Accuracy of Bioelectrical Impedance Analysis in Estimated Longitudinal Fat-Free Mass Changes in Male Army Cadets

Influence of the Type of Physical Training on the Physical Fitness of Army Cadets

INTRODUCTION

Military Physical Training programs represent a constant challenge for the Armed Forces to improve body composition indices and physical fitness levels. This study aimed to verify the influence of the type of training on the physical fitness and body composition of army cadets, using 2 strategies: a sports program (Athletes group) and conventional physical training (Traditional group).

MATERIALS AND METHODS

This study involved new entrant male cadets who were selected for the Athletes group (n = 243, 19.08 ± 1.23 years) or the Traditional group (n = 356, 18.89 ± 0.97 years), with assessments occurring before and after the period of training (31.4 ± 3.9 weeks). Dual-energy X-ray absorptiometry was used to measure the bone mineral content (BMC), fat mass (FM), percentage of fat mass (%FM), and lean soft tissue (LST). The physical tests evaluated the 3,000 m run (RUN), 50 m swim (SWIM), pull-ups (PULL) on the horizontal bar, push-ups (PUSH) on the ground, and abdominal flexion (SITUP).

RESULTS

Time × group interaction effects were observed for BMC (P < .001), LST (P < .023), RUN (P < .001), PULL (P = .006), PUSH (P < .001), SITUP (P = .007), and SWIM (P < .001). A significant effect of time was noted for all variables analyzed in both groups, except %FM. The Athletes group achieved better results in body composition and in PULL, PUSH, RUN, and SWIM at baseline and after the training period. The Traditional group demonstrated higher relative changes (%) after the training period in the LST (P = .001), PULL (P < .001), PUSH (P < .001), SITUP (P < .004), and SWIM (P < .001) compared to the Athletes group.

CONCLUSION

Both physical training programs improved body composition and physical performance.

Sex Differences in Body Composition and Fitness Scores in Military Reserve Officers' Training Corps Cadets

INTRODUCTION

The cadets in the U.S. Army Reserve Officers' Training Corps (ROTC) consist of students from varied backgrounds. As part of collegiate ROTC programs, cadets must pass fitness tests and adhere to body composition standards in addition to completing their education. The previous fitness test of record was the Army Physical Fitness Test (APFT), but it was recently changed to the Army Combat Fitness Test (ACFT) to better test soldiers for combat capabilities. As part of the standardized scoring, the ACFT is no longer separated by sex or age as in the APFT, but rather by job duty. The purpose of this study was to characterize the modern ROTC cadet based on body composition measures and APFT and ACFT scores and then determine how those factors are related.

MATERIALS AND METHODS

We calculated body mass index (BMI), fat mass, fat-free mass (FFM), fat-free mass index (FFMI), and fat mass index (FMI) (n = 68, 42 males, 26 females). We used Pearson correlations to compare the scores to body composition assessments and Student's t-tests to determine if there were differences between sexes. We hypothesized that those with higher FFM and FFMI will have a higher passing rate on the ACFT and that males would perform better on the ACFT because of having more FFM.

RESULTS

We found that cadets, regardless of sex, were borderline overweight using BMI standards and that BMI did not correlate with any fitness tests. When comparing sexes, both males and females had high passing rates on the APFT, but females struggled to pass the ACFT mostly because of the leg tuck. We also found that ACFT scores were strongly correlated with FFM and FFMI, yet no body composition measures were correlated with APFT scores.

CONCLUSIONS

It is clear from our data that structured training programs and nutrition guidance are needed with an emphasis on changing body composition to increase lean mass and strength to increase the performance of ROTC cadets on the ACFT.

Positive association of lean mass and negative association of protein intake on bone mass and bone geometry of adolescent soccer players

OBJECTIVE

The aim of this study was to evaluate the association of food consumption and body composition on bone parameters in adolescent soccer players.

METHODS

There were 148 male soccer players 12 to 18 y who participated in the study. Body composition was assessed by dual energy x-ray absorptiometry, comprising bone mineral density (BMD) and bone mineral content (BMC) of total body without head (TBLH), lumbar spine (L1-L4), and right femoral neck (RFN). The bone geometry variables measured were femoral strength index (FSI), buckling ratio (BR), section modulus (Z), cross-sectional moment of inertia (CSMI), and cross-sectional area (CSA). Food intake was analyzed using the 24-h food recall. Somatic maturation was estimated by the peak height velocity equation. For the statistical analysis, the stepwise multiple linear regression was used, with P < 0.05.

