Body Composition Prediction Equations Research Articles

Variations in bioelectrical impedance devices impact raw measures comparisons and subsequent prediction of body composition using recommended estimation equations

Background & AimsBioelectrical impedance analysis (BIA) for body composition estimation is increasingly used in clinical and field settings to guide nutrition and training programs. Due to variations among BIA devices and the proprietary prediction equations used, studies have recommended the use of raw measures of resistance (R) and reactance (Xc) within population-specific equations to predict body composition. ObjectiveWe compared raw measures from three BIA devices to assess inter-device variation and the impact of differences on body composition estimations. MethodsRaw R, Xc, impedance (Z) parameters were measured on a calibrated phantom and athletes using tetrapolar supine (BIASUP4), octapolar supine (BIASUP8), and octapolar standing (BIASTA8) devices. Measures of R and Xc were compared across devices and graphed using BIA vector analysis (BIVA) and raw parameters were entered into recommended athlete-specific equations for predicting fat-free mass (FFM) and appendicular lean soft tissue (ALST). Whole-body FFM and regional ALST were compared across devices and to a criterion five-compartment (5C) model and dual energy X-ray absorptiometry for ALST. ResultsData from 73 (23.2 ± 4.8y) athletes were included in the analyses. Technical differences were observed between Z (range 12.2 - 50.1Ω) measures on the calibrated phantom. Differences in whole-body impedance were apparent due to posture (technological) and electrode placement (biological) factors. This resulted in raw measures for all three devices showing greater dehydration on BIVA compared to published norms for athletes using a separate BIA device. Compared to the 5C FFM, significant differences (p < 0.05) were observed on all three equations for BIASUP8 and BIASTA8, with constant error (CE) from -2.7 to -4.6 kg; no difference was observed for BIASUP4 or when device-specific algorithms were used. Published equations resulted in differences as large as 8.8 kg FFM among BIA devices. For ALST, even after a correction in the error of the published empirical equations, all three devices showed significant (p<0.01) CE from -1.6 to -2.9 kg. ConclusionsRaw bioimpedance measurements differ among devices due to technical, technological, and biological factors, limiting interchangeability of data across BIA systems. Professionals should be aware of these factors when purchasing systems, comparing data to published reference ranges, or when applying published empirical body composition prediction equations

Prediction equations for body composition of children and adolescents aged 8–19 years in Uganda using deuterium dilution as the reference technique

Body composition is important as a marker of both current and future health status. Bioelectrical impedance analysis (BIA) is a simple and accurate method for estimating body composition in field, clinical and research settings, if standard protocol procedures are followed. However, BIA requires population-specific equations since applicability of existing equations to diverse populations has been questioned. This study aimed to derive predictive equations for Total Body Water (TBW), Fat Free Mass (FFM) and Fat Mass (FM) determinations with BIA and anthropometric measurements in a population of children and adolescents aged 8-19 years in Uganda. A cross-sectional study was conducted among 203 children and adolescents aged 8-19 years attending schools in Kampala district (also referred to as Kampala city since the city is conterminous with the district), Uganda through a two-stage cluster sample design. Deuterium dilution method (DDM) was used as the reference measure while BIA and anthropometric measures were used to create the new body composition prediction equations through multivariate regression. The new prediction equations explained 88%, 87% and 71% of the variance in TBW, FFM % and of FM respectively with no statistical shrinkage upon cross-validation. The linear regression models proposed in this study were well adjusted with respect to TBW, FFM and FM. Log of TBW obtained by DDM=0.0129×Impedance index+0.0055×Age+0.0049 Waist Circumference+0.1219Ht2+2.0388. Log of FFM obtained by DDM=0.0197×FFM obtained by BIA - 0.0181 sex code - 0.00055×Impedance+3.1761. Log of FM obtained by DDM=0.0634×FM obtained by BIA - 0.1881 sex code+0.0252×Weight+0.5273. The use of these equations for more accurate body composition assessment may facilitate identification of effective intervention strategies to prevent or combat overweight and obesity among children and adolescents. They may also assist in treatment of conditions where more accurate information on body composition measures is required.

