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 Assoc...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3544805?pdf=render |
| _version_ | 1819014531018391552 |
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| author | Jean A Hall Lynda D Melendez Dennis E Jewell |
| author_facet | Jean A Hall Lynda D Melendez Dennis E Jewell |
| author_sort | Jean A Hall |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T02:17:19Z |
| format | Article |
| id | doaj.art-2c73246ed29d4771a2677a7260954a14 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T02:17:19Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2c73246ed29d4771a2677a7260954a142022-12-21T19:19:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0181e5440510.1371/journal.pone.0054405Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality.Jean A HallLynda D MelendezDennis E JewellBecause 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.http://europepmc.org/articles/PMC3544805?pdf=render |
| spellingShingle | Jean A Hall Lynda D Melendez Dennis E Jewell Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality. PLoS ONE |
| title | Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality. |
| title_full | Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality. |
| title_fullStr | Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality. |
| title_full_unstemmed | Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality. |
| title_short | Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality. |
| title_sort | using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality |
| url | http://europepmc.org/articles/PMC3544805?pdf=render |
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