Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements benefit from gathering exact measurements of body composition or body nutrient contents. Those are acquired by standardized dissection or by grinding the body followed by wet chemical analysis, respectively. The two methods do not result in the same type of information, but both are destructive. Harnessing human medical imaging techniques for animal science can enable repeated measurements of individuals over time and reduce the number of individuals required for research. Among imaging techniques, dual-energy X-ray absorptiometry (DXA) is particularly promising. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. Several calibration regressions have been published, but comparative studies of those regression equations and whether they are applicable to different data sets are pending. Thus, it is currently not clear whether existing regression equations can be directly used to convert DXA measurements into chemical values or whether each individual DXA device will require its own calibration. Our study builds prediction equations that relate body composition to the content of single nutrients in growing entire male pigs (BW range 20–100 kg) as determined by both DXA and chemical analyses, with R² ranging between 0.89 for ash and 0.99 for water and CP. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses, with the prediction error ranging between 4.3% for CP and 12.6% for ash. Finally, we compare existing prediction equations for pigs of a similar range of BWs with the equations derived from our DXA measurements and evaluate their fit with our chemical analysis data. We found that existing equations for absolute contents that were built using the same DXA beam technology predicted our data more precisely than equations based on different technologies and percentages of fat and lean mass. This indicates that the creation of generic regression equations that yield reliable estimates of body composition in pigs of different growth stages, sexes and genetic breeds could be achievable in the near future. DXA may be a promising tool for high-throughput phenotyping for genetic studies, because it efficiently measures body composition in a large number and wide array of animals.     

Age at castration (surgical or immunological) impacts carcass characteristics and meat quality of male pigs

In order to accurately estimate body composition at slaughter and to meet specific market targets, the influence of age at time of castration (surgical or immunological) on body composition and boar taint indicators must be determined for male pigs. In all, 48 males were randomly assigned to one of four management regimens: (1) entire male pigs (EM), (2) EM surgically castrated at ~40 kg BW and 10 weeks of age (late castrates; LC), (3) conventional, early surgical castrates (within 4 days of birth; EC) and (4) EM immunized with a gonadotropin-releasing hormone (GnRH) analog (primary dose at 30 kg BW and 8 weeks of age; booster dose at 70 kg and 14 weeks of age; IM). Pigs were fed corn and soybean meal-based diets that were not limiting in essential nutrients. Back fat was sampled on days −3, 8, 18 and 42, relative to administering the booster dose of GnRH analog at day 0, to determine androstenone concentrations (n=8 or 9/group). Fat androstenone concentrations in IM were lower than EM between days 8 and 42 after administering the booster dose (173 v. 863 ng/g, respectively; P<0.01), and were not different from surgically castrated males (EC and LC) after day 18. Slaughter occurred at ~115 kg BW, 42 days (6 weeks) after administering the booster dose for IM, and 10 and 20 weeks after surgical castration for LC and EC, respectively (n=8 or 9/group). At slaughter, live BW, liver weight as a percent of live BW, dissectible bone as a percent of cold carcass side, body protein and water contents and whole-body protein deposition decreased with time after surgical castration (linear; P<0.05), whereas dressing percentage, dissectible fat, probe fat depth and body fat content increased with time after surgical castration (linear; P<0.05). The IM had intermediate dressing percentage and dissected fat to EM and EC, whereas liver weight as a percent of live BW and body protein and lipid contents were not different from EM. Whole-body lipid deposition tended to be greater in IM than in EM between 14 and 20 weeks of age (373 v. 286 g/d; P=0.051). In conclusion, castration of male pigs after 6 weeks of age has a lasting effect on physical and chemical body composition. The relationship between time after castration and body composition may be developed to predict carcass composition and can be used to determine the ideal immunization schedule aimed at specific markets in the future.     

