Abstract
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.
Highlights
Alzheimer’s disease (AD) is a chronic neurodegenerative brain disease that has no available effective medications or supplemental treatments
In our previous research [10], we identified two Mild cognitive impairment (MCI) subtypes based on similarity network fusion (SNF) in ADNI1 and we applied the label propagation algorithm to assign a subtype label to each MCI
Two MCI subtypes were identified based on our method and were compared by the following factors: the time difference of the conversion from MCI to AD, cognitive scales and Structural magnetic resonance imaging (sMRI) images and significantly enriched pathways based on differentially expressed genes separately
Summary
Alzheimer’s disease (AD) is a chronic neurodegenerative brain disease that has no available effective medications or supplemental treatments. The population of AD patients will rise sharply in the coming years, which will attract increased public attention. Mild cognitive impairment (MCI) is known to be the prodromal stage of AD. MCI is a neurological disorder in which an elderly person has mild but measurable changes in cognition. It is worth mentioning that not all people with MCI or in other preclinical stages of Alzheimer’s disease will develop AD [4]. Studies suggest that MCI patients progress to AD at a rate of approximately 10% every year [5]. The goal of this study is to evaluate patients’ characteristics and predict which MCI patients will be more likely to convert from
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