Using Gross Energy Improves Metabolizable Energy Predictive Equations for Pet Foods Whereas Undigested Protein and Fiber Content Predict Stool Quality

Because animal studies are labor intensive, predictive equations are used extensively for calculating metabolizable energy (ME) concentrations of dog and cat pet foods. The objective of this retrospective review of digestibility studies, which were conducted over a 7-year period and based upon Association of American Feed Control Officials (AAFCO) feeding protocols, was to compare the accuracy and precision of equations developed from these animal feeding studies to commonly used predictive equations. Feeding studies in dogs and cats (331 and 227 studies, respectively) showed that equations using modified Atwater factors accurately predict ME concentrations in dog and cat pet foods (r² = 0.97 and 0.98, respectively). The National Research Council (NRC) equations also accurately predicted ME concentrations in pet foods (r² = 0.97 for dog and cat foods). For dogs, these equations resulted in an average estimate of ME within 0.16% and 2.24% of the actual ME measured (equations using modified Atwater factors and NRC equations, respectively); for cats these equations resulted in an average estimate of ME within 1.57% and 1.80% of the actual ME measured. However, better predictions of dietary ME in dog and cat pet foods were achieved using equations based on analysis of gross energy (GE) and new factors for moisture, protein, fat and fiber. When this was done there was less than 0.01% difference between the measured ME and the average predicted ME (r² = 0.99 and 1.00 in dogs and cats, respectively) whereas the absolute value of the difference between measured and predicted was reduced by approximately 50% in dogs and 60% in cats. Stool quality, which was measured by stool score, was influenced positively when dietary protein digestibility was high and fiber digestibility was low. In conclusion, using GE improves predictive equations for ME content of dog and cat pet foods. Nondigestible protein and fiber content of diets predicts stool quality

Sequencing and comparative genomic analysis of 1227 Felis catus cDNA sequences enriched for developmental, clinical and nutritional phenotypes

<p>Abstract</p> <p>Background</p> <p>The feline genome is valuable to the veterinary and model organism genomics communities because the cat is an obligate carnivore and a model for endangered felids. The initial public release of the Felis catus genome assembly provided a framework for investigating the genomic basis of feline biology. However, the entire set of protein coding genes has not been elucidated.</p> <p>Results</p> <p>We identified and characterized 1227 protein coding feline sequences, of which 913 map to public sequences and 314 are novel. These sequences have been deposited into NCBI's genbank database and complement public genomic resources by providing additional protein coding sequences that fill in some of the gaps in the feline genome assembly. Through functional and comparative genomic analyses, we gained an understanding of the role of these sequences in feline development, nutrition and health. Specifically, we identified 104 orthologs of human genes associated with Mendelian disorders. We detected negative selection within sequences with gene ontology annotations associated with intracellular trafficking, cytoskeleton and muscle functions. We detected relatively less negative selection on protein sequences encoding extracellular networks, apoptotic pathways and mitochondrial gene ontology annotations. Additionally, we characterized feline cDNA sequences that have mouse orthologs associated with clinical, nutritional and developmental phenotypes. Together, this analysis provides an overview of the value of our cDNA sequences and enhances our understanding of how the feline genome is similar to, and different from other mammalian genomes.</p> <p>Conclusions</p> <p>The cDNA sequences reported here expand existing feline genomic resources by providing high-quality sequences annotated with comparative genomic information providing functional, clinical, nutritional and orthologous gene information.</p