Development of equations to predict the influence of floor space on average daily gain,average daily feed intake and gain : feed ratio of finishing pigs

Floor space allowance for pigs has substantial effects on pig growth and welfare. Data from 30 papers examining the influence of floor space allowance on the growth of finishing pigs was used in a meta-analysis to develop alternative prediction equations for average daily gain (ADG), average daily feed intake (ADFI) and gain : feed ratio (G : F). Treatment means were compiled in a database that contained 30 papers for ADG and 28 papers for ADFI and G : F. The predictor variables evaluated were floor space (m²/pig), k (floor space/final BW^0.67), Initial BW, Final BW, feed space (pigs per feeder hole), water space (pigs per waterer), group size (pigs per pen), gender, floor type and study length (d). Multivariable general linear mixed model regression equations were used. Floor space treatments within each experiment were the observational and experimental unit. The optimum equations to predict ADG, ADFI and G : F were: ADG, g=337.57+(16 468×k)−(237 350×k²)−(3.1209×initial BW (kg))+(2.569×final BW (kg))+(71.6918×k×initial BW (kg)); ADFI, g=833.41+(24 785×k)−(388 998×k²)−(3.0027×initial BW (kg))+(11.246×final BW (kg))+(187.61×k×initial BW (kg)); G : F=predicted ADG/predicted ADFI. Overall, the meta-analysis indicates that BW is an important predictor of ADG and ADFI even after computing the constant coefficient k, which utilizes final BW in its calculation. This suggests including initial and final BW improves the prediction over using k as a predictor alone. In addition, the analysis also indicated that G : F of finishing pigs is influenced by floor space allowance, whereas individual studies have concluded variable results.     

Predicting the carcass chemical composition and describing its growth in live pigs of different sexes using computed tomographys

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 (RMSEP_CV) of 1.31 and 1.34, respectively, and R²=0.91 for both cases) and moisture relative content (g/100 g) (RMSEP_CV=1.19 and 1.38 and R²=0.94 and 0.93, respectively) and were less accurate for the protein (RMSEP_CV=0.65 and 0.67 and R²=0.54 and 0.63, respectively) and mineral content (RMSEP_CV 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) (RMSEP_CV from 0.26 to 1.14 and R² from 0.91 to 0.99) as well as Ca and P (g) (RMSEP_CV=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.     

Effect of high contents of dietary animal-derived protein or carbohydrates on canine faecal microbiota

Background

Few equations have been developed in veterinary medicine compared to human medicine to predict body composition. The present study was done to evaluate the influence of weight loss on biometry (BIO), bioimpedance analysis (BIA) and ultrasonography (US) in cats, proposing equations to estimate fat (FM) and lean (LM) body mass, as compared to dual energy x-ray absorptiometry (DXA) as the referenced method. For this were used 16 gonadectomized obese cats (8 males and 8 females) in a weight loss program. DXA, BIO, BIA and US were performed in the obese state (T0; obese animals), after 10% of weight loss (T1) and after 20% of weight loss (T2). Stepwise regression was used to analyze the relationship between the dependent variables (FM, LM) determined by DXA and the independent variables obtained by BIO, BIA and US. The better models chosen were evaluated by a simple regression analysis and means predicted vs. determined by DXA were compared to verify the accuracy of the equations.

Results

The independent variables determined by BIO, BIA and US that best correlated (p?<?0.005) with the dependent variables (FM and LM) were BW (body weight), TC (thoracic circumference), PC (pelvic circumference), R (resistance) and SFLT (subcutaneous fat layer thickness). Using Mallows??Cp statistics, p value and r ² , 19 equations were selected (12 for FM, 7 for LM); however, only 7 equations accurately predicted FM and one LM of cats.

Conclusions

The equations with two variables are better to use because they are effective and will be an alternative method to estimate body composition in the clinical routine. For estimated lean mass the equations using body weight associated with biometrics measures can be proposed. For estimated fat mass the equations using body weight associated with bioimpedance analysis can be proposed.     

Estimation of carcass composition and cut composition from computed tomography images of live growing pigs of different genotypes

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.     

