[Quantitative endogenous peptidomics analysis of the type-2 diabetic clinical serum samples]

Diabetes is a systemic metabolic disorder syndrome, mainly characterized by hyperglycemia, and is associated with the dysfunction of various organs, such as liver, pancreas, intestine, adipose muscle tissue, kidney and brain. It has become a global epidemic disease that seriously threatens human health. Therefore, mapping the global molecular signatures of diabetes-related disease spectrum can provide more comprehensive data to understand early clinical diagnosis, molecular typing, and pathological processes involved in diabetes mellitus. In this study, we performed a quantitative differential analysis on the endogenous peptidome of the serum samples obtained from healthy, prediabetes and type 2 diabetes groups to explore the peptidomics evolution in the development of diabetes. Partial least squares-discriminant analysis (PLS-DA) was used for pattern recognition. A nonparametric test was examined to find out the significantly changed endogenous peptides. As a result, 690 serum endogenous peptides were identified totally, among which 163 endogenous peptides were statistically different among the three groups. This could be promising quantitative peptidomics data for early screening, diagnosis and molecular typing of type 2 diabetes mellitus.