Live the Unab - Number 478

Una de las personas más felices con la noticia que Telepacífico estaba transmitiendo en la noche del pasado domingo 2 de junio era su cómplice y abuela Martha Cecilia Serrano, quien con más de la mitad de su escuálido salario en una cafetería escolar hizo el sacrificio para obsequiarle la primera guitarra a los 14 años. El otro fue Rafael Ardila Duarte, presidente de la Junta Directiva de la Universidad Autónoma de Bucaramanga, quien en una tertulia en la capital guanentina el año anterior tuvo la ocasión de escucharlo y le instó a que viniera a estudiar en la UNAB.Ganador del ‘Gran Mono Núñez’ y alumno de Música en la UNAB; Por Pastor Virviescas Gómez…01 Juan Manuel Santos estará en Ulibro con “La batalla por la paz”; Por Lynda Vanessa Bula Barbosa y Vilma Alexandra Blanco Melendez…04 La Sinfónica UNAB ‘embrujó’ al público del Teatro Santander; Por Pastor Virviescas Gómez…06 Los estudiantes de Derecho también realizan experimentos; Por Pastor Virviescas Gómez…08 La diáspora venezolana supera cualquier cálculo; Por Pastor Virviescas Gómez…10 Las dos primeras patentes UNAB; Por Ludy Carolina Toscano Vargas…14 UNAB abre espacios para entender los procesos de justicia transicional; Por Lynda Vanessa Bula Barbosa…15 Misión a Washington…15 Partieron los ‘Embajadores’ UNAB…16 Actualización en vacaciones…17 Dirección de actores…17 Se marcha María Nuria…17 Camino a la Acreditación Internacional…18 Tecnología en Dirección Comercial…18 Los mejores de ExpoConCiencia…18 Primer y tercer puestos…19 Foros Canal 1 en la UNAB…19 Diplomado de conciliación…19 Inducción a docentes Instituto Caldas…20 Partió Vladimir Rojas…20 “Divertimento”…20One of the happiest people with the news that Telepacífico was transmitting last Sunday night, June 2, was his accomplice and grandmother Martha Cecilia Serrano, who with more than half of her meager salary in a school cafeteria made the sacrifice to give him a gift. the first guitar at age 14. The other was Rafael Ardila Duarte, president of the Board of Directors of the Autonomous University of Bucaramanga, who in a gathering in the Guanentina capital the previous year had the opportunity to listen to him and urged him to come to study at UNAB

Relationship between lean body mass and serum renal biomarkers in healthy dogs

Background: Symmetric dimethylarginine (SDMA) is an accurate and precise biomarker for estimating glomerular filtration rate (GFR) in humans and cats. Serum creatinine (sCr) also correlates with GFR, but has limitations as a biomarker of renal function because nonrenal factors can influence its concentration. Hypothesis: Differences in lean body mass (LBM) influence sCr, but not serum SDMA concentrations. Animals: Forty-one healthy Beagles, mean age 9.9 years (range: 3.1–14.8 years), were studied over a 6 month period. Methods: Serum biomarkers of renal function were measured prospectively at baseline, and 1, 3, and 6 months. SDMA concentrations were measured by liquid chromatography-mass spectroscopy and sCr concentrations by enzymatic colorimetry. Body composition was determined by dual energy x-ray absorptiometry. Results: LBM (P females; P = .02). Mature adult dogs (<8 years) had greater LBM compared with geriatric dogs (≥8 years; P < .001). Conclusion and Clinical Importance: sCr concentrations, but not SDMA concentrations, are influenced by LBM, which limits sCr utility as a biomarker for monitoring renal function in dogs with decreased LBM. Reductions in LBM can lower sCr concentration and overestimate GFR. SDMA concentrations, but not sCr concentrations were influenced by time on food. SDMA could have clinical advantages over sCr in monitoring response to nutritional interventions.Copyright © 2015 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine. This is the publisher’s final pdf. The published article can be found at: http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291939-1676Keywords: Creatinine, Canine, Urea nitrogen, Symmetric dimethylarginin

Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality.

