In this research communication we describe the creation of an equation for the prediction of milk yield (MY) from udder volume (UV). A total of 280 measurements were collected between 5 and 15 d postpartum (pp) from 36 multiparous Pelibuey ewes. Study variables were measured between 2 and 9 weeks pp and MY was measured by manual milking, UV prior to and following milking was measured using the technique of making moulds from aluminium foil. The MY ranged from 0.09 to 0.83 kg/d, meanwhile UV prior and following milking ranged from 155 to 1940 and 90 to 1520 cm3, respectively. Measurements of UV had a moderate to high (P < 0.01; 0.58 ≤ r ≤ 0.78) correlation with MY. The UV prior to milking was the best prediction model for MY, which explained 62% of the variation in MY. This equation presented moderate precision (r2 = 0.61) and high accuracy (bias correction factor = 0.94), confirming a good reproducibility index (concordance correlation coefficient = 0.73). Modelling efficiency (MEF = 0.59) showed moderate concordance between observed and predicted values. In conclusion, MY in lactating Pelibuey ewes could be predicted in a moderate way using the predictor variable UV measured with the technique of moulds made with aluminium foil.

Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris–Benedict, Schofield, Henry, Mifflin–St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17–44 kg/m2). Agreement between methods was assessed by Bland–Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin–St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin–St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly (P < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin–St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.

Plane of nutrition (PN) offered to ewe replacements during the rearing phase (8 to 17 months) affects BW at joining and may affect reproductive performance when joined at ~19 months. The effects of PN offered to ewe replacements during their first winter (winter_1) and second summer (summer_2) were evaluated over 3 consecutive years, using 287 spring-born ewe lambs, representing four genotypes (Belclare (Bel), Charmoise×Scottish Blackface (C×SBF), Bel×SBF (Bel×SBF), Bel×SBF that were heterozygous either FecGH or FecXG mutations that increase ovulation rate (OR) (BelMG×SBF)). During extended (deferred) grazing in winter_1 (November to March) the lambs were offered herbage DM allowances (HA) of 0.75 (L) or 1.75 (H) kg/day. During summer_2 (March to August) the replacements were set stocked to maintain sward heights of 4 (L) or 6 (H) cm, thus yielding a 2×2 factorial design for the nutrition treatments (71 to 73 animals/treatment). Increasing HA during winter_1, and residual sward height during summer_2, increased (P<0.001) BW and body size at joining (19 months) but had no effect (P>0.05) on OR. Mean BW at joining was 53.3, 56.8, 56.6 and 61.7 (SEM 0.74) kg for ewes offered the LL, LH, HL and HH treatments, respectively. Mean OR of C×SBF and BelMG×SBF differed (P<0.001) from that of Bel×SBF by −0.58 and +0.47, respectively. Correlations between linear measurements (LMs) of body size and BW at 7, 12 and 17 months were all positive and significant (P<0.001). The precision of the set of LMs as a predictor of BW at joining improved with age (R2 0.46, 0.54 and 0.74 at 7, 12 and 17 months) but BW at a given age was a better predictor at all age time points. Chest girth was the best predictor, among the LMs, of BW at joining and its explanatory power increased with age. Equations developed using LMs at 7, 12 or 17 months, to predict BW at joining exhibited biases of −2.1, −1.5 and +0.9 kg, respectively. It is concluded that whilst altering PN during the rearing phase changed BW by 16% it had no effect on OR. There was no interaction (P>0.05) between the PN offered during winter_1 and summer_2, or PN and ewe genotype for BW at joining or OR. LMs, either at 7, 12 or 17 months of age, are useful predictors of BW at joining.

