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A blood-based signature of cerebral spinal fluid Aβ1-42 status

By Benjamin Goudey, Bowen J Fung, Christine Schieber, for the Alzheimer’s Disease Metabolomics Consortium, for the Alzheimer’s Disease Neuroimaging Initiative, Noel G Faux

Posted 19 Sep 2017
bioRxiv DOI: 10.1101/190207 (published DOI: 10.1016/j.jalz.2018.06.2473)

It is increasingly recognized that Alzheimer's 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 β1-42 (Aβ1-42) may be an earlier indicator of Alzheimer's 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 individual's 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 Aβ1-42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEϵ4 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 Aβ1-42 levels transitioned to an AD diagnosis over 120 months significantly faster than those predicted with normal CSF Aβ1-42 levels and that the resulting model also performs reasonably across PET Aβ1-42 status. This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEϵ4 carrier status, is able to predict CSF Aβ1-42 status, the earliest risk indicator for AD, with high accuracy.

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