Multi-factor combined biomarker screening strategy to rapidly diagnose Alzheimer's disease and evaluate drug effect based on a rat model

Alzheimer's disease (AD) represents the main form of dementia; however, valid diagnosis and treatment measures are lacking. The discovery of valuable biomarkers through omics technologies can help solve this problem. For this reason, metabolomic analysis using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS) was carried out on plasma, hippocampus, and cortex samples of an AD rat model. Based on the metabolomic data, we report a multi-factor combined biomarker screening strategy to rapidly and accurately identify potential biomarkers. Compared with the usual procedure, our strategy can identify fewer biomarkers with higher diagnostic specificity and sensitivity. In addition to diagnosis, the potential biomarkers identified using our strategy were also beneficial for drug evaluation. Multi-factor combined biomarker screening strategy was used to identify differential metabolites from a rat model of amyloid beta peptide 1–40 (Aβ(1−40)) plus ibotenic acid-induced AD (compared with the controls) for the first time; lysophosphatidylcholine (LysoPC) and intermediates of sphingolipid metabolism were screened as potential biomarkers. Subsequently, the effects of donepezil and pine nut were successfully reflected by regulating the levels of the abovementioned biomarkers and metabolic profile distribution in partial least squares-discriminant analysis (PLS-DA). This novel biomarker screening strategy can be used to analyze other metabolomic data to simultaneously enable disease diagnosis and drug evaluation.