論文

査読有り 国際誌
2023年6月29日

Classification and deep-learning–based prediction of Alzheimer disease subtypes by using genomic data

Translational Psychiatry
  • Daichi Shigemizu
  • ,
  • Shintaro Akiyama
  • ,
  • Mutsumi Suganuma
  • ,
  • Motoki Furutani
  • ,
  • Akiko Yamakawa
  • ,
  • Yukiko Nakano
  • ,
  • Kouichi Ozaki
  • ,
  • Shumpei Niida

13
1
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1038/s41398-023-02531-1

Late-onset Alzheimer’s disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk factors for LOAD but not for LOAD subtypes. Here, we examined the genetic architecture of LOAD based on Japanese GWAS data from 1947 patients and 2192 cognitively normal controls in a discovery cohort and 847 patients and 2298 controls in an independent validation cohort. Two distinct groups of LOAD patients were identified. One was characterized by major risk genes for developing LOAD (APOC1 and APOC1P1) and immune-related genes (RELB and CBLC). The other was characterized by genes associated with kidney disorders (AXDND1, FBP1, and MIR2278). Subsequent analysis of albumin and hemoglobin values from routine blood test results suggested that impaired kidney function could lead to LOAD pathogenesis. We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort and 0.687 (2162/3145) in the validation cohort. These findings provide new insights into the pathogenic mechanisms of LOAD.

リンク情報
DOI
https://doi.org/10.1038/s41398-023-02531-1
ID情報
  • DOI : 10.1038/s41398-023-02531-1
  • eISSN : 2158-3188

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