RESULTS

Regarding food consumption, there was a high protein intake and low calcium intake. Lean mass was a predictor of BMC of TBLH (R² = 0.524), L1-L4 (R² = 0.492), and RFN (R² = 0.405); BMD of L1-L4 (R² = 0.407) and RFN (R² = 0.27); Z (R² = 0.683), CSMI (R² = 0.630), and CSA (R² = 0.640). There was a negative correlation between protein intake with bone mass and bone geometry parameters.

CONCLUSION

In adolescent soccer players, lean mass was a predictor of bone parameters, and high protein intake was negatively associated with bone mass and geometry.

Correlation of body composition in early pregnancy on gestational diabetes mellitus under different body weights before pregnancy

OBJECTIVE

The prediction of gestational diabetes mellitus (GDM) by body composition-related indicators in the first trimester was analyzed under different body mass index (BMI) values before pregnancy.

METHODS

This was a retrospective analysis of pregnant women who were treated, had documented data, and received regular perinatal care at the Third Affiliated Hospital of Zhengzhou University from January 1, 2021, to December 31, 2021. Women with singleton pregnancies who did not have diabetes before pregnancy were included. In the first trimester (before the 14th week of pregnancy), bioelectric impedance assessment (BIA) was used to analyze body composition-related indicators such as protein levels, mineral levels, fat volume, and the waist-hip fat ratio. The Pearman's correlation coefficient was used to evaluate the linear relationship between the continuous variables and pre-pregnancy body mass index (BMI). In the univariate body composition analysis, the association with the risk of developing GDM was included in a multivariate analysis using the relative risk and 95% confidence interval obtained from logarithmic binomial regression, and generalized linear regression was used for multivariate regression analysis. Furthermore, the area under the curve (AUC) was calculated by receiver operating characteristic (ROC) curves. The optimal cutoff value of each risk factor was calculated according to the Youden Index.

RESULTS

In a retrospective study consisting of 6698 pregnant women, we collected 1109 cases of gestational diabetes. Total body water (TBW), protein levels, mineral levels, bone mineral content (BMC), body fat mass (BFM), soft lean mass (SLM), fat-free mass (FMM), skeletal muscle mass (SMM), percent body fat (PBF), the waist-hip ratio (WHR), the visceral fat level (VFL), and the basal metabolic rate (BMR) were significantly higher in the GDM group than in the normal group (P<0.05). Under the pre-pregnancy BMI groupings, out of 4157 pregnant women with a BMI <24 kg/m², 456 (10.97%) were diagnosed with GDM, and out of 2541 pregnant women with a BMI ≥24 kg/m², 653 (25.70%) were diagnosed with GDM. In the generalized linear regression model, it was found that in all groups of pregnant women, pre-pregnancy BMI, age, gestational weight gain (GWG) in the first trimester, and weight at the time of the BIA had a certain risk for the onset of GDM. In Model 1, without adjusting for confounders, the body composition indicators were all positively correlated with the risk of GDM. In Model 3, total body water, protein levels, mineral levels, bone mineral content, soft lean mass, fat-free mass, skeletal muscle mass, and the basal metabolic rate were protective factors for GDM. After Model 4 was adjusted for confounders, only the waist-hip ratio was positively associated with GDM onset. Among pregnant women with a pre-pregnancy BMI <24 kg/m², the body composition-related indicators in Model 2 were all related to the onset of GDM. In Model 3, total body water, soft lean mass, fat-free mass, and the basal metabolic rate were negatively correlated with GDM onset. In the body composition analysis of among women with a pre-pregnancy BMI ≥ 24 kg/m², only Model 1 and Model 2 were found to show positive associations with GDM onset. In the prediction model, in the basic data of pregnant women, the area under the receiver operating characteristic curve predicted by gestational weight gain for GDM was the largest (0.795), and its cutoff value was 1.415 kg. In the body composition results, the area under the receiver operating characteristic curve of body fat mass for predicting GDM risk was larger (0.663) in all pregnant women.

CONCLUSIONS

Through this retrospective study, it was found that the body composition-related indicators were independently associated with the onset of GDM in both the pre-pregnancy BMI <24 kg/m² and pre-pregnancy BMI ≥24 kg/m² groups. Body fat mass, the visceral fat level, and the waist-hip ratio had a higher correlation with pre-pregnancy BMI. Total body water, protein levels, mineral levels, bone mineral content, soft lean mass, fat-free mass, skeletal muscle mass, and the basal metabolic rate were protective factors for GDM after adjusting for some confounders. In all pregnant women, the waist-hip ratio was found to be up to 4.562 times the risk of GDM development, and gestational weight gain had the best predictive power for GDM. Gestational weight gain in early pregnancy, body fat mass, and the waist-hip ratio can assess the risk of GDM in pregnant women, which can allow clinicians to predict the occurrence of GDM in pregnant women as early as possible and implement interventions to reduce adverse perinatal outcomes.

