A blood-based signature of cerebral spinal fluid Aβ1-42 status
It is increasingly recognized that Alzheimers disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebral spinal fluid (CSF) amyloid {beta}1-42 (A{beta}1-42) may be an earlier indicator of Alzheimers disease risk than measures of amyloid obtained from Positron Emission Topography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individuals CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF A{beta}1-42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOE{varepsilon}4 carrier status and four analytes are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A{beta}1-42 levels transitioned to an AD diagnosis over 120 months significantly faster than those predicted with normal CSF A{beta}1-42 levels and that the resulting model also performs reasonably across PET A{beta}1-42 status.\n\nThis is the first study to show that a machine learning approach, using plasma protein levels, age and APOE{varepsilon}4 carrier status, is able to predict CSF A{beta}1-42 status, the earliest risk indicator for AD, with high accuracy.