Data-driven symptom dimensions reveal familial patterns in bipolar disorder

Bipolar disorder (BD) is a highly heritable psychiatric illness whose clinical and genetic heterogeneity complicates efforts to identify biologically-relevant subtypes. Traditional categorical approaches often fail to capture the multidimensional nature of BD symptomatology. This study aimed to evaluate whether data-driven dimensions show familial aggregation, suggesting potential genetic underpinnings.

Using two independent cohorts: a primary sample from Halifax (N = 368) and a replication sample from the NIMH Genetics Initiative Bipolar Disorder Consortium (N = 1356), latent dimensions were derived from 21 clinical variables with principal component analysis (PCA). The similarity of relatives in the PCA-derived space was quantified and compared to their similarity with unrelated BD subjects. Mixed-effects models assessed whether familial similarity on latent dimensions increased with degree of relatedness.

Across both cohorts, the first two principal components (PCs; i.e., mood episode frequency and age of illness onset) were consistent. Overall clinical phenotype was more similar among relatives than among unrelated cases (Halifax: β = 0.316, p = 0.025; NIMH: β = 0.406, p < 0.001; Combined: β = 0.388, p < 0.001). PC 2 (onset) showed significant familial similarity in both cohorts, and PC 1 (episode frequency) showed similarity in the NIMH sample.

These findings suggest that latent clinical dimensions, especially those reflecting mood episode recurrence and age of onset, aggregate within families and may reflect underlying genetic liability in BD. Dimensional, data-driven phenotypes could provide more genetically informative traits than traditional diagnostic subtypes and offer promising targets for future genetic and neurobiological research.