Bioelectrical Impedance Phase Angle and Its Determinants in Patients with Classic Congenital Adrenal Hyperplasia

BACKGROUND

Patients with congenital adrenal hyperplasia due to 21-hydroxylase deficiency (CAH21OHD) have increased fat mass and metabolic alterations. The bioelectrical impedance phase angle (PhA) is an indicator of cellular integrity in several diseases. This study aimed to determine the influence of adiposity levels, sex, CAH21OHD, pubertal development, body composition, and treatment on the PhA of patients with CAH21OHD.

METHODS

Forty girls and 30 boys with CAH21OHD aged 15.3 ± 5.8 years were evaluated. Sexual maturation was assessed by a pediatrician. The PhA was assessed using bioelectrical impedance, percentage of fat mass (% FM), and lean soft tissue (LST) with dual-energy X-ray absorptiometry. Adiposity levels were compared using % FM tertiles and body mass index (BMI). Glucocorticoid dosage was converted using hydrocortisone dose equivalent (HDE).

RESULTS

No differences were found in the PhA values among the clinical form (p = 0.103), BMI (p = 0.498), and % FM (p = 0.654) groups. High PhA values were observed in boys (p = 0.011) and postpubertal (p < 0.001) patients. LST, HDE, and height in girls (r² = 0.68, p < 0.001) and age, HDE, and FM in boys (r² = 0.82, p < 0.001) determined the PhA variations. BMI explained 14% (p = 0.032) of the PhA variations, whereas LST, height, HDE, and FM (kg) explained 66% (p < 0.001) in the prepubertal, pubertal, and postpubertal groups, respectively.

CONCLUSION

LST determined the PhA variations in girls and the postpubertal group. Age and BMI were determinants in boys and the pre- and pubertal groups, respectively.

No differences in the body fat after violating core bioelectrical impedance measurement assumptions

Objective

It is unclear to what degree acutely violating bioelectrical impedance analysis (BIA) measurement assumptions will alter the predicted percent fat mass (%FM) and whether this differs by sex or body mass index (BMI).

Methods

%FM was assessed under control, dehydration, exercise, water, food intake and non-voided bladder conditions with three BIA devices (Tanita: BC-418, TBF-314, & Omron HBF-306CN) for men (n = 23, age: 24.0 ± 5.2 years old) and women (n = 17, age: 22.5 ± 3.4 years old) separately.

Results

For all BIA devices, there were no differences in the %FM values between the control and the other conditions in men or women (− 1.9 to 0.4%, p > 0.05). Across the three devices and five conditions, 97% of %FM tests returned values within 5% of control (2 tests), and 86% of tests were within 2% of control despite violating an assumption. The errors were greatest with dehydration and women were more likely to have a %FM difference greater than 2% than men with dehydration using the hand-to-foot device (Tanita TBF-314: 59% versus 9%). There were no differences in %FM between control and the conditions when examined by BMI (overweight/obesity: − 2.8 to 0.1% and normal weight: − 1.7 to 0.5%; BMI*trial, p = 0.99).

Conclusion

%FM estimates were similar despite acutely violating the preliminary measurement BIA assumptions across a range of different BMIs. The minor variations in %FM are smaller than what would be expected with day-to-day variability or weight loss intervention but may be larger in women than men.

Tailoring Body Mass Index for Prediction of Obesity in Young Adults: A Multi-Centric Study on MBBS Students of Southeast India

INTRODUCTION

Body mass index (BMI) has been used for a long period as a surrogative measure for obesity. But BMI does not differentiate between fat and nonfat tissue (blood, bone, and muscle) due to which it is not considered accurate anymore. But since BMI is easier to estimate and used widely for assessment of obesity, it is better if it is re-standardized according to the body fat percentage (BFP) of a specific population, community, and their ethnicity.

OBJECTIVE

To estimate and propose the BMI cut-off values in young Indian population especially MBBS students taking BFP as a standard.

DESIGN

This is a cross-sectional study. Anthropometric data (age, gender, height, weight, waist circumference, and hip circumference) were collected from the participants after taking consent. BMI was calculated using Quetelet's Rule. BFP was estimated using Omron Body fat Monitor (HBF 385). It measures the BFP by the bioelectrical impedance (BI) method. Data were analyzed with appropriate statistical tests and receiver operating curve (ROC) curves were drawn to find the cut-off values of BMI to determine obesity.

