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Prevalence and risk factors of hepatic lipid changes in bearded dragons ( Pogona vitticeps ).

Trinita K BarbozaLeonardo SustaDrury ReavillHugues Beaufrère
Published in: Veterinary pathology (2022)
Hepatic lipidosis is a common disease of captive bearded dragons ( Pogona vitticeps ). Diagnosis, prevention, and treatment of this condition are challenging, as there is minimal information in the literature. Our study determined the prevalence and epidemiological risk factors associated with the grade and severity of hepatic lipid changes in bearded dragons submitted for necropsy in 2 North American institutions. A total of 571 postmortem cases were retrieved, and from each pathology report the demographic data (age, sex) and the list of final diagnoses were extracted. For each case diagnosed with hepatic lipidosis, the archived sections of liver were reviewed and the severity of lipid change was stratified using a standardized histologic grading system. Descriptive statistics were used to estimate the prevalence of each grade and severity class. Associations between grade and severity, as well as demographic data and concurrent diseases, were explored using ordinal logistic regression analysis. On multiple logistic models, the occurrence of infectious disease and neoplasia was associated with decreased grade and severity of hepatic lipid changes, while the female sex and adult age were associated with an increased grade and severity. None of the other variables were significantly associated with hepatic lipid changes. These results suggest that reproductively active females and adult bearded dragons are predisposed to increasing hepatic lipid changes, while those with an underlying disease process have reduced hepatic lipid accumulation and changes, possibly due to increased fat catabolism. Data in this study can serve to benchmark the prevalence of hepatic lipidosis in bearded dragons and allow further investigations.
Keyphrases
  • fatty acid
  • big data
  • machine learning
  • adipose tissue
  • radiation therapy
  • young adults
  • infectious diseases
  • artificial intelligence