AUTHOR=Yang Jingzhi , Wu Shuo , Yang Jun , Zhang Qun , Dong Xin 

TITLE=Amyloid beta-correlated plasma metabolite dysregulation in Alzheimer's disease: an untargeted metabolism exploration using high-resolution mass spectrometry toward future clinical diagnosis

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 15 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2023.1189659

DOI=10.3389/fnagi.2023.1189659

ISSN=1663-4365

ABSTRACT=Alzheimer's disease (AD) is a leading cause of dementia, and it has rapidly become an increasingly burdensome and fatal disease in society. Despite medical research advances, accurate recognition of AD remains challenging. Epidemiological evidence suggests that metabolic abnormalities are tied to higher AD risk. Thus, this study conducted case-control analyses with plasma samples and identified a metabolite panel of 27 metabolites in both AD and cognitively normal (CN) groups using high-resolution mass spectrometry. All the identified variables were confirmed using MS/MS with detected fragmented ions and through public metabolite databases. The results suggested that the metabolites PAGln and L-arginine significantly fluctuated in AD patients’ peripheral blood. Additionally, the ELISA results revealed a significant elevation in amyloid beta 42 (Aβ42) in AD as compared to CN, while amyloid beta 40 (Aβ40) showed no significant changes between groups. Furthermore, this study observed positive correlations between Aβ42/Aβ40 and PAGln or L-arginine, indicating that both metabolites could be involved in the pathology of Aβ. Binary regression analysis with two metabolites resulted in an optimal model of ROC (AUC=0.95, p<0.001) for discriminating AD from CN. The study demonstrated the potential of advanced HRMS technology for novel plasma metabolite discovery with good stability and sensitivity and paving the way for future clinical studies. Overall, these findings demonstrate that the combination of PAGln and L-arginine holds great potential for improving the diagnosis of AD in clinical settings.