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Early Detection of Alzheimer’s 2 Disease-Related Pathology Using a 3 Multi-Disease Diagnostic Platform 4 Employing Autoantibodies as Blood-Based 5 Bio

Mary Wilson

Well-known member
(NOTE: Uncorrected Author Proof)

Accepted 31 January 2023
Pre-press 21 February 2023

DOI 10.3233/JAD-221091

Cassandra A. DeMarshalla, Jeffrey Vivianoa, Sheina Emranib,c, Umashanger Thayasivama,d,
George A. Godseya, Abhirup Sarkara, Benjamin Belinkaa, David J. Libonb, Robert G. Nagelea,e,∗ and on behalf of the Parkinson’s Study Group and the Alzheimer’s Disease Neuroimaging Initiative1


Abstract

Background: Evidence for the universal presence of IgG autoantibodies in blood and their potential utility for the diagnosis of Alzheimer’s disease (AD) and other neurodegenerative diseases has been extensively demonstrated by our laboratory. The fact that AD-related neuropathological changes in the brain can begin more than a decade before tell-tale symptoms emerge has made it difficult to develop diagnostic tests useful for detecting the earliest stages of AD pathogenesis.

Objective: To determine the utility of a panel of autoantibodies for detecting the presence of AD-related pathology along the early AD continuum, including at pre-symptomatic [an average of 4 years before the transition to mild cognitive impairment (MCI)/AD)], prodromal AD (MCI), and mild-moderate AD stages.

Methods: A total of 328 serum samples from multiple cohorts, including ADNI subjects with confirmed pre-symptomatic, prodromal, and mild-moderate AD, were screened using Luminex xMAP® technology to predict the probability of the
presence of AD-related pathology. A panel of eight autoantibodies with age as a covariate was evaluated using random Forest and receiver operating characteristic (ROC) curves.

Results: Autoantibody biomarkers alone predicted the probability of the presence of AD-related pathology with 81.0% accuracy and an area under the curve (AUC) of 0.84 (95% CI = 0.78–0.91). Inclusion of age as a parameter to the model improved the AUC (0.96; 95% CI = 0.93–0.99) and overall accuracy (93.0%).

https://content.iospress.com/downlo...91?id=journal-of-alzheimers-disease/jad221091
 
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