Proposal of new body composition prediction equations from bioelectrical impedance for Indonesian men.

Bioelectrical impedance analysis (BIA) is one of the most affordable and feasible body composition assessment techniques for clinical and field settings. However, it is important to use an equation appropriate for the study population. This study aimed to propose and validate prediction equations to estimate body composition using BIA for Indonesian men. Total body water (TBW), fat-free mass (FFM) and fat mass (FM) were determined using the deuterium dilution technique in 292 Indonesian males. Participants were divided equally into development and validation groups to develop prediction equations and to cross-validate the proposed prediction equations, respectively. In addition, selected prediction equations using BIA were cross-validated. The proposed BIA equations were valid in our cross-validation samples. The best performance equations obtained from the absolute measure of body composition (TBW, FFM and FM) showed that r ranged between 0.89 and 0.91 and standard error of the estimate=1.8-2.6 kg. Cross-validation analysis indicated that the proposed equations had a bias of 0.1-0.3 kg, pure error of 1.3-1.8 kg and limits of agreement (mean difference±1.96 s.d.) of -0.26 to 0.13±4.09 to 5.59 kg. Among existing prediction equations examined, those by Deurenberg et al. (1989) and Lukaski et al. (1987) significantly overestimated FM by 4.0 and 3.2 kg, respectively, whereas the equation by Deurenberg et al. (1991) significantly (P<0.001) underestimated FFM by 5.0 kg compared with the reference FFM. The new BIA prediction equations may provide more precise and accurate estimation of body composition in Indonesian men than the existing equations.

Body composition estimation using skinfolds in children with and without health conditions affecting growth and body composition

Background: Body composition prediction equations using skinfolds are useful alternatives to advanced techniques, but their utility across diverse paediatric populations is unknown.Aim: To evaluate published and new prediction equations across diverse samples of children with health conditions affecting growth and body composition.Subjects and methods: Anthropometric and dual-energy X-ray absorptiometry (DXA) body composition measures were obtained in children with Down syndrome (n = 59), Crohn disease (n = 128), steroid-sensitive nephrotic syndrome (n = 67) and a healthy reference group (n = 835). Published body composition equations were evaluated. New equations were developed for ages 3–21 years using the healthy reference sample and validated in other groups and national survey data.Results: Fat mass (FM), fat-free mass (FFM) and percentage body fat (%BF) from published equations were highly correlated with DXA-derived measures (r = 0.71–0.98), but with poor agreement (mean difference = 2.4 kg, −1.9 kg and 6.3% for FM, FFM and %BF). New equations produced similar correlations (r = 0.85–1.0) with improved agreement for the reference group (0.2 kg, 0.4 kg and 0.0% for FM, FFM and %BF, respectively) and in sub-groups.Conclusions: New body composition prediction equations show excellent agreement with DXA and improve body composition estimation in healthy children and those with selected conditions affecting growth.

In vivo prediction of goat kids body composition from the deuterium oxide dilution space determined by isotope-ratio mass spectrometry.

