High-throughput lipidomics analysis to discover lipid biomarkers and profiles as potential targets for evaluating efficacy of Kai-Xin-San against APP/PS1 transgenic mice based on UPLC-Q/TOF-MS.

Lipid metabolism has a significant function in the central nervous system and Alzheimer's disease (AD) is an age-related senile disease characterized by central nerve degeneration. The pathological development of AD is closely related to lipid metabolism disorders. To reveal the influence of Kai-Xin-San (KXS) on lipid metabolism in APP/PSI transgenic mice and potential therapeutic targets for treating AD, brain tissue samples were collected and analyzed by high-throughput lipidomics based on UPLC-Q/TOF-MS. The collected raw data were processed by multivariate data analysis to discover the potential biomarkers and lipid metabolic profiles. Compared with the control wild-type mouse group, nine potential lipid biomarkers were found in the AD model group, of which seven were up-regulated and two were down-regulated. Orally administrated KXS can reverse the changes in these potential biomarkers. Compared with the model group, a total of six differential metabolites showed a recovery trend and may be potential targets for KXS to treat AD. This study showed that high-throughput lipidomics can be used to discover the perturbed pathways and lipid biomarkers as potential targets to reveal the therapeutic effects of KXS.