Obesity, insulin resistance, adiponectin, and PPAR-γ gene expression in broiler chicks fed diets supplemented with fat and green tea (Camellia sinensis) extract.

Adipose tissue is an active endocrine organ secreting several adipokines, especially adiponectin, that play an important role in regulating insulin function in the body of mammals. Therefore, this study was aimed to investigate the association between abdominal fat deposit, insulin resistance, peroxisome proliferator-activated receptor gamma (PPAR-γ), and adiponectin gene (AG) expression in broiler chicks fed diets high in unsaturated fat supplemented with green tea extract (GTE). A total of 300 one-day-old female Ross 308 broiler chicks were allocated to 6 dietary treatments in a completely randomized design with a factorial arrangement of two levels of GTE (0 and 500 mg/kg diet) × three levels of fat inclusion [without fat (control group), soybean oil (SO), and tallow (Ta)]. Each treatment was replicated five times. At the end of the experiment (day 49), two chicks from each replicate weighing an average of pen weight were bled and then slaughtered for further analysis. Abdominal fat percentage, fasting concentration of blood glucose, triglyceride and insulin, glycogen reserves of breast and liver tissues, and PPAR-γ and AG expression were determined. The insulin resistance index of the Quantitative Insulin Sensitivity Check Index (QUICKI) was calculated using the fasting plasma glucose and insulin concentrations. The highest abdominal fat percentage and the lowest carcass yield were obtained in chicks fed SO-supplemented diet (P < 0.05). Chicks fed diet supplemented with SO showed the highest PPAR-γ gene expression (P < 0.05). SO-rich diets suppressed AG expression in chickens' abdominal fat tissue, and the birds fed with SO-supplemented diet showed a significant decrease in AG expression compared with the control (P < 0.05). Chicks fed diet supplemented with SO showed lower QUICKI and breast glycogen reserve compared with the control group (P < 0.05). A significant increase in blood glucose and triglyceride concentrations was observed in birds fed SO-supplemented diets (P < 0.05). AG and PPAR-γ expression increased and decreased by GTE, respectively. QUICKI tended (P = 0.09) to be greater in GTE-supplemented chicks; however, the effect of GTE supplementation on carcass yield, abdominal fat percentage, and blood insulin and glucose concentration was not significant. The findings of this study showed that SO-rich diets via increased PPAR-γ gene expression and decreased AG expression in abdominal fat may lead to insulin resistance in female broiler chicks.

Modelling cumulative egg production in laying hens and parent stocks of broiler chickens using classical growth functions.

1. The objective of the present study is to introduce fresh insight into modelling of egg production by applying classical growth functions to egg production records reported by Aviagen Management Guide to laying hens and the parent stock of broiler chickens. 2. The functions (monomolecular, logistic, Gompertz, Richards and Morgan) were fitted using nonlinear regression procedures of SAS software, and their performance was assessed using goodness-of-fit statistics (coefficient of determination, residual mean squares, Akaike information criterion and Bayesian information criterion). 3. Overall, except for the logistic and Gompertz, the growth functions evaluated gave an acceptable fit to the cumulative egg production curves, with the Morgan equation ranking first followed by the Richards equation. The Morgan and Richards equations provided satisfactory predictions of weekly egg yield at different egg production stages, from early to late production, whereas the least accurate estimates were obtained with the logistic equation. 4. In conclusion, classical growth functions proved feasible alternatives to fit cumulative egg production curves of laying hens and parent stock of broiler chickens, resulting in suitable statistical performance and accurate estimates of production.

A sinusoidal function and the Nelder-Mead simplex algorithm applied to growth data from broiler chickens.

There has been much recent interest in mathematical developments for the analysis of growth in poultry. In this paper, we present a sinusoidal function to describe the evolution of growth as a function of time based on real life experiments. The function was evaluated with regard to its ability to describe the relationship between body weight and age in broilers and was compared to 4 standard growth functions: Gompertz, logistic, Lopez, and Richards. In order to estimate the model parameters, we adopted a global optimization method based on a direct search method instead of using gradient-based techniques. The results of this study show that both the sinusoidal function and the direct search method precisely describe the growth dynamics of broiler chickens. Fitting the growth functions to different data profiles nearly always led to the same or less maximized log-likelihood values for the sinusoidal equation, which is an indication of its superiority in describing growth data from broiler chickens.

