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Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis

Mild cognitive impairment (MCI) is a clinical precursor of Alzheimer’s disease (AD). Recent genetic studies have reported on associations between AD risk genes and immunity. Here, we obtained samples and data from 317 AD, 432 MCI, and 107 cognitively normal (CN) subjects and investigated immune-cell...

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Autores principales: Shigemizu, Daichi, Akiyama, Shintaro, Mitsumori, Risa, Niida, Shumpei, Ozaki, Kouichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636153/
https://www.ncbi.nlm.nih.gov/pubmed/36333348
http://dx.doi.org/10.1038/s41514-022-00096-9
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author Shigemizu, Daichi
Akiyama, Shintaro
Mitsumori, Risa
Niida, Shumpei
Ozaki, Kouichi
author_facet Shigemizu, Daichi
Akiyama, Shintaro
Mitsumori, Risa
Niida, Shumpei
Ozaki, Kouichi
author_sort Shigemizu, Daichi
collection PubMed
description Mild cognitive impairment (MCI) is a clinical precursor of Alzheimer’s disease (AD). Recent genetic studies have reported on associations between AD risk genes and immunity. Here, we obtained samples and data from 317 AD, 432 MCI, and 107 cognitively normal (CN) subjects and investigated immune-cell type composition and immune clonal diversity of T-cell receptor (TRA, TRB, TRG, and TRD) and B-cell receptor (IGH, IGK, and IGL) repertoires through bulk RNA sequencing. We found the proportions of plasma cells, γδ T cells, neutrophils, and B cells were significantly different and the diversities of IGH, IGK, and TRA were significantly small with AD progression. We then identified a differentially expressed gene, WDR37, in terms of risk of MCI-to-AD conversion. Our prognosis prediction model using the potential blood-based biomarkers for early AD diagnosis, which combined two immune repertoires (IGK and TRA), WDR37, and clinical information, successfully classified MCI patients into two groups, low and high, in terms of risk of MCI-to-AD conversion (log-rank test P = 2.57e-3). It achieved a concordance index of 0.694 in a discovery cohort and of 0.643 in an independent validation cohort. We believe that further investigation, using larger sample sizes, will lead to practical clinical use in the near future.
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spelling pubmed-96361532022-11-06 Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis Shigemizu, Daichi Akiyama, Shintaro Mitsumori, Risa Niida, Shumpei Ozaki, Kouichi NPJ Aging Article Mild cognitive impairment (MCI) is a clinical precursor of Alzheimer’s disease (AD). Recent genetic studies have reported on associations between AD risk genes and immunity. Here, we obtained samples and data from 317 AD, 432 MCI, and 107 cognitively normal (CN) subjects and investigated immune-cell type composition and immune clonal diversity of T-cell receptor (TRA, TRB, TRG, and TRD) and B-cell receptor (IGH, IGK, and IGL) repertoires through bulk RNA sequencing. We found the proportions of plasma cells, γδ T cells, neutrophils, and B cells were significantly different and the diversities of IGH, IGK, and TRA were significantly small with AD progression. We then identified a differentially expressed gene, WDR37, in terms of risk of MCI-to-AD conversion. Our prognosis prediction model using the potential blood-based biomarkers for early AD diagnosis, which combined two immune repertoires (IGK and TRA), WDR37, and clinical information, successfully classified MCI patients into two groups, low and high, in terms of risk of MCI-to-AD conversion (log-rank test P = 2.57e-3). It achieved a concordance index of 0.694 in a discovery cohort and of 0.643 in an independent validation cohort. We believe that further investigation, using larger sample sizes, will lead to practical clinical use in the near future. Nature Publishing Group UK 2022-11-04 /pmc/articles/PMC9636153/ /pubmed/36333348 http://dx.doi.org/10.1038/s41514-022-00096-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Shigemizu, Daichi
Akiyama, Shintaro
Mitsumori, Risa
Niida, Shumpei
Ozaki, Kouichi
Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis
title Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis
title_full Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis
title_fullStr Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis
title_full_unstemmed Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis
title_short Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis
title_sort identification of potential blood biomarkers for early diagnosis of alzheimer’s disease through immune landscape analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636153/
https://www.ncbi.nlm.nih.gov/pubmed/36333348
http://dx.doi.org/10.1038/s41514-022-00096-9
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