University of Worcester Worcester Research and Publications

Psychosocial markers of age at onset in bipolar disorder: a machine learning approach

Bolton, S., Joyce, D. W., Gordon-Smith, Katherine ORCID:, Jones, Lisa ORCID:, Jones, I., Geddes, J. and Saunders, K. E. A. (2022) Psychosocial markers of age at onset in bipolar disorder: a machine learning approach. BJPsych Open, 8 (4). e133. ISSN 2056-4724

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Bipolar disorder is a chronic and severe mental health disorder. Early stratification of individuals into subgroups based on age at onset (AAO) has the potential to inform diagnosis and early intervention. Yet, the psychosocial predictors associated with AAO are unknown.

We aim to identify psychosocial factors associated with bipolar disorder AAO.

Using data from the Bipolar Disorder Research Network UK, we employed least absolute shrinkage and selection operator regression to identify psychosocial factors associated with bipolar disorder AAO. Twenty-eight factors were entered into our model, with AAO as our outcome measure.

We included 1022 participants with bipolar disorder (μ = 23.0, s.d. ± 9.86) in our model. Six variables predicted an earlier AAO: childhood abuse (β = −0.2855), regular cannabis use in the year before onset (β = −0.2765), death of a close family friend or relative in the 6 months before onset (β = −0.2435), family history of suicide (β = −0.1385), schizotypal personality traits (β = −0.1055) and irritable temperament (β = −0.0685). Five predicted a later AAO: the average number of alcohol units consumed per week in the year before onset (β = 0.1385); birth of a child in the 6 months before onset (β = 0.2755); death of parent, partner, child or sibling in the 6 months before onset (β = 0.3125); seeking work without success for 1 month or more in the 6 months before onset (β = 0.3505) and a major financial crisis in the 6 months before onset (β = 0.4575).

The identified predictor variables have the potential to help stratify high-risk individuals into likely AAO groups, to inform treatment provision and early intervention.

Item Type: Article
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.

Uncontrolled Discrete Keywords: Bipolar affective disorders, childhood experience, psychosocial interventions, statistical methodology, aetiology
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: College of Health, Life and Environmental Sciences > School of Allied Health and Community
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Copyright Info: © The Author(s), 2022. Published by Cambridge University Press on behalf of the Royal College of Psychiatrists
Depositing User: Katherine Gordon-Smith
Date Deposited: 20 Jul 2022 08:25
Last Modified: 20 Jul 2022 08:25

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