A blood-based signature of cerebrospinal fluid Aβ status.

TitleA blood-based signature of cerebrospinal fluid Aβ status.
Publication TypeJournal Article
Year of Publication2019
AuthorsGoudey B, Fung BJ, Schieber C, Faux NG
Corporate AuthorsAlzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative
JournalSci Rep
Date Published2019 03 11

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 cerebrospinal fluid (CSF) amyloid β (Aβ) may be an earlier indicator of Alzheimer's disease risk than measures of amyloid obtained from Positron Emission Tomography (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 Aβ 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 plasma analytes (CGA, Aβ, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ levels and that the resulting model also validates reasonably across PET Aβ status (0.78 AUC). 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 Aβ status, the earliest risk indicator for AD, with high accuracy.

Alternate JournalSci Rep
PubMed ID30853713
PubMed Central IDPMC6409361
Grant ListP30 AG010129 / AG / NIA NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
UL1 TR002369 / TR / NCATS NIH HHS / United States

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