Importance of Serum Albumin in Deep Learning-Based Prediction of Cognitive Function Data in the Aged Using a Basic Blood Test.

Journal: Advances in experimental medicine and biology
PMID: 39400832

Abstract

BACKGROUND: Recently, a method using deep learning has been developed to estimate the risk of developing dementia. This method uses general blood test data from routine health examinations that reveal lifestyle-related diseases, which can lead to vascular cognitive impairment via arteriosclerosis, as well as systemic metabolic disorders that are unrelated to lifestyle, such as nutritional disorders. In this study, we investigated the differences in the accuracy of estimating the risk of dementia based on the presence or the absence of blood test parameters reflecting nutritional disorders while focusing on the association between malnutrition and the risk of dementia in frail, elderly individuals.

Authors

  • Kenji Karako
    Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Takeo Hata
    Department of Hospital Quality and Safety Management, Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Japan.
  • Atsushi Inoue
    Department of Urology, Yokohama Rosai Hospital, Yokohama, Kanagawa, Japan.
  • Katsunori Oyama
    Department of Computer Science, College of Engineering, Nihon University, Chiyoda City, Tokyo, Japan.
  • Eiichiro Ueda
    Department of Hospital Quality and Safety Management, Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Japan.
  • Katsuya Iijima
    Institute of Gerontology, The University of Tokyo, Tokyo, Japan.
  • Yu Chen
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • Kaoru Sakatani
    Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Bunkyo City, Tokyo, Japan. k.sakatani@edu.k.u-tokyo.ac.jp.

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