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

ABSTRACT:

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 pre

SUBMITTER: Bolton S 

PROVIDER: S-EPMC9344222 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature