Examining the Dimensionality of Anxiety and Depression: a Latent Profile Approach to Modeling Transdiagnostic Features

Depression and anxiety are highly prevalent psychological disorders; our understanding of these conditions remains limited. Efforts to explain anxiety and depression have been constrained in part by binary classification systems. Dimensional approaches to understanding psychopathology may be more effective. The present study used latent profile analysis (LPA) to assess whether unique subgroups exist within a tri-level model of anxiety and depression. Participants (N = 627) completed self-report questionnaires from which tri-level model factors were derived. LPA was conducted on those factors. A 4-profile model offered optimal fit to the data at baseline. This model was replicated at a second time point. Models derived included profiles labelled ‘Mixed Fears,’ ‘Anxious Arousal,’ ‘Low Mood/Anhedonia,’ and ‘Sub-Clinical.’ Profiles were validated at Time 1 using diagnostic status and clinical severity ratings associated with mood and anxiety presentations. Profiles demonstrated flexibility in accommodating breadth in clinical presentations and common comorbidities. Latent variable models may offer more ecologically valid approaches to understanding psychopathology.