Data-driven symptom dimensions reveal familial patterns in bipolar disorder
Bibliographic record
Abstract
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. • Data-driven symptom dimensions were derived using PCA on mixed-type clinical data. • Mood episode frequency and age of onset emerged as consistent latent dimensions. • Relatives showed greater overall similarity in symptom dimensions than unrelated BD cases. • Familial aggregation was strongest for age of onset across both cohorts. • Latent symptom dimensions may aid in identifying genetically informative traits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".