Development of a bioelectrical impedance analysis-based prediction equation for body composition of rural children aged 4-8 years in Myanmar.

Background: Reliable and accurate estimates of body composition are essential when studying the various health correlates of disease. Bioelectrical impedance analysis (BIA) is an affordable and feasible body composition assessment technique for clinical and field settings. Total body water (TBW) and hence fat-free mass is estimated by predictive regression algorithms using anthropometric measurements plus the resistance index. Aim: The study aimed to develop a BIA prediction equation for TBW in children in Myanmar using the deuterium dilution technique as the reference method. Methods: The study design was cross-sectional in a school setting with convenience sampling of participants. One hundred and two healthy children (57 boys and 45 girls) with aged 4 and 8 years participated; randomly divided into the prediction group (29 boys and 22 girls) and cross-validation group (28 boys and 23 girls). Whole-body impedance, anthropometric and TBW (by D2O dilution) measurements. The prediction equation was cross-validated using a split-group design and compared to published equations for contemporaneous populations. Results: TBW could be predicted by the following equation. TBW = 0.4597 * Weight (kg) + 0.1564 * Impedance index + 0.6075 (R2 = 0.891, P < 0.0001) with a correlation coefficient of 0.942 and limits of agreement of 0.98 kg TBW on cross-validation. Conclusions: This equation can be used to predict body composition in young (aged 4-8 years) children in Myanmar but because the age range of the participants in the present study was relatively narrow, more research in different age groups is required to establish its broader applicability.

Comparison of Volume Measurements and Bioimpedance Spectroscopy Using A Stand-on Device for Assessment of Unilateral Breast Cancer-Related Lymphedema.

Objective: Breast cancer related lymphedema (BCRL) may be assessed through objective measurement of limb swelling with common techniques including volumetric measurement using a tape measure or perometry, and measurement of extracellular water using bioimpedance spectroscopy (BIS). This study aimed to evaluate the performance of a stand-on BIS device for detection of BCRL, introduce a novel graphical method to compare volumetric and BIS methods alongside traditional specificity and sensitivity analysis, and determine and compare BIS thresholds with those published previously. Materials and Methods: Female participants with indocyanine green lymphography confirmed unilateral arm lymphedema (n = 197) and healthy controls (n = 267) were assessed using a cross-sectional study design. BIS and volumetric measures were obtained in a single session. Results: The BIS lymphedema index (L-Dex) method had a significantly higher sensitivity than the excess volume approach (area under the curve = 0.832 vs. 0.649, p = 0.0001). A threshold of L-Dex 6.5 had a higher true positive rate (70.6%) than L-Dex 10 (68.5%) although false positive rate increased from 0.4% to 2.6%. A threshold of 5% excess volume improved the true positive rate (68.5%) compared with 10% excess volume (49.7%) however the false positive rate increased to an unacceptable 47%. The L-Dex ranges in this study were not significantly different from previously published ranges. Conclusion: BIS was superior for identifying BCRL compared with volume measurements, reaffirming the value of this technique. However, it is recommended that BIS be used in conjunction with comprehensive evaluation of symptoms and clinical presentation. The proposed graphical method provides a simple and easily interpretable approach to compare and define concordance between the two commonly used methods for BCRL assessment namely limb volume and BIS L-Dex indices. The existing BIS (L-Dex) thresholds for presence of BCRL were also validated.

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.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults.

BACKGROUND: Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. OBJECTIVES: To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. METHODS: TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. RESULTS: Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. CONCLUSIONS: These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Longitudinal changes in body composition and diet after acute spinal cord injury.