Deuterium oxide dilution space (DOS) determination is one of the most accurate methods for in vivo estimation of ruminant body composition. However, the time-consuming vacuum sublimation of blood preceding infrared spectroscopy analysis, which is traditionally used to determine deuterium oxide (DO) concentration, limits its current use. The use of isotope-ratio mass spectrometry (IRMS) to determine the deuterium enrichment and thus quantify DO in plasma could counteract this limitation by reducing the sample preparation for plasma deproteinisation through centrifugal filters. The aim of this study was to validate the DOS technique using IRMS in growing goat kids to establish in vivo prediction equations of body composition. Seventeen weaned male Alpine goat kids (8.6 wk old) received a hay-based diet supplemented with 2 types of concentrates providing medium ( = 9) or high ( = 8) energy levels. Kids were slaughtered at 14.0 ( = 1, medium-energy diet), 17.2 ( = 4, medium-energy diet, and = 4, high-energy diet), or 21.2 wk of age ( = 4, medium-energy diet, and = 4, high-energy diet). Two days before slaughter, DOS was determined after an intravenous injection of 0.2 g DO/kg body mass (BM) and the resulting study of DO dilution kinetics from 4 plasma samples (+5, +7, +29, and +31 h after injection). The deuterium enrichment was analyzed by IRMS. After slaughter, the gut contents were discarded, the empty body (EB) was minced, and EB water, lipid, protein, ash, and energy contents were measured by chemical analyses. Prediction equations for body components measured postmortem were computed from in vivo BM and DOS. The lack of postmortem variation of fat-free EB composition was confirmed (mean of 75.3% [SD 0.6] of water), and the proportion of lipids in the EB tended ( = 0.06) to be greater for the high-energy diet (13.1%) than for the medium-energy diet (11.1%). There was a close negative relationship (residual CV [rCV] = 3.9%, = 0.957) between EB water and lipid content, whereas DOS was closely related to total body water (rCV = 2.9%, = 0.944) but DOS overestimated it by 5.8%. Adding DOS to BM improved the in vivo predictions of EB lipid and energy content (rCV = 13.1% and rCV = 7.9%, respectively) but not those of protein or ash. Accuracy of the obtained prediction equations was similar to those reported in studies determining DOS by infrared spectroscopy. Therefore, the use of IRMS to quantify DOS provides a highly accurate measure of the in vivo body composition in goat kids.

Length-weight relationship and prediction equations of body composition for growing-finishing cage-farmed Nile tilapia

The objective of the present study was to develop models for predicting live weight from the length-weight relationship and body composition of Nile tilapia. A total of 3,000 juvenile fish (initial weight = 28.6±4.16 g and standard length = 13.8±0.16 cm) were distributed into three circular cages (12 m3 each). The fish were hand-fed extruded diets containing 332 g kg-1 of crude protein and 3,230 kcal kg-1 of digestible energy, until apparent satiety, twice daily, for 100 days. Twelve fish were collected from each cage every 20 days for measurements of body weight and length, and proximate composition analysis; statistical analysis was conducted using linear regression. The value of the slope b and the intercept for the length-weight relationship were 3.0604 and 0.0203, respectively. The prediction equations obtained for body moisture (MO), crude protein (CP), crude lipid (CL), and ash against body weight (BW) in g/100 g of fish were as follows: MO = 70.0090 - 0.0071BW; CP = 13.7550 + 0.0037BW; CL = 9.2636 + 0.0057BW; and ash = 4.2392 - 0.0024BW. It is possible to develop equations to predict body weight and composition, which can be used to control the production of Nile tilapia and improve its commercial value.

Anthropometric predictive equations for estimating body composition.

Background:Precise and accurate measurements of body composition are useful in achieving a greater understanding of human energy metabolism in physiology and in different clinical conditions, such as, cardiovascular disease and overall mortality. Dual-energy x-ray absorptiometry (DXA) can be used to measure body composition, but the easiest method to assess body composition is the use of anthropometric indices. This study has been designed to evaluate the accuracy and precision of body composition prediction equations by various anthropometric measures instead of a whole body DXA scan.Materials and Methods:We identified 143 adult patients underwent DXA evaluation of the whole body. The anthropometric indices were also measured. Datasets were split randomly into two parts. Multiple regression analysis with a backward stepwise elimination procedure was used as the derivation set and then the estimates were compared with the actual measurements from the whole-body scans for a validation set. The SPSS version 20 for Windows software was used in multiple regression and data analysis.Results:Using multiple linear regression analyses, the best equation for predicting the whole-body fat mass (R2 = 0.808) included the body mass index (BMI) and gender; the best equation for predicting whole-body lean mass (R2 = 0.780) included BMI, WC, gender, and age; and the best equation for predicting trunk fat mass (R2 = 0.759) included BMI, WC, and gender.Conclusions:Combinations of anthropometric measurements predict whole-body lean mass and trunk fat mass better than any of these single anthropometric indices. Therefore, the findings of the present study may be used to verify the results in patients with various diseases or diets.

