Coronary artery calcium in middle-aged adults with and without rheumatoid arthritis: a case-cohort analysis
Bibliographic record
Abstract
Abstract Background Rheumatoid Arthritis (RA) is a chronic autoimmune disease associated with systemic inflammation and a potential increased risk of subclinical atherosclerosis. Coronary Artery Calcium (CAC) is a marker of atherosclerotic disease events. Purpose The aim of this study was to evaluate the cross-sectional association of CAC prevalence and the prospective 4-year follow-up associations of CAC incidence and progression in individuals with and without RA, free of prior cardiovascular conditions at baseline, using data from a large multicentre cohort study. Methods A case-cohort study was conducted with 76 participants with RA and 441 controls without RA, selected from a randomly assigned subcohort at baseline from a prospective cohort study of adults aged 35–74 years. This analysis included baseline (2008–2010) and 4-year follow-up data (2012–2014). RA diagnosis was based on physician confirmation, use of disease-modifying antirheumatic drugs, or positive serological biomarkers. CAC was measured using computed tomography, and Agatston scores were calculated. Associations between RA and CAC prevalence, incidence, and progression were assessed using linear regression for log (CAC+1), logistic regression for CAC > 0, and Poisson regression for CAC incidence and progression. CAC progression was evaluated using Berry, Hokanson, and Raggi criteria. Models were adjusted for sociodemographic and cardiovascular risk factors. P-value < 0.05 was considered statistically significant. Results The mean ± SD age of participants was 50.5 ± 8.5 years, with 55.5% being women. Participants with RA were significantly older (53.2 ± 8.7 vs 50.0 ± 8.3 years) and more likely to be female (82.9% vs 50.8%). At baseline, no significant differences were observed between groups for CAC prevalence when assessed as a continuous log-transformed variable (log [CAC+1]: β = 0.18, 95% CI: -0.24 to 0.60; p = 0.402) or as a categorical variable (CAC ≥ 0: Odds Ratio (OR) = 1.18, 95% CI: 0.59 to 2.28; p = 0.635). Similarly, for CAC incidence after 4 years of follow-up, no significant difference was found (Relative Risk (RR) = 0.84, 95% CI: 0.34 to 2.07, p = 0.700), and CAC progression also showed no significant results using the Berry, Hokanson, and Raggi criteria (Table 1). Conclusion There was no evidence of a significant association between RA and CAC prevalence, incidence after 4 years of follow-up, or progression. The relatively short follow-up period may have contributed to the lack of association observed in our findings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".