Depression Polygenicity and Disease Activity and Disability Worsening in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: A better understanding of factors associated with multiple sclerosis (MS) disease activity and disability is needed. Given the strong link between comorbid depression and MS disease activity and disability, we aimed to determine whether the depression genetic burden, as modelled using its polygenic score, is associated with MS disease activity and disability worsening. METHODS: In this cohort study, we used samples from neurologist-defined adult people with MS (PwMS) followed in clinical care or during a clinical trial from existing cohorts: Canada, the United States (US), and Sweden with extensive longitudinal phenotypes. We computed the depression polygenic score (PGS) and tested its association with annualized relapse rate and worsening disability. In the US cohort, we additionally explored the time to relapse, number of enhancing lesions, and confirmed Expanded Disability Status Scale (EDSS) worsening during the study period. RESULTS: We included 3,420 relapsing-onset PwMS of European genetic ancestry with a median follow-up of 3 to 5 years. Meta-analyses revealed for each 1-standard deviation increase in the depression PGS, the relapse rate increased (incidence rate ratio: 1.23, 95% confidence interval [CI] = 1.01-1.50). In the US cohort, higher depression PGS was associated with protocol-defined relapses (hazard ratio [HR] = 1.58, 95% CI = 1.03-2.43), and time to confirmed EDSS worsening (HR = 1.51, 95% CI = 1.03-2.22) with this effect largely direct. INTERPRETATION: Meta-analyses showed a higher depression genetic burden was associated with increased MS disease activity. In the US clinical trial cohort only, we found a significant association between higher depression PGS and time to relapse and confirmed EDSS worsening. These findings may provide insights into MS disease activity and disability worsening. ANN NEUROL 2025;98:1057-1069.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".