Length-weight relationships and prediction equations of body composition of farm raised Astyanax aff. Fasciatus (Actinopterygii: Characiformes: Characidae)

This study ascertains the length-weight relationship and predicts body composition equations in cultivated Astyanax aff. fasciatus. A total of 300 fish were distributed in five groups according to body weight: 1.19 ± 0.18, 3.7 ± 0.70, 7.55 ± 0.97, 12.28 ± 2.32 and 22.16 ± 2.64 g. The length-weight relationship was elaborated using linear (y i = b0 + b1x i) or second-order (y i = b0 + b1x i + b1x i²) regression analysis. The value of the b slope in the length-weight relationship was 3.6971 and the intercept was 0.0031. The prediction equations obtained for body moisture, crude protein, crude lipid and ash were, y = 71.680 - 0.404BW, y = 17.140 - 0.095BW, y = 8.432 +0.364BW and y = 3.720 - 0.032BW, respectively, where BW is the body weight of fish (g). The use of prediction equations to describe body composition as a tool to support fish production and commercialization is suggested.

Body Composition and Non-Communicable Diseases: A study on Diabetic Subjects.

Non-communicable disease (NCD), is a medical condition or disease which by definition is noninfectious and non-transmissible between persons. It may be chronic diseases of long duration and slow progression, or they may result in more rapid death such as some types of sudden stroke. They include autoimmune diseases, heart disease, stroke, many cancers, asthma, diabetes, chronic kidney disease, osteoporosis, Alzheimer's disease, cataracts and more. Out of these, India is facing an epidemic of diet-related non-communicable disease namely diabetes along with widely prevalent under-nutrition resulting in substantial socio-economic burden over the country. According to data released by International Diabetes Federations, in India the number of persons suffering from diabetes have increased from 19 million in 1995 to 51 million in 2010 and it is projected to be increased to 87 million by 2030 (Ramachandran, 2010). Diabetes mellitus is a chronic metabolic disorders characterized by high blood glucose levels resulting from defects in insulin secretion, insulin action or both, which prevents the body to utilize glucose completely or partially (zimmet, 1983). The prevalence of diabetes in developing countries is due to adoption of western life-style, sedentary life, smoking, high fat diet and lack of physical exercise. Indians are at special risk because of fat accumulating phenotype/ genotype, high percentage of body fat at lower level of BMI as well as central and truncal distribution of fat. All these factors have led to substantial increase in the prevalence of obesity. People who eat too much food and lead a sedentary life become overweight and obese. Obesity reduces the sensitivity of tissues to the action of insulin in the utilization of glucose. Lack of physical activity and obesity increases the risk for development of diabetes in the later stage of a person’s life (Raghuram et al, 1998). Extensive research published over the past two decades has demonstrated a positive association between increased levels of body fatness, obesity and type-2 diabetes (Pi-Sunyer, 1991). In particular abdominal obesity has been identified as a significant risk factor for the development of insulin resistance, glucose intolerance and type-2 diabetes (Despres, 1993). Although not all Individuals with increased body fatness and abdominal obesity manifest the metabolic complications of diabetes, approximately 85% of patients with type-2 diabetes display increased levels of body fatness (Tsui Eyl et al, 1998). In order to make the most valid assessment of body composition, it is necessary to understand the underlying theoretical models and reference methods used to develop body composition prediction equations. The two-component model of body composition divides the body into a fat component and fat free body (FFB) component (Brozek et al, 1963; siri, 1961). The FFB consists of all residual chemicals and tissues including water, muscles (protein) and bone (minerals). This model assumes that (a) the density of the FFB is 1.1 g/cc and (b) the densities of the fat and the FFB components are same for all individuals (c) the individual being measured differs from the reference body only in the amount of fat, and (4) the FFB of the reference body is 73.8% water, 19.4% protein and 6.8% mineral. So, the present study was planned to know the relationship between body composition and diabetes.

