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Predictive equations of maintenance energy requirement for healthy and chronically ill adult dogs.

Vivian PedrinelliMariana Yukari Hayasaki PorsaniDaniel Magalhães LimaFabio Alves TeixeiraCaio Nogueira DuarteThiago Henrique Annibale VendraminiMárcio Antonio Brunetto
Published in: Journal of animal physiology and animal nutrition (2019)
Maintenance energy requirement (MER) is the energy amount necessary for dogs to maintain their weight and body condition. Some factors can influence the MER, such as gender, age, neutering status and also diseases. The present retrospective study aimed to evaluate MER of adult dogs with several diseases and compare with the MER of healthy adult dogs, observing the influence of parameters such as body condition score (BCS), neutering status, gender, age, diagnosis and type of food on MER of these dogs. A total of 165 adult dogs with weight changes of ≤5% were included and divided in groups according to diagnosis. Mean MER for healthy dogs was 86.09 kcal/BW0.75 , which differed from NRC and FEDIAF recommendations for inactive adult dogs (p = .047). Lowest MERs were of the endocrinopathies (78.52 ± 19.32 kcal/BW0.75 ), orthopaedic diseases (59.71 ± 19.30 kcal/BW0.75 ) and neurologic diseases (78.83 ± 32.66 kcal/BW0.75 ) groups. Gastrointestinal diseases (99.59 ± 20.36 kcal/BW0.75 ), orthopaedic diseases (59.71 ± 19.30 kcal/BW0.75 ) and neoplasia (95.61 ± 21.02 kcal/BW0.75 ) groups were the only groups that differed from the mean MER of healthy adult dogs. Regarding BCS, for each increasing point in a 9-point scale, there was a decrease of 9.8 kcal/BW0.75 on MER, independent of diagnosis (p < .0001; r2  = .55). There was no difference regarding breed size gender and age, but neutered dogs presented lower MER (p = .031). Based on data obtained from the present study, it can be concluded that it is necessary to consider BCS, age, neutering status and diagnosis when calculating MER, both in healthy dogs and chronically ill dogs.
Keyphrases
  • body mass index
  • physical activity
  • weight loss
  • sars cov
  • weight gain
  • machine learning
  • clinical practice
  • childhood cancer
  • climate change
  • electronic health record
  • body weight