A Robust Blood-based Signature of Cerebrospinal Fluid Aβ42 Status

Abstract

Early detection of AD is of vital importance in the development of disease-modifying therapies. This necessitates the use of early pathological indicators of the disease such as amyloid abnormality to identify individuals at early disease stages where intervention is likely to be most effective. Recent evidence suggests that cerebrospinal fluid (CSF) amyloid β1-42 (Aβ42) level may indicate AD risk earlier compared to amyloid positron emission tomography (PET). However, the method of collecting CSF is invasive. Blood-based biomarkers indicative of CSF Aβ42 status may remedy this limitation as blood collection is minimally invasive and inexpensive. In this study, we show that APOE4 genotype and blood markers comprising EOT3, APOC1, CGA, and Aβ42 robustly predict CSF Aβ42 with high classification performance (0.84 AUC, 0.82 sensitivity, 0.62 specificity, 0.81 PPV and 0.64 NPV) using machine learning approach. Due to the method employed in the biomarker search, the identified biomarker signature maintained high performance in more than a single machine learning algorithm, indicating potential to generalise well. A minimally invasive and cost-effective solution to detecting amyloid abnormality such as proposed in this study may be used as a first step in a multi-stage diagnostic workup to facilitate