POS0031 Exploring the Potential for Cardiorenal-Metabolic Therapies to Target Comorbidities in Early Rheumatoid Arthritis
Bibliographic record
Abstract
Background: Cardiorenal-metabolic (CRM) therapies, such as SGLT-2 inhibitors and GLP-1 agonists, are medications that target interconnected pathways between cardiovascular, renal, and metabolic systems and may provide additional benefits for patients with rheumatoid arthritis (RA) by potentially reducing systemic inflammation. Objectives: (1) To assess the prevalence of CRM conditions and eligibility for CRM therapies based on approved indications and investigational (off-label) use in early RA (ERA). (2) To examine differences in clinical characteristics, including sex-based variations, for patients with and without CRM conditions. Methods: Data were from the Canadian Early Arthritis Cohort, a cross-sectional study of patients recruited between 2017 and 2023, including baseline BMI, creatinine, Clinical Disease Activity Index (CDAI), and CDAI at 12 months. We estimated the baseline prevalence of approved Canadian CRM indications: (1) type 2 diabetes, (2) obesity (BMI ≥30 kg/m²), (3) heart failure, or (4) overweight (BMI ≥27 and <30 kg/m²) + ≥ 1 weight-related complication of hypertension and/or dyslipidemia. Off-label indications included (5) chronic kidney disease (< eGFR <60 mL/min/1.73 m²), (6) overweight BMI with elevated C-reactive protein (CRP>5mg/L), or (7) large joint osteoarthritis [1]. Descriptive statistics were done for the overall cohort. Stratification by sex was used to identify differences associated with CRM conditions. Results: Out of 855 recruited patients, the final sample included 278 patients and 67% were female. The mean age was 57 ± 14 years, and mean symptom duration was 5.1 ± 2.7 months. At baseline, nearly all patients (90%) had moderate or high Clinical Disease Activity Index (CDAI) scores. Overall, 54% had one CRM condition and 45% met approved indications for CRM therapy, primarily for obesity (Table 1). Overlapping conditions increased (14% had ≥2 CRM) when off-label indications such as knee OA or overweight BMI + elevated CRP were considered. Patients with CRM conditions were older and had more non-metabolic comorbidities. However, there were no significant differences in disease activity components beyond inflammatory makers (mean CRP 9.5 mg/L vs. 4.8mg/L, p=0.01), nor were there differences in initial DMARD or corticosteroid strategies. In sex-stratified analyses, no differences were observed between male and females for the number or type of CRM conditions (Table 1). Conclusion: In this real-world ERA cohort, 45% of patients met criteria for approved CRM therapy, primarily for obesity and diabetes. Obesity is known to negatively affect RA disease activity and treatment response, making it a critical target for intervention. The lack of significant sex-based differences in CRM conditions further highlights the universal relevance of CRM conditions in RA management. Future research should explore how CRM therapies, particularly those targeting obesity, could improve metabolic health and RA outcomes, warranting their study in this population. REFERENCES: [1] Bliddal H et al. Once-Weekly Semaglutide in Persons with Obesity and Knee Osteoarthritis. N Engl J Med. 2024 Oct 31;391(17):1573-1583. Table 1Baseline prevalence of approved and off-label cardiorenal-metabolic (CRM) indications, stratified by sex.Approved CRM IndicationsApproved and Off-Label CRM IndicationsTotalN=278FemaleN = 185MaleN=93P-valueTotalN = 278FemaleN=185MaleN = 93P-valueAny CRM Condition126 (45%)79 (43%)47 (51%)0.22151 (54%)94 (51%)57 (61%)0.09Type 2 Diabetes31 (11%)16 (9%)15 (16%)0.0631 (11%)16 (9%)15 (16%)0.06Obesity88 (32%)62 (34%)26 (28%)0.3588 (32%)62 (34%)26 (28%)0.34Heart Failure000NA000NAOverweight BMI + ≥ 1 weight-related complication:25 (9%)14 (8%)11 (12%)0.2441 (15%)24 (13%)17 (18%)0.24Hypertension21 (8%)12 (7%)9 (10%)0.3421 (8%)12 (6%)9 (10%)0.34Dyslipidemia16 (6%)9 (5%)7 (8%)0.3716 (6%)9 (5%)7 (8%)0.37CRP levels (>5mg/L)____28 (10%)17 (9%)11 (12%)0.49Chronic Renal Disease____12 (4%)9 (5%)3 (3%)0.76Knee Osteoarthritis____28 (10%)18 (10%)10 (11%)0.79Number of Conditions0 conditions152 (55%)106 (57%)46 (50%)0.30127 (46%)91 (49%)36 (39%)0.281 condition108 (39%)66 (36%)42 (45%)107 (38%)63 (34%)44 (47%)2 conditions18 (6%)13 (7%)5 (5%)39 (14%)27 (15%)12 (13%)3 conditions0005 (2%)4 (2%)1 (1%)≥ 4 conditions000000 Acknowledgements: On behalf of CATCH Investigators. Disclosure of Interests: Bindee Kuriya Abbvie, Pfizer, Abbive, Pfizer, UCB Canada, BMS, Sanofi, Abbvie, Pfizer, Susan J. Bartlett Janssen, Sandoz, Nordic, Marie-France Valois: None declared, Hugues Allard-Chamard AstraZeneca, Abbvie, Amgen, Astrazeneca, BMS, Celltrion, Eli Lilly, GSK, Hoffmann-La Roche, Janssen, Novartis, Otsuka, Sandoz, Pfizer, Sobi, AstraZeneca, Eli Lilly, Fresenius Kabi, Pfizer, Carol A Hitchon Sandoz, Pfizer, Astra Zeneca, Carter Thorne Medexus, Accord, AbbVie, Acccord, BIOGEN, Pfizer, Roche, Medexus, Nordic, Organon, JAMP, Pfizer, Glen Hazlewood: None declared, Louis Bessette Amgen, BMS, Janssen, UCB, Abbvie, Pfizer, Lilly, Novartis, Sanofi, TEVA, Fresenius Kabi, Sandoz, JAMP Pharma, Organon, Amgen, BMS, Janssen, UCB, Abbvie, Pfizer, Celgene, Lilly, Novartis, Sanofi, TEVA, Fresenius Kabi, Sandoz, Organon, Sobi, Amgen, BMS, Janssen, UCB, Abbvie, Pfizer, Celgene, Sanofi, Lilly, Novartis, AstraZeneca, JAMP Pharma, Janet Pope AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Certa, Eli Lilly, Frensenius Kabi, Janssen, Nordic Pharma, Novartis, Organon, Otsuka, Palleon, Pfizer, Sandoz, Sanofi, UCB, Zura, AbbVie, Amgen, Astra Zeneca, Boehringer Ingelheim, Boxer Capital, Bristol Myers Squibb, Celltrion Healthcare, Eli Lilly, Frensenius Kabi, GSK, Janssen, Merck, Novartis, Pfizer, Sandoz, Sanofi, DSMB: Astra Zeneca, Horizon, Novartis, BMS, Janssen, Mallinckrodt, Pfizer (Seattle Genetics), Gilles Boire Abbvie, Janssen, Lilly, Mylan, Novartis, Pfizer, Sanofi, Teva, Viatris, BMS, Biocon, Pfizer, Vivian Bykerk: 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".