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Goudey, B.* ; Fung, B.J.* ; Schieber, C.* ; Faux, N.G.* ; Alzheimer's Disease Neuroimaging Initiative (Kastenmüller, G. ; Arnold, M.)

A blood-based signature of cerebrospinal fluid Aβ1-42 status.

Sci. Rep. 9:4163 (2019)
Verlagsversion Forschungsdaten DOI
Open Access Gold
Creative Commons Lizenzvertrag
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 beta(1-42) (A beta(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 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 epsilon 4 carrier status and four plasma analytes (CGA, A beta(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 A beta(1-42) levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF A beta(1-42) levels and that the resulting model also validates reasonably across PET A beta(1-42) status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOE epsilon 4 carrier status, is able to predict CSF A beta(1-42) status, the earliest risk indicator for AD, with high accuracy.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Amyloid-beta Deposition; Apolipoprotein-e Levels; Alzheimers-disease; Chromogranin-a; Plasma; Biomarkers; Apoe; Diagnosis; Markers; Protein
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Zeitschrift Scientific Reports
Quellenangaben Band: 9, Heft: 1, Seiten: , Artikelnummer: 4163 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed