Depression in older adults and its associations with sleep and synaptic density.

Depression Longitudinal study Mortality Older adults Sleep Synaptic density

Journal

Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073

Informations de publication

Date de publication:
29 Aug 2024
Historique:
received: 20 06 2024
revised: 24 08 2024
accepted: 28 08 2024
medline: 1 9 2024
pubmed: 1 9 2024
entrez: 31 8 2024
Statut: aheadofprint

Résumé

Depression among older adults is a global concern, contributing to disability and overall illness burden. Understanding its trajectory, associated risk factors, and implications for mortality is essential for effective intervention. Moreover, the relationship between depression, sleep disturbances, and synaptic density in the ageing brain remains complex and poorly understood. Using data from the University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age cohort, comprising 6375 participants, we conducted comprehensive assessments of depression trajectories using generalized linear mixed models and mortality risks using Cox mixed-effects models. Generalized structural equation modelling was performed to explore longitudinal associations between sleep duration and depression. Lastly, associations between post-mortem synaptic density and depression were investigated. Our findings revealed that depression rates declined until age 80 before increasing again. Depression was associated with a 10 % increased risk of mortality in older adults. Reduced sleep was correlated with depression, and depression measured early in the study predicted future reduced sleep. Post-mortem analysis showed a global reduction in synaptic density associated with depression, particularly pronounced in the frontal lobe. Limitations include recall bias, limiting generalizability due to dominantly including White British participants and difficulty in establishing causation between synaptic density and depression. Our study underscores the significance of addressing depression in older adults, not only for mental health but also for mortality risk and neurobiological health. Early detection and intervention strategies are crucial for improving outcomes in elderly populations, potentially mitigating adverse effects on sleep, synaptic density, cognitive health, and longevity.

Sections du résumé

BACKGROUND BACKGROUND
Depression among older adults is a global concern, contributing to disability and overall illness burden. Understanding its trajectory, associated risk factors, and implications for mortality is essential for effective intervention. Moreover, the relationship between depression, sleep disturbances, and synaptic density in the ageing brain remains complex and poorly understood.
METHODS METHODS
Using data from the University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age cohort, comprising 6375 participants, we conducted comprehensive assessments of depression trajectories using generalized linear mixed models and mortality risks using Cox mixed-effects models. Generalized structural equation modelling was performed to explore longitudinal associations between sleep duration and depression. Lastly, associations between post-mortem synaptic density and depression were investigated.
RESULTS RESULTS
Our findings revealed that depression rates declined until age 80 before increasing again. Depression was associated with a 10 % increased risk of mortality in older adults. Reduced sleep was correlated with depression, and depression measured early in the study predicted future reduced sleep. Post-mortem analysis showed a global reduction in synaptic density associated with depression, particularly pronounced in the frontal lobe.
LIMITATIONS CONCLUSIONS
Limitations include recall bias, limiting generalizability due to dominantly including White British participants and difficulty in establishing causation between synaptic density and depression.
CONCLUSION CONCLUSIONS
Our study underscores the significance of addressing depression in older adults, not only for mental health but also for mortality risk and neurobiological health. Early detection and intervention strategies are crucial for improving outcomes in elderly populations, potentially mitigating adverse effects on sleep, synaptic density, cognitive health, and longevity.

Identifiants

pubmed: 39216641
pii: S0165-0327(24)01419-8
doi: 10.1016/j.jad.2024.08.186
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest All authors report no conflict of interest.

Auteurs

Altug Didikoglu (A)

Division of Neuroscience, Faculty of Science, Izmir institute of Technology, Izmir, Turkey. Electronic address: altugdidikoglu@iyte.edu.tr.

Esin Simge Guler (ES)

Division of Molecular Biology and Genetics, Faculty of Science, Izmir Institute of Technology, Izmir, Turkey.

Halil Kaan Turk (HK)

Division of Molecular Biology and Genetics, Faculty of Science, Izmir Institute of Technology, Izmir, Turkey.

Kubilay Can (K)

Division of Molecular Biology and Genetics, Faculty of Science, Izmir Institute of Technology, Izmir, Turkey.

Aleyna Nur Erim (AN)

Division of Molecular Biology and Genetics, Faculty of Science, Izmir Institute of Technology, Izmir, Turkey.

Antony Payton (A)

Division of Informatics, Imaging & Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.

Chris Murgatroyd (C)

School of Healthcare Science, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, UK.

Eduwin Pakpahan (E)

Applied Statistics Research Group, Department of Mathematics, Physics & Electrical Engineering, Northumbria University, Newcastle upon Tyne, UK.

James Minshull (J)

Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Neuroscience, University of Manchester, Manchester, UK.

Andrew C Robinson (AC)

Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Neuroscience, University of Manchester, Manchester, UK.

Asri Maharani (A)

Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester and Manchester Academic Health Science Centre (MAHSC), Manchester, UK.

Classifications MeSH