Two experiments were conducted to determine the effect of phytase on energy availability in pigs. In Exp. 1, barrows (initial and final BW of 26 and 52 kg) were allotted to four treatments in a 2 x 2 factorial arrangement. Corn-soybean meal (C-SBM) diets were fed at two energy levels (2.9 and 3.2 x maintenance [M]) with and without the addition of 500 phytase units/kg of diet. The diets contained 115% of the requirement for Ca, available P (aP), and total lysine, and Ca and aP were decreased by 0.10% in diets with added phytase. Pigs were penned individually and fed daily at 0600 and 1700, and water was available constantly. Eight pigs were killed and ground to determine initial body composition. At the end of Exp. 1, all 48 pigs were killed for determination of carcass traits and protein and fat content by total-body electrical conductivity (TOBEC) analysis. Six pigs per treatment were ground for chemical composition. In Exp. 2, 64 barrows and gilts (initial and final BW of 23 and 47 kg) were allotted to two treatments (C-SBM with 10% defatted rice bran or that diet with reduced Ca and aP and 500 phytase units/kg of diet), with five replicate pens of barrows and three replicate pens of gilts (four pigs per pen). In Exp. 1, ADG was increased (P < 0.01) in pigs fed at 3.2 x M. Based on chemical analyses, fat deposition, kilograms of fat, retained energy (RE) in the carcass and in the carcass + viscera, fat deposition in the organs, and kilograms of protein in the carcass were increased (P < 0.10) in pigs fed the diets at 3.2 vs. 2.9 x M. Based on TOBEC analysis, fat deposition, percentage of fat increase, and RE were increased (P < 0.09) in pigs fed at 3.2 x M. Plasma urea N concentrations were increased in pigs fed at 3.2 x M with no added phytase but were not affected when phytase was added to the diet (phytase x energy, P < 0.06). Fasting plasma glucose measured on d 28, ultrasound longissimus muscle area (LMA), and 10th-rib fat depth were increased (P < 0.08) in pigs fed phytase, but many other response variables were numerically affected by phytase addition. In Exp. 2, phytase had no effect (P > 0.10) on ADG, ADFI, gain:feed, LMA, or 10th-rib fat depth. These results suggest that phytase had small, mostly nonsignificant effects on energy availability in diets for growing pigs; however, given that phytase increased most of the response variables measured, further research on its possible effects on energy availability seems warranted.

During puberty young people undergo significant hormonal changes which affect metabolism and, subsequently, health. Evidence suggests there is a period of transient pubertal insulin resistance, with this effect greater in girls than boys. However, the response to everyday high and low glycaemic index (GI) meals remains unknown. Following ethical approval, forty adolescents consumed a high GI or low GI breakfast, in a randomised cross-over design. Capillary blood samples were taken during a 2-h postprandial period, examining the glycaemic and insulinaemic responses. Maturity offset and homoeostatic model assessment (HOMA) were also calculated. The glycaemic response to the breakfasts was similar between boys and girls, as shown by similar peak blood glucose concentrations and incremental AUC (IAUC) following both high and low GI breakfasts (all P>0·05). Girls exhibited a higher peak plasma insulin concentration 30 min post-breakfast following both high GI (P=0·043, g=0·69) and low GI (P=0·010, g=0·84) breakfasts, as well as a greater IAUC following high GI (P=0·041, g=0·66) and low GI (P=0·041, g=0·66) breakfasts. HOMA was positively correlated with the insulinaemic responses (all P<0·0005) and maturity offset (P=0·037). The findings of the present study suggest that pubertal insulin resistance affects the postprandial insulinaemic responses to both high and low GI meals. Specifically, girls exhibit a greater insulinaemic response than boys to both meals, despite similar glycaemic responses. This study is the first to report the glycaemic and insulinaemic responses to everyday meals in boys and girls, supporting the recommendation for young people to base their diet on low GI carbohydrates.

ObjectiveTo investigate the impact of match and training load on time-loss incidence in elite, professional Rugby Union players.Materials and MethodsEighty-nine Rugby Union players were monitored over two seasons of training and competition. Load was measured for all training sessions and matches using subjective [session ratings of perceived exertion (sRPE) load; RPE × session duration] and objective [global positioning systems (GPS); distance and high-speed running distance] methods and quantified using multiple approaches; absolute match and training load, acute:chronic workload ratio (ACWR), exponentially weighted moving average (EWMA) and cumulative 7, 14, 21, and 28 d sums. Mixed effect models were used to assess the effect of each variable on time-loss incidence.ResultsOf the 474 time-loss incidences that occurred across the two seasons, 50.0% were contact injuries (86.5% occurred during matches and 13.5% during training), 34.8% were non-contact injuries (31.5% occurred during matches and 68.5% during training) and 15.2% were cases of illness. The absolute match and training load variables provided the best explanation of the variance in time-loss incidence occurrence [sRPE load: p < 0.001, Akaike information criterion (AIC) = 2936; distance: p < 0.001, AIC = 3004; high-speed running distance: p < 0.001, AIC = 3025]. The EWMA approach (EWMA sRPE load: p < 0.001, AIC = 2980; EWMA distance: p < 0.001, AIC = 2980; EWMA high-speed running distance: p = 0.002, AIC = 2987) also explained more of the variance in time-loss incidence occurrence than the ACWR approach (ACWR sRPE load: p = 0.091, AIC = 2993; ACWR distance: p = 0.008, AIC = 2990; ACWR high-speed running distance: p = 0.153, AIC = 2994).ConclusionOverall, the absolute sRPE load variable best explained the variance in time-loss incidence, followed by absolute distance and absolute high-speed running distance. Whilst the model fit using the EWMA approach was not as good as the absolute load variables, it was better than when the same variables were calculated using the ACWR method. Overall, these findings suggest that the absolute match and training load variables provide the best predictors of time-loss incidence rates, with sRPE load likely to be the optimal variant of those examined here.

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