Because animal studies are labor intensive, predictive equations are used extensively for calculating metabolizable energy (ME) concentrations of dog and cat pet foods. The objective of this retrospective review of digestibility studies, which were conducted over a 7-year period and based upon Association of American Feed Control Officials (AAFCO) feeding protocols, was to compare the accuracy and precision of equations developed from these animal feeding studies to commonly used predictive equations. Feeding studies in dogs and cats (331 and 227 studies, respectively) showed that equations using modified Atwater factors accurately predict ME concentrations in dog and cat pet foods (r²= 0.97 and 0.98, respectively). The National Research Council (NRC) equations also accurately predicted ME concentrations in pet foods (r² = 0.97 for dog and cat foods). For dogs, these equations resulted in an average estimate of ME within 0.16% and 2.24% of the actual ME measured (equations using modified Atwater factors and NRC equations, respectively); for cats these equations resulted in an average estimate of ME within 1.57% and 1.80% of the actual ME measured. However, better predictions of dietary ME in dog and cat pet foods were achieved using equations based on analysis of gross energy (GE) and new factors for moisture, protein, fat and fiber. When this was done there was less than 0.01% difference between the measured ME and the average predicted ME (r² = 0.99 and 1.00 in dogs and cats, respectively) whereas the absolute value of the difference between measured and predicted was reduced by approximately 50% in dogs and 60% in cats. Stool quality, which was measured by stool score, was influenced positively when dietary protein digestibility was high and fiber digestibility was low. In conclusion, using GE improves predictive equations for ME content of dog and cat pet foods. Nondigestible protein and fiber content of diets predicts stool quality

Food composition, expressed as means and standard deviation (SD), of canine foods used in digestibility studies.^*,†

*<p>All analytical values are expressed as percentage of food as fed, unless otherwise indicated.</p>†<p>Food composition of the experimental foods was determined by a commercial laboratory (Eurofins Scientific, Inc., Des Moines, IA) using AOAC methods.</p

Relationship between measured metabolizable energy (ME) concentrations (x-axis) and ME concentrations predicted using National Research Council (NRC) equations [2] (y-axis) for dog and pet foods.

<p>Ideally, all points should be on the line <i>x</i> = <i>y</i>. <b>A</b>) Measured ME concentrations were determined from 331 total digestibility studies in dogs, of which 259 used dry dog foods and 72 used canned dog foods. The NRC equations for dogs first calculate gross energy (GE) using the equation GE  = 5.7× g protein +9.4× g fat +4.1× (g NFE + g fiber). Energy digestibility coefficients are then calculated for dogs as (91.2–1.43× percentage crude fiber in DM). These digestibility coefficients then allow calculation of digestible energy (DE) in dogs as DE  =  GE × percentage energy digestibility/100 and, subsequent calculation of ME as ME  =  DE – (1.04× g protein). <b>B</b>) Measured ME concentrations were determined from 227 total digestibility studies in cats, of which 173 used dry cat foods and 54 used canned cat foods. The NRC equations for cats first calculate GE using the equation GE  = 5.7× g protein +9.4× g fat +4.1× (g NFE + g fiber). Energy digestibility coefficients are then calculated for cats as (87.9–0.88× percentage crude fiber in DM). These digestibility coefficients then allow calculation of DE in cats as DE  =  GE × percentage energy digestibility/100 and, subsequent calculation of ME as ME  =  DE – (0.77× g protein).</p

Metabolizable energy (ME; kcal/kg; means and standard deviation, SD), were determined in canine digestibility studies and compared to those calculated using predictive equations.

a<p>Predicted ME using equation with modified Atwater factors <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054405#pone.0054405-AAFCO1" target="_blank">[4]</a>.</p>b<p>The difference between measured and estimated ME.</p>c<p>The absolute value of the difference between measured and estimated ME.</p>d<p>Predicted ME using NRC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054405#pone.0054405-NRC1" target="_blank">[2]</a> equations.</p>e<p>Predicted ME using equation developed from the experimental animal feeding studies.</p

Digestibility coefficients, expressed as means and standard deviation (SD), of feline foods used in digestibility studies.^*

*<p>All analytical values are expressed as percentages.</p>a,b<p>Means with different superscripts in the same row are different (<i>P</i>≤0.01).</p

Food composition, expressed as means and standard deviation (SD), of feline foods used in digestibility studies.^*,†

*<p>All analytical values are expressed as percentage of food as fed, unless otherwise indicated.</p>†<p>Food composition of the experimental foods was determined by a commercial laboratory (Eurofins Scientific, Inc., Des Moines, IA) using AOAC methods.</p