OBJECTIVE: Spinal cord injury (SCI) is associated with low muscle mass and adiposity, however, to our knowledge, few studies have monitored the trajectory of changes over time. This study aimed to evaluate the timing, rate, magnitude, and site-specific changes in body composition and related changes in diet after SCI. METHODS: We assessed 39 patients with SCI. The analysis included five women. Of the participants, 51% had American Spinal Injury Association Impairment Scale (AIS) criteria A/B (motor complete) injuries, 18% had AIS C (sensory/motor incomplete) injuries, and 31% had AIS D (motor incomplete) injuries. The mean age of the patients was 43.2 y. They were 48.1 d post-injury and had their weight, diet, and body composition (bioimpedance spectroscopy) assessed every 2 wk. RESULTS: No significant linear changes were observed for any body composition measure. Total body fat mass (FM) changed 0.01 kg/2 wk when fitted to a quadratic model (P = 0.004), decreasing to week 15 and returning to baseline at week 28. Subgroup analysis revealed that arm lean tissue mass (LTM) increased in paraplegic versus tetraplegic participants (0.05 versus -0.01 kg/2 wk, P = 0.007). Participants with AIS A/B injuries lost FM (-0.17 kg; P = 0.010), whereas those with AIS C injuries gained appendicular LTM (ALTM; 0.15 kg; P = 0.017) and leg LTM (0.12 kg; P = 0.008) every 2 wk. Body composition remained stable in the AIS D group. Mean fortnightly changes were greater in the AIS A/B group than the C group for weight (mean difference -0.30 kg; P = 0.021), FM (-0.25 kg; P = 0.002), and leg LTM (-0.11 kg; P = 0.021) and AIS A/B versus D for FM (-0.42 kg; P = 0.013). Baseline energy and protein intakes were 2150 kcal (±741) and 102 g (±40) and decreased by 21.5 kcal (P = 0.016) and 1.3 g (P = 0.004) every 2 wk but were not associated with body composition changes. CONCLUSIONS: Neurologic level and severity of SCI, but not changes in diet, were the main determinants of heterogeneous body composition changes.

Bioelectrical impedance analysis as a clinical marker of health status in adult patients with benign gastrointestinal disease: A systematic review.

BACKGROUND: Body composition reflects nutritional status, disease status and progression, and treatment responses. Mounting evidence supports the use of bioelectrical impedance analysis (BIA) as a non-invasive tool to assess body composition. Patients with benign gastrointestinal (GI) disease experience disease-related alterations in their body composition, and bioimpedance outcomes in patients with benign GI diseases have not previously been summarized. We aimed to evaluate BIA as a clinical body composition marker for benign GI diseases and describe its association with physical health status. METHODS: We systematically searched PubMed, Scopus, Web of Science, Embase, and CINAHL from inception to October 2023 (PROSPERO registration: CRD42021265866). Of 971 screened studies, 26 studies were included in the final analysis, comprising a total of 2398 adult patients with benign GI disease. The main outcome was raw impedance data. RESULTS: The most frequently reported BIA outcome was phase angle (PhA) (reported in 18 of 26 studies), followed by fat-free-mass (FFM) (reported in 13 of 26 studies). The consensus view of the included studies illustrates that BIA can be a useful tool for evaluating body composition in patients with benign GI diseases, and low PhA and FFM were associated with increased nutritional risk, abnormal physical characteristics, and increased mortality risk. CONCLUSION: To fully utilize BIA as a clinical marker of health in patients with benign GI disease, standardized protocols specific to this population are needed and prospective studies testing cut-offs and ranges, accuracy, and other raw BIA parameters for classifying disease status.

Effects of Body Positioning When Assessing Lymphedema of the Lower Limb Using Bioimpedance Spectroscopy.

Background: Bioimpedance spectroscopy (BIS) measurements are conventionally performed in supine position with a lead device attached to gel-backed electrodes, and more recently, with a stand-on device that uses fixed stainless-steel electrodes under the hands and feet. The aim of this study was to assess and compare BIS measurements made in supine, sitting, and standing positions using lead and stand-on impedance devices in participants with and without unilateral leg lymphedema. Materials and Methods: Participants with self-ascribed unilateral leg lymphedema (n = 24) and healthy controls (n = 71) were recruited using a cross-sectional study design. Triplicate BIS measurements were taken for each device in each position. Results: Impedance measurements with either device were reliable with coefficient of variation of 0.6% or lower. The magnitude of mean differences in absolute impedance values between devices were between 1% and 6% dependent on condition. L-Dex scores between the two devices were highly correlated (r = 0.82) and ∼70% of participants in the lymphedema group were classified as having lymphedema using the recommended cut-off with either device. There was no significant interleg difference of controls using the lead device; however, small, but significant differences (p = 0.0001) were found when using the stand-on device. Conclusion: The findings demonstrate that reliable impedance measurements of the legs can be made with either device in lying, sitting, or standing positions. However, data between the devices were not directly interchangeable. Although the risk of misidentification was small, reference ranges appropriate to the device and measurement position should be used when converting data to L-Dex scores.

The bioelectrical impedance analysis (BIA) international database: aims, scope, and call for data.

