Markers of Cerebrovascular Injury, Inflammation, and Plasma Lipids Are Associated with Alzheimer's Disease Cerebrospinal Fluid Biomarkers in Cognitively Normal Persons

Background: Alzheimer's disease (AD) is a multifactorial process that takes years to manifest clinically. We propose that brain-derived indicators of cerebrovascular dysfunction and inflammation would inform on AD-related pathological processes early in, and perhaps prior to neurodegenerative disease development. Objective: Define the relationship between cerebrospinal fluid (CSF) markers of cerebrovascular dysfunction and neuroinflammation with AD CSF biomarkers in cognitively normal individuals. Methods: Analytes were measured from CSF and plasma collected at baseline from two randomized control trials. We performed Pearson correlation analysis (adjusting for age, sex, APOE haplotype, and education) between markers of central nervous system (CNS) barrier disruption, cerebrovascular dysfunction, CSF inflammatory cytokines and chemokines, and plasma lipid levels. We then developed a statistical prediction model using machine learning to test the ability of measured CSF analytes and blood lipid profiles to predict CSF AD biomarkers (total tau, phospho-tau (181), A beta(42)) in this clinical population. Results: Our analysis revealed a significant association between markers of CNS barrier dysfunction and markers of cerebrovascular dysfunction, acute inflammatory responses, and CSF inflammatory cytokines. There was a significant association of blood lipid profiles with cerebrovascular injury markers, and CSF inflammatory cytokine levels. Using machine learning, we show that combinations of blood lipid profiles, CSF markers of CNS barrier disruption, cerebrovascular dysfunction and CSF inflammatory cytokines predict CSF total tau, p-tau, and, to a lesser extent, A beta(42) in cognitively normal subjects. Conclusion: This suggests that these parallel pathological processes may contribute to the development of AD-related neuropathology in the absence of clinical manifestations.

Automated summary

This study found that cerebrovascular dysfunction and inflammation are related to the pathological processes of Alzheimer's disease in the absence of clinical manifestations. Blood lipid levels are associated with cerebrovascular injury markers and CSF inflammatory cytokine levels. Machine learning can predict the levels of Alzheimer's disease biomarkers in CSF.