Body composition prediction equations based on deuterium oxide dilution method in Mexican children: a national study

Obesity and undernutrition co-exist in many regions of Mexico. However, accurate assessments are difficult because epidemiological data on body composition are not available. The aim of this study was to facilitate assessments of body composition in Mexican school children of different geographical regions and ethnicity by developing equations for bioelectrical impedance and anthropometry based on deuterium oxide dilution. We evaluated 336 subjects (143 belonged to six major indigenous groups) from Northern, Central and Southern Mexico. We measured height (Ht), weight (Wt), tricipital skinfold (Tricp-SKF) and resistance (R) based on a bioimpedance analysis (BIA). Fat-free mass (FFM) and fat mass (FM) were estimated from measurements of total body water with the deuterium dilution technique. The final BIA equation was FFM (kg)=0.661 × Ht²/R+0.200 × Wt-0.320. The R² was 0.96; the square root of the mean square error (SRMSE) was 1.39 kg. The final anthropometric equation was FM (kg)=-1.067 × sex+0.458 × Tricp-SKF+0.263 × Wt-5.407. The R² was 0.91; SRMSE was 1.60 kg. The BIA equation had a bias of 0.095 kg and precision of 1.43 kg. The anthropometric equation had a bias of 0.047 kg and precision of 1.58 kg. We validated two equations for evaluating body composition in Mexican indigenous and non-indigenous children and youth from three main regions of the country. These equations provided reliable estimates and will promote a better understanding of both obesity and undernutrition.

Development of bioelectrical impedance analysis-based equations for estimation of body composition in postpartum rural Bangladeshi women

Equations for predicting body composition from bioelectrical impedance analysis (BIA) parameters are age-, sex- and population-specific. Currently there are no equations applicable to women of reproductive age in rural South Asia. Hence, we developed equations for estimating total body water (TBW), fat-free mass (FFM) and fat mass in rural Bangladeshi women using BIA, with ²H₂O dilution as the criterion method. Women of reproductive age, participating in a community-based placebo-controlled trial of vitamin A or β-carotene supplementation, were enrolled at 19·7 (SD 9·3) weeks postpartum in a study to measure body composition by ²H₂O dilution and impedance at 50 kHz using multi-frequency BIA (n 147), and resistance at 50 kHz using single-frequency BIA (n 82). TBW (kg) by ²H2O dilution was used to derive prediction equations for body composition from BIA measures. The prediction equation was applied to resistance measures obtained at 13 weeks postpartum in a larger population of postpartum women (n 1020). TBW, FFM and fat were 22·6 (SD 2·7), 30·9 (SD 3·7) and 10·2 (SD 3·8) kg by ²H₂O dilution. Height²/impedance or height²/resistance and weight provided the best estimate of TBW, with adjusted R² 0·78 and 0·76, and with paired absolute differences in TBW of 0·02 (SD 1·33) and 0·00 (SD 1·28) kg, respectively, between BIA and ²H₂O. In the larger sample, values for TBW, FFM and fat were 23·8, 32·5 and 10·3 kg, respectively. BIA can be an important tool for assessing body composition in women of reproductive age in rural South Asia where poor maternal nutrition is common.

Body fat assessment in Sri Lankan adolescent girls; development of a simple field tool

Background: Development of accurate, simple measures of body fat is essential in developing countries due to the rising prevalence of overweight and obesity. Application of skin-fold thickness (SFT) to body fat prediction equations is an inexpensive method suitable for field settings.Aim: To determine percentage fat mass (%FM) of Sri Lankan adolescent girls using Fourier Transform Infrared spectrometry (FTIR) as a reference method and develop a SFT-prediction equation to estimate %FM.Methods: Weight, height and SFT were measured and %FM determined by FTIR in 131 adolescent, post-menarcheal girls (15–19 years). SFT-measurements were applied to available body composition equations for validation in the population. A new equation for %FM was derived and validated.Results: Mean %FM (FTIR) was 19.13 ± 8.2.Selected body composition prediction equations over-estimated %FM. The multiple regression procedure yielded a final equation consisting of two anthropometric and one demographic variable, %FM = 9.701 − (0.460)* age+(0.640)*TricepsSFT+(0.583)*Supra-iliacSFT.Conclusions: Existing SFT prediction-equations are unsuitable for predicting %FM in this population. The new equation utilizing one peripheral and one truncal skin-fold, is appropriate for prediction of %FM in this population and may be applicable to other South Asian/Asian-Indian adolescents following validation.

