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Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype
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 subt...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224289/ https://www.ncbi.nlm.nih.gov/pubmed/34064186 http://dx.doi.org/10.3390/brainsci11060674 |
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author | Li, Hai-Tao Yuan, Shao-Xun Wu, Jian-Sheng Gu, Yu Sun, Xiao |
author_facet | Li, Hai-Tao Yuan, Shao-Xun Wu, Jian-Sheng Gu, Yu Sun, Xiao |
author_sort | Li, Hai-Tao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8224289 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82242892021-06-25 Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype Li, Hai-Tao Yuan, Shao-Xun Wu, Jian-Sheng Gu, Yu Sun, Xiao Brain Sci Article 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. MDPI 2021-05-21 /pmc/articles/PMC8224289/ /pubmed/34064186 http://dx.doi.org/10.3390/brainsci11060674 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Hai-Tao Yuan, Shao-Xun Wu, Jian-Sheng Gu, Yu Sun, Xiao Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype |
title | Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype |
title_full | Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype |
title_fullStr | Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype |
title_full_unstemmed | Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype |
title_short | Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype |
title_sort | predicting conversion from mci to ad combining multi-modality data and based on molecular subtype |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224289/ https://www.ncbi.nlm.nih.gov/pubmed/34064186 http://dx.doi.org/10.3390/brainsci11060674 |
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