Influence of body mass index on cardiovascular risk in rheumatoid arthritis varies across anti-citrullinated protein antibody status and biologic use
Bibliographic record
Abstract
OBJECTIVES: The impact of body mass index (BMI) on cardiovascular risk in rheumatoid arthritis (RA) is unclear. RA characteristics may influence the association between BMI and risk. Disease activity, which predicts cardiovascular risk, is associated with obesity only among anticitrullinated antibody (ACPA)-positive patients. Biologics alter body composition and mitigate cardiovascular risk in RA. We explored the association of BMI with cardiovascular risk and whether this varied across ACPA status and biologic use. METHODS: We evaluated 3982 patients from an international observational cohort. Outcomes included (a) first major adverse cardiovascular event (MACE) encompassing myocardial infarction, stroke or cardiovascular death; and (b) all events comprising MACE, angina, revascularisation, transient ischaemic attack, peripheral arterial disease and heart failure. Multivariable Cox models stratified by centre risk evaluated the impact of BMI, ACPA, biologics and their two- and three-way interactions on outcomes. RESULTS: We recorded 192 MACE and 319 total events. No main effects of BMI, ACPA or biologics were observed. A three-way interaction between them on MACE (p-interaction<0.001) and all events (p-interaction=0.028) was noted. Among ACPA negative patients, BMI was inversely associated with MACE (HR 0.38 (95% CI 0.25 to 0.57)) and all events (HR 0.67 (0.49 to 0.92)) in biologic users but not non-users (p-for-interaction <0.001 and 0.012). Among ACPA-positive patients, BMI was associated with MACE (HR 1.04 [1.01-1.07]) and all events (HR 1.03 (1.00 to 1.06)) independently of biologic use. CONCLUSIONS: BMI is inversely associated with cardiovascular risk only among ACPA-negative biologic users. In contrast, BMI is associated with cardiovascular risk in ACPA-positive patients independently of biologic use.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".