BACKGROUND: Bioelectrical impedance analysis (BIA) is a technique widely used for estimating body composition and health-related parameters. The technology is relatively simple, quick, and non-invasive, and is currently used globally in diverse settings, including private clinicians' offices, sports and health clubs, and hospitals, and across a spectrum of age, body weight, and disease states. BIA parameters can be used to estimate body composition (fat, fat-free mass, total-body water and its compartments). Moreover, raw measurements including resistance, reactance, phase angle, and impedance vector length can also be used to track health-related markers, including hydration and malnutrition, and disease-prognostic, athletic and general health status. Body composition shows profound variability in association with age, sex, race and ethnicity, geographic ancestry, lifestyle, and health status. To advance understanding of this variability, we propose to develop a large and diverse multi-country dataset of BIA raw measures and derived body components. The aim of this paper is to describe the 'BIA International Database' project and encourage researchers to join the consortium. METHODS: The Exercise and Health Laboratory of the Faculty of Human Kinetics, University of Lisbon has agreed to host the database using an online portal. At present, the database contains 277,922 measures from individuals ranging from 11 months to 102 years, along with additional data on these participants. CONCLUSION: The BIA International Database represents a key resource for research on body composition.

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.

Can change in phase angle predict the risk of morbidity and mortality during an 18-year follow-up period? A cohort study among adults.

Introduction: Phase angle (PhA, degrees), measured via bioimpedance (BIA, 50 kHz), is an index that has been used as an indicator of nutritional status and mortality in several clinical situations. This study aimed to determine the relationship between 6-year changes in PhA and total mortality as well as the risk of incident morbidity and mortality from cardiovascular disease (CVD) and coronary heart disease (CHD) during 18 years of follow-up among otherwise healthy adults. Methods: A random subset (n = 1,987) of 35-65 years old men and women was examined at the baseline in 1987/1988 and 6 years later in 1993/1994. Measures included weight, height, and whole-body BIA, from which PhA was calculated. Information on lifestyle was obtained through a questionnaire. The associations between 6-year PhA changes (ΔPhA) and incident CVD and CHD were assessed by Cox proportional hazard models. The median value of ΔPhA was used as the reference value. The hazard ratio (HR) model and confidence intervals (CIs) of incident CVD and CHD were used according to the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ΔPhA. Results: During 18 years of follow-up, 205 women and 289 men died. A higher risk of both total mortality and incident CVD was present below the 50th percentile (Δ = -0.85°). The highest risk was observed below the 5th percentile (ΔPhA = -2.60°) in relation to total mortality (HR: 1.55; 95% CI: 1.10-2.19) and incident CVD (HR: 1.52; 95% CI: 1.16-2.00). Discussion: The larger the decrease in PhA, the higher the risk of early mortality and incident CVD over the subsequent 18 years. PhA is a reliable and easy measure that may help identify those apparently healthy individuals who may be at increased risk of future CVD or dying prematurely. More studies are needed to confirm our results before it can be definitively concluded that PhA changes can improve clinical risk prediction.

Development and validation of new predictive equations for the resting metabolic rate of older adults aged ≥65 y.

BACKGROUND: The aging process alters the resting metabolic rate (RMR), but it still accounts for 50%-70% of the total energy needs. The rising proportion of older adults, especially those over 80 y of age, underpins the need for a simple, rapid method to estimate the energy needs of older adults. OBJECTIVES: This research aimed to generate and validate new RMR equations specifically for older adults and to report their performance and accuracy. METHODS: Data were sourced to form an international dataset of adults aged ≥65 y (n = 1686, 38.5% male) where RMR was measured using the reference method of indirect calorimetry. Multiple regression was used to predict RMR from age (y), sex, weight (kg), and height (cm). Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split and leave one out cross-validation were performed. The newly generated prediction equations were compared with the existing commonly used equations. RESULTS: The new prediction equation for males and females aged ≥65 y had an overall improved performance, albeit marginally, when compared with the existing equations. It is described as follows: RMR (kJ/d) = 31.524 × W (kg) + 25.851 × H (cm) - 24.432 × Age (y) + 486.268 × Sex (M = 1, F = 0) + 530.557. Equations stratified by age (65-79.9 y and >80 y) and sex are also provided. The newly created equation estimates RMR within a population mean prediction bias of ∼50 kJ/d (∼1%) for those aged ≥65 y. Accuracy was reduced in adults aged ≥80 y (∼100 kJ/d, ∼2%) but was still within the clinically acceptable range for both males and females. Limits of agreement indicated a poorer performance at an individual level with 1.96-SD limits of approximately ±25%. CONCLUSIONS: The new equations, using simple measures of weight, height, and age, improved the accuracy in the prediction of RMR in populations in clinical practice. However, no equation performs optimally at the individual level.