Predicting caloric and feed efficiency in turkeys using the group method of data handling-type neural networks.

Neural networks (NN) are a relatively new option to model growth in animal production systems. One self-organizing submodel of artificial NN is the group method of data handling (GMDH)-type NN. The use of such self-organizing networks has led to successful application of the GMDH algorithm over a broad range of areas in engineering, science, and economics. The present study aimed to apply the GMDH-type NN to predict caloric efficiency (CE, g of gain/kcal of caloric intake) and feed efficiency (FE, kg of gain/kg of feed intake) in tom and hen turkeys fed diets containing different energy and amino acid levels. Involved effective input parameters in prediction of CE and FE were age, dietary ME, CP, Met, and Lys. Quantitative examination of the goodness of fit for the predictive models was made using R2 and error measurement indices commonly used to evaluate forecasting models. Statistical performance of the developed GMDH-type NN models revealed close agreement between observed and predicted values of CE and FE. In conclusion, using such powerful models can enhance our ability to predict economic traits, make precise prediction of nutrition requirements, and achieve optimal performance in poultry production.

Flexible alternatives to the Gompertz equation for describing growth with age in turkey hens.

A total of 49 profiles of growing turkey hens from commercial flocks were used in this study. Three flexible growth functions (von Bertalanffy, Richards, and Morgan) were evaluated with regard to their ability to describe the relationship between BW and age and were compared with the Gompertz equation with its fixed point of inflection, which might result in its overestimation. For each function, 4 ways of analysis were implemented. A basic model was fitted first, followed by implementation of a first-order autoregressive correlation structure. A model that considered only mature BW varied with year and another that considered only the rate coefficient varied with different years were applied. The results showed that the fixed point of inflection of the Gompertz equation can be a limitation and that the relationship between BW and age in turkeys was best described using flexible growth functions. However, the Richards equation failed to converge when fitted to the turkey growth data; therefore, it was not considered further. Inclusion of an autoregressive process of the first order rendered a substantially improved fit to data for the 3 growth functions. The Morgan equation provided the best fit to the data set and was used for characterizing mean growth curves for the 7 yr of production. It was estimated that the maximum growth rate occurred at 3.74, 3.65, 3.99, 4.18, 4.05, 4.01, and 3.77 kg BW for production years from 1997 to 2003, respectively. It is recommended that flexible growth functions should be considered as an alternative to the simpler functions (with a fixed point of inflection) for describing the relationship between BW and age in turkeys because they were easier to fit and very often gave a closer fit to data points because of their flexibility, and therefore a smaller residual MS value, than simpler models. It can also be recommended that studies should consider adding a first-order autoregressive process when analyzing repeated measures data with nonlinear models.

An evaluation of different growth functions for describing the profile of live weight with time (age) in meat and egg strains of chicken.

A total of 86 profiles from meat and egg strains of chickens (male and female) were used in this study. Different flexible growth functions were evaluated with regard to their ability to describe the relationship between live weight and age and were compared with the Gompertz and logistic equations, which have a fixed point of inflection. Six growth functions were used: Gompertz, logistic, Lopez, Richards, France, and von Bertalanffy. A comparative analysis was carried out based on model behavior and statistical performance. The results of this study confirmed the initial concern about the limitation of a fixed point of inflection, such as in the Gompertz equation. Therefore, consideration of flexible growth functions as an alternatives to the simpler equations (with a fixed point of inflection) for describing the relationship between live weight and age are recommended for the following reasons: they are easy to fit, they very often give a closer fit to data points because of their flexibility and therefore a smaller RSS value, than the simpler models, and they encompasses simpler models for the addition of an extra parameter, which is especially important when the behavior of a particular data set is not defined previously.