What is it and what does it include?
There is an urgent need to identify biomarkers that can accurately detect and diagnose Alzheimer's disease (AD). Autoantibodies are abundant and ubiquitous in human sera and can be used as noninvasive and effective blood-based biomarkers for early diagnosis and staging of AD.
In this study, sera from 236 subjects, including 50 mild cognitive impairment (MCI) subjects with confirmed low CSF Aβ42 levels, were screened with human protein microarrays to identify potential biomarkers for MCI.
Microarray data was analyzed using several statistical significance algorithms, and autoantibodies that demonstrated significant differences in group prevalence were selected as potential biomarkers of disease.
Differentiation of MCI 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 AD samples assessed their utility for use in disease staging.
How can I use this dataset to advance my research?
This dataset is ideal if:
studying antibodies as blood-based biomarkers for early diagnosis of AD.
- you’re interested in analyzing protein profiling data by protein array from sera obtained from subjects with diagnosis of AD and other neurodegenerative and neurological conditions.
Has this dataset helped researchers understand Alzheimer’s and other dementias better?
- AD & AI, predictive diagnostics:
In 2021, researchers used AI to model PD (Parkinson’s), AD, and MCI data and analyzed the possible connections between them. Data included human blood protein microarray profiles from the present dataset. Researchers classified the disease types more carefully into early and late stages of AD, MCI, and PD, respectively, and found that early PD may occur earlier than early MCI. Finally, there were 24 proteins that were both differentially expressed proteins and upstream regulators in the disease group versus the normal group, and these proteins may serve as potential therapeutic targets and targets for subsequent studies. In conclusion, building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. Building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. October 2021 – DOI: https://www.frontiersin.org/articles/10.3389/fbinf.2021.764497/full
- AD & Autoantibodies, Blood-based biomarkers:
In 2016, investigators found that autoantibody biomarkers can differentiate MCI patients from age-matched and gender-matched controls with an overall accuracy, sensitivity, and specificity of 100.0%. They were also capable of differentiating MCI patients from those with mild-moderate AD and other neurologic and non-neurologic controls with high accuracy. April 2016 – DOI: https://alz-journals.onlinelibrary.wiley.com/doi/10.1016/j.dadm.2016.03.002