The reference organ-level body composition measurement method is MRI. Practical estimations of total adipose tissue mass (TATM), total adipose tissue fat mass (TATFM) and total body fat are valuable for epidemiology, but validated prediction equations based on MRI are not currently available. We aimed to derive and validate new anthropometric equations to estimate MRI-measured TATM/TATFM/total body fat and compare them with existing prediction equations using older methods. The derivation sample included 416 participants (222 women), aged between 18 and 88 years with BMI between 15·9 and 40·8 (kg/m2). The validation sample included 204 participants (110 women), aged between 18 and 86 years with BMI between 15·7 and 36·4 (kg/m2). Both samples included mixed ethnic/racial groups. All the participants underwent whole-body MRI to quantify TATM (dependent variable) and anthropometry (independent variables). Prediction equations developed using stepwise multiple regression were further investigated for agreement and bias before validation in separate data sets. Simplest equations with optimal R2 and Bland–Altman plots demonstrated good agreement without bias in the validation analyses: men: TATM (kg)=0·198 weight (kg)+0·478 waist (cm)−0·147 height (cm)−12·8 (validation: R2 0·79, CV=20 %, standard error of the estimate (SEE)=3·8 kg) and women: TATM (kg)=0·789 weight (kg)+0·0786 age (years)−0·342 height (cm)+24·5 (validation: R2 0·84, CV=13 %, SEE=3·0 kg). Published anthropometric prediction equations, based on MRI and computed tomographic scans, correlated strongly with MRI-measured TATM: (R2 0·70−0·82). Estimated TATFM correlated well with published prediction equations for total body fat based on underwater weighing (R2 0·70–0·80), with mean bias of 2·5–4·9 kg, correctable with log-transformation in most equations. In conclusion, new equations, using simple anthropometric measurements, estimated MRI-measured TATM with correlations and agreements suitable for use in groups and populations across a wide range of fatness.

Very few effective field methods are available for accurate, non-invasive estimation of skeletal muscle volume (SMV) and mass in children. We aimed to develop regression-based prediction equations for SMV, using ultrasonography, in Japanese prepubertal children, and to assess the validity of these equations. In total, 145 healthy Japanese prepubertal children aged 6–12 years were randomly divided into two groups: the model development group (sixty boys, thirty-seven girls) and the validation group (twenty-nine boys, nineteen girls). Reference data in the form of contiguous MRI with 1-cm slice thickness were obtained from the first cervical vertebra to the ankle joints. The SMV was calculated by the summation of digitised cross-sectional areas. Muscle thickness was measured using B-mode ultrasonography at nine sites in different regions. In the model development group, strong, statistically significant correlations were observed between the site-matched SMV (total, arms, trunk, thigh and lower legs) measured by MRI and the muscle thickness×height measures obtained by ultrasonography, for both boys and girls. When these SMV prediction equations were applied to the validation groups, the measured total and regional SMV were also very similar to the values predicted for boys and girls, respectively. With the exception of the trunk region in girls, the Bland–Altman analysis for the validation group did not indicate any bias for either boys or girls. These results suggest that ultrasonography-derived prediction equations for boys and girls are useful for the estimation of total and regional SMV.

The aims of this study were (1) to evaluate the ability of computed tomography (CT) to predict the chemical composition of live pigs and carcasses, (2) to compare the chemical composition of four different sex types at a commercial slaughter weight and (3) to model and evaluate the chemical component growth of these sex types. A total of 92 pigs (24 entire males (EM), 24 surgically castrated males (CM), 20 immunocastrated males (IM) and 24 females (FE)) was used. A total of 48 pigs (12 per sex type) were scanned repeatedly in vivo using CT at 30, 70, 100 and 120 kg and slaughtered at the end of the experiment. The remaining 44 were CT scanned in vivo and slaughtered immediately: 12 pigs (4 EM, 4 CM and 4 FE) at 30 kg and 16 pigs each at 70 kg and 100 kg (4 per sex type). The left carcasses were CT scanned, and the right carcasses were minced and analysed for protein, fat, moisture, ash, Ca and P content. Prediction equations for the chemical composition were developed using Partial Least Square regression. Allometric growth equations for the chemical components were modelled. By using live animal and carcass CT images, accurate prediction equations were obtained for the fat (with a root mean square error of prediction (RMSEPCV) of 1.31 and 1.34, respectively, and R2=0.91 for both cases) and moisture relative content (g/100 g) (RMSEPCV=1.19 and 1.38 and R2=0.94 and 0.93, respectively) and were less accurate for the protein (RMSEPCV=0.65 and 0.67 and R2=0.54 and 0.63, respectively) and mineral content (RMSEPCV from 0.28 to 1.83 and R2 from 0.09 to 0.62). Better equations were developed for the absolute amounts of protein, fat, moisture and ash (kg) (RMSEPCV from 0.26 to 1.14 and R2 from 0.91 to 0.99) as well as Ca and P (g) (RMSEPCV=144 and 71, and R2=0.76 to 0.66, respectively). At 120 kg, CM had a higher fat and lower moisture content than EM. For protein, CM and IM had lower values than FE and EM. The ash content was higher in EM and IM than in FE and CM, while IM had a higher Ca and P content than the others. The castrated animals showed a higher allometric coefficient for fat and a lower one for moisture, with IM having intermediate values. However, for the Ca and P models, IM presented higher coefficients than EM and FE, and CM were intermediate.

