Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank.


Journal

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

Informations de publication

Date de publication:
15 08 2023
Historique:
received: 28 07 2022
revised: 25 04 2023
accepted: 29 04 2023
medline: 9 6 2023
pubmed: 9 5 2023
entrez: 8 5 2023
Statut: ppublish

Résumé

Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes. Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC). For MD vs. controls (n Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary. Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.

Sections du résumé

BACKGROUND
Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes.
METHODS
Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC).
RESULTS
For MD vs. controls (n
LIMITATIONS
Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary.
DISCUSSION
Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.

Identifiants

pubmed: 37156273
pii: S0165-0327(23)00624-9
doi: 10.1016/j.jad.2023.04.138
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

83-94

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S003061/1
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest None.

Auteurs

Laura M Lyall (LM)

School of Health and Wellbeing, University of Glasgow, Glasgow, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. Electronic address: laura.lyall@glasgow.ac.uk.

Natasha Sangha (N)

School of Health and Wellbeing, University of Glasgow, Glasgow, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

Xingxing Zhu (X)

School of Health and Wellbeing, University of Glasgow, Glasgow, UK.

Donald M Lyall (DM)

School of Health and Wellbeing, University of Glasgow, Glasgow, UK.

Joey Ward (J)

School of Health and Wellbeing, University of Glasgow, Glasgow, UK.

Rona J Strawbridge (RJ)

School of Health and Wellbeing, University of Glasgow, Glasgow, UK; Health Data Research, UK; Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden.

Breda Cullen (B)

School of Health and Wellbeing, University of Glasgow, Glasgow, UK.

Daniel J Smith (DJ)

School of Health and Wellbeing, University of Glasgow, Glasgow, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

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Classifications MeSH