Familial aggregation of candidate phenotypes for borderline personality disorder.
Bibliographic record
Abstract
Borderline personality disorder (BPD) and its core Diagnostic and Statistical Manual of Mental Disorders (DSM) factor-analytically derived phenotypes aggregate in families. To potentially inform future conceptualizations of BPD, this study examined the familial aggregation and co-aggregation with BPD of 3 additional candidate phenotypes for BPD psychopathology: anxiousness, aggressiveness, and cognitive dysregulation. Participants included 347 probands (126 with BPD, 128 without BPD, and 93 with major depressive disorder) and 814 parents and siblings of probands. All participants completed diagnostic assessments and scales assessing the candidate phenotypes. The familial aggregation of phenotypes (correlation of level of phenotype between family members), the familial co-aggregation of phenotypes with BPD (correlation of phenotype with BPD between family members), and the within-individual correlation of phenotypes with BPD were assessed. All 3 candidate phenotypes showed high levels of familial aggregation (rs = .14 - .53, ps < .001), the magnitudes of which were comparable with DSM-based core sectors of psychopathology. Anxiousness and cognitive dysregulation showed strong within-individual associations with BPD (rs = .55 and .46, respectively; ps < .001) and substantial familial co-aggregation with BPD (rs = .12 and .13, respectively; ps ≤ .002). In contrast, aggressiveness showed a weak within-individual association with BPD (r = .11, p = .12) and little familial co-aggregation with BPD (r = .05, p = .21). These findings suggest that anxiousness and cognitive dysregulation are promising phenotypes for BPD psychopathology that move beyond factor-analytically based conceptualizations. In contrast, aggressiveness was only weakly related to BPD, suggesting that this phenotype may not represent an essential feature of this disorder.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".