Relationships between Insulin Sensitivity and Measures of Body Fat in Asymptomatic Men and Women

Objective: To assess whether measures of body fat by DXA scanning can improve prediction of insulin sensitivity (S_I) beyond what is possible with traditional measures, such as BMI, waist circumference, and waist‐to‐hip ratio (WHR). Research Methods and Procedures: Frequently sampled intravenous glucose tolerance tests were performed in 256 asymptomatic non‐Hispanic white subjects from Rochester, MN (age 19‐60 years; 123 men and 133 women) to determine the S_I index by Bergman's minimal model technique. Height, weight, and waist and hip circumferences were measured for calculation of BMI and WHR; DXA was used to determine fat in the head, upper body, abdomen, and lower body. Linear regression was used to assess their relationships with S_I after sex stratification and adjustment for age. Results: After controlling for age, increases in traditional and DXA measures of fat were consistently associated with smaller declines in S_I among women than among men. In men, after controlling for age, all of the predictive information of S_I was provided by waist circumference (additional R² = 0.39, p < 0.001); none of the DXA measures improved the ability to predict S_I. In women, after adjustment for age, BMI, and WHR, the only DXA measure that improved the prediction of S_I was percentage head fat (additional R² = 0.03, p < 0.001). Discussion: Equivalent increases in most measures of body fat had lesser impact on S_I in women than in men. In both sexes, the predictive information provided by DXA measures is approximately equal to, but not additive to, that provided by simpler, traditional measures.     

Validation of DXA Body Composition Estimates in Obese Men and Women

The aim of this study was to determine the accuracy of dual‐energy X‐ray absorptiometry (DXA)‐derived percentage fat estimates in obese adults by using four‐compartment (4C) values as criterion measures. Differences between methods were also investigated in relation to the influence of fat‐free mass (FFM) hydration and various anthropometric measurements. Six women and eight men (age 22–54 years, BMI 28.7–39.9 kg/m², 4C percent body fat (%BF) 31.3–52.6%) had relative body fat (%BF) determined via DXA and a 4C method that incorporated measures of body density (BD), total body water (TBW), and bone mineral mass (BMM) via underwater weighing, deuterium dilution, and DXA, respectively. Anthropometric measurements were also undertaken: height, waist and gluteal girth, and anterior‐posterior (A‐P) chest depth. Values for both methods were significantly correlated (r² = 0.894) and no significant difference (P = 0.57) was detected between the means (DXA = 41.1%BF, 4C = 41.5%BF). The slope and intercept for the regression line were not significantly different (P > 0.05) from 1 and 0, respectively. Although both methods were significantly correlated, intraindividual differences between the methods were sizable (4C‐DXA, range = ?3.04 to 4.01%BF) and significantly correlated with tissue thickness (chest depth) or most surrogates of tissue thickness (body mass, BMI, waist girth) but not FFM hydration and gluteal girth. DXA provided cross‐sectional %BF data for obese adults without bias. However, individual data are associated with large prediction errors (±4.2%BF). This error appears to be associated with tissue thickness indicating that the DXA device used may not be able to accurately account for beam hardening in obese cohorts.     

Validation of Dual Energy X-Ray Absorptiometry Measures of Abdominal Fat by Comparison with Magnetic Resonance Imaging in an Indian Population

Objective

Abdominal adiposity is an important risk factor for diabetes and cardiovascular disease in Indians. Dual energy X-ray absorptiometry (DXA) can be used to determine abdominal fat depots, being more accessible and less costly than gold standard measures such as magnetic resonance imaging (MRI). DXA has not been fully validated for use in South Asians. Here, we determined the accuracy of DXA for measurement of abdominal fat in an Indian population by comparison with MRI.

Design

146 males and females (age range 18–74, BMI range 15–46 kg/m²) from Hyderabad, India underwent whole body DXA scans on a Hologic Discovery A scanner, from which fat mass in two abdominal regions was calculated, from the L1 to L4 vertebrae (L1L4) and from the L2 to L4 vertebrae (L2L4). Abdominal MRI scans (axial T1-weighted spin echo images) were taken, from which adipose tissue volumes were calculated for the same regions.

Results

Intra-class correlation coefficients between DXA and MRI measures of abdominal fat were high (0.98 for both regions). Although at the level of the individual, differences between DXA and MRI could be large (95% of DXA measures were between 0.8 and 1.4 times MRI measures), at the sample level, DXA only slightly overestimated MRI measures of abdominal fat mass (mean difference in L1L4 region: 2% (95% CI:0%, 5%), mean difference in L2L4 region:4% (95% CI: 1%, 7%)). There was evidence of a proportional bias in the association between DXA and MRI (correlation between difference and mean −0.3), with overestimation by DXA greater in individuals with less abdominal fat (mean bias in leaner half of sample was 6% for L1L4 (95%CI: 2, 11%) and 7% for L2L4 (95% CI:3,12%).

