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

Nutritional physiology and body composition changes during a rapid ascent to high altitude

Exposure to high altitude might cause the body to adapt with negative energy and fluid balance that compromise body composition and physical performance. In this field study involving 12 healthy adults, sex-balanced, and aged 29 ± 4 years with a body mass index of 21.6 ± 1.8 kg/m2, we investigated the effects of a 4-day trekking up to 4556 m a.s.l. on Monte Rosa (Alps, Italy). The food intake was recorded using food diaries and nutrient averages were calculated. The bio-impedance analysis was performed at low and high altitudes, and a wearable biosensor (Swemax) was used to track hydro-saline losses in two participants. Daily total energy intake was 3348 ± 386 kcal for males and 2804 ± 415 kcal for females (13%-14% protein, 35% fat, 44%-46% carbohydrates). Although there was a significant body weight loss (65.0 ± 9.3 vs. 64.2 ± 9.10 kg, p < 0.001, d = 1.398), no significant changes in body composition parameter were found but a trend in the increase of the bioelectrical phase angle in males (p = 0.059, d = -0.991). Body water percentage significantly changed (p = 0.026, η2 p = 0.440), but the absolute water did not, suggesting that the weight loss was not due to water loss. Salivary and urinary osmolality did not change. A reduction in sweat rate at higher altitudes was observed in both participants. Interestingly, salivary leptin increased (p = 0.014, η2 p = 0.510), and salivary ghrelin decreased (p = 0.036, η2 p  = 0.403). Therefore, the 4-day trekking at altitude of hypoxia exposure induced changes in satiety and appetite hormones. High altitude expeditions require more specific nutritional guidance, and using multiplex analysis could help in monitoring fluid balance and body composition.

Are accelerometer-measured sitting and physical activity times associated with muscle mass and strength in healthy young adults in the UAE?

Background

A high prevalence of obesity, sedentary behavior, and physical inactivity could affect muscle mass and strength in young adults in the United Arab Emirates (UAE). Therefore, we investigated the association of sex, body mass index (BMI), and accelerometer-measured sitting and physical activity (PA) times with skeletal muscle mass index (SMI), hand grip, and thigh muscle strength in healthy young adults in the UAE.

Methods

In this cross-sectional study, 156 healthy young adults (age 21.68 ± 3.01 years, BMI 25.40 ± 4.79 kg/m2, 52.6 % women) were included. BMI and muscle mass were recorded using a bioelectrical impedance analyzer. Maximum hand grip strength and thigh muscle torque were assessed using the Jamar-smart hand-dynamometer and Biodex System-4-Pro, respectively. Participants wore a triaxial Fibion accelerometer on their anterior thigh for >10 h per day for 4-7 days to measure their sitting and PA times. Multiple linear regression analyses were used.

Results

Participants spent most of their time sitting (11.37 ± 1.10 h), followed by standing (2.92 ± 0.86 h), walking (1.58 ± 0.55 h), and vigorous intensity PA (4.79 ± 5.85 min) per 16-h day. Sex (p < 0.001) and BMI (p < 0.001) were negatively associated with all muscle mass and strength variables. Men had more muscle mass and strength than women. As BMI increased, muscle mass and muscle strength decreased. Accelerometer-measured sitting and walking times were negatively associated with concentric hamstrings (p = 0.044) and quadriceps torques (p = 0.031), respectively.

Conclusion

Sex, BMI, and accelerometer-measured sitting and walking times were associated with muscle mass and/or muscle strength in healthy young adults. Women and those with a high BMI need interventions to improve their muscle mass and strength. The paradox regarding the association of PA with muscle mass and strength in younger adults may be due to possible influences from other factors (e.g., resistance training, dietary intakes, etc.) superseding that of accelerometer-measured PA.

Association of sociodemographic, anthropometric, and sleep quality factors with accelerometer-measured sitting and physical activity times among Emirati working women during the COVID-19 pandemic: A cross-sectional study

BACKGROUND

Although a significant lack of physical activity has been linked to an increase in obesity among Emirati women, the factors associated with accelerometer-measured sitting and physical activity times in Emirati women remain unclear.

OBJECTIVES

To explore the association of accelerometer-measured sitting and physical activity times with sociodemographic, anthropometric, and sleep quality factors among Emirati working women.

DESIGN

A cross-sectional study.

METHODS

A convenience sample of 163 healthy working Emirati women aged 18-45 years was used. Sedentary and physical activity times were measured using the Fibion accelerometers worn on the thighs for 4-7 days. General demographic information, anthropometric measurements, and self-reported sleep quality (Pittsburgh sleep quality index score) were collected. Only participants who had valid data (i.e. wear time of ⩾600 min (10 h) per day for a minimum of 4-7 days) were evaluated. All values were normalized to a 16-h day to mitigate differences in wear time among the participants.

