{"title":"多组学和人工智能使基于外周血的健忘轻度认知障碍预测成为可能","authors":"Yota Tatara , Hiromi Yamazaki , Fumiki Katsuoka , Mitsuru Chiba , Daisuke Saigusa , Shuya Kasai , Tomohiro Nakamura , Jin Inoue , Yuichi Aoki , Miho Shoji , Ikuko N. Motoike , Yoshinori Tamada , Katsuhito Hashizume , Mikio Shoji , Kengo Kinoshita , Koichi Murashita , Shigeyuki Nakaji , Masayuki Yamamoto , Ken Itoh","doi":"10.1016/j.retram.2022.103367","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>Multiomics analysis of amnestic MCI (aMCI) peripheral blood (<em>n</em> = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (<em>n</em> = 12). Artificial intelligence was used to identify the most important features for predicting aMCI.</p></div><div><h3>Findings</h3><p>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.</p></div><div><h3>Interpretation</h3><p>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.</p></div>","PeriodicalId":54260,"journal":{"name":"Current Research in Translational Medicine","volume":"71 1","pages":"Article 103367"},"PeriodicalIF":3.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment\",\"authors\":\"Yota Tatara , Hiromi Yamazaki , Fumiki Katsuoka , Mitsuru Chiba , Daisuke Saigusa , Shuya Kasai , Tomohiro Nakamura , Jin Inoue , Yuichi Aoki , Miho Shoji , Ikuko N. Motoike , Yoshinori Tamada , Katsuhito Hashizume , Mikio Shoji , Kengo Kinoshita , Koichi Murashita , Shigeyuki Nakaji , Masayuki Yamamoto , Ken Itoh\",\"doi\":\"10.1016/j.retram.2022.103367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>Multiomics analysis of amnestic MCI (aMCI) peripheral blood (<em>n</em> = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (<em>n</em> = 12). Artificial intelligence was used to identify the most important features for predicting aMCI.</p></div><div><h3>Findings</h3><p>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.</p></div><div><h3>Interpretation</h3><p>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.</p></div>\",\"PeriodicalId\":54260,\"journal\":{\"name\":\"Current Research in Translational Medicine\",\"volume\":\"71 1\",\"pages\":\"Article 103367\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Research in Translational Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2452318622000356\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Research in Translational Medicine","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452318622000356","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment
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.
期刊介绍:
Current Research in Translational Medicine is a peer-reviewed journal, publishing worldwide clinical and basic research in the field of hematology, immunology, infectiology, hematopoietic cell transplantation, and cellular and gene therapy. The journal considers for publication English-language editorials, original articles, reviews, and short reports including case-reports. Contributions are intended to draw attention to experimental medicine and translational research. Current Research in Translational Medicine periodically publishes thematic issues and is indexed in all major international databases (2017 Impact Factor is 1.9).
Core areas covered in Current Research in Translational Medicine are:
Hematology,
Immunology,
Infectiology,
Hematopoietic,
Cell Transplantation,
Cellular and Gene Therapy.