Exigências nutricionais de bezerros da raça Holandesa alimentados com concentrado e feno de capim-elefante

Foram utilizados 13 bezerros machos da raça Holandesa, com idade média de 14 dias. Três animais foram abatidos no início do experimento (referência) e os outros dez foram distribuídos aleatoriamente em dois tratamentos (ração farelada ou peletizada) e abatidos aos 112 dias de idade para determinação das exigências nutricionais. Foram ajustadas equações de regressão do logaritmo da quantidade corporal de proteína, gordura e energia em função do peso de corpo vazio (PCVZ). As exigências líquidas de proteína e energia para ganho de 1 kg de PCVZ foram obtidas por derivação das equações de predição da composição corporal. Os conteúdos de proteína, gordura e energia aumentaram de acordo com o peso corporal. Os requerimentos líquidos de proteína e energia para ganho de peso de corpo vazio (GPCVZ) elevaram quando o PCVZ variou de 25,74 até 85,81 kg. Foi obtida a equação para estimar a proteína retida em função do ganho médio diário (GMD) e da energia retida (ER): PR = - 0,297083 + 4,19797 ganho de peso corporal em jejum (GPCJ) + 0,0926327 ER (R² = 0,92). A equação obtida para descrever a relação entre a energia retida (ER), em Mcal, e o ganho diário de PCVZ (GPCVZ), em kg, para determinado PCVZ a partir dos dados deste trabalho, foi a seguinte: ER = 0,1004 × PCVZ0,75 × GDPCVZ0,5552. Os valores de energia líquida de mantença (ELm) variaram de 1,10 para 2,72 Mcal/dia e energia líquida para ganho (ELg) de 1,42 para 2,46 Mcal/dia quando o peso corporal aumentou de 30 para 100 kg. Dos 28 aos 112 dias de idade, aumenta a composição de proteína, gordura e energia no corpo vazio de bezerros machos da raça Holandesa.

New Percentage Body Fat Prediction Equations for Japanese Females

Anthropometry is a simple and cost-efficient method for the assessment of body composition. However prediction equations to estimate body composition using anthropometry should be 'population-specific'. Most popular body composition prediction equations for Japanese females were proposed more than 40 years ago and there is some concern regarding their usefulness in Japanese females living today. The aim of this study was to compare percentage body fat (%BF) estimated from anthropometry and dual energy x-ray absorptiometry (DXA) to examine the applicability of commonly used prediction equations in young Japanese females. Body composition of 139 Japanese females aged between 18 and 27 years of age (BMI range: 15.1-29.1 kg/m(2)) was measured using whole-body DXA (Lunar DPX-LIQ) scans. From anthropometric measurements %BF was estimated using four equations developed from Japanese females. The results showed that the traditionally employed prediction equations for anthropometry significantly (p<0.01) underestimate %BF of young Japanese females and therefore are not valid for the precise estimation of body composition. New %BF prediction equations were proposed from the DXA and anthropometry results. Application of the proposed equations may assist in more accurate assessment of body fatness in Japanese females living today.

