ORIGINAL RESEARCH article

Front. Genet.

Sec. Computational Genomics

Volume 16 - 2025 | doi: 10.3389/fgene.2025.1552063

This article is part of the Research TopicAdvancements in AI for the Analysis and Interpretation of Large-scale Data by Omics TechniquesView all 5 articles

Prediction of Mild Cognitive Impairment Using Blood Multi-Omics Data

Provisionally accepted
  • 1Rice University, Houston, Texas, United States
  • 2Icahn School of Medicine at Mount Sinai, New York, New York, United States

The final, formatted version of the article will be published soon.

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.

Keywords: Mild Cognitive Impairment, Alzheimer's disease, machine learning, copy number variation, Gene Expression

Received: 09 Jan 2025; Accepted: 29 Apr 2025.

Copyright: © 2025 Zhang, Sevim Bayrak, Zeng, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Bin Zhang, Icahn School of Medicine at Mount Sinai, New York, 10029, New York, United States

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