A predictive microarray-based biomarker for early detection of Alzheimer’s disease intended for clinical diagnostic application

Abstract

Objective: Microarray-based signatures for clinical application are often plagued by processing variability or batch effects that compromise the robustness of the test performance.

Methods: A splice variant array-based signature for early detection of Alzheimer’s disease (AD) was developed using 315 AD or normal subjects processed in three disparate microarray batches.

Results: A modified top scoring pair classifier using the signature, is robust to batch effects and outperforms other common classifiers, with sensitivity and specificity of 88.3% (95% CI:81.2%, 93.4%) and 88.9% (95% CI:65.3%, 98.6%), respectively, on an independent cohort.

Conclusions: This splice-variant array-based signature shows promise for clinical diagnostic use in AD.

• Alzheimer’s disease
• diagnostic
• gene expression
• microarray
• splice variant

Acknowledgements

We would like to thank Dr Brandon W. Higgs for his statistical contributions in this study and Dr Eric Martens for scientific writing assistance.