Adipose tissue (AT) fatty acid (FA) composition partly reflects habitual dietary intake. Circulating NEFA are mobilised from AT and might act as a minimally invasive surrogate marker of AT FA profile. Agreement between twenty-eight FA in AT and plasma NEFA was assessed using concordance coefficients in 204 male and female participants in a 12-month intervention using supplements to increase the intake of EPA and DHA. Concordance coefficients generally showed very poor agreement between AT FA and plasma NEFA at baseline SFA: 0·07; MUFA: 0·03; n-6 PUFA: 0·28; n-3 PUFA: 0·01). Participants were randomly divided into training (70 %) and validation (30 %) data sets, and models to predict AT and dietary FA were fitted using data from the training set, and their predictive ability was assessed using data from the validation set. AT n-6 PUFA and SFA were predicted from plasma NEFA with moderate accuracy (mean absolute percentage error n-6 PUFA: 11 % and SFA: 8 %), but predicted values were unable to distinguish between low, medium and high FA values, with only 25 % of n-6 PUFA and 33 % of SFA predicted values correctly assigned to the appropriate tertile group. Despite an association between AT and plasma NEFA EPA (P=0·001) and DHA (P=0·01) at baseline, there was no association after the intervention. To conclude, plasma NEFA are not a suitable surrogate for AT FA.

The aim of the present work was (1) to study the relationship between cross-sectional computed tomography (CT) images obtained in live growing pigs of different genotypes and dissection measurements and (2) to estimate carcass composition and cut composition from CT measurements. Sixty gilts from three genotypes (Duroc×(Landrace×Large White), Pietrain×(Landrace×Large White), and Landrace×Large White) were CT scanned and slaughtered at 30 kg (n=15), 70 kg (n=15), 100 kg (n=12) or 120 kg (n=18). Carcasses were cut and the four main cuts were dissected. The distribution of density volumes on the Hounsfield scale (HU) were obtained from CT images and classified into fat (HU between −149 and −1), muscle (HU between 0 and 140) or bone (HU between 141 and 1400). Moreover, physical measurements were obtained on an image of the loin and an image of the ham. Four different regression approaches were studied to predict carcass and cut composition: linear regression, quadratic regression and allometric equations using volumes as predictors, and linear regression using volumes and physical measurements as predictors. Results show that measurements from whole animal taken in vivo with CT allow accurate estimation of carcass and cut composition. The prediction accuracy varied across genotypes, BW and variable to be predicted. In general, linear models, allometric models and linear models, which included also physical measurements at the loin and the ham, produced the lowest prediction errors.

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.

Accurate assessment of neonatal body composition is essential to studies investigating neonatal nutrition or developmental origins of obesity. Bioelectrical impedance analysis or bioimpedance analysis is inexpensive, non-invasive and portable, and is widely used in adults for the assessment of body composition. There are currently no prediction algorithms using bioimpedance analysis in neonates that have been directly validated against measurements of fat-free mass (FFM). The aim of the study was to evaluate the use of bioimpedance analysis for the estimation of FFM and percentage of body fat over the first 4 months of life in healthy infants born at term, and to compare these with estimations based on anthropometric measurements (weight and length) and with skinfolds. The present study was an observational study in seventy-seven infants. Body fat content of infants was assessed at birth, 6 weeks, 3 and 4·5 months of age by air displacement plethysmography, using the PEA POD body composition system. Bioimpedance analysis was performed at the same time and the data were used to develop and test prediction equations for FFM. The combination of weight+sex+length predicted FFM, with a bias of < 100 g and limits of agreement of 6–13 %. Before 3 months of age, bioimpedance analysis did not improve the prediction of FFM or body fat. At 3 and 4·5 months, the inclusion of impedance in prediction algorithms resulted in small improvements in prediction of FFM, reducing the bias to < 50 g and limits of agreement to < 9 %. Skinfold measurements performed poorly at all ages.

