Multiple regression models to predict electrical bioimpedance variables through anthropometric and simple measures in children aged 6-12 years.

Overweight and obesity are conditions associated with sedentary lifestyle and accumulation of abdominal fat, determining increased mortality, favoring chronic diseases, and increasing cardiovascular risk. Although the evaluation of body composition and fat distribution are highly relevant, the high cost of the gold standard techniques limits their wide utilization. Therefore, the aim of this work was to explore the relationships between simple anthropometric measures and BIA variables using multivariate linear regression models to estimate body composition and fat distribution in adults. In this cross-sectional study, sixty-eight adult individuals (20 males and 48 females) were subjected to bioelectrical impedance analysis (BIA), anthropometric measurements (waist circumference (WC), neck circumference (NC), mid-arm circumference (MAC)), allowing the calculation of conicity index (C-index), fat mass/fat-free mass (FM/FFM) ratios, body mass index (BMI) and body shape index (ABSI). Statistical analyzes were performed with the R program. Nonparametric Statistical tests were applied to compare the characteristics of participants of the groups (normal weight, overweight and obese). For qualitative variables, the Fisher's exact test was applied, and for quantitative variables, the paired Wilcoxon signed-rank test. To evaluate the linear association between each pair of variables, the Pearson correlation coefficient was calculated, and Multivariate linear regression models were adjusted using the stepwise variable selection method, with Akaike Information Criterion (p ≤ 0.05). BIA variables with the highest correlations with anthropometric measures were total body water (TBW), body fat percentage (BFP), FM, FFM and FM/FFM. The multiple linear regression analysis showed, in general, that the same variables can be estimated through simple anthropometric measures. The assessment of fat distribution in the body is desirable for the diagnosis and definition of obesity severity. However, the high cost of the instruments (dual energy X-ray absorptiometry, hydrostatic weighing, air displacement plethysmography, computed tomography, magnetic resonance) to assess it, favors the use of BMI in the clinical practice. Nevertheless, BMI does not represent a real fat distribution and body fat percentage. This highlights the relevance of the findings of the current study, since simple anthropometric variables can be used to estimate important BIA variables that are related to fat distribution and body composition.

Pilates is a popular type of exercise, aimed at improving core muscle strength and endurance, core stability, and joint flexibility through a variety of whole-body exercises. Research has shown that Pilates improves body composition, muscle endurance, and joint flexibility. Adequate protein intake is a key factor in supporting the adaptive response of skeletal muscle to exercise training. However, whether protein supplementation augments the adaptations to Pilates training remains unknown. Thus, the aim of the present study was to investigate the effects of protein supplementation during Pilates training on body composition, core muscle endurance, and joint flexibility in trained women. Nineteen Pilates-trained women (31 ± 9 y) performed 10 weeks of Pilates training using the Reformer and Cadillac apparatuses, at least 2 times per week. Participants were randomly allocated to either a placebo (n = 10) or protein supplementation group (n = 9) in a quadruple-blind (participants, intervention providers, investigators, and outcome assessors) design. Participants received 0.6 g of maltodextrin or whey protein per kg body weight daily, respectively. Habitual dietary intake was monitored throughout the study. Before and after the intervention, anthropometric measures (body weight, body mass index, waist and hip circumferences), body composition [through full-scan dual-energy X-ray absorptiometry (DXA) and multifrequency bioelectrical impedance analysis (BIA)], core muscle endurance (through the McGill's torso muscular endurance test battery), and joint flexibility (through the sit-and-reach test) were assessed. Data were analyzed by 2-way ANOVA (supplement × time) with repeated measures on time. Common DXA and BIA variables (whole-body fat and lean mass) were compared through paired Student's t tests and subjected to Pearson's correlation analysis. The level of statistical significance was set at α = 0.05. Participants received, on average, 1.3 g protein/kg body weight/day from their habitual diet. After 10 weeks of Pilates training and regardless of supplementation, body fat (assessed by BIA) and hip circumference decreased; lean mass, total water, and extracellular water (by BIA) increased; and arm lean mass, trunk bone mineral content, and trunk bone area (by DXA) increased (all p < 0.05). The common BIA and DXA variables were highly correlated (r > 0.78, p < 0.001) and did not differ pre-intervention (p > 0.1), although they differed post-intervention (p < 0.001), with BIA overestimating lean mass compared with DXA. Core muscle endurance and joint flexibility increased with training (p < 0.05), with no effect of supplementation. Ten weeks of Pilates training improved core muscle endurance, joint flexibility, and aspects of body composition in healthy trained women, but these adaptations were not enhanced by daily supplementation with 0.6 g of protein per kilogram body weight.

