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Converging multi-modal evidences for the primary prevention of Alzheimer's dementia...International Society for Gerontechnology 13th World Conference, October 22-26, 2022, Daegu, South Korea.

Purpose Predicting risk of Alzheimer's disease for the primary prevention and early intervention. Method We investigated the performance of combining multi-modal biomarkers obtained from GWAS study (n=14,000). We first predict the lifetime risk of AD using the ethnicity adjusted SNVs incorporating t...

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Publicado en:Gerontechnology Vol. 21; pp. 4 - 5
Autores principales: Gim, J. S., Kim, Y. T., Lee, K. H.
Formato: abstract proceedings research Journal Article
Publicado: International Society for Gerontechnology Oct2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2022
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      pub: International Society for Gerontechnology
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        atl: Converging multi-modal evidences for the primary prevention of Alzheimer's dementia...International Society for Gerontechnology 13th World Conference, October 22-26, 2022, Daegu, South Korea.
      aug:
        au:
          Gim, J. S.
          Kim, Y. T.
          Lee, K. H.
        affil: Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
      sug:
        subj:
          Alzheimer's Disease Prevention and Control
          Alzheimer's Disease Risk Factors
          Risk Assessment
          Early Intervention
          Prediction Models
          Congresses and Conferences South Africa
          South Africa
      ab: Purpose Predicting risk of Alzheimer's disease for the primary prevention and early intervention. Method We investigated the performance of combining multi-modal biomarkers obtained from GWAS study (n=14,000). We first predict the lifetime risk of AD using the ethnicity adjusted SNVs incorporating the prior genome-wide association (GWA) knowledge and predict the onset year for those with high lifetime risk by learning neuropsychological test scores and life-log (sleep quality and daily activity) information. Results and discussion When validated with three independent samples, the lifetime risk of AD using the proposed genomic prediction showed 75.3% of average accuracy (max 79.1%) with 74.8% and 73.7% of average sensitivity (max 80.5%) and specificity (73.7%), respectively. For those with high risk, we predicted onset within two years, our deep learning model showed 77.4% of accuracy. Strict blind test and application of the proposed model on GARD cohort shows its validity in application for screening and prevention of AD patients, especially in preclinical stage.
      pubtype: Academic Journal
      doctype:
        abstract
        proceedings
        research
        Journal Article
      ougenre: Article
    language: English
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