A Standardized Protocol for Measuring Bioelectrical Impedance in Green Turtles (Chelonia mydas).

AbstractBioelectrical impedance analysis (BIA) is gaining popularity in wildlife studies as a portable technology for immediate and nondestructive predictions of body composition components, such as fat-free and fat masses. Successful application of BIA for field-based research requires the identification and control of potential sources of error, as well as the creation of and adherence to a standardized protocol for measurement. The aim of our study was to determine sources of error and to provide a standardization protocol to improve measurement precision of BIA on juvenile green turtles (Chelonia mydas; n=35). We assessed the effects of altered environmental temperature (20°C-30°C), postprandial state (2-72 h), and time out of the water (2 h) on five impedance parameters (resistance at infinite frequency [Rinf], resistance at zero frequency [R0], resistance at 50 kHz [R50], phase angle at 50 kHz [PhA50], and intracellular resistance [Ri]) using a bioimpedance spectroscopy device. Technical reproducibility of measurements and interanimal variability were also assessed. We found an inverse exponential relationship between change in environmental temperature and impedance parameters Rinf, R0, and R50. Postprandial state significantly increased Rinf and Ri 72 h after feeding. BIA measurements were reproducible within individual juvenile green turtles at temperatures from 20°C to 30°C. Significant variation in impedance values was found between animals at all temperatures, sampling times, and postprandial states, but the relative differences (%) were small in magnitude. Our study suggests that measurement precision is improved by measuring animals at consistent environmental temperatures close to their preferred thermal range. We propose a standardized protocol of measurement conditions to facilitate laboratory and field use of BIA for body composition assessment studies in turtles.

Bioimpedance basics and phase angle fundamentals.

Measurement of phase angle using bioimpedance analysis (BIA) has become popular as an index of so-called "cellular health". What precisely is meant by this term is not always clear but strong relationships have been found between cellular water status (the relative amounts of extra- and intracellular water), cell membrane integrity and cellular mass. Much of the current research is empirical observation and frequently pays little regard to the underlying biophysical models that underpin the BIA technique or attempts to provide mechanistic explanations for the observations. This brief review seeks to provide a basic understanding of the electrical models frequently used to describe the passive electrical properties of tissues with particular focus on phase angle. In addition, it draws attention to some practical concerns in the measurement of phase angle and notes the additional understanding that can be gained when phase angle are obtained with bioimpedance spectroscopy (BIS) rather than single frequency BIA (SFBIA) along with the potential for simulation modelling.

A longitudinal analysis of resting energy expenditure and body composition in people with spinal cord injury undergoing surgical repair of pressure injuries: a pilot study.

BACKGROUND: Data informing energy needs of people with spinal cord injury (SCI) and pressure injuries are scarce, the impact of surgical repair unknown, and the role of body composition in healing unexplored. The study aims were to investigate resting energy expenditure (REE) over the course of pressure injury surgical repair, compare with available energy prediction equations, and explore associations between body composition and wound healing. METHODS: Indirect calorimetry measured REE pre-surgery, post-surgery, at suture removal and hospital discharge. A clinically significant change was defined as +/-10% difference from pre-surgery. Eight SCI-specific energy prediction equations were compared to pre-surgery REE. Wound breakdown (Yes/No), weight, waist circumference (WC), and body composition (fat mass [FM], fat-free mass [FFM], bioimpedance spectroscopy) were measured. RESULTS: Twenty people underwent pressure injury surgical repair (95% male, mean age 56 ± 12 years, 70% paraplegia). Between pre-surgery and discharge, mean REE increased (+118 kcal/d, p = 0.005), but with <10% change at any timepoint. An energy prediction equation incorporating FFM showed greatest agreement (rc = 0.779, 95% CI: 0.437, 0.924). Those with wound breakdown (65%) had a higher weight (12.7 kg, 95% CI: -4.0, 29.3), WC (17.8 cm, 95% CI: -5.1, 40.7), and FM % (36.0% [IQR 31.8, 40.2] vs 26.0% [IQR 15.6, 41.3]) than those without wound breakdown, although statistical significance was not reached. CONCLUSION: The presence of pressure injuries and subsequent surgical repair did not impact REE and energy prediction equations incorporating FFM performed best. While not statistically significant, clinically important differences in body composition were observed in those with wound breakdown.

