Interface of Multiple Sclerosis, Depression, Vascular Disease, and Mortality
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To assess whether the association among depression, vascular disease, and mortality differs in people with multiple sclerosis (MS) compared with age-, sex-, and general practice-matched controls. METHODS: We conducted a population-based retrospective matched cohort study between January 1, 1987, and September 30, 2018, that included people with MS and matched controls without MS from England, stratified by depression status. We used time-varying Cox proportional hazard regression models to test the association among MS, depression, and time to incident vascular disease and mortality. Analyses were also stratified by sex. RESULTS: We identified 12,251 people with MS and 72,572 matched controls. At baseline, 21% of people with MS and 9% of controls had depression. Compared with matched controls without depression, people with MS had an increased risk of incident vascular disease regardless of whether they had comorbid depression. The 10-year hazard of all-cause mortality was 1.75-fold greater in controls with depression (95% confidence interval [CI] 1.59-1.91), 3.88-fold greater in people with MS without depression (95% CI 3.66-4.10), and 5.43-fold greater in people with MS and depression (95% CI 4.88-5.96). Overall, the interaction between MS status and depression was synergistic, with 14% of the observed effect attributable to the interaction. Sex-stratified analyses confirmed differences in hazard ratios. DISCUSSION: Depression is associated with increased risks of incident vascular disease and mortality in people with MS, and the effects of depression and MS on all-cause mortality are synergistic. Further studies should evaluate whether effectively treating depression is associated with a reduced risk of vascular disease and mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".