Untargeted Analysis of the Serum Metabolome in Cats with Exocrine Pancreatic Insufficiency: A Pilot Study
Exocrine pancreatic insufficiency (EPI) is known to cause chronic digestive dysfunction in cats but its pathogenesis and pathophysiology are poorly understood. The purpose of this preliminary study was to use untargeted analysis of the serum metabolome to discover novel aspects of the pathobiology of EPI in cats. Serum samples were collected from 5 cats with EPI and 8 healthy controls. The diagnosis of EPI was confirmed via measurement of subnormal serum feline trypsin-like immunoreactivity (fTLI). Untargeted quantification of serum metabolites utilized ultra-high-performance liquid chromatography-tandem mass spectroscopy. Cats with EPI had significantly increased serum quantities of long-chain fatty acids, polyunsaturated fatty acids, mevalonate pathway intermediates, and endocannabinoids compared with healthy controls. Diacylglycerols, phosphatidylethanolamines, amino acid derivatives, and microbial metabolites were significantly decreased in cats with EPI compared to healthy controls. Diacyclglycerols and amino acid metabolites were positively correlated, and sphingolipids and long-chain fatty acids were negatively correlated with serum fTLI, respectively. These results suggest that EPI in cats is associated with increased lipolysis of peripheral adipose stores, dysfunction of the mevalonate pathway, and altered amino acid metabolism. Differences in microbial metabolites indicate that feline EPI is also associated with enteric microbial dysbiosis.
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Serum metabolite profiles were generated via ultrahigh performance liquid chromatography-tandem mass spectroscopy by a commercial laboratory (Metabolon Inc.). All methods utilized a Waters ACQUITY ultra-performance liquid chromatographer and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization source and Orbitrap mass analyzer operated at 35,000 mass resolution. Data extraction and compound identification were performed using a proprietary software platform. Compounds were identified by comparison to library entries of purified and authenticated standards. Metabolites were quantified by measuring the area-under-the-curve of the chromatographic peak. The raw data was processed by data scientists at Metabolon Inc. using a proprietary platform, generating the fEPI_metab_raw and fEPI_metab_scaledimp data files. The fEPI_metab_scaledimp data were analyzed using custom scripts in the R language for statistical programming. The code used to analyze these data are present in our github repository: https://github.com/pcbarko/Barko-fEPI-Metabolome.