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.