Depressive symptom trajectories and polygenic risk scores in individuals with an immune-mediated inflammatory disease
Bibliographic record
Abstract
OBJECTIVE: To develop group-based trajectories of depressive symptoms in immune-mediated inflammatory disease (IMID) to understand their evolution and identify any associated factors, with the overall goal of identifying those at highest risk of higher depressive symptom burden. METHOD: 922 participants had an IMID or anxiety/depression. The PHQ-9 was administered at four visits, and polygenic risk scores (PRS) for major depressive disorder, depressive symptoms, and body mass index (BMI) were generated. Group-based trajectory modelling of PHQ-9 scores estimated distinct trajectories. Regression tested whether specific factors were associated with the trajectories. Mediation analyses assessed whether IMID mediated the association between BMI PRS and trajectories. RESULTS: Three trajectories were identified. Regression demonstrated those in Group 3 ('high symptoms') had significantly higher PRS for the three traits, compared to Group 1 ('minimal symptoms') (OR: 1.34-1.66, P < 0.01). Stratified analyses in the IMID subgroup revealed an increased effect for BMI PRS in Group 3 (OR: 2.31, P < 0.001), in contrast, BMI PRS was no longer associated in the non-IMID sample. No significant indirect effect of BMI PRS on depressive symptoms trajectories was identified via IMID. CONCLUSIONS: A significant association between polygenicity and PHQ-9 trajectories supports a role for genetic inheritance in the variability in depressive symptoms in IMID.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".