Conclusions

DXA measures of abdominal fat are suitable for use in Indian populations and provide a good indication of abdominal adiposity at the population level.     

Genetic determinism of boar taint and relationship with growth traits,meat quality and lesions

Breeding entire males is an alternative to surgical castration to improve their welfare. However, entire males may have a major quality defect called boar taint. Boar taint is partly due to the presence of androstenone in fat. In this study, we estimated the genetic parameters between androstenone and production traits to evaluate the consequences of selection against boar taint for traits of interest. We focused on growth traits, meat quality, lesions, hormone levels and computerised tomography measurements in purebred Piétrain (P) or Piétrain cross Large White (X) entire males. The number of measured animals varied from 670 P and 734 X for hormones concentrations to 553 P and 645 X for computerised tomography measurements. Skin lesions were measured on live pigs shortly after mixing, at the end of the fattening period, and on carcasses. Heritabilities of traits measured by tomography ranged from low to high: femur density (P: 0.34, X: 0.69), loin eye area (P: 0.53, X: 0.88) and loin eye density (P: 0.12, X: 0.18). The mean number of lesions at each stage was lower in purebred pigs than in crossbreds (entering the fattening stage 4.01 in P and 4.68 in X; before slaughter 3.72 in P and 4.22 in X; on carcass 4.50 in P and 4.96 in X). We also observed a decrease in the average number of lesions between the two stages in live pigs. We found high genetic correlations between stages in purebred pigs (0.74 to 0.76) but low correlations (−0.30 to 0.29) in crossbred pigs. Selection aiming to decrease fat androstenone is feasible (h² = 0.57 in P and h² = 0.71 in X). It would have overall positive effects on meat production and quality traits. Selection aiming to reduce plasma oestradiol would strongly reduce the level of fat androstenone (rg = 0.89 in P and rg = 0.84 in X). Selection against oestradiol is easier and less invasive since it would only require a blood sample rather than a fat biopsy in live animals.     

Independent Influences of Central Fat and Skeletal Muscle Lipids on Insulin Sensitivity

Objective: Insulin resistance is closely associated with two disparate aspects of lipid storage: the intracellular lipid content of skeletal muscle and the magnitude of central adipose beds. Our aim was to determine their relative contribution to impaired insulin action. Research Methods and Procedures: Eighteen older (56 to 75 years of age) men were studied before elective knee surgery. Insulin sensitivity (M/ΔI) was determined by hyperinsulinemic–euglycemic clamp. Central abdominal fat (CF) was assessed by DXA. Skeletal muscle was excised at surgery and assayed for content of metabolically active long‐chain acyl‐CoA esters (LCAC). Results: Significant inverse relationships were observed between LCAC and M/ΔI (R² = 0.34, p = 0.01) and between CF and M/ΔI (R² = 0.38, p = 0.006), but not between CF and LCAC (R² = 0.0005, p = 0.93). In a multiple regression model (R² = 0.71, p < 0.0001), both CF (p = 0.0006) and LCAC (p = 0.0009) were independent statistical predictors of M/ΔI. Leptin levels correlated inversely with M/ΔI (R² = 0.60, p = 0.0002) and positively with central (R² = 0.41, p = 0.006) and total body fat (R² = 0.63, p = 0.0001). Discussion: The mechanisms by which altered lipid metabolism in skeletal muscle influences insulin action may not be related directly to those linking central fat and insulin sensitivity. In particular, it is unlikely that muscle accumulation of lipids directly derived from labile central fat depots is a principal contributor to peripheral insulin resistance. Instead, our results imply that circulating factors, other than nonesterified fatty acids or triglyceride, mediate between central fat depots and skeletal muscle tissue. Leptin was not exclusively associated with central fat, but other factors, secreted specifically from central fat cells, could modulate muscle insulin sensitivity.     

