Abstract

Oral Sessions

Day 3, June 24（Fri.）　10:00-10:20　Room A (Main Hall)

3A-O1-1000　　PDF

Blood-based prediction of mild cognitive impairment using multi-omics and machine learning

(¹Hirosaki Univ., ²Tohoku Univ., ³Hirosaki Univ., ⁴HMT)
^oYota Tatara¹, Hiromi Yamazaki¹, Fumiki Katsuoka², Mitsuru Chiba³, Daisuke Saigusa², Shuya Kasai¹, Tomohiro Nakamura², Naotaka Kameya⁴, Hiroyuki Yamamoto⁴, Dai Ujihara⁴, Tetta Fujimoto⁴, Miho Shoji⁴, Ikuko Motoike², Yoshinori Tamada¹, Katsuhito Hashizume⁴, Mikio Shoji¹, Kengo Kinoshita², Shigeyuki Nakaji¹, Masayuki Yamamoto², Ken Itoh¹

Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment, are urgently needed. Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n=25) was performed covering the transcriptome, miRNA, proteome, and metabolome. Validation analysis for miRNAs was conducted in an independent cohort (n=12). Artificial intelligence was used to identify the most important features for predicting aMCI. 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 amino acid starvation stress response are associated with aMCI. This study proposes a framework for generating new hypotheses including additional research, i.e., large-scale studies to validate biomarkers for clinical use and to clarify functions of the miRNAs.