A blood-based signature of cerebrospinal fluid Aβ _1–42 status

Authors

Benjamin Goudey, IBM Research
Bowen J. Fung, IBM Research
Christine Schieber, IBM Research
Noel G. Faux, IBM Research
Michael W. Weiner, University of California, San Francisco
Paul Aisen, UC San Diego School of Medicine
Ronald Petersen, Mayo Clinic
Clifford R. Jack, Mayo Clinic
William Jagust, University of California, Berkeley
John Q. Trojanowki, University of Pennsylvania
Arthur W. Toga, University of Southern California
Laurel Beckett, University of California, Davis
Robert C. Green, Harvard Medical School
Andrew J. Saykin, Indiana University Bloomington
John Morris, Washington University in St. Louis
Leslie M. Shaw, University of Pennsylvania
Jeffrey Kaye, Oregon Health & Science University
Joseph Quinn, University of California, San Diego
Lisa Silbert, University of California, San Diego
Betty Lind, University of California, San Diego
Raina Carter, Oregon Health & Science University
Sara Dolen, Oregon Health & Science University
Lon S. Schneider, Oregon Health & Science University
Sonia Pawluczyk, Oregon Health & Science University
Mauricio Beccera, Oregon Health & Science University
Liberty Teodoro, University of Southern California
Bryan M. Spann, University of Southern California
James Brewer, University of California, San Diego
Helen Vanderswag, University of California, San Diego
Adam Fleisher, University of California, San Diego
Judith L. Heidebrink, University of Michigan, Ann Arbor
Joanne L. Lord, University of Michigan, Ann Arbor

Document Type

Article

Publication Date

12-1-2019

Journal

Scientific Reports

Volume

9

Issue

1

URL with Digital Object Identifier

10.1038/s41598-018-37149-7

Abstract

© 2019, The Author(s). 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 1−42 (Aβ 1−42 ) 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 1−42 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 plasma analytes (CGA, Aβ 1−42 , 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 1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ 1−42 levels and that the resulting model also validates reasonably across PET Aβ 1−42 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 1−42 status, the earliest risk indicator for AD, with high accuracy.