Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

BACKGROUND: 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. AIMS: We aim to identify psychosocial factors associated with bipolar disorder AAO. METHOD: 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. RESULTS: 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). CONCLUSIONS: The identified predictor variables have the potential to help stratify high-risk individuals into likely AAO groups, to inform treatment provision and early intervention.

Original publication

DOI

10.1192/bjo.2022.536

Type

Journal article

Journal

BJPsych Open

Publication Date

18/07/2022

Volume

8

Keywords

Bipolar affective disorders, aetiology, childhood experience, psychosocial interventions, statistical methodology