Early Detection of Alzheimer's Disease-Related Pathology Using a Multi-Disease Diagnostic Platform Employing Autoantibodies as Blood-Based Biomarkers

使用以自身抗体作为血液生物标志物的多疾病诊断平台早期检测阿尔茨海默病相关病理

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作者:Cassandra A DeMarshall, Jeffrey Viviano, Sheina Emrani, Umashanger Thayasivam, George A Godsey, Abhirup Sarkar, Benjamin Belinka, David J Libon, Robert G Nagele; Parkinson’s Study Group and the Alzheimer’s Disease Neuroimaging Initiative

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.

Conclusion

Blood-based autoantibodies can be used as an accurate, non-invasive, inexpensive, and widely accessible diagnostic screener for detecting AD-related pathology at pre-symptomatic and prodromal AD stages that could aid clinicians in diagnosing AD.

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 randomForest and receiver operating characteristic (ROC) curves.

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.

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%).

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