The primary purpose of this study was to investigate the accuracy of the DF50 (ImpediMed Ltd, Eight Mile Plains, Queensland, Australia) bioelectrical impedance analysis device using dual-energy x-ray absorptiometry as the criterion in two groups: endurance athletes and power athletes. The secondary purpose was to develop accurate body fat percentage prediction equations for each group based on bioelectrical impedance analysis data and/or the combination of bioelectrical impedance analysis and anthropometric data. Eighty male athletes (40 elite endurance athletes and 40 were power athletes), age 19–48 with body mass indexes ranging from 18.9 to 37.4 were recruited. Anthropometric measurements were taken. Body composition was assessed by dual-energy x-ray absorptiometry and bioelectrical impedance analysis. An athlete-specific bioelectrical impedance analysis prediction equation was developed by stepwise regression analysis using dual-energy x-ray absorptiometry as the criterion and bioelectrical impedance analysis data and anthropometric measurements as predictor variables. The DF50 bioelectrical impedance analysis significantly overestimated body fat percentage by 6.4 ± 0.5 in the entire group (p < .001) and in both the endurance group (6.1 ± .6, p < .001) and the power group (6.7 ± 0.7, p < .001). The endurance and power group showed no significant difference in the error of estimation by bioelectrical impedance analysis (p = .554), indicating that bioelectrical impedance analysis has the same error in both groups. The final prediction equation incorporated both anthropometric variables as well as bioelectrical impedance analysis variables and produced an adjusted r2 of .982 and a standard error of the estimate (SEE) of 1.98 for the entire group. This prediction equation used bioelectrical impedance analysis measurements and anthropometric measurements, specifically trunk measurements, to account for trunk size, a common source of error in bioelectrical impedance analysis equations. Follow-up validation studies are necessary to further validate the equations produced.

Predicting abdominal adipose tissue among women with familial partial lipodystrophy

Development of predictive equations for total and segmental body fat in HIV-seropositive patients

Skeletal Muscle Mass and Extracellular Water/Total Body Water Independently Predict Phase Angle Percentile in Young Men

Although visceral adipose tissue (VAT) is the component of body composition most highly associated with cardiovascular risk factors, its measurement requires expensive procedures, such as magnetic resonance imaging. This study examined the ability of simple demographic and anthropometric measurements to predict magnetic resonance imaging-derived VAT in 76 apparently healthy, black and white youths with obesity who were 7 years to 16 years of age. Stepwise multiple linear regression was used to develop a prediction equation for VAT based on 13 simple anthropometric variables (height, weight, body mass index, triceps skinfold, calf skinfold, sagittal diameter, waist circumference, hip circumference, thigh circumference, waist/hip ratio, waist/thigh ratio, sagittal diameter/thigh ratio, and percent body fat from the sum of calf and triceps skinfolds) and three demographic variables (age, gender and ethnicity). The stepwise multiple regression procedure yielded a final model that included two anthropometric variables (sagittal diameter and waist/hip ratio) and one demographic variable (ethnicity). The prediction equation was: VAT = - 124.06+ 16.67 (ethnicity)+4.15 (sagittal diameter)+100.89 (waist/hip ratio), where ethnicity was coded as 0= black and 1 = white. The model explained 63% of the variance in VAT and was associated with a measurement error of 23.9%. Although the model seems to lack sufficient explanatory power for routine use in clinical settings with individual patients, it may have some utility in epidemiological studies given its relatively small (<25%) standard error of estimate.