RESULTS

Overall, 110 Emirati women were included. The mean sitting time per 16-h day was 11.6 ± 1.1 h; mean moderate activity time per day, 40.88 ± 17.99 min; and mean vigorous activity time per day, 2.41 ± 1.21 min. Longer sitting time was associated with high body fat, secondary education, and divorce. Sitting time was reduced in those with good sleep quality. Moderate-to-vigorous physical activity time was increased in women with postgraduate education and was decreased in women with a longer work experience and with comorbidities. The total activity time increased with increasing age and good sleep quality, whereas it decreased with increasing body fat, presence of at least one comorbidity, secondary education, and divorce.

CONCLUSION

Certain demographic, anthropometric, and sleep quality factors were associated with accelerometer-measured sitting and physical activity times among Emirati working women. Future longitudinal studies should consider these factors when investigating predictors of physical activity levels in this population.

Differences in calculated body fat percentage estimated from published equations based on bioelectric impedance analysis in healthy young South African adults

Background

Adult overweight and obesity, in addition to the intake of saturated fat and total serum cholesterol must be monitored as biological risk factors for non-communicable diseases (NCDs). Bioelectric impedance analysis (BIA) provides data on body fat for use in epidemiological settings. However, optimized equations should be used to calculate percentage body fat (%BF). The purpose of this study was to assess the differences between %BF calculated using different published BIA equations and %BF measured by BIA in young South African adults.

Design and methods

In this observational study, differences in calculated %BF were assessed, with different BIA equations retrieved from the literature used in 1128 healthy young adults aged 20-30 years. The %BF (measured by BIA) was compared between equations, between Black and White men and women, respectively.

Results

The results showed statistically significant differences in the %BF calculated from published BIA equations when used in young South African adults (χ² = 946, χ² = 2528, χ² = 2088, respectively, p < 0.0001). In Black and White men and women, respectively, %BF levels were significantly higher when calculated by equations, than when measured by BIA (p < 0.0001).

Conclusion

There seem to be large discrepancies in estimating %BF by BIA equations and these values cannot be used interchangeably for young South African adults. A South African age, ethnicity and sex-specific BIA equation needs to be developed to accurately estimate %BF in young South African adults.

Comparisons of Baseline Obesity Prevalence and Its Association with Perceived Health and Physical Performance in Military Officers

Increasing obesity rates among USA military members vary by age, pay grade, and measurement methods and threaten force fitness and readiness. Limited research has directly measured obesity among officers; those enrolled in graduate school at the Command and General Staff College (CGSC) face additional demands and increased obesity risk. This study compared obesity measurements and performance on the Army Physical Fitness test and self-rated health for a sample of mostly CGSC officers. Participants (n = 136, 75.7% male, 79.6% officers) completed body composition measures; BMI, body fat percentage (BF%), waist circumference (WC), and waist-to-height ratio (WHtR) estimated obesity rates and were compared for discordance. Army Physical Fitness Test pass/fail rates and self-reported perceived health were compared with each obesity classification. Obesity prevalence was 18.5-39.7%, differing by sex and measure. BMI, WC, and WHtR underestimated obesity compared to BF% at least 21% of the time. WHtR had the lowest discordance with BF%. Soldiers categorized as obese were significantly more likely to fail the fitness test and report lower self-rated health than non-obese soldiers. Overall, obesity rates among our primarily officer sample were higher than previous studies, particularly for women. Interventions to address obesity are necessary among military officers to optimize human performance.

Prediction of fat-free mass in a multi-ethnic cohort of infants using bioelectrical impedance: Validation against the PEA POD

Background

Bioelectrical impedance analysis (BIA) is widely used to measure body composition but has not been adequately evaluated in infancy. Prior studies have largely been of poor quality, and few included healthy term-born offspring, so it is unclear if BIA can accurately predict body composition at this age.

Aim

This study evaluated impedance technology to predict fat-free mass (FFM) among a large multi-ethnic cohort of infants from the United Kingdom, Singapore, and New Zealand at ages 6 weeks and 6 months (n = 292 and 212, respectively).

Materials and methods

Using air displacement plethysmography (PEA POD) as the reference, two impedance approaches were evaluated: (1) empirical prediction equations; (2) Cole modeling and mixture theory prediction. Sex-specific equations were developed among ∼70% of the cohort. Equations were validated in the remaining ∼30% and in an independent University of Queensland cohort. Mixture theory estimates of FFM were validated using the entire cohort at both ages.

