Quantification of lean bodyweight

Background: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA - a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18-82 years), bodyweight (40.7-216.5kg) and BMI values (17.1-69.9 kg/m(2)). Patients in population B had BMI values of 18.7-38.4 kg/m(2). A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r(2) = 0.78, mean error [ME] = 2.30 x 10(-3), root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r(2) = 0.93, ME = -0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r(2) = 0.85, ME -0.04, RMSE = 4.39 [approximately 7% of mean]). Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.

Anthropometry profiles of elite rugby players: quantifying changes in lean mass

Objective: To demonstrate the utility of a practical measure of lean mass for monitoring changes in the body composition of athletes. Methods: Between 1999 and 2003 body mass and sum of seven skinfolds were recorded for 40 forwards and 32 backs from one Super 12 rugby union franchise. Players were assessed on 13 (7) occasions ( mean (SD)) over 1.9 (1.3) years. Mixed modelling of log transformed variables provided a lean mass index (LMI) of the form mass/skinfolds(x), for monitoring changes in mass controlled for changes in skinfold thickness. Mean effects of phase of season and time in programme were modelled as percentage changes. Effects were standardised for interpretation of magnitudes. Results: The exponent x was 0.13 for forwards and 0.14 for backs ( 90% confidence limits +/- 0.03). The forwards had a small decrease in skinfolds ( 5.3%, 90% confidence limits +/- 2.2%) between preseason and competition phases, and a small increase ( 7.8%, 90% confidence limits +/- 3.1%) during the club season. A small decrease in LMI (similar to 1.5%) occurred after one year in the programme for forwards and backs, whereas increases in skinfolds for forwards became substantial (4.3%, 90% confidence limits +/- 2.2%) after three years. Individual variation in body composition was small within a season (within subject SD: body mass, 1.6%; skinfolds, 6.8%; LMI, 1.1%) and somewhat greater for body mass (2.1%) and LMI (1.7%) between seasons. Conclusions: Despite a lack of substantial mean changes, there was substantial individual variation in lean mass within and between seasons. An index of lean mass based

Responses in carcass lean pH at 24 hours post-mortem in lines of Large White pigs selected for growth rate on a fixed ration over a set time

A restricted maximum likelihood analysis applied to an animal model showed no significant differences (P > 0.05) in pH value of the longissimus dorsi measured at 24 h post-mortem (pH24) between high and low lines of Large White pigs selected over 4 years for post-weaning growth rate on restricted feeding. Genetic and phenotypic correlations between pH24 and production and carcass traits were estimated using all performance testing records combined with the pH24 measurements (5.05–7.02) on slaughtered animals. The estimate of heritability for pH24 was moderate (0.29 ± 0.18). Genetic correlations between pH24 and production or carcass composition traits, except for ultrasonic backfat (UBF), were not significantly different from zero. UBF had a moderate, positive genetic correlation with pH24 (0.24 ± 0.33). These estimates of genetic correlations affirmed that selection for increased growth rate on restricted feeding is likely to result in limited changes in pH24 and pork quality since the selection does not put a high emphasis on reduced fatness.

Change in lean body mass Is a major determinant of change in areal bone mineral density of the proximal femur: A 12-year observational study

Our objective was to assess the contribution of lean body mass (LBM) and fat body mass (FBM) to areal bone mineral density (aBMD) in women during the years surrounding menopause. We used a 12-year observational design. Participants included 75 Caucasian women who were premenopausal, 53 of whom were available for follow-up. There were two measurement periods: baseline and 12-year follow-up. At both measurement periods, bone mineral content and aBMD of the proximal femur, posterior-anterior lumbar spine, and total body was assessed using dual-energy X-ray absorptiometry (DXA). LBM and FBM were derived from the total-body scans. General health, including current menopausal status, hormone replace therapy use, medication use, and physical activity, was assessed by questionnaires. At the end of the study, 44% of the women were postmenopausal. After controlling for baseline aBMD, current menopausal status, and current hormone replacement therapy, we found that change in LBM was independently associated with change in aBMD of the proximal femur (P = 0.001). The cross-sectional analyses also indicated that LBM was a significant determinant of aBMD of all three DXA-scanned sites at both baseline and follow-up. These novel longitudinal data highlight the important contribution of LBM to the maintenance of proximal femur bone mass at a key time in women's life span, the years surrounding menopause.

Genetic parameters and predicted response to selection for lean growth in an Australian commercial nucleus pig herd

Genetic parameters for performance traits in a pig population were estimated using a multi-trait derivative-free REML algorithm. The 2590 total data included 922 restrictively fed male and 1668 ad libitum fed female records. Estimates of heritability (standard error in parentheses) were 0.25 (0.03), 0.15 (0.03), and 0.30 (0.05) for lifetime daily gain, test daily gain, and P2-fat depth in males, respectively; and 0.27 (0.04) and 0.38 (0.05) for average daily gain and P2-fat depth in females, respectively. The genetic correlation between P2-fat depth and test daily gain in males was -0.17 (0.06) and between P2-fat and lifetime average daily gain in females 0.44 (0.09). Genetic correlations between sexes were 0.71 (0.11) for average daily gain and -0.30 (0.10) for P2-fat depth. Genetic response per standard deviation of selection on an index combining all traits was predicted at $AU120 per sow per year. Responses in daily gain and backfat were expected to be higher when using only male selection than when using only female selection. Selection for growth rate in males will improve growth rate and carcass leanness simultaneously.