Artificial neural network model effectively estimates muscle and fat mass using simple demographic and anthropometric measures

ObjectiveTo examine the changes in body composition, water compartment, and bioimpedance in mild cognitive impairment (MCI) individuals.MethodsWe obtained seven whole-body composition variables and seven pairs of segmental body composition, water compartment, and impedance variables for the upper and lower extremities from the segmental multi-frequency bioelectrical impedance analysis (BIA) of 939 elderly participants, including 673 cognitively normal (CN) people and 266 individuals with MCI. Participants’ characteristics, anthropometric information, and the selected BIA variables were described and statistically compared between the CN participants and those with MCI. The correlations between the selected BIA variables and neuropsychological tests such as the Korean version of the Mini-Mental State Examination and Seoul Neuropsychological Screening Battery – Second Edition were also examined before and after controlling for age and sex. Univariate and multivariate logistic regression analyses with estimated odds ratios (ORs) were conducted to investigate the associations between these BIA variables and MCI prevalence for different sexes.ResultsParticipants with MCI were slightly older, more depressive, and had significantly poorer cognitive abilities when compared with the CN individuals. The partial correlations between the selected BIA variables and neuropsychological tests upon controlling for age and sex were not greatly significant. However, after accounting for age, sex, and the significant comorbidities, segmental lean mass, water volume, resistance, and reactance in the lower extremities were positively associated with MCI, with ORs [95% confidence interval (CI)] of 1.33 (1.02–1.71), 1.33 (1.03–1.72), 0.76 (0.62–0.92), and 0.79 (0.67–0.93), respectively; with presumably a shift of water from the intracellular area to extracellular space. After stratifying by sex, resistance and reactance in lower extremities remained significant only in the women group.ConclusionAn increase in segmental water along with segmental lean mass and a decrease in body cell strength due to an abnormal cellular water distribution demonstrated by reductions in resistance and reactance are associated with MCI prevalence, which are more pronounced in the lower extremities and in women. These characteristic changes in BIA variables may be considered as an early sign of cognitive impairment in the elderly population.

OBJECTIVE: The aim of the present study was to determine the impedance of Wistar rats treated with high-fat and high-sucrose diets and correlate their biochemical and anthropometric parameters with chemical analysis of the carcass. METHODS: Twenty-four male Wistar rats were fed a standard (AIN-93), high-fat (50% fat) or high-sucrose (59% of sucrose) diet for 4 weeks. Abdominal and thoracic circumference and body length were measured. Bioelectrical impedance analysis was used to determine resistance and reactance. Final body composition was determined by chemical analysis. RESULTS: Higher fat intake led to a high percentage of liver fat and cholesterol and low total body water in the High-Fat group, but these changes in the biochemical profile were not reflected by the anthropometric measurements or bioelectrical impedance analysis variables. Anthropometric and bioelectrical impedance analysis changes were not observed in the High-Sucrose group. However, a positive association was found between body fat and three anthropometric variables: body mass index, Lee index and abdominal circumference. CONCLUSION: Bioelectrical impedance analysis did not prove to be sensitive for detecting changes in body composition, but body mass index, Lee index and abdominal circumference can be used for estimating the body composition of rats.