Results

Sex-specific equations based on weight and length explained 75-81% of FFM variance at 6 weeks but only 48-57% at 6 months. At both ages, the margin of error for these equations was 5-6% of mean FFM, as assessed by the root mean squared errors (RMSE). The stepwise addition of clinically-relevant covariates (i.e., gestational age, birthweight SDS, subscapular skinfold thickness, abdominal circumference) improved model accuracy (i.e., lowered RMSE). However, improvements in model accuracy were not consistently observed when impedance parameters (as the impedance index) were incorporated instead of length. The bioimpedance equations had mean absolute percentage errors (MAPE) < 5% when validated. Limits of agreement analyses showed that biases were low (< 100 g) and limits of agreement were narrower for bioimpedance-based than anthropometry-based equations, with no clear benefit following the addition of clinically-relevant variables. Estimates of FFM from BIS mixture theory prediction were inaccurate (MAPE 11-12%).

Conclusion

The addition of the impedance index improved the accuracy of empirical FFM predictions. However, improvements were modest, so the benefits of using bioimpedance in the field remain unclear and require further investigation. Mixture theory prediction of FFM from BIS is inaccurate in infancy and cannot be recommended.

Gestational weight change and childhood body composition trajectories from pregnancy to early adolescence

OBJECTIVE

A mother-child dyad trajectory model of weight and body composition spanning from conception to adolescence was developed to understand how early life exposures shape childhood body composition.

METHODS

African American (49.3%) and Dominican (50.7%) pregnant mothers (n = 337) were enrolled during pregnancy, and their children (47.5% female) were followed from ages 5 to 14. Gestational weight gain (GWG) was abstracted from medical records. Child weight, height, percentage body fat, and waist circumference were measured. GWG and child body composition trajectories were jointly modeled with a flexible latent class model with a class membership component that included prepregnancy BMI.

RESULTS

Four prenatal and child body composition trajectory patterns were identified, and sex-specific patterns were observed for the joint GWG-postnatal body composition trajectories with more distinct patterns among girls but not boys. Girls of mothers with high GWG across gestation had the highest BMI z score, waist circumference, and percentage body fat trajectories from ages 5 to 14; however, boys in this high GWG group did not show similar growth patterns.

CONCLUSIONS

Jointly modeled prenatal weight and child body composition trajectories showed sex-specific patterns. Growth patterns from childhood though early adolescence appeared to be more profoundly affected by higher GWG patterns in females, suggesting sex differences in developmental programming.

Accuracy of Body Mass Index and Obesity Status in Police Trainees

The first aim of this study was to compare body mass index (BMI) (indirect method) classification with the body fat percent (PBF) (direct method) and to determine how BMI classifies subjects with different levels of skeletal muscle mass percent (PSMM). The second aim was to determine the prevalence of overweight and obesity status among police trainees (PTs). A total of 103 male PTs participated in this research: age = 21.46 ± 0.64 years, body mass (BM) = 75.97 ± 8.10 kg, body height (BH) = 174.07 ± 6.31 cm, BMI = 25.05 ± 2.12 kg/m². The InBody 370 multichannel bioelectrical impedance analysis (BIA) measured body composition. Study results indicated that muscular PTs could be misclassified as overweight and that PBF identified more subjects as obese. Namely, three PTs were obese according to BMI, while 13 were obese according to PBF. The information provided by this research could be used to help professionals understand the importance of measuring body composition, and the inaccuracies in BMI classification. In conclusion, whenever possible PSMM and PBF should replace the utilization of BMI to screen overweight and obesity in PTs. Agencies may think of using BIA as non-invasive, quick and inexpensive measurement tool.

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.

The Possibility of Using Bioelectrical Impedance Analysis in Pregnant and Postpartum Women

Pregnancy is a time of significant changes occurring in the composition of a woman’s body in order to provide support for the growth and development of the foetus. Bioelectrical impedance analysis (BIA) is used to assess the body composition and hydration status. This technique represents a non-invasive, reliable, and fast clinical approach, which is well tolerated by patients. A segmental impedance measurement might be advantageous in pregnant women, particularly in late pregnancy. The purpose of this paper is to provide a review of different applications of BIA in pregnant and postpartum women. It seems that BIA has a better prognostic potential for gestational and post-partum outcomes than body mass index. The BIA method can be successfully used to study the effect of excessive gestational weight gain in pregnancy on the development of obstetric complications. Studying the mother’s body composition and correlating it with her energy balance could facilitate the development of dietary recommendations for women. Evaluation of the body composition can provide important clues for diagnosis of gestational diabetes mellitus in pregnant women with a low risk of this disease. BIA is also used as one of the additional tests in assessing the risk of developing gestational hypertension and preeclampsia.