Untargeted metabolomic analysis in non-fasted diabetic dogs by UHPLC–HRMS
We recently identified variances in serum metabolomic profiles between fasted diabetic and healthy dogs, some having similarities to those identified in human type 1 diabetes.
Compare untargeted metabolomic profiles in the non-fasted state.
Serum from non-fasted diabetic (n = 6) and healthy control (n = 6) dogs were analyzed by liquid chromatography-high resolution mass spectrometry.
Clear clustering of metabolites between groups were observed, with multiple perturbations identified that were similar to those previously observed in fasted diabetic dogs.
These findings further support the development of targeted assays capable of detecting metabolites that may be useful as biomarkers of canine diabetes.
KeywordsCanine diabetes mellitus Untargeted metabolomics Type 1 diabetes Metabolites Biomarkers Ultra high performance liquid chromatography High resolution mass spectrometry
ALO conceived of the study, collected the data, analyzed and interpreted the data, and wrote the manuscript; TJG conducted the analytical measurements, performed statistical analysis, analyzed and interpreted the data, and wrote the manuscript. CW conceived of the study, analyzed and interpreted the data, contributed to the discussion, and reviewed/edited the manuscript; MAA conceived of the study, contributed to discussion and reviewed/edited the manuscript.
This study was funded by grants from the National Institutes of Health: P01 AI42288 (MAA), U24 DK097209 (TJG), K08DK116735 (ALO), and KL2TR001429 (ALO).
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflicts of interest.
All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
- Deac, O. M., Mills, J. L., Shane, B., Midttun, Ø, Ueland, P. M., Brosnan, J. T., Brosnan, M. E., Laird, E., Gibney, E. R., Fan, R., Wang, Y., Brody, L. C., & Molloy, A. M. (2015). Tryptophan catabolism and vitamin B-6 status are affected by gender and lifestyle factors in healthy young adults. Journal of Nutrition, 145, 701–707.CrossRefGoogle Scholar
- Dutta, T., Kudva, Y. C., Persson, X. M., Schenck, L. A., Ford, G. C., Singh, R. J., Carter, R., & Nair, K. S. (2016). Impact of long-term poor and good glycemic control on metabolomics alterations in type 1 diabetic people. The Journal of Clinical Endocrinology and Metabolism, 101, 1023–1033.CrossRefGoogle Scholar
- Emwas, A.-H. M. (2015). The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. In Bjerrum, J. (Ed.), Metabonomics. Methods in molecular biology. New York: Humana Press.Google Scholar
- Li, Q., Freeman, L. M., Rush, J. E., Huggins, G. S., Kennedy, A. D., Labuda, J. A., Laflamme, D. P., & Hannah, S. S. (2015). Veterinary medicine and multi-omics research for future nutrition targets: Metabolomics and transcriptomics of the common degenerative mitral valve disease in dogs. OMICS, 19, 461–470.CrossRefGoogle Scholar
- Montgomery, T. M., Nelson, R. W., Feldman, E. C., Robertson, K., & Polonsky, K. S. (1996). Basal and glucagon-stimulated plasma C-peptide concentrations in healthy dogs, dogs with diabetes mellitus, and dogs with hyperadrenocorticism. Journal of Veterinary Internal Medicine, 10, 116–122.CrossRefGoogle Scholar
- O’Kell, A. L., Wasserfall, C., Catchpole, B., Davison, L. J., Hess, R. S., Jushner, J., & Atkinson, M. A. (2017b). Comparative pathogenesis of autoimmune diabetes in humans, NOD mice, and canines: Has a valuable animal model of type 1 diabetes been overlooked? Diabetes, 66, 1443–1452.CrossRefGoogle Scholar
- Oresic, M., Simell, S., Sysi-Aho, M., Näntö-Salonen, K., Seppänen-Laakso, T., Parikka, V., Katajamaa, M., Hekkala, A., Mattila, I., Keskinen, P., Yetukuri, L., Reinikainen, A., Lähde, J., Suortti, T., Hakalax, J., Simell, T., Hyöty, H., Veijola, R., Ilonen, J., Lahesmaa, R., Knip, M., & Simell, O. (2008). Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. Journal of Experimental Medicine, 205, 2975–2984.CrossRefGoogle Scholar
- Slupsky, C. M., Rankin, K. N., Wagner, J., Fu, H., Chang, D., Weljie, A. M., Saude, E. J., Lix, B., Adamko, D. J., Shah, S., Greiner, R., Sykes, B. D., & Marrie, T. J. (2007). Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Analytical Chemistry, 79, 6995–7004.CrossRefGoogle Scholar
- Yao, J., Lu, H., Wang, Z., Wang, T., Fang, F., Wang, J., Yu, J., & Gao, R. (2018). A sensitive method for the determination of the gender difference of neuroactive metabolites in tryptophan and dopamine pathways in mouse serum and brain by UHPLC-MS/MS. Journal of Chromatography B, 1093–1094, 91–99.CrossRefGoogle Scholar