Substituting sedentary time with sleep or physical activity and subsequent weight-loss maintenance.

OBJECTIVE: In this study, the associations between the substitution of sedentary time with sleep or physical activity at different intensities and subsequent weight-loss maintenance were examined. METHODS: This prospective study included 1152 adults from the NoHoW trial who had achieved a successful weight loss of ≥5% during the 12 months prior to baseline and had BMI ≥25 kg/m2 before losing weight. Physical activity and sleep were objectively measured during a 14-day period at baseline. Change in body weight was included as the primary outcome. Secondary outcomes were changes in body fat percentage and waist circumference. Cardiometabolic variables were included as exploratory outcomes. RESULTS: Using isotemporal substitution models, no associations were found between activity substitutions and changes in body weight or waist circumference. However, the substitution of sedentary behavior with moderate-to-vigorous physical activity was associated with a decrease in body fat percentage during the first 6 months of the trial (-0.33% per 30 minutes higher moderate-to-vigorous physical activity [95% CI: -0.60% to -0.07%], p = 0.013). CONCLUSIONS: Sedentary behavior had little or no influence on subsequent weight-loss maintenance, but during the early stages of a weight-loss maintenance program, substituting sedentary behavior with moderate-to-vigorous physical activity may prevent a gain in body fat percentage.

Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning.

Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass - (-0.03 [intercept] - 0.29 * length2/resistance at 50 kHz + 1.07 * body mass - 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%-0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.

Wasabi (Eutrema japonicum) Reduces Obesity and Blood Pressure in Diet-Induced Metabolic Syndrome in Rats.

6-(Methylsulfinyl)hexyl isothiocyanate (6-MSITC) has several biological functions. The present study aimed to evaluate the composition of hydroponically grown Tasmanian wasabi (Eutrema japonicum (Miq.) Koidz.) for 6-MSITC in all plant tissues and investigate the influence of wasabi (rhizome and stem blend) in high-carbohydrate, high-fat (H) diet-fed rats. Male Wistar rats were fed either a corn starch (C) or H diet. After the initial 8 weeks, half of the animals on the C and H diets were given 5% (w/w) wasabi powder in their respective diets for an 8-week duration (CW and HW). The control animals received diets without supplementation throughout the 16-week experiment. Our findings demonstrated that wasabi grown under hydroponic conditions contained 6-MSITC in all parts of the plant such as the stem, leaf and flower, as well as the commonly used rhizome, albeit at lower concentrations. Rats treated with wasabi showed reductions in body weight (H, 460.0 ± 9.5; HW, 416.0 ± 3.6 g), fat mass (H, 178 ± 14; HW, 120 ± 23 g), plasma triglycerides (H, 1.7 ± 0.3; HW, 0.9 ± 0.3 mmol/L) and total cholesterol (H, 1.5 ± 0.1; HW, 1.0 ± 0.04 mmol/L), and the plasma activities of aspartate transaminase. Systolic blood pressure and the area under the curve of blood glucose concentration were decreased by wasabi treatment. Thus, wasabi may be a novel alternative treatment to assist in the management of obesity and related metabolic disorders.

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.

Individualized body geometry correction factor (KB) for use when predicting body composition from bioimpedance spectroscopy.

Objective. Prediction of body composition from bioimpedance spectroscopy (BIS) measurements using mixture theory-based biophysical modelling invokes a factor (KB) to account for differing body geometry (or proportions) between individuals. To date, a single constant value is commonly used. The aim of this study was to investigate variation inKBacross individuals and to develop a procedure for estimating an individualizedKBvalue.Approach.Publicly available body dimension data, primarily from the garment industry, were used to calculateKBvalues for individuals of varying body sizes across the life-span. The 3D surface relationship between weight, height andKB, was determined and used to create look-up tables to enable estimation ofKBin individuals based on height and weight. The utility of the proposed method was assessed by comparing fat-free mass predictions from BIS using either a constantKBvalue or the individualized value.Results.ComputedKBvalues were well fitted to height and weight by a 3D surface (R2 = 0.988). Body composition was predicted more accurately compared to reference methods when using individualizedKBthan a constant value in infants and children but improvement in prediction was less in adults particularly those with high body mass index.Significance.Prediction of body composition from BIS and mixture theory is improved by using an individualized body proportion factor in those of small body habitus, e.g. children. Improvement is small in adults or non-existent in those of large body size. Further improvements may be possible by incorporating a factor to account for trunk size, i.e. waist circumference.