@article{82ae1e6db53b44099fae2356022e9e96,
title = "Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment",
abstract = "Background: Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment (MCI), are urgently needed. Methods: Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (n = 12). Artificial intelligence was used to identify the most important features for predicting aMCI. Findings: We found that hsa-miR-4455 is the best biomarker in all omics analyses. The diagnostic index taking a ratio of hsa-miR-4455 to hsa-let-7b-3p predicted aMCI patients against healthy subjects with 97% overall accuracy. An integrated review of multiomics data suggested that a subset of T cells and the GCN (general control nonderepressible) pathway are associated with aMCI. Interpretation: The multiomics approach has enabled aMCI biomarkers with high specificity and illuminated the accompanying changes in peripheral blood. Future large-scale studies are necessary to validate candidate biomarkers for clinical use.",
keywords = "Biomarker, Cohort, Metabolome, Mild cognitive impairment, Alzheimer's disease with dementia, Proteome, Regulatory T cells, Transcriptome, miRNA",
author = "Yota Tatara and Hiromi Yamazaki and Fumiki Katsuoka and Mitsuru Chiba and Daisuke Saigusa and Shuya Kasai and Tomohiro Nakamura and Jin Inoue and Yuichi Aoki and Miho Shoji and Motoike, {Ikuko N.} and Yoshinori Tamada and Katsuhito Hashizume and Mikio Shoji and Kengo Kinoshita and Koichi Murashita and Shigeyuki Nakaji and Masayuki Yamamoto and Ken Itoh",
note = "Funding Information: We thank Mr. Kenichi Kawatani and Ms. Naori Ishiyama of the Hirosaki University Center of Healthy Aging Innovation for research assistance. The authors thank Mr. Takahiro Nakayama (Tohoku Chemical Co. Ltd.) for help with transcriptome data analysis. The authors are grateful to Ms. Miyu Miyazaki at the Center for Scientific Equipment Management, Hirosaki University Graduate School of Medicine, for help with LC–MS/MS analysis. We also thank JPSC-AD JP16dk0207025 for letting us to use the cohort samples. This work was supported by “JST/COI (Grant No. JPMJCE1302 and JPMJCA2201) and Collaborative Research Fund of Young Scientists (Project No. H29 W08 and H30W14).” The funders had no role in the study design, data collection, analysis, publishing decision, or manuscript preparation. Funding Information: We thank Mr. Kenichi Kawatani and Ms. Naori Ishiyama of the Hirosaki University Center of Healthy Aging Innovation for research assistance. The authors thank Mr. Takahiro Nakayama (Tohoku Chemical Co. Ltd.) for help with transcriptome data analysis. The authors are grateful to Ms. Miyu Miyazaki at the Center for Scientific Equipment Management, Hirosaki University Graduate School of Medicine, for help with LC–MS/MS analysis. We also thank JPSC-AD JP16dk0207025 for letting us to use the cohort samples. This work was supported by “JST/COI (Grant No. JPMJCE1302 and JPMJCA2201) and Collaborative Research Fund of Young Scientists (Project No. H29 W08 and H30W14).” The funders had no role in the study design, data collection, analysis, publishing decision, or manuscript preparation. Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2023",
month = jan,
day = "1",
doi = "10.1016/j.retram.2022.103367",
language = "English",
volume = "71",
journal = "Current Research in Translational Medicine",
issn = "2452-3186",
publisher = "Elsevier Masson",
number = "1",
}