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Metabolite differences between senior Burmese and Non-Burmese Cats, and associations with measures of glucose metabolism

Reeve-Johnson, M., Rand, J., Anderson, S., Vankan, D., Dias, D., Boughton, B.ORCID: 0000-0001-6342-9814, De Livera, A., Ishioka, K. and Roessner, U. (2016) Metabolite differences between senior Burmese and Non-Burmese Cats, and associations with measures of glucose metabolism. Journal of Veterinary Internal Medicine, 30 (4). p. 1452.

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Phenotypic identification of cats at risk of developing diabetes has yet to be defined. Burmese cats are reported to have a dyslipidemia and are 3 to 4 times more likely to develop diabetes than other breeds.

To identify plasma metabolite differences between healthy senior Burmese and matched non-Burmese cats ranging from lean to obese, and to determine if significant correlations exist between these metabolites and measures of glucose metabolism, hormonal and biochemical related to diabetes.

Of the 69 cats tested, 49 non-Burmese; 20 lean (BCS 4-5/9), 12 overweight (BCS 6-7/9) and 17 obese (BCS 8-9/9) and 20 were Burmese (6 lean, 12 overweight and 2 obese).

Food was withheld 18 - 24 hours, and a cephalic vein catheter implanted. A venous blood sample was collected and the serum and plasma separated and frozen at _80°C. A glucose tolerance test (glucose 0.5 g/kg) was performed. Biochemical (screening, fasting and 2-hr blood glucose in the glucose tolerance test, triglyceride), hormonal (leptin, adiponectin, leptin:adiponectin ratio, insulin, glucose:insulin ratio) and enzymatic (fPLI, MCP-1) analyses were performed on plasma and serum samples. After extraction, analyses using GC-MS was performed. Linear modeling was used to identify statistically significant (adjusted P-value<0.05) metabolites associated with obesity and whether these responses were affected by sex and age. Pearson correlations were used to identify relationships between metabolites and other parameters.

GC-MS analyses identified 25 metabolites significantly (P < 0.05) different between Burmese and non-Burmese cats. In Burmese cats, metabolites that are involved in amino acid metabolism were either increased (phenylalanine, tyrosine, valine, serine, threonine, cysteine, proline and arginine) or decreased (cysteine, alanine and b-alanine) compared to non-Burmese cats. Some metabolites in carbohydrate metabolism were decreased (glucose, fructose, lactic acid), as were some involved in lipid metabolism (glucose 3 phosphate, phosphoric acid, stearic acid, cholesterol). When these metabolites were correlated with known measures of glucose metabolism, numerous amino acids were positively correlated with insulin and triglyceride (tyrosine, phenylalanine cysteine, proline, glutamine and arginine were all positively correlated with insulin, and tyrosine and phenylalanine were also correlated with triglyceride). These results agreed with human studies where future risk of developing diabetes was found to be associated with these changes in metabolites. The amino acid metabolites alanine, threonine and tyrosine were negatively correlated with adiponectin and glucose and fructose were positively associated with 2-hr blood glucose following a glucose tolerance test. However, some metabolites considered discriminators of impaired fasting glucose and increased in humans at risk of diabetes (phosphoric acid, alanine and stearic acid) were decreased in Burmese cats, as were metabolites of glycerolipid metabolism (glycerate, glycerol 3 phosphate, glucose and fructose).

This study is the first to report metabolite differences between healthy non-Burmese and Burmese cats and identifies some biomarkers involved in amino acid, carbohydrate and lipid metabolism that are altered in Burmese cats in a similar pattern to those identified in humans at risk of diabetes. Further research is required to determine if these are useful as markers of metabolic dysfunction in cats at risk of developing diabetes.

Item Type: Journal Article
Publisher: Wiley-Blackwell
Copyright: © 2016 The Authors.
Other Information: Conference title: 2016 ACVIM Forum Research Abstract Program Denver, Colorado, June 9–10.
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