A blood-based signature of cerebrospinal fluid Aβ 1–42 status

Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number4163
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

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Cerebrospinal Fluid
Amyloid
Alzheimer Disease
Positron-Emission Tomography
Area Under Curve
Dementia
Blood Proteins
Sensitivity and Specificity

ASJC Scopus subject areas

  • General

Cite this

Alzheimer’s Disease Metabolomics Consortium, & Alzheimer’s Disease Neuroimaging Initiative (2019). A blood-based signature of cerebrospinal fluid Aβ 1–42 status Scientific reports, 9(1), [4163]. https://doi.org/10.1038/s41598-018-37149-7

A blood-based signature of cerebrospinal fluid Aβ 1–42 status . / Alzheimer’s Disease Metabolomics Consortium; Alzheimer’s Disease Neuroimaging Initiative.

In: Scientific reports, Vol. 9, No. 1, 4163, 01.12.2019.

Research output: Contribution to journalArticle

Alzheimer’s Disease Metabolomics Consortium & Alzheimer’s Disease Neuroimaging Initiative 2019, ' A blood-based signature of cerebrospinal fluid Aβ 1–42 status ', Scientific reports, vol. 9, no. 1, 4163. https://doi.org/10.1038/s41598-018-37149-7
Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative. A blood-based signature of cerebrospinal fluid Aβ 1–42 status Scientific reports. 2019 Dec 1;9(1). 4163. https://doi.org/10.1038/s41598-018-37149-7
Alzheimer’s Disease Metabolomics Consortium ; Alzheimer’s Disease Neuroimaging Initiative. / A blood-based signature of cerebrospinal fluid Aβ 1–42 status In: Scientific reports. 2019 ; Vol. 9, No. 1.
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AU - Schieber, Christine

AU - Faux, Noel G.

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AU - Morris, John

AU - Shaw, Leslie M.

AU - Kaye, Jeffrey

AU - Quinn, Joseph

AU - Silbert, Lisa

AU - Lind, Betty

AU - Carter, Raina

AU - Dolen, Sara

AU - Schneider, Lon S.

AU - Pawluczyk, Sonia

AU - Beccera, Mauricio

AU - Teodoro, Liberty

AU - Spann, Bryan M.

AU - Brewer, James

AU - Vanderswag, Helen

AU - Fleisher, Adam

AU - Heidebrink, Judith L.

AU - Lord, Joanne L.

AU - Mason, Sara S.

AU - Albers, Colleen S.

AU - Knopman, David

AU - Johnson, Kris

AU - Doody, Rachelle S.

AU - Villanueva-Meyer, Javier

AU - Chowdhury, Munir

AU - Rountree, Susan

AU - Dang, Mimi

AU - Stern, Yaakov

AU - Honig, Lawrence S.

AU - Bell, Karen L.

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AU - Morris, John C.

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