Urea space and body condition score to predict body composition of meat goats

Yearling Boer × Spanish goat wethers (40) were used to develop and compare body composition prediction equations for mature meat goats based on urea space (US) and body condition score (BCS). Before the experiment, one-half of the animals were managed to have high BW and BCS (1–5, with 1 being extremely thin and 5 very fat) and the others were managed to have low BW and BCS. During the 24-week experiment, initially fat wethers were fed to lose BW and BCS and initially thin wethers were fed to increase BW and BCS. BCS, US, and whole body chemical composition were determined after 0, 12, and 24 weeks. Mean, minimum, and maximum values were 42.1 (S.E. = 1.12), 24.5, and 59.0 kg for shrunk BW; 3.0 (S.E. = 0.11), 1.5, and 4.0 for BCS; 61.3 (S.E. = 1.01), 53.7, and 76.5% for water; 20.2 (S.E. = 1.11), 4.7, and 29.7% for fat; 15.6 (S.E. = 0.19), 13.3, and 18.1% for protein; and 2.9 (S.E. = 0.062), 2.2, and 3.7% for ash, respectively. For water, fat, and ash concentrations and mass, simplest equations explaining greatest variability (with independent variables of US, BCS, and (or) shrunk BW) based on BCS accounted for more variation than ones based on US, although in some cases differences were not large (i.e., water and ash concentrations and mass). Neither US nor BCS explained variability in protein concentration. Equations to predict protein mass based on shrunk BW and US or BCS were nearly identical in R 2 and the root mean square error. A 1 unit change in BCS corresponded to change in full BW of 8.9 kg (full BW (kg) = 17.902 + (8.9087 × BCS); R 2 = 0.653), fat concentration of 7.54% (%fat = −5.076 + (7.5361 × BCS); R 2 = 0.612), and energy concentration of 3.01 MJ/kg (energy (MJ/kg) = 0.971 + (3.0059 × BCS); R 2 = 0.615). In summary, BCS may be used as or more effectively to predict body composition of meat goats than US. The primary determinant of BCS, within the range of BCS observed in this experiment, was body fat content.

Prediction of body weight and composition in lactating dairy cows: Prediction of empty body contents of lipid and crude protein

The accurate prediction of body composition of dairy cows is important for developing appropriate nutritional and management regimes. The objective of the present study was to develop prediction equations for empty body (EB) composition of lactating dairy cows using body weight and other live animal data.

Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys

Previous studies to develop and validate bioelectrical impedance analysis (BIA) equations to predict body composition were limited by small sample sizes, sex specificity, and reliance on reference methods that use a 2-component model. This study was designed to develop sex-specific BIA equations to predict total body water (TBW) and fat-free mass (FFM) with the use of a multicomponent model for children and adults. Data from 5 centers were pooled to create a sample of 1474 whites and 355 blacks aged 12-94 y. TBW was measured by dilution, and FFM was estimated with a multicomponent model based on densitometry, isotope dilution, and dual-energy X-ray absorptiometry. The final race-combined TBW prediction equations included stature(2)/resistance and body weight (R(2) = 0.84 and 0.79 and root mean square errors of 3.8 and 2.6 L for males and females, respectively; CV: 8%) and tended to underpredict TBW in black males (2.0 L) and females (1.4 L) and to overpredict TBW in white males (0.5 L) and females (0.3 L). The race-combined FFM prediction equations contained the same independent variables (R(2) = 0.90 and 0.83 and root mean square errors of 3.9 and 2.9 kg for males and females, respectively; CV: approximately 6%) and tended to underpredict FFM in black males (2.1 kg) and females (1.6 kg) and to overpredict FFM in white males (0.4 kg) and females (0.3 kg). These equations have excellent precision and are recommended for use in epidemiologic studies to describe normal levels of body composition.

Composição corporal e exigências nutricionais de energia e proteína para cordeiros em crescimento

