Cardiovascular Disease Incidence Among Aging Patients with Rheumatoid Arthritis: Results from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Background and Aims Rheumatoid arthritis (RA) is associated with a higher risk of cardiovascular disease (CVD). This study examined CVD incidence among middle-aged and older Canadians with and without RA and identified the role of potential risk factors for CVD in this population. Methods and Results Data were obtained from the Canadian Longitudinal Study on Aging (CLSA) using three time points between 2011 and 2021. Incidence rate ratios (IRR) for CVD were calculated to compare CVD incidence among RA and non-RA individuals. Cox proportional hazards regression models identified the potential risk factors associated with CVD incidence. Stratification based on sex, age and education levels were conducted. The analysis included 19,844 participants. RA patients (N = 553) experienced significantly higher CVD incidence, compared to the non-RA individuals (IRR = 1.69, 95% CI: 1.34 – 2.11). Multivariable analysis showed that RA, elevated C-reactive protein (CRP) level, disease-modifying antirheumatic drugs (DMARDs) other than methotrexate, male sex, older age, low physical activity, smoking, dissatisfaction with sleep quality, diabetes, hypertension and mood disorders were significant risk factors for CVD. Stratified analyses revealed stronger associations between RA and CVD among females, individuals < 65 years old and those with lower education levels. Conclusions This large population-based study confirmed the elevated risk of CVD among RA patients and identified high-risk subgroups, including females, younger individuals and those with lower socioeconomic status. This highlights the need for targeted prevention and management strategies in these vulnerable populations. Future research should explore underlying mechanisms and tailored interventions to mitigate the risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".