In vivo body composition in autochthonous and conventional pig breeding groups by dual-energy X-ray absorptiometry and magnetic resonance imaging under special consideration of Cerdo Ibérico

The improvement of carcass quality is one of the main breeding goals in pig production. To select appropriate breeding animals, it is of major concern to exactly and reliably analyze the body composition in vivo. Therefore, the objective of the study was to examine whether the combination of dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) offers the opportunity to reliably analyze quantitative and qualitative body composition characteristics of different pig breeding groups in vivo. In this study, a total of 77 pigs were studied by DXA and MRI at an average age of 154 days. The pigs originated from different autochthonous or conventional breeds or crossbreeds and were grouped into six breed types: Cerdo Ibérico (Ib); Duroc × Ib (Du_Ib); White Sow Lines (WSL, including German Landrace and German Large White); Hampshire/Pietrain (Pi_Ha, including Hampshire, Pietrain × Hampshire (PiHa) and Pietrain × PiHa); Pietrain/Duroc (Pi_Du, including Pietrain × Duroc (PiDu) and Pietrain × PiDu); crossbred WSL (PiDu_WSL, including Pietrain × WSL and PiDu × WSL). A whole-body scan was performed by DXA with a GE Lunar DPX-IQ in order to measure the amount and percentage of fat tissue (FM; %FM), lean tissue (LM; %LM) and bone mineral, whereas a Siemens Magnetom Open with a large body coil was used for MRI in the thorax region between 13th and 14th vertebrae in order to measure the area of the loin (LA) and the above back fat area (FA) of both body sides. A GLM procedure using SAS 9.2 was used to analyze the data. As expected, the native breed Ib followed by Du_Ib crossbreeds showed the highest %FM (27.2%, 25.0%) combined with the smallest LA (46.2 cm², 73.6 cm²), whereas Ib had the lowest BW at an average age of 154 days. Pigs with Pi_Ha origin presented the least %FM (12.4%) and largest LA (99.5 cm²). The WSL and PiDu_WSL showed an intermediate body composition. Therefore, it could be concluded that DXA and MRI and especially their combination are very suitable methods to reliably identify differences in body composition and carcass traits among different pig lines in vivo.     

Prediction of physical characteristics of the lamb carcass using in vivo bioimpedance analysis

The increase of sheep meat competitiveness in international markets can be attributed to the rise of the quantity and the improvement of the quality of the edible portion of sheep carcasses. Usually, carcass yield is established after the slaughter of the animals. Yet, when carcass yield is determined in vivo, it can be both a costly and subjective method. This study proposes models for predicting the physical characteristics of lamb carcass using bioimpedance analysis (BIA) in live animals. Thirty-one Texel × Ile de France crossbreed ram lambs were slaughtered at 20, 26, 32 or 38 kg of BW. Before the slaughter, values of resistance (Rs) and reactance (Xc) were collected using a single-frequency BIA equipment (Model RJL Quantum II Bioelectrical Body Composition Analyzer). Then, BIA main variables such as body bioelectrical volume (V), phase angle (PA), resistive density (RsD) and reactive density (XcD) were calculated. After slaughter, cold carcass weight (CCW), cold carcass yield (CCY), subcutaneous fat thickness (SFT), soft tissue weight (STW) and soft tissue yield (STY) were also measured. Multiple regression analyses were carried out using the physical characteristics as dependent variables and the bioimpedance values as independent variables. Predictive performance of the models was assessed using leave-one-out cross-validation. The prediction model of CCW was obtained using the V, PA and RsD (R² = 0.97), STW through the V, RsD and XcD (R² = 0.97), CCY by Rs, Z and XcD (R² = 0.69), STY by V and XcD (R² = 0.67), and SFT only for XcD (R² = 0.84). The results indicated that BIA has the potential to predict carcass characteristics of lambs at different body masses.     

Predictive equations for body fat and abdominal fat with DXA and MRI as reference in Asian Indians

