Human serum samples from Alzheimer's disease driven mild cognitive impairment and non-diseased controls were probed onto human protein microarrays in order to identify differentially expressed autoantibody biomarkers that could be used as diagnostic indicators. Other neurodegenerative and non-neurodegenerative diseases were also used to help measure the specificity of the selected biomarkers.
In the study presented here, 50 Alzheimer's disease driven mild cognitive impairment samples and 50 control human serum samples were probed onto human protein microarrays in order to identify differentially expressed autoantibodies. Microarray data was analyzed using several statistical significance algorithms, and autoantibodies that demonstrated significant differences in group prevelance were selected as potential biomarkers of disease. Prediction classification analysis using Invitrogen's Prospector v5.2.1 and Random Forest tested the diagnostic efficacy of the identified biomarkers. Differentiation of mild cognitive impairment samples from other neurodegenerative and non-neurodegenerative controls (Parkinson's disease, multiple sclerosis, and *** cancer) assessed the specificity of the selected biomarkers, while comparison with mild-moderate Alzheimer's disease samples assessed their utility for use in disease staging.
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Manuscripts citing this dataset
- Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data. 2021. DOI: https://doi.org/10.3389/fbinf.2021.76
- Detection of Alzheimer's disease at mild cognitive impairment and disease progression using autoantibodies as blood-based biomarkers. 2016. DOI: https://doi.org/10.1016/j.dadm.2016.03.002
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