Polygenicity of Comorbid Depression in Multiple Sclerosis
Bibliographic record
Abstract
Background and Objectives Depression is common in multiple sclerosis (MS) and is associated with faster disability progression. The etiology of comorbid depression in MS remains poorly understood. Identification of individuals with a high risk of depression, through polygenic scores (PGS), may facilitate earlier identification. Previous genetic studies of depression considered depression as a primary disorder, not a comorbidity, and thus, findings may not generalize to MS. Body mass index (BMI) is a risk factor of both MS and depression, and its association may highlight differences in depression in MS. To improve the understanding of comorbid depression in MS, we will investigate PGS in people with MS, with the hypothesis that a higher depression PGS is associated with increased odds for comorbid depression in MS. Methods Samples from 3 sources (Canada, UK Biobank, and the United States) were used. Individuals were grouped into cases (MS/comorbid depression) and compared with 3 control groups: MS/no depression, depression/no immune disease, and healthy persons. We used 3 depression definitions: lifetime clinical diagnoses, self-reported diagnoses, and depressive symptoms. The PGS were tested in association with depression using regression. Results A total of 106,682 individuals of European genetic ancestry were used: Canada (n = 370; 213 with MS), UK Biobank (n = 105,734; 1,390 with MS), and the United States (n = 578 with MS). Meta-analyses revealed individuals with MS and depression had a higher depression PGS compared with both individuals with MS without depression (odds ratio range per SD 1.29–1.38, p < 0.05) and healthy controls (odds ratio range per SD 1.49–1.53, p < 0.025), regardless of the definition applied and when sex stratified. The BMI PGS was associated with depressive symptoms (p ≤ 0.001). The depression PGS did not differ between depression occurring as a comorbid condition with MS or as the primary condition (odds ratio range per SD 1.03–1.13, all p > 0.05). Discussion A higher depression genetic burden was associated with approximately 30%–40% increased odds of depression in European genetic ancestry participants with MS compared with those without depression and was no different compared with those with depression and no comorbid immune disease. This study paves the way for further investigations into the possible use of PGS for assessing psychiatric disorder risk in MS and its application to non-European genetic ancestries.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".