE) have been associated with pre-operative risk, ϕ has not been studied in orthopaedics, despite potential to serve as a pre-operative marker. This study aims to identify relationships between ϕ, EE, and body composition metrics, laboratory values, patient reported outcomes, and comorbidities.

Methods

Multi-frequency bioimpedance analysis (BIA) records, laboratory values, and patient reported outcomes of adult patients presenting to an academic arthroplasty clinic were retrospectively reviewed. Correlation coefficients between ϕ, EE, and reviewed information were conducted.

Results

ϕ was significantly correlated (p<0.001) most positively with measures of lean tissue such as skeletal muscle mass (r=0.48), appendicular skeletal muscle index (r=0.39), lean body mass (r=0.43), and dry lean mass (r=0.47), while it held negative correlations (p<0.001) with age (r= -0.55), and body fat mass (r= -0.11). ϕ was not correlated with body mass index (BMI, p = 0.204). In contrast, EE demonstrated its strongest positive correlations (p<0.001) with body fat mass (r=0.32), age (r=0.50), and BMI (r=0.26), and its strongest negative correlations (p<0.001) with serum albumin (r= -0.37) and total protein (r= -0.23).

Conclusions

Based on their associations with markers of health and fitness, BIA determined ϕ and EE demonstrate relationships to markers currently implemented in orthopaedic practice. This likely indicates that ϕ has potential as a comprehensive surrogate for several commonly used markers to quantify pre-operative risk. In the future, ϕ may aid in developing risk-stratifications for intervention and prevention of complications.

The Use of Bioelectrical Impedance Analysis Measures for Predicting Clinical Outcomes in Critically Ill Children

Background

The study aimed to investigate the association of bioelectrical impedance analysis (BIA) for predicting clinical outcomes in critically ill children.

Methods

This single-center prospective observational study included patients admitted to a mixed Pediatric Intensive Care Unit (PICU). All patients underwent anthropometric measurement and BIA measurements in the first 24 h of admission. The patients were classified into different groups based on body mass index (BMI) for age. Electronic hospital medical records were reviewed to collect clinical data for each patient. All the obtained data were analyzed by the statistical methods.

Results

There were 231 patients enrolled in our study, of which 31.6% were diagnosed with malnutrition. The phase angle (PhA) of 90-day survivors was significantly higher than that of the non-survivors (4.3° ± 1.1°vs. 3.1° ± 0.9°, P = 0.02). The age-adjusted Spearman partial correlation analysis showed a weak negative correlation between PhA and duration of medical ventilation (r_s = -0.42, P < 0.05). Furthermore, length of stay in PICU has a very weak correlation with ECW/TBW (r_s = 0.29, P < 0.05), and a negative correlation with protein (r_s = -0.27, P < 0.05). Multivariate analysis found that PhA was a significant predictor associated with the 90-day mortality when it was adjusted for PRISM III score (adjusted OR = 1.51, CI: 1.10–2.07, p = 0.01). The area under the ROC (AUROC) of PhA for predicting 90-day mortality was 0.69 (95% CI: 0.53–0.85, p < 0.05), and the cutoff value of PhA was 3.0°, with a sensitivity and specificity of 83 and 53%, respectively.

Conclusion

BIA-derived PhA was found to be an independent predictor of 90-day mortality among critically ill children. A low PhA was associated with a prolonged duration of medical ventilation.

Intensity Dependent Effects of Interval Resistance Training on Myokines and Cardiovascular Risk Factors in Males With Obesity

OBJECTIVE

To determine the effects of different intensities of interval resistance training (IRT) protocols on the levels of select myokines (decorin, follistatin, myostatin, activin A, transforming growth factor beta-1 [TGF-β1]), and cardiometabolic and anthropometric measures in males with obesity.

METHODS

Forty-four obese males (age: 27.5 ± 9.4 yr.; height: 165.4 ± 2.8 cm; weight: 97.9 ± 2.6 kg and BMI: 35.7 ± 4.3 kg/m²) were randomly assigned to one of four groups (n=11 per group): low-intensity interval resistance training (LIIRT), moderate-intensity interval resistance training (MIIRT), high-intensity interval resistance training (HIIRT) or control (C). The LIIRT group performed 10 exercises in 3 sets of 40% (20 repetitions), the MIIRT group performed 10 exercises in three sets of 60% (13 repetitions), and the HIIRT group performed 10 exercises in three sets of 80% (10 repetitions) of one maximum repetition (1RM), which were followed with active rest of 20% of 1RM and 15 repetitions. The resistance training groups exercised ~70 min per session, 3 days per week, for 12 weeks. Measurements were taken at baseline and after 12 weeks of exercise training.

RESULTS

Baseline levels of myokines, cardiovascular risk factors, anthropometry, body composition, and cardio-respiratory fitness were not different between the four groups (p>0.05). The group x time interactions for decorin, activin A, follistatin, myostatin, and TGF-β1, total cholesterol (TC), triglyceride (TG), high-density cholesterol (HDL), low-density cholesterol (LDL), anthropometry, body composition, and cardio-respiratory fitness were statistically significant (p<0.05). There were increases in post-test values for decorin, follistatin, HDL (p<0.05) and decreases in TC, TG, TGF-β1, LDL, and myostatin levels in the LIIRT, MIIRT, and HIIRT groups compared to pretest values (p<0.05). Changes in fat mass, VO_2peak, HDL, TG, glucose, activin A, decorin were not significant in LIIRT compared to the control group, while changes in activin A, follistatin, and TFG-β1 levels were greater in HIIRT and MIIRT groups compared to the LIIRT group (p<0.05).

CONCLUSION

The LIIRT, MIIRT, and HIIRT protocols all produced beneficial changes in decorin, activin A, follistatin, myostatin, and TGF-β1 levels, and cardiometabolic risk factors, with greater effects from the MIIRT and HIIRT protocols compared to LIIRT.