BIOELECTRICAL IMPEDANCE ANALYSIS AND PHYSICAL FITNESS IN ASSESSING THE NUTRITIONAL STATUS OF CHILDREN AND ADOLESCENTS ABSTRACT Background The evaluation of body composition and the assessment of physical fitness are both relevant issues in public health nutrition. In children/adolescents different aspects of body composition may be related to growth, health and well being, including in-creased body fat content and/or abdominal fat as well as a decreases fat-free mass, appendicular muscle mass and/or body cell mass. Bioelectrical impedance analysis (BIA) is a field method providing estimates of FFM by means of predictive equations which include BIA variables and frequently other variables such as age and weight. As an alternative, the use of directly-measured raw BIA variables, such as impedance ratio (IR) or phase angle (PhA) has gained attention, because these latter are thought to be related to body cell mass, ex-tracellular/intracellular water ratio and cell health. Health related physical fitness is an important component of overall health in the first two decades of life being associated with cardiovascular risk factors, bone health, ac-ademic performance, etc. It can be evaluted by using a number of tests assessing dif-ferent aspects of physical fitness such a muscolar strenght and endurance, flexibility, and cardiorespiratory endurance. To date there is a scarcity of data available in the literature regarding raw BIA varia-bles in the first two decades of life, as well as the relationships between physical fit-ness tests and body composition. Aim The aim of the Thesis was to evaluate: 1) the changes of raw BIA variables with time and their differences due to the gen-eral characteristics of the individual; 2) the relationships of physical fitness test with general characteristics of the individ-ual, BIA-derived estimates of body composition, and BIA variables; 3) changes in 1) and 2) due to overweight/obesity. Subjects In this thesis the evaluation of body composition and physical fitness was carried out in 1) general population sample: children and adolescents (n=396) recruited in two schools (Studies 1-2); 2) obese sample: overweight and obese children and adoles-cents (n=277) recruited from a outpatient clinic for obesity (Study 3). Methods Standard procedures were used for collecting data on the following variables: 1) stature, weight, waist circumference; 2) BIA: impedance (Z) at 5-10-50-100-250 kHz and phase angle at 50 kHz for both dominant and non-dominant body sides (whole body, upper limbs, and lower limbs); 3) Physical fitness tests: handgrip strength, standing long jump, five-long jump, chair (sit-to-stand), six-minute walk test (6MWT); 4) derived variables: BI index as stature²/Z, impedance ratios (IRs) as ratios between Z at higher frequencies (5,10 and 50 kHz) and Z at lower frequencies (5 and 10 kHz). Results Study 1: Consistent data on changes in BIA raw variables were obtained over the age span 6-13in the general population sample and systematically presented for either the whole body or upper and lower limbs. An increase in BI indexes and phase angle with age was apparent, while IRs declined progressively. Significant differences in BIA raw variables were observed between males and females, with some dissimilari-ties when upper and lower limbs were compared to each other. Study 2: We identified several determinants of fitness-related tests in the subjects. In addition to gender and age, BIA-derived FFM, BI index, IRs and phase angle were all significant predictors of handgrip strenght, and less consistently of standing long jump and standing five jumps. IR and phase angle were also predictors of the 6MWT, but not of the chair test. Study 3: Phase angle did not differ significantly between genders in over-weight/obese children and adolescents, but still remain a significant predictor, con-currently with the BI index, of HGS, standing long jump and standing five jumps. Weight and BMI-SDS were weak predictors of the 6MWT. Conclusion The results presented in the Thesis increase to a considerable extent the information available on row BIA data in children and adolescents, first of all with regard to changes with time and between-gender differences. Raw BIA variables (BI indexes, IRs and phase angle) emerged as significant predictors of physical fitness tests in normal-weight, but also in overweight/obese children and adolescents, the stronger relationship being the one with HGS.

Body fat mass and distribution as predictors of metabolic outcome and weight loss after Roux-en-Y gastric bypass

Background/Objectives: Aging is linked to a loss of muscle mass and strength, which increases the risk of falls, disability, and lower quality of life. Identifying individuals at risk of sarcopenia, cachexia, or malnutrition is important. In settings with limited access to advanced diagnostics, simple anthropometric measurements can serve as practical alternatives. This study aimed to evaluate simple, quick, and non-invasive anthropometric measurements as indicators of muscle mass in older adults, based on body composition analysis (BIA). Methods: The study included 361 patients admitted to a geriatric ward. Standardized protocols were used to measure anthropometric and body composition parameters, including bioelectrical impedance analysis (BIA). Various body composition indices were calculated, such as body mass index (BMI), body adiposity index (BAI), waist-to-hip ratio (WHR), visceral adiposity index (VAI), fat-free mass index (FFMI), and fat mass index (FMI). Results: Strong positive correlations were found between body mass and muscle mass in both women and men. Among anthropometric measures, calf circumference showed the strongest correlation with muscle mass (R = 0.798 in women; R = 0.744 in men, p < 0.001). Other indices, including BMI (R = 0.733 in women; R = 0.606 in men, p < 0.001), FFMI (R = 0.697 in women; R = 0.721 in men, p < 0.001), and FMI (R = 0.600 in women; R = 0.354 in men, p < 0.001), were also positively correlated with muscle mass. Conclusions: Calf circumference is a simple, quick, and non-invasive measure that can effectively estimate muscle mass in older adults. Although other anthropometric parameters, such as body mass, BMI, FMI, and FFMI, also showed positive correlations with muscle mass, calf circumference appears to be the most practical, as it requires minimal equipment, little time, and limited preparation. This makes it particularly useful for routine assessments in primary care and long-term care settings, where access to advanced diagnostic tools like BIA or DXA is limited. These findings support the use of simple anthropometric measurements as cost-effective and easily accessible alternatives for estimating muscle mass in older adults. Further research is needed to determine appropriate cut-off points for specific populations and sexes.

The aim of the study was to evaluate the relationship between basal metabolic rate (BMR) and bioelectrical impedance analysis (BIA) in undernourished female patients with anorexia nervosa. Participants were 86 female patients with anorexia nervosa (age 20.8 ± 4.7 years; weight 39.3 ± 5.2 kg; body mass index 15.4 ± 1.6 kg m−2). BMR was measured by indirect calorimetry and single-frequency BIA was determined at 50 kHz on the whole body. The BIA variables considered were resistance, reactance, phase angle and the bioimpedance index (height2/resistance). Fat-free mass was calculated from subcutaneous skin fold thickness. In the study group BMR was 3782 ± 661 kJ d−1 while bioimpedance index varied between 27.6 and 49.9 cm2 Ω−1 and phase angle between 2.54° and 6.49°. BMR was significantly correlated with weight, height, body mass index and fat-free mass, and, among BIA variables, with reactance and phase angle. Multiple regression analysis indicated that phase angle was a predictor of BMR not only when solely BIA variables were considered, but also in combination with either weight and age or fat-free mass. In conclusion, phase angle emerged as a strong predictor of BMR in female patients with anorexia nervosa. Nevertheless, further studies are necessary to confirm this finding in other forms of protein energy malnutrition and justify the inclusion of BIA variables in the equations used to predict BMR in the clinical setting.

The total body water (TBW) and body condition of 86 female southern elephant seals was estimated from tritiated water (HTO) dilution space analysis. HTO blood samples were analysed using two distillation methods (direct serum counts and evaporative freeze capture) that yielded significantly different estimates. Evaporative freeze capture is recommended for use because it is faster, cheaper, and provides a more precise TBW estimate of dilution space. Estimates of TBW were then compared with those derived from bioelectrical impedance analysis (BIA) and morphometric models. There were significant, positive relationships between TBW and BIA variables, but the level of accuracy was inadequate for BIA to be more useful than the other methods trialled. Morphometric models accurately estimated TBW (kg). Models developed from surface area (SA) (TBW = [SA * 82.58] – 86.94) and from a combination of mass (M), length (L), and girth (G) (TBW = [(M * 0.72) + (L * 5.49) + ( G * 134.94) + 164.36)] provided the most accurate TBW estimates. In contrast, condition indices did not give accurate or reliable estimates of relative body condition.