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

Publication ,  Journal Article
Goudey, B; Fung, BJ; Schieber, C; Faux, NG; Alzheimer’s Disease Metabolomics Consortium, ; Alzheimer’s Disease Neuroimaging Initiative,
Published in: Sci Rep
March 11, 2019

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 β1-42 (Aβ1-42) 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 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 plasma analytes (CGA, Aβ1-42, 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 Aβ1-42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1-42 levels and that the resulting model also validates reasonably across PET Aβ1-42 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 Aβ1-42 status, the earliest risk indicator for AD, with high accuracy.

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Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

March 11, 2019

Volume

9

Issue

1

Start / End Page

4163

Location

England

Related Subject Headings

  • Predictive Value of Tests
  • Peptide Fragments
  • Male
  • Humans
  • Female
  • Chromogranin A
  • Chemokine CCL26
  • Biomarkers
  • Apolipoproteins E
  • Amyloid beta-Peptides
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Goudey, B., Fung, B. J., Schieber, C., Faux, N. G., Alzheimer’s Disease Metabolomics Consortium, ., & Alzheimer’s Disease Neuroimaging Initiative, . (2019). A blood-based signature of cerebrospinal fluid Aβ1-42 status. Sci Rep, 9(1), 4163. https://doi.org/10.1038/s41598-018-37149-7
Goudey, Benjamin, Bowen J. Fung, Christine Schieber, Noel G. Faux, Noel G. Alzheimer’s Disease Metabolomics Consortium, and Noel G. Alzheimer’s Disease Neuroimaging Initiative. “A blood-based signature of cerebrospinal fluid Aβ1-42 status.Sci Rep 9, no. 1 (March 11, 2019): 4163. https://doi.org/10.1038/s41598-018-37149-7.
Goudey B, Fung BJ, Schieber C, Faux NG, Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative. A blood-based signature of cerebrospinal fluid Aβ1-42 status. Sci Rep. 2019 Mar 11;9(1):4163.
Goudey, Benjamin, et al. “A blood-based signature of cerebrospinal fluid Aβ1-42 status.Sci Rep, vol. 9, no. 1, Mar. 2019, p. 4163. Pubmed, doi:10.1038/s41598-018-37149-7.
Goudey B, Fung BJ, Schieber C, Faux NG, Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative. A blood-based signature of cerebrospinal fluid Aβ1-42 status. Sci Rep. 2019 Mar 11;9(1):4163.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

March 11, 2019

Volume

9

Issue

1

Start / End Page

4163

Location

England

Related Subject Headings

  • Predictive Value of Tests
  • Peptide Fragments
  • Male
  • Humans
  • Female
  • Chromogranin A
  • Chemokine CCL26
  • Biomarkers
  • Apolipoproteins E
  • Amyloid beta-Peptides