JOURNAL ARTICLE

Depression and the Risk of Dementia and All-Cause Mortality Among Japanese Older Adults: A 9-Year Longitudinal Study From JAGES.

  • Published In: Journals of Gerontology Series B: Psychological Sciences & Social Sciences, 2024, v. 79, n. 7. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wu, Shan; Zhong, Xiangbin; Gong, Yajie; Yao, Yao; Shirai, Kokoro; Kondo, Katsunori; Wang, Xinlei; Guan, Liqi; Chen, Qiqing; Liu, Keyang; Li, Yuting 3 of 3

Abstract

This article investigates the association and dose–response relationship between depression severity, dementia, and all-cause mortality among Japanese adults aged 65 and older, using data from the longitudinal Japanese Gerontological Evaluation Study (JAGES) from 2010 to 2019. Depressive symptoms were assessed by the Geriatric Depression Scale-15 (GDS-15), and dementia was identified through Japan's long-term care insurance (LTCI) registry. The study found that both minor and major depressive symptoms were significantly associated with increased risks of dementia and all-cause mortality in a dose-dependent manner, with stronger effects observed in the younger elderly group (65–79 years). Additionally, dementia partially mediated the relationship between depression and mortality. These findings suggest that preventing and treating major depression in older adults may reduce the incidence of dementia and premature death.

Additional Information

  • Source:Journals of Gerontology Series B: Psychological Sciences & Social Sciences. 2024/07, Vol. 79, Issue 7, p1
  • Document Type:Article
  • Subject Area:Nursing and Allied Health
  • Publication Date:2024
  • ISSN:1079-5014
  • DOI:10.1093/geronb/gbae084
  • Accession Number:178300176
  • Copyright Statement:Copyright of Journals of Gerontology Series B: Psychological Sciences & Social Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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