Metabolomic serum abnormalities in dogs with hepatopathies

• Published in: Scientific reports Vol. 12; no. 1; p. 5329
• Main Authors: Imbery, Carolin A, Dieterle, Frank, Ottka, Claudia, Weber, Corinna, Schlotterbeck, Götz, Müller, Elisabeth, Lohi, Hannes, Giger, Urs
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 29.03.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Amino acids; Animals; Dog Diseases; Dogs; Enzymatic activity; Lipoproteins; Liver diseases; Liver Diseases - veterinary; Magnetic resonance spectroscopy; Metabolism; Metabolites; Metabolome; Metabolomics; NMR; Nuclear magnetic resonance; Older people; Phenylalanine; Principal components analysis; Shunts; Tyrosine; Vascular Malformations
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-09056-5

Hepatopathies can cause major metabolic abnormalities in humans and animals. This study examined differences in serum metabolomic parameters and patterns in left-over serum samples from dogs with either congenital portosystemic shunts (cPSS, n = 24) or high serum liver enzyme activities (HLEA, n = 25) compared to control dogs (n = 64). A validated targeted proton nuclear magnetic resonance spectroscopy platform was used to assess 123 parameters. Principal component analysis of the serum metabolome demonstrated distinct clustering among individuals in each group, with the cluster of HLEA being broader compared to the other groups, presumably due to the wider spectrum of hepatic diseases represented in these samples. While younger and older adult control dogs had very similar metabolomic patterns and clusters, there were changes in many metabolites in the hepatopathy groups. Higher phenylalanine and tyrosine concentrations, lower branched-chained amino acids (BCAAs) concentrations, and altered fatty acid parameters were seen in cPSS dogs compared to controls. In contrast, dogs with HLEA had increased concentrations of BCAAs, phenylalanine, and various lipoproteins. Machine learning based solely on the metabolomics data showed excellent group classification, potentially identifying a novel tool to differentiate hepatopathies. The observed changes in metabolic parameters could provide invaluable insight into the pathophysiology, diagnosis, and prognosis of hepatopathies.