Este trabalho foi realizado com o objetivo de determinar a composição corporal para gordura, proteína e cinzas, as exigências líquidas de energia e proteína para ganho de peso e as exigências de proteína metabolizável de cordeiros em crescimento. Foram utilizados 22 cordeiros machos inteiros, filhos de carneiros Texel e ovelhas cruzas (Texel x Ideal); destes, quatro foram abatidos no início do experimento (24 horas após o nascimento) e grupos de seis cordeiros, ao desmame, aos 28 e 33 kg de peso vivo. As equações de predição da composição corporal de proteína, gordura, cinzas e energia foram realizadas de acordo com o ARC (1980), por intermédio da regressão do logaritmo de proteína, gordura, cinzas e energia no corpo vazio, em relação ao logaritmo do peso de corpo vazio (PCV). As exigências líquidas de proteína e energia para ganho de peso foram obtidas por meio das equações de predição da composição corporal, ao passo que as exigências de proteína metabolizável foram estimadas de acordo com o AFRC (1993). Os conteúdos corporais de proteína, gordura, energia e matéria mineral para cordeiros de 5 a 30 kg PCV variaram de 141,69 a 186,24 g/kg PCV; 24,49 a 158,11 g/kg PCV; 1088,75 a 2451,55 kcal/kg PCV; e 27,55 a 33,70 g/kg PCV, respectivamente. As estimativas das exigências líquidas para ganho foram: energia, 399,93 e 892,21 kcal/250g de ganho, e proteína de 40,98 e 53,70 g/250g de ganho, para cordeiros de 5 e 30 kg de PCV, respectivamente. As exigências de proteína metabolizável para animais de 5 e 30 kg de peso vivo foram de 79,09 a 121,13 g/250g de ganho.

Body composition prediction equations for elderly men.

The purpose of this study was to develop and validate anthropometric body composition prediction equations for elderly (i.e., > or =65 years old) men. This was necessary because of a lack of accurate and reliable predictive equations specifically developed for this population. Seventy-five elderly men were randomly assigned to either an equation development sample (on = 50) or an equation validation sample (n = 25). Subject anthropometric measures were analyzed in a regression procedure with hydrodensitometry-determined body density, percentage of fat, fat-free mass, and fat weight to develop prediction equations for each body composition variable. The equation estimates were then validated against the hydrostatically determined measures. Four equations were developed and validated for the estimation of elderly male body composition variables [one each for body density (R2 = .66, SEE = +/- .01, where SEE is the standard error of estimate), percentage of fat (R2 = .66, SEE = +/- 4.43), fat-free mass (R2 = .88, SEE = +/- 3.94, and fat weight (R2 = .90, SEE = +/- 4.11)]. The equations provided estimates of body density, percentage of fat, fat-free mass, and fat weight, which were not statistically different from the hydrostatically determined criterion variables. The results of this study indicate that accurate and reliable anthropometric predictive equations can be developed for an active and healthy elderly male population. These equations may be used for accurate epidemiological testing of this group's body composition variables.

Growth and body composition of highly selected boars and gilts

AbstractAn experiment was conducted to measure the growth and body composition changes of highly selected boars and gilts from 10 to 150 kg live weight. Thirty boars and 30 gilts were given food ad libitum and two pigs of each sex were slaughtered at 10-kg increments from 10 kg to 150 kg live weight at which time the chemical composition of the body was determined. Boars and gilts exhibited different patterns of growth, nitrogen deposition rate (NDR) and lipid deposition rate (LDR) with boars exhibiting a sharp peak in daily live-weight gain and NDR while gilts exhibited almost a flat response curve over the age and weight range tested. Gilts experienced a peak in LDR at a lighter weight than boars (75·8 v. 100·5 kg) while NDR peaked at the same weight for both sexes (70·8 kg). Maximum NDR for boars and gilts was 37·7 and 28·1 glday (235·5 and 175·5 glday protein deposition rate) respectively. The Gompertz growth equation [Y = A + C × EXP (−EXP (−B ×(X−M)))] was shown to accurately represent the growth trajectory, while the logarithmic derivative of the allometric equation [Y = aXb] was used to determine live weight and body composition relationships. Combined sex relationships indicated that total body nitrogen and lipid concentrations increased at the same rate. A quadratic equation for the prediction of NDR based on live weight was developed for this genotype (NDR = 24·06 + 0·34 W − 0·002W2). In conclusion, the results provide a basis for comparison of body composition and growth patterns between the highly selected genotype tested and pigs from other genetic backgrounds. Sex effects exist for growth and body composition but combined sex prediction equations can be used to estimate NDR potential.