Prediction of mild cognitive impairment using blood multi-omics data Article Swipe
Daniel Frank Zhang
,
Çiğdem Sevim Bayrak
,
Qi Zeng
,
Minghui Wang
,
Bin Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fgene.2025.1552063
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fgene.2025.1552063
Mild cognitive impairment (MCI) represents an initial phase of memory or other cognitive function decline and is viewed as an intermediary stage between normal aging and Alzheimer’s disease (AD), the most prevalent type of dementia. Individuals with MCI face a heightened risk of progressing to AD, and early detection of MCI can facilitate the prevention of such progression through timely interventions. Nonetheless, diagnosing MCI is challenging because its symptoms can be subtle and are easily missed. Using genomic data from blood samples has been proposed as a non-invasive and cost-efficient approach to build machine learning predictive models for assisting MCI diagnosis. However, these models often exhibit poor performance. In this study, we developed an XGBoost-based machine learning model with AUC (the Area Under the receiver operating characteristic Curve) of 0.9398 utilizing gene expression and copy number variation (CNV) data from patient blood samples. We demonstrated, for the first time, that data at a genome structure level such as CNVs could be as informative as gene expression data to classify MCI patients from normal controls. We identified 149 genomic features that are important for MCI prediction. Notably, these features are enriched in the pathways associated with neurodegenerative diseases, such as neuron development and G protein-coupled receptor activity. Overall, our study not only demonstrates the effectiveness of utilizing blood sample-based multi-omics for predicting MCI, but also provides insights into crucial molecular characteristics of MCI.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fgene.2025.1552063
- https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1552063/pdf
- OA Status
- gold
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410752269
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410752269Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fgene.2025.1552063Digital Object Identifier
- Title
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Prediction of mild cognitive impairment using blood multi-omics dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-26Full publication date if available
- Authors
-
Daniel Frank Zhang, Çiğdem Sevim Bayrak, Qi Zeng, Minghui Wang, Bin ZhangList of authors in order
- Landing page
-
https://doi.org/10.3389/fgene.2025.1552063Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1552063/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1552063/pdfDirect OA link when available
- Concepts
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Cognitive impairment, Omics, Cognition, Computer science, Computational biology, Bioinformatics, Data mining, Artificial intelligence, Biology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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