Disease-syndrome combination modeling: metabolomic strategy for the pathogenesis of chronic kidney disease

Abstract

Abstract Conventional disease animal models have limitations on the conformity to the actual clinical situation. Disease-syndrome combination (DS) modeling may provide a more efficient strategy for biomedicine research. Disease model and DS model of renal fibrosis in chronic kidney disease were established by ligating the left ureter and by ligating unilateral ureteral combined with exhaustive swimming, respectively. Serum metabolomics was conducted to evaluate disease model and DS model by using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Potential endogenous biomarkers were identified by multivariate statistical analysis. There are no differences between two models regarding their clinical biochemistry and kidney histopathology, while metabolomics highlights their difference. It is found that abnormal sphingolipid metabolism is a common characteristic of both models, while arachidonic acid metabolism, linolenic acid metabolism and glycerophospholipid metabolism are highlighted in DS model. Metabolomics is a promising approach to evaluate experiment animal models. DS model are comparatively in more coincidence with clinical settings, and is superior to single disease model for the biomedicine research.