POS0162 THE INFLUENCE OF BODY MASS INDEX ON CARDIOVASCULAR RISK IN RHEUMATOID ARTHRITIS VARIES ACROSS ANTICITRULLINATED PROTEIN ANTIBODY STATUS AND BIOLOGIC USE
Bibliographic record
Abstract
Background: The impact of body mass index (BMI) as a surrogate of body fat content on cardiovascular risk in rheumatoid arthritis (RA) is unclear. Obesity associated with higher RA activity among anticitrullinated antibody (ACPA) positive but not negative patients. Since RA activity predicts cardiovascular risk, we hypothesized that obesity may associate with risk differently in ACPA positive versus negative patients. Biologic disease modifying antirheumatic drugs (bDMARDs) control inflammation, mitigate cardiovascular risk and may alter body composition in RA. Therefore BMI may relate to cardiovascular risk differently in bDMARD users versus nonusers. Lastly, ACPA status influenced effectiveness of certain bDMARDs. Objectives: We hence explored the association of BMI with cardiovascular risk and whether this varied across ACPA status and bDMARD use. Methods: We evaluated 3982 patients free of cardiovascular disease upon registration in An InTernation A l Consortium for Cardiovascular disease in RA (ATACC-RA). Outcomes included: (a) first major adverse cardiovascular event (MACE) encompassing non-fatal myocardial infarction, non-fatal stroke, or cardiovascular death and (b) all events additionally comprising angina, revascularization, transient ischemic attack, peripheral arterial disease and heart failure. Missing data were imputed using multiple imputation with 10 repetitions. Multivariable Cox models stratified by center risk evaluated the impact of BMI, ACPA positivity, bDMARD use, and their two- and three-way interactions on prespecified outcomes after adjusting for age, gender, diabetes, hypertension, family history of CV disease, smoking, total cholesterol to high-density lipoprotein cholesterol ratio, 28-joint disease activity score with ESR, and RA duration. A sensitivity analysis using inverse probability of treatment weighting to balance the differences across bDMARD treatment groups and ACPA status was undertaken to support the robustness of our findings. Results: We recorded 192 MACE and 319 total events. No main effects of BMI, bDMARDs or ACPA were observed on either MACE (HR 1.02, 95% CI 0.99-1.05, 1.47, 95% CI 0.74-2.92 and 1.37, 95% CI 0.99-1.90 respectively) or any cardiovascular events (HR 1.02, 95% CI 0.99-1.04, 1.35, 95% CI 0.79-2.28 and 1.07, 95% CI 0.84-1.36 correspondingly). Notably, 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 (Figure 1A and B), BMI inversely associated with MACE (HR 0.38, 95%CI 0.25-0.57) and all-event risk (HR 0.67, 95%CI 0.49-0.92) in bDMARD users but not nonusers (p-for-interaction<0.001 and 0.012). Results of the inverse probability weighted analysis were similar. Among ACPA positive patients, while the bDMARD × BMI interaction and bDMARD main effect were not significant for either outcome, BMI directly associated with MACE (HR 1.04, 95%CI 1.01-1.07) and all event risk (HR 1.03, 95%CI 1.00-1.06) independently of biologic use (Figure 2A and B). Conclusion: Among ACPA negative patients, BMI inversely associated with cardiovascular risk only among bDMARD users. In contrast, BMI directly associated with risk in ACPA positive patients. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: George Karpouzas Scipher, Janssen, Pfizer, Elena Myasoedova: None declared, Miguel Ángel González-Gay: None declared, Alfonso Corrales Martinez: None declared, Solbritt Rantapää Dahlqvist: None declared, Petros P. Sfikakis: None declared, Patrick Dessein: None declared, Carol Hitchon: None declared, Virginia Pascual: None declared, Irazú Contreras-Yáñez: None declared, Iris J. Colunga-Pedraza: None declared, Dionicio A. Galarza-Delgado: None declared, Jose R. Azpiri-Lopez: None declared, Anne Grete Semb: None declared, Piet van Riel: None declared, Durga Prasanna Misra: None declared, Patrick Durez: None declared, Brian Bridal-Logstrup: None declared, Ellen-Margrethe Hauge: None declared, George Kitas: None declared, Sarah Ormseth: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.011 | 0.002 |
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".