Abstract
Objectives
Our goal was to investigate whether cardiovascular disease (CVD) risk factors are associated with the retention of biologic disease‐modifying antirheumatic drugs (bDMARDs) or targeted‐synthetic DMARDs (tsDMARDs) in patients with rheumatoid arthritis (RA).
Methods
We included participants in the Ontario Best Practices Initiative RA registry who initiated their first bDMARD or tsDMARD. Participants were grouped by the number of baseline CVD risk factors (0, 1, or ≥2). The primary outcome was time‐to‐discontinuation of therapy for any reason. Secondary outcomes included discontinuation for primary failure, secondary failure, or due to adverse events. Competing risks hazards model, adjusted for clinically important confounders, estimated the association between CVD risk factors and treatment retention.
Results
The sample included 872 patients, of which 58% (n = 508) discontinued their b/tsDMARD after a median of 13 months from the time of initiation. The most common causes for treatment discontinuation were primary failure (n = 72), secondary failure (n = 126), or adverse events (n = 133). Patients with no CVD risk factors experienced significantly longer treatment survival compared to patients with 1 or ≥2 CVD risk factors. In multivariable‐adjusted analysis, there was no association between all‐cause discontinuation and CVD risk factors. However, there was a significant association between the presence of >1 CVD risk factor and treatment discontinuation, notably due to secondary treatment failure, but not due to adverse events.
Conclusion
Multiple CVD risk factors increase the risk of treatment failure in RA, particularly for secondary treatment failure. To improve patient outcomes, future research should focus on developing strategies to identify early treatment nonresponse and investigate the potential modifiability of this association.
INTRODUCTION
There is a well‐established association between rheumatoid arthritis (RA) and cardiovascular disease (CVD). Chronic inflammation and a high prevalence of traditional CVD risk factors—which are often underdiagnosed and undertreated—contribute to the excess CVD risk for RA patients. 1 The presence of CVD risk factors can have a significant impact on overall RA care and outcomes. We previously demonstrated that CVD risk factors are independently associated with higher disease activity and disability in a large cohort of patients with RA. 2 Similar results have been observed in studies showing an association between worse disease activity and obesity, insulin resistance, hypertension, and smoking. 3 , 4
SIGNIFICANCE & INNOVATIONS.
A high proportion (62%) of biologic naïve patients with rheumatoid arthritis (RA) initiating their first biologic or targeted‐synthetic disease‐modifying antirheumatic drug (b/tsDMARD) have at least one comorbid cardiovascular disease (CVD) risk factor.
Patients with CVD risk factors are a high‐risk group for discontinuation of their initial b/tsDMARD.
The presence of >1 CVD risk factor was associated with discontinuation of b/tsDMARD due to primary or secondary treatment failure but not due to adverse events.
The mechanisms by which CVD comorbidity negatively impact RA outcomes are not fully understood. Some studies have suggested that obesity could potentially lower the response to biologic disease‐modifying antirheumatic drugs (bDMARDs) or targeted‐synthetic DMARDs (tsDMARDs) through altered pharmacokinetics and pharmacodynamics. 3 Similarly, smoking has been identified as a negative predictor of treatment response in patients with RA, with higher disease activity and lower medication retention rates. 4 It is theorized that polypharmacy used to treat multiple comorbidities can also increase the risk of medication interactions, adverse effects, and nonadherence. 5 This in turn can compromise the overall management of both RA and CVD. However, it is unknown how much of the poor treatment response observed in the setting of CVD risk factors may relate to medication retention or discontinuation. Specifically, no studies have examined if CVD risk factors increase the risk of primary or secondary treatment failure.