SETTING

The present study is a multi-centric study conducted in four medical colleges (two in each state; Odisha and Andhra Pradesh, India).

PARTICIPANTS

Apparently healthy MBBS students aged 18-24 years were included in this study. Students having any chronic or acute illnesses were excluded from the study. Out of 904 students contacted from four medical colleges, 863 (430 males and 433 females) consented and participated.

RESULTS

Some 863 MBBS students have participated in this study. After adjusting for age, BMI was found to be higher in males. BMI was found to be 29.33 for males and in females it was 29.06. BFP was higher in females (34.23) as compared to males (20.77). Waist hip ratio was found to be higher in females (0.92) than in males (0.84). Whereas, fat free mass (FFM) and fat free mass index (FFMI) are higher in males, i.e., 56.24 and 18.48 respectively. Most appropriate cut-off value for obesity on ROC curve was found to be 22.09 (sensitivity 84.5%, specificity 83.46%) in males and that of females was 23.73 (sensitivity 85.26, specificity 81.23). Whereas, the conventional cut-off of 25 for males had sensitivity of only 46% and that of females was 70.5%. For total population BMI cut-off value was found to be 22.2 with 81% sensitivity and 74% specificity.  Conclusion: We propose the cut-off value for overweight/obesity in males to be 22.09 kg/m² and for females to be 23.73 kg/m² in young adult Indian population. These values were found to have more sensitivity and specificity than current BMI cut-off value.

Use of bioelectrical impedance analysis to monitor changes in fat-free mass during recovery from colorectal cancer- a validation study

BACKGROUND & AIMS

Although previous research show high correlation between fat-free mass (FFM) measured by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), the validity of BIA to track longitudinal changes in FFM is uncertain. Thus, the aim of this study was to validate the ability of BIA to assess changes in FFM during 6 months of recovery from non-metastatic colorectal cancer (CRC).

METHODS

A total of 136 women and men (50-80 years) with stage I-III CRC and a wide range of baseline FFM (35.7-73.5 kg) were included in the study. Body composition was measured at study baseline within 2-9 months of surgery and again 6 months later. Whole-body BIA FFM estimates (FFM_BIA) were calculated using three different equations (manufacturer's, Schols' and Gray's) before comparison to FFM estimates obtained by DXA (FFM_DXA).

RESULTS

Correlation between changes in FFM_BIA and FFM_DXA was intermediate regardless of equation (r ≈ 0.6). The difference in change of FFM_BIA was significant compared to FFM_DXA, using all three equations and BIA overestimated both loss and gain. However, BIA showed 100% sensitivity and about 90% specificity to identify individuals with ≥5% loss in FFM, using all three equations. Sensitivity of FFM_BIA to detect a smaller loss of FFM (60-76%) or a gain in FFM of ≥5% (33-62%) was poor.

CONCLUSION

In a well-nourished population of non-metastatic CRC patients, a single-frequency whole-body BIA device yielded imprecise data on changes in FFM, regardless of equation. BIA is thus not a valid option for quantifying changes in FFM in individuals. However, BIA could be used to identify patients with loss in FFM ≥5% in this population. The validity of BIA to monitor changes in FFM warrants further investigation before implementation in clinical praxis.

Phase Angle is Determined by Body Composition and Cardiorespiratory Fitness in Adolescents

The phase angle is used to evaluate nutritional status and is an indicator of cellular health. Cardiorespiratory fitness and body composition are strong indicators of health during adolescence. We aimed to evaluate if body composition and cardiorespiratory fitness have an association with phase angle among adolescents. 203 girls and 220 boys (12.8±1.3 years) were evaluated. Peak height velocity, percentage fat mass, fat-free mass, cardiorespiratory fitness, and phase angle measurements were collected. Linear regression adjusted by peak height velocity was used to verify if predictor variables were associated with phase angle among adolescents. Phase angle showed correlation with fat-free mass (girls: r=0.42 and boys: r=0.37); with percent fat mass (girls: r=0.23); and with cardiorespiratory fitness (boys: r=0.19). Linear regression showed that percentage fat mass (in girls) and cardiorespiratory fitness (in boys) had an effect of 11 and 17% in phase angle, respectively, while fat-free mass had an effect of 22 and 26% in phase angle for girls and boys, respectively. Changes in phase angle seem to be more associated with the percentage fat mass in girls, cardiorespiratory fitness in boys, and fat-free mass in both when controlled by peak height velocity.