The present study was performed to develop regression-based prediction equations for fat mass by ultrasound in Japanese children and to investigate the validity of these equations. A total of 127 healthy Japanese pre-pubertal children aged 6–12 years were randomly separated into two groups: the model development group (fifty-four boys and forty-four girls) and the validation group (eighteen boys and eleven girls). Total body, trunk, arm and leg fat masses were initially determined by dual-energy X-ray absorptiometry (DXA, Delphi A-QDR whole-body scanner; Hologic, Inc., Bedford, MA, USA). Then, fat thickness was measured by B-mode ultrasound (5 MHz scanning head) at nine sites (arm: lateral forearm, anterior and posterior upper arm; trunk: abdomen and subscapular; leg: anterior and posterior thigh, anterior and posterior lower leg). Regression analyses were used to describe the relationships between the site-matched fat masses (total body, arm, trunk and leg) obtained by DXA and ultrasound in the development group. When these fat mass prediction equations were applied to the validation group, the measured total and regional fat mass was very similar to the predicted fat mass (mean difference calculated as predicted −  measured fat mass ± 2 sd; total body 0·1 (sd 0·5) kg, arm 0·1 (sd 0·3) kg, trunk − 0·1 (sd 0·3) kg, leg 0·1 (sd 0·5) kg for boys; total body 0·5 (sd 1·3) kg, arm 0·0 (sd 0·3) kg, trunk 0·1 (sd 0·8) kg, leg 0·3 (sd 0·6) kg for girls), and the Bland–Altman analysis did not indicate a bias. These results suggest that ultrasound-derived prediction equations for boys and girls are useful for estimating total and regional fat mass.

The purpose of the present study was to investigate whether ultrasound-derived prediction equations for estimating total and regional skeletal muscle (SM) mass in adults are applicable for prepubertal children and adolescents. Ten Japanese prepubertal children and twenty-one adolescents volunteered for the study. Contiguous MRI images with a 1 cm slice thickness were obtained from the first cervical vertebra to the ankle joints as reference data. The SM volume was calculated from the summation of digitised cross-sectional areas. The regional SM volume was determined by anatomical landmarks visible in the scanned images. The volume units were converted into mass by an assumed SM density (1·041 g/cm3). Muscle thickness was measured by B-mode ultrasound at nine sites on different muscles (lateral forearm, anterior and posterior upper arm, abdomen, subscapular, anterior and posterior thigh, anterior and posterior lower leg). Total and regional SM mass was estimated using adult prediction equations. Mean values between measured and predicted total and regional segments of SM mass were not significantly different for adolescents, but were for prepubertal children. There was a relatively large range of the 95 % limits of agreement both in prepubertal children and adolescents. These results suggest that the adult ultrasound-derived prediction equations are useful for estimating total and regional SM mass for adolescents at the group level, but the relatively high degree of variability suggested limited reliability at the individual level both in prepubertal children and adolescents.

The concentrations of utilisable energy per unit of price is important information to allow choices to be made between feedstuffs for practical dietary formulations. There has been a long lasting debate as to whether net energy (NE) or metabolisable energy (ME) is preferable. Only one large data set of empirically determined net energy concentrations (NEp) of feedstuffs for poultry has been published. Although the ME system for describing the utilisable energy concentrations of feedstuffs is now widely used, different metabolised nutrients (protein, fat and carbohydrate) are utilised with different relative efficiencies for energy deposition. More recently, methods of predicting the NE of feeds from equations have been suggested. These include information on nutrient digestibility with or without determined ME values with corrections according to the efficiency by which the nutrients are utilised. The objectives of the present study were, firstly, to evaluate the relationship between determined apparent metabolisable energy (AME) and determined NEp concentrations for a range of poultry feedstuffs and, secondly, the relationship between four different NE prediction equations and determined NEp concentrations was evaluated using the same data set. There was a positive linear regression (p < 0.001) between the determined AME and NEp concentrations in a data set of 62 feedstuffs with a range of AME from 0.1 to 20.5 MJ/kg. This regression equation accounted for 92.8% of the variance in NEp. Each 1.0 MJ increase in AME gave a 0.69 MJ increase in NEp. However, this data set had a large proportion of low energy feedstuffs that are unlikely to be used in modern proprietary poultry diets. A subset of these data that included 40 feedstuffs with an AME range of 8.0–18.0 MJ/kg was also examined. The regression between determined AME and NEp concentrations in this smaller data set only accounted for 77.7% of the variance in NEp. There was evidence (p < 0.01) that AME overestimated the NEp concentrations of high protein feeds of animal origin compared with cereals, cereal byproducts and high protein vegetable feeds. Three NE prediction equations that require knowledge of the digestibility coefficients for protein, ether extract and carbohydrates all improved the precision of the NEp prediction. However, a fourth prediction equation that requires only AME and crude protein determinations gave an equally precise estimate of NEp.