Objective: To develop accurate and reliable equations from simple anthropometric parameters that would predict percentage of total body fat (%BF), total abdominal fat (TAF), subcutaneous abdominal adipose tissue (SCAT), and intra‐abdominal adipose tissue (IAAT) with a fair degree of accuracy. Methods and Procedures: Anthropometry, %BF by dual‐energy X‐ray absorptiometry (DXA) in 171 healthy subjects (95 men and 76 women) and TAF, IAAT, and SCAT by single slice magnetic resonance imaging (MRI) at L3–4 intervertebral level in 100 healthy subjects were measured. Mean age and BMI were 32.2 years and 22.9 kg/m², respectively. Multiple regression analysis was used on the training data set (70%) to develop equations, by taking anthropometric and demographic variables as potential predictors. Predicted equations were applied on validation data set (30%). Results: Multiple regression analysis revealed the best equation for predicting %BF to be: %BF = 42.42 + 0.003 × age (years) + 7.04 × gender (M = 1, F = 2) + 0.42 × triceps skinfold (mm) + 0.29 × waist circumference (cm) ? 0.22 × weight (kg) ? 0.42 × height (cm) (R ² = 86.4%). The most precise predictive equation for estimating IAAT was: IAAT (mm²) = ?238.7 + 16.9 × age (years) + 934.18 × gender (M = 1, F = 2) + 578.09 × BMI (kg/m²) ? 441.06 × hip circumference (cm) + 434.2 × waist circumference (cm) (R ² = 52.1%). SCAT was best predicted by: SCAT (mm²) = ?49,376.4 ? 17.15 × age (years) + 1,016.5 × gender (M = 1, F = 2) +783.3 × BMI (kg/m²) + 466 × hip circumference (cm) (R ² = 67.1). Discussion: We present predictive equations to quantify body fat and abdominal adipose tissue sub‐compartments in healthy Asian Indians. These equations could be used for clinical and research purposes.     

Effects of dietary CP and amino acid restriction on the growth dynamics of organs and body components in entire male,castrated and female pigs

The optimized use of dietary nutrients and the accurate knowledge of the growth dynamics of body components is important for efficient pig production. This study aimed at evaluating the growth of carcass components and organs of Swiss Large White pigs from birth to 140 kg BW depending on the CP and amino acid (AA) supply. A total of 66 entire males (EM), 58 castrates (CA) and 66 female (FE) pigs were used. From 20 kg BW onwards, they had either ad libitum access to a control (C) or a diet (LP) with 20% lower CP, lysine, methionine + cystine, threonine and tryptophan content compared to C. The weight of organs, primal cuts and external fat were recorded in eight EM and eight FE; at 10 kg BW, on two EM, CA and FE each, and at 20 kg BW, on eight pigs from each sex. From 40 to 140 kg BW at 20 kg intervals, measurements were recorded on four pigs per sex and dietary treatment. The weight of each component was related to empty body (EB) using allometric regressions. Kidneys were heavier (P<0.05) in C- than LP-pigs and in EM than CA and FE. Above 21 kg EB weight, growth rate of LP-FE overpassed (P<0.05) the one of C-pigs. Consequently, LP-FE had heavier (P<0.05) lean cuts than C-pigs in the finisher period. However, LP-CA and LP-EM displayed lower (P<0.05) weights and growth rates of the lean cuts than C-CA and C-EM. Shoulder and loin weights and growth rates were reduced (P<0.05) in LP-pigs when compared to C-pigs. Growth rates of the ham were greater (P<0.05) in LP-FE than C-FE, whereas in LP-EM and LP-CA they were lower (P<0.05) than their C-counterparts. Total amounts of subcutaneous fat, backfat, ham fat and shoulder fat were lower (P<0.05) in C- than LP-pigs. The total amount of subcutaneous fat, backfat and shoulder fat of C-CA was, regardless of diet, greater (P<0.05) than of C-FE. In the LP group, this difference was even more pronounced. The amount of deposited ham fat was greater (P<0.05) in LP-CA than LP-FE, but not in C-CA v. C-FE. Differences in kidney weights suggested a greater nitrogen clearance required in C-pigs. Overall, dietary restriction and sex did not affect all body parts to the same extent. This study further suggests the possibility to reduce the CP and AA supply in FE without compromising the yield of primal lean cuts or increasing the amount of subcutaneous fat.     

A New Equation to Estimate Muscle Mass from Creatinine and Cystatin C

Background

With evaluation for physical performance, measuring muscle mass is an important step in detecting sarcopenia. However, there are no methods to estimate muscle mass from blood sampling.

Methods

To develop a new equation to estimate total-body muscle mass with serum creatinine and cystatin C level, we designed a cross-sectional study with separate derivation and validation cohorts. Total body muscle mass and fat mass were measured using dual-energy x-ray absorptiometry (DXA) in 214 adults aged 25 to 84 years who underwent physical checkups from 2010 to 2013 in a single tertiary hospital. Serum creatinine and cystatin C levels were also examined.

Results

Serum creatinine was correlated with muscle mass (P < .001), and serum cystatin C was correlated with body fat mass (P < .001) after adjusting glomerular filtration rate (GFR). After eliminating GFR, an equation to estimate total-body muscle mass was generated and coefficients were calculated in the derivation cohort. There was an agreement between muscle mass calculated by the novel equation and measured by DXA in both the derivation and validation cohort (P < .001, adjusted R² = 0.829, β = 0.95, P < .001, adjusted R² = 0.856, β = 1.03, respectively).

Conclusion

The new equation based on serum creatinine and cystatin C levels can be used to estimate total-body muscle mass.     

Comparison of three bioelectrical impedance methods with DXA in overweight and obese men

Objective: To compare bioelectrical impedance analysis (BIA) of body composition using three different methods against DXA in overweight and obese men. Research Methods and Procedures: Forty‐three healthy overweight or obese men (ages 25 to 60 years; BMI, 28 to 43 kg/m²) underwent BIA assessment of body composition using the ImpediMed SFB7 (version 6; ImpediMed, Ltd., Eight Mile Plains, Queensland, Australia) in multifrequency mode (Imp‐MF) and DF50 single‐frequency mode (Imp‐SF) and the Tanita UltimateScale (Tanita Corp., Tokyo, Japan). Validity was assessed by comparison against DXA using linear regression and limits of agreement analysis. Results: All three BIA methods showed good relative agreement with DXA [Imp‐MF: fat mass (FM), r² = 0.81; fat‐free mass (FFM), r² = 0.81; percentage body fat (BF%), r² = 0.69; Imp‐SF: FM, r² = 0.65; FFM, r² = 0.76; BF%, r² = 0.40; Tanita: BF%, r² = 0.44; all p < 0.001]. Absolute agreement between DXA and Imp‐MF was poor, as indicated by a large bias and wide limits of agreement (bias, ±1.96 standard deviation; FM, ?6.6 ± 7.7 kg; FFM, 8.0 ± 7.1 kg; BF%, ?7.0 ± 6.6%). Imp‐SF and Tanita exhibited a smaller bias but wide limits of agreement (Imp‐SF: FM, ?1.1 ± 8.5 kg; FFM, 2.5 ± 7.9 kg; BF%, ?1.7 ± 7.3% Tanita: BF%, 1.2 ± 9.5%). Discussion: Compared with DXA, Imp‐MF produced large bias and wide limits of agreement, and its accuracy estimating body composition in overweight or obese men was poor. Imp‐SF and Tanita demonstrated little bias and may be useful for group comparisons, but their utility for assessment of body composition in individuals is limited.     

Upper extremity soft and rigid tissue mass prediction using segment anthropometric measures and DXA

Regression equations for predicting bone mineral content (BMC), fat mass (FM), lean mass (LM), and wobbling mass (WM) of living people from simple anthropometric measures (segment lengths, circumferences, breadths, and skin folds) have been reported in the literature for the lower extremities, but are lacking for the upper extremities. Multiple linear stepwise regression was used to generate such equations for the arm, forearm, and forearm and hand segments of healthy university aged people (38 males, 38 females). Actual tissue masses were obtained from full body Dual-energy X-ray Absorptiometry (DXA) scans and were used to validate the developed equations with an independent sample of 24 participants (12 male, 12 female). Prediction equations exhibited very high adjusted R² values (range from 0.854 to 0.968), with more explained variance for LM and WM than for BMC and FM. Scatter plots of actual versus predicted tissue masses revealed a close relationship (R² range from 0.681 to 0.951). Relative errors between the predicted and actual tissue masses for the validation group ranged from ?2.2% to 15.5%, and the root-mean-squared error (RMS_error) ranged from 7.92 to 180.26 g, for BMC of the forearm and LM of the arm, respectively. These results suggest that accurate estimates of in-vivo tissue masses for the upper extremities can be predicted from simple anthropometric measurements in young adults. Access to tissue masses such as these will enable the development of more accurate models for predicting dynamic in-vivo response of the body to activities involving impact.     

