Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype
Open accessAbstract
Alzheimer’s disease (AD) is a neurodegenerative brain disease in the elderly. Identifying patients with mild cognitive impairment (MCI) who are more likely to progress to AD is a key step in AD prevention. Recent studies have shown that AD is a heterogeneous disease. In this study, we propose a subtyping-based prediction strategy to predict the conversion from MCI to AD in three years according to MCI patient subtypes. Structural magnetic resonance imaging (sMRI) data and multi-omics data, including genotype data and gene expression profiling derived from peripheral blood samples, from 125 MCI patients were used in the Alzheimer’s Disease Neuroimaging Initiative (ADNI)-1 dataset and from 98 MCI patients in the ADNI-GO/2 dataset. A variational Bayes approximation model based on the multiple kernel learning method was constructed to predict whether an MCI patient will progress to AD within three years. In internal fivefold cross-validation within ADNI-1, we achieved an overall AUC of 0.83 (79.20% accuracy, 81.25% sensitivity, 77.92% specificity) compared to the model without subtyping, which achieved an AUC of 0.78 (76.00% accuracy, 77.08% sensitivity, 75.32% specificity). In external validation using ADNI-1 as a training set and ADNI-GO/2 as an independent test set, we attained an AUC of 0.78 (74.49% accuracy, 74.19% sensitivity, 74.63% specificity). Identifying MCI patient subtypes with omics data would improve the accuracy of predicting the conversion from MCI to AD. In addition to evaluating statistics, obtaining the significant sMRI, single nucleotide polymorphism (SNP) and mRNA expression data from peripheral blood of MCI patients is noninvasive and cost-effective for predicting conversion from MCI to AD.
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Identifying molecular subtypes within mild cognitive impairment cohorts improves the prediction of conversion to dementia compared to unstratified models.
“For instance, identifying molecular subtypes within mild cognitive impairment cohorts improves the prediction of conversion to dementia compared to unstratified models273.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [273]
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- Authors
- Li, H.-T., Yuan, S.-X., Wu, J.-S., Gu, Y., & Sun, X.
- Affiliations
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- State Key Laboratory of Digital Medical Engineering (CN)
- Corresponding author
- Sun, Xiao
- Venue
- Brain Sciences
- Publisher
- MDPI AG
- Published
- 2021-05-21
- Volume
- 11
- Issue
- 6
- Pages
- 674
- ISSN
- 2076-3425 (ISSN-L)
- PMC
- PMC8224289
- OpenAlex
- W3164268453
- Cited by
- 27
- References
- 37
- Citations by year
- 2026: 9 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 3 · 2021: 1
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Brain Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81830053, 61972084)
- Jiangsu Provincial Key Research and Development Program (BE2016002-3)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, mild cognitive impairment, multi-omics integration, structural MRI, peripheral blood gene expression, single nucleotide polymorphisms, multiple kernel learning, disease heterogeneity, ADNI
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