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Prediction of mild cognitive impairment using blood multi-omics data
Daniel Frank Zhang, Çiğdem Sevim Bayrak, Qi Zeng, Minghui Wang, Bin Zhang. Frontiers in Genetics, 2025-05-26
Topic: Risk, prevention and care in population studies
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.
Key findings
- 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.
- 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.
Why it matters
This study developed a machine learning model using blood gene expression and copy number variation data that classified MCI patients from normal controls with an AUC of 0.9398. The work is about developing a non-invasive diagnostic prediction tool and does not test any treatment or prevention.
Study details
- Evidence strengthLow
How this study scored 36/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in Genetics) |
| Journal's citation record | 2 / 4 | Frontiers in Genetics: journal h-index 173 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 2 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Bin Zhang: h-index 66 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Icahn School of Medicine at Mount Sinai: institution h-index 1,331 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40491569) |
| Free to read | 2 / 2 | Free to read |
| Total | 36 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fgene.2025.1552063
- PubMed: 40491569
- Original source →
- Free full text: Frontiers in Genetics →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Zhang, D. F., Bayrak, Ç. S., Zeng, Q., Wang, M., & Zhang, B. (2025). Prediction of mild cognitive impairment using blood multi-omics data. Frontiers in Genetics. https://doi.org/10.3389/fgene.2025.1552063
Vancouver
Zhang DF, Bayrak ÇS, Zeng Q, Wang M, Zhang B. Prediction of mild cognitive impairment using blood multi-omics data. Frontiers in Genetics. 2025. doi:10.3389/fgene.2025.1552063. PMID: 40491569.
BibTeX
@article{zhang2025-40491569,
author = {Zhang, Daniel Frank and Bayrak, Çiğdem Sevim and Zeng, Qi and Wang, Minghui and Zhang, Bin},
title = {{Prediction of mild cognitive impairment using blood multi-omics data}},
journal = {Frontiers in Genetics},
year = {2025},
doi = {10.3389/fgene.2025.1552063},
pmid = {40491569},
url = {https://doi.org/10.3389/fgene.2025.1552063}
}
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