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Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data

Mild cognitive impairment (MCI) is a high-risk condition for conversion to Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneous patterns of pathology and conversion to AD dementia. Thus, detailed subtyping of MCI subjects and accurate prediction of the patients in...

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Autores principales: Kikuchi, Masataka, Kobayashi, Kaori, Itoh, Sakiko, Kasuga, Kensaku, Miyashita, Akinori, Ikeuchi, Takeshi, Yumoto, Eiji, Kosaka, Yuki, Fushimi, Yasuto, Takeda, Toshihiro, Manabe, Shirou, Hattori, Satoshi, Nakaya, Akihiro, Kamijo, Kenichi, Matsumura, Yasushi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513733/
https://www.ncbi.nlm.nih.gov/pubmed/36212530
http://dx.doi.org/10.1016/j.csbj.2022.08.007
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author Kikuchi, Masataka
Kobayashi, Kaori
Itoh, Sakiko
Kasuga, Kensaku
Miyashita, Akinori
Ikeuchi, Takeshi
Yumoto, Eiji
Kosaka, Yuki
Fushimi, Yasuto
Takeda, Toshihiro
Manabe, Shirou
Hattori, Satoshi
Nakaya, Akihiro
Kamijo, Kenichi
Matsumura, Yasushi
author_facet Kikuchi, Masataka
Kobayashi, Kaori
Itoh, Sakiko
Kasuga, Kensaku
Miyashita, Akinori
Ikeuchi, Takeshi
Yumoto, Eiji
Kosaka, Yuki
Fushimi, Yasuto
Takeda, Toshihiro
Manabe, Shirou
Hattori, Satoshi
Nakaya, Akihiro
Kamijo, Kenichi
Matsumura, Yasushi
author_sort Kikuchi, Masataka
collection PubMed
description Mild cognitive impairment (MCI) is a high-risk condition for conversion to Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneous patterns of pathology and conversion to AD dementia. Thus, detailed subtyping of MCI subjects and accurate prediction of the patients in whom MCI will convert to AD dementia is critical for identifying at-risk populations and the underlying biological features. To this end, we developed a model that simultaneously subtypes MCI subjects and predicts conversion to AD and performed an analysis of the underlying biological characteristics of each subtype. In particular, a heterogeneous mixture learning (HML) method was used to build a decision tree-based model based on multimodal data, including cerebrospinal fluid (CSF) biomarker data, structural magnetic resonance imaging (MRI) data, APOE genotype data, and age at examination. The HML model showed an average F1 score of 0.721, which was comparable to the random forest method and had significantly more predictive accuracy than the CART method. The HML-generated decision tree was also used to classify-five subtypes of MCI. Each MCI subtype was characterized in terms of the degree of abnormality in CSF biomarkers, brain atrophy, and cognitive decline. The five subtypes of MCI were further categorized into three groups: one subtype with low conversion rates (similar to cognitively normal subjects); three subtypes with moderate conversion rates; and one subtype with high conversion rates (similar to AD dementia patients). The subtypes with moderate conversion rates were subsequently separated into a group with CSF biomarker abnormalities and a group with brain atrophy. The subtypes identified in this study exhibited varying MCI-to-AD conversion rates and differing biological profiles.
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spelling pubmed-95137332022-10-06 Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data Kikuchi, Masataka Kobayashi, Kaori Itoh, Sakiko Kasuga, Kensaku Miyashita, Akinori Ikeuchi, Takeshi Yumoto, Eiji Kosaka, Yuki Fushimi, Yasuto Takeda, Toshihiro Manabe, Shirou Hattori, Satoshi Nakaya, Akihiro Kamijo, Kenichi Matsumura, Yasushi Comput Struct Biotechnol J Research Article Mild cognitive impairment (MCI) is a high-risk condition for conversion to Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneous patterns of pathology and conversion to AD dementia. Thus, detailed subtyping of MCI subjects and accurate prediction of the patients in whom MCI will convert to AD dementia is critical for identifying at-risk populations and the underlying biological features. To this end, we developed a model that simultaneously subtypes MCI subjects and predicts conversion to AD and performed an analysis of the underlying biological characteristics of each subtype. In particular, a heterogeneous mixture learning (HML) method was used to build a decision tree-based model based on multimodal data, including cerebrospinal fluid (CSF) biomarker data, structural magnetic resonance imaging (MRI) data, APOE genotype data, and age at examination. The HML model showed an average F1 score of 0.721, which was comparable to the random forest method and had significantly more predictive accuracy than the CART method. The HML-generated decision tree was also used to classify-five subtypes of MCI. Each MCI subtype was characterized in terms of the degree of abnormality in CSF biomarkers, brain atrophy, and cognitive decline. The five subtypes of MCI were further categorized into three groups: one subtype with low conversion rates (similar to cognitively normal subjects); three subtypes with moderate conversion rates; and one subtype with high conversion rates (similar to AD dementia patients). The subtypes with moderate conversion rates were subsequently separated into a group with CSF biomarker abnormalities and a group with brain atrophy. The subtypes identified in this study exhibited varying MCI-to-AD conversion rates and differing biological profiles. Research Network of Computational and Structural Biotechnology 2022-08-22 /pmc/articles/PMC9513733/ /pubmed/36212530 http://dx.doi.org/10.1016/j.csbj.2022.08.007 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Kikuchi, Masataka
Kobayashi, Kaori
Itoh, Sakiko
Kasuga, Kensaku
Miyashita, Akinori
Ikeuchi, Takeshi
Yumoto, Eiji
Kosaka, Yuki
Fushimi, Yasuto
Takeda, Toshihiro
Manabe, Shirou
Hattori, Satoshi
Nakaya, Akihiro
Kamijo, Kenichi
Matsumura, Yasushi
Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data
title Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data
title_full Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data
title_fullStr Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data
title_full_unstemmed Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data
title_short Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data
title_sort identification of mild cognitive impairment subtypes predicting conversion to alzheimer’s disease using multimodal data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513733/
https://www.ncbi.nlm.nih.gov/pubmed/36212530
http://dx.doi.org/10.1016/j.csbj.2022.08.007
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