Higher insulin, triglycerides, and blood pressure with greater trunk fat in Tanner 1 Chinese

Objective: The aims of this study were to investigate the body fat distribution pattern in prepubertal Chinese children and to investigate the relationship between central fat distribution and specific biomarkers of cardiovascular disease. Research Methods and Procedures: The study was conducted in an urban Mainland Chinese (Jinan, Shandong) sample of children using a cross‐sectional design. Pubertal status was determined by Tanner criteria. Measurements included weight, height, waist circumference, DXA measures of total body fat and trunk fat; fasting serum measures of glucose, insulin, triglyceride, cholesterol, high‐density lipoprotein‐cholesterol; and systolic and diastolic blood pressure. Multiple regression models were developed with the biomarkers of cardiovascular risk factor as the dependent variables, and adjustments were made for significant covariates, including sex, age, height, weight, waist circumference, total body fat, trunk fat, and interactions. Results: A total of 247 healthy prepubertal subjects were studied. After co‐varying for age, weight, height, and extremity fat (the sum of arm fat and leg fat), girls had greater trunk fat than boys (p < 0.0001, R² for model = 0.95). Insulin and triglyceride were positively related to central fat measured by DXA‐trunk fat (p < 0.05) but not related to the waist circumference. In the blood pressure model, waist circumference was a significant predictor of both systolic blood pressure and diastolic blood pressure, while DXA‐trunk fat was associated with diastolic blood pressure only. Significant interactions between sex and trunk fat, and sex and total fat, were found in relation to diastolic blood pressure. Discussion: In prepubertal Chinese children, greater trunk fat was significantly associated with higher insulin and triglyceride in boys and girls and was associated with higher diastolic blood pressure in boys only.     

Predicting total, abdominal, visceral and hepatic adiposity with circulating biomarkers in caucasian and Japanese american women

Background

Characterization of abdominal and intra-abdominal fat requires imaging, and thus is not feasible in large epidemiologic studies.

Objective

We investigated whether biomarkers may complement anthropometry (body mass index [BMI], waist circumference [WC], and waist-hip ratio [WHR]) in predicting the size of the body fat compartments by analyzing blood biomarkers, including adipocytokines, insulin resistance markers, sex steroid hormones, lipids, liver enzymes and gastro-neuropeptides.

Methods

Fasting levels of 58 blood markers were analyzed in 60 healthy, Caucasian or Japanese American postmenopausal women who underwent anthropometric measurements, dual energy X-ray absorptiometry (DXA), and abdominal magnetic resonance imaging. Total, abdominal, visceral and hepatic adiposity were predicted based on anthropometry and the biomarkers using Random Forest models.

Results

Total body fat was well predicted by anthropometry alone (R² = 0.85), by the 5 best predictors from the biomarker model alone (leptin, leptin-adiponectin ratio [LAR], free estradiol, plasminogen activator inhibitor-1 [PAI1], alanine transaminase [ALT]; R² = 0.69), or by combining these 5 biomarkers with anthropometry (R² = 0.91). Abdominal adiposity (DXA trunk-to-periphery fat ratio) was better predicted by combining the two types of predictors (R² = 0.58) than by anthropometry alone (R² = 0.53) or the 5 best biomarkers alone (25(OH)-vitamin D₃, insulin-like growth factor binding protein-1 [IGFBP1], uric acid, soluble leptin receptor [sLEPR], Coenzyme Q10; R² = 0.35). Similarly, visceral fat was slightly better predicted by combining the predictors (R² = 0.68) than by anthropometry alone (R² = 0.65) or the 5 best biomarker predictors alone (leptin, C-reactive protein [CRP], LAR, lycopene, vitamin D₃; R² = 0.58). Percent liver fat was predicted better by the 5 best biomarker predictors (insulin, sex hormone binding globulin [SHBG], LAR, alpha-tocopherol, PAI1; R² = 0.42) or by combining the predictors (R² = 0.44) than by anthropometry alone (R² = 0.29).

Conclusion

The predictive ability of anthropometry for body fat distribution may be enhanced by measuring a small number of biomarkers. Studies to replicate these data in men and other ethnic groups are warranted.