Primary failure refers to the lack of response or inadequate response usually within a certain time frame (within three to six months), whereas secondary failure occurs when a previously good response that was obtained is lost over time. 6 This is an important distinction, especially early in the line of RA therapy, because the reason for treatment failure guides future treatment selection and management. Our goal was to study the impact of CVD risk factors on medication retention and reasons for discontinuation among biologic‐naïve patients initiating their first bDMARD or tsDMARD. We hypothesized that CVD risk factors are associated with lower medication retention, primarily mediated by treatment failure.
PATIENTS AND METHODS
Study design and population
This study used data from the Ontario Best Practices Research Initiative (OBRI), a provincial registry that prospectively collects data from approximately one‐third of rheumatologists in Ontario, Canada. Further details of the OBRI have been described previously.2 Adult patients are eligible if they have a confirmed RA diagnosis by a rheumatologist according to the American College of Rheumatology (ACR)/EULAR 2010 RA Classification Criteria and at least one swollen joint. 7 Treatment is not protocolized and left at the discretion of the rheumatologist, following a treat‐to‐target approach. In Ontario, patients must try and fail conventional synthetic DMARDs (csDMARDs), including methotrexate, before accessing advanced therapeutics.
For this study, we included patients who were new initiators of their first bDMARD (tumour necrosis factor inhibitor [TNFi], interleukin 6 inhibitor [IL6i], rituximab, or abatacept) or tsDMARD (tofacitinib, baricitinib, upadacitinib) after cohort entry between January 2008 and April 2021. Concomitant treatment with csDMARDs or steroids was permitted. Patients with any prior use of b/tsDMARDs or a history of CVD were excluded. Patients were required to have ≥2 consecutive visits spanning a minimum of 12 months and were followed until drug discontinuation or the end of follow‐up period, whichever came first. Ethics approval was obtained from the University Health Network (REB# 07‐0729 AE). Participants provided verbal and written informed consent for both the OBRI study participation and the reporting and publication of the aggregated de‐identified data.
Data collection
Data on demographics, disease characteristics, non‐CVD comorbidities (depression, respiratory disease, gastrointestinal or liver disease, malignancy, and osteoarthritis), current and past medications, health assessment questionnaire‐disability index (HAQ‐DI), clinical disease activity index (CDAI), and disease activity score 28 joints‐erythrocyte sedimentation rate (DAS28‐ESR) were collected. The baseline number of CVD risk factors was determined based on physician report.
Exposures
The study population was divided into three mutually exclusive categories based on the number of baseline CVD risk factors: 0, 1, ≥2. The CVD risk factors included were diabetes (DM), dyslipidemia (DLD), hypertension (HTN), obesity (body mass index of ≥30), and current smoking status. Ten patients with CVD disease and no reported CVD risk factors were excluded from analysis.
Outcomes
The primary outcome of interest was treatment discontinuation of the first b/tsDMARD for any reason (primary failure, secondary failure, adverse event, reimbursement issues, patient decision, physician decision, improvement, completed treatment, and other). The main reason for discontinuation was selected and recorded by the treating rheumatologist. Our secondary outcomes of interest were treatment discontinuation for the specific reasons of primary failure, secondary failure, or due to an adverse event. We also explored these as composite outcomes.
Statistical analysis
Descriptive statistics were used to summarize baseline demographic and clinical characteristics, using mean and SD for continuous variables or proportions for categorical variables. Comparisons of variables by the groups were conducted using the chi‐square test and Fisher's exact test for categorical variables and the analysis of variance (ANOVA) and Kruskal‐Wallis test for continuous variables, as appropriate.
A competing risk regression model was used to estimate the probability of b/tsDMARD retention, considering the competing risks of possible discontinuation of therapy due to treatment failure, adverse events, or other reasons. Based on a cumulative incidence function, a subdistribution hazard ratio (sHR) was calculated based on the Fine and Gray model, a proportional hazards model for the subdistrubtion of a competing risk. In this multivariable model, CVD risk factors were the main exposure of interest, and we adjusted for potential confounders that were selected a priori (age, sex, CDAI score, disease duration, HAQ‐DI score, number of non‐CVD comorbidities, and csDMARD or oral glucocorticoid use). Results were reported as sHR with 95% confidence intervals (CIs). Statistical significance level was set for P < 0.05. We additionally ran multiple imputation techniques to impute missing data using the full conditional specification (FCS) methods for missing covariates at treatment initiation.
Last, we conducted a sensitivity analysis separating composite CVD risk factors to the individual components (DM, DLD, HTN, obesity, and current smoking status) to determine their association with drug survival. All analyses were performed using version 9.4 (SAS/STAT 13.1) of SAS software.
RESULTS
A total of 872 patients were included in this study. The baseline demographics and disease characteristics are presented in Table 1. A notable proportion of the patients (62%) presented with at least one CVD risk factor. Thirty‐six percent of patients had only one CVD risk factor, whereas 25% had two or more. The most common risk factors were hypertension (33%), obesity (25%), and current smoking status (18%).
Table 1.
Baseline characteristics of study participants, stratified by number of cardiovascular risk factors*
| Baseline characteristics | All (n = 872) | No CVD RF (n = 336) | 1 CVD RF (n = 318) | ≥2 CVD RF (n = 218) | P value |
|---|---|---|---|---|---|
| Age, mean (SD), y | 57.5 (12.7) | 53.9 (12.7) | 58.4 (13.0) | 61.5 (10.6) | <0.01 |
| Female, n (%) | 695 (79.7) | 276 (82.1) | 248 (78.0) | 171 (78.4) | 0.36 |
| White, n (%) | 692 (82.1) | 244 (78.7) | 268 (85.4) | 180 (82.6) | 0.09 |
| RA duration, mean (SD), y | 7.8 (8.3) | 7.7 (7.9) | 8.6 (8.9) | 7.0 (7.9) | 0.09 |
| RF seropositivity, n (%) | 609 (74.5) | 243 (76.4) | 225 (76.5) | 141 (68.5) | 0.07 |
| PtGA (0–10), mean (SD) | 4.2 (2.7) | 4.0 (2.8) | 4.1 (2.7) | 4.6 (2.7) | 0.04 |
| PhGA (0–10), mean (SD) | 3.5 (2.5) | 3.3 (2.4) | 3.6 (2.5) | 3.7 (2.4) | 0.10 |
| ESR, mean (SD), mm/hr | 20.2 (19.4) | 19.8 (20.9) | 19.3 (17.8) | 22.0 (19.4) | 0.05 |
| CRP, mean (SD), mg/L | 8.7 (15.6) | 8.9 (18.5) | 8.1 (12.5) | 9.1 (15.1) | <0.01 |
| DAS28‐ESR, mean (SD) | 3.8 (1.6) | 3.6 (1.7) | 3.7 (1.5) | 4.0 (1.5) | 0.01 |
| CDAI score, mean (SD) | 16.5 (12.1) | 15.4 (12.2) | 16.4 (11.9) | 18.4 (11.9) | 0.03 |
| HAQ score, mean (SD) | 1.27 (0.73) | 1.00 (0.75) | 1.12 (0.77) | 1.29 (0.76) | <0.01 |
| Non‐CVD comorbidity, n (%) | |||||
| Depression | 91 (10) | 27 (8) | 38 (12) | 26 (12) | 0.18 |
| Respiratory disease | 107 (12) | 30 (9) | 45 (14) | 32 (15) | 0.06 |
| Gastrointestinal disease | 90 (10) | 24 (7) | 30 (9) | 36 (17) | <0.01 |
| Liver disease | 27 (3) | 5 (1) | 14 (4) | 8 (4) | 0.08 |
| Malignancy | 49 (6) | 19 (6) | 17 (5) | 13 (6) | 0.95 |
| Osteoarthritis | 175 (20) | 48 (14) | 71 (22) | 56 (26) | <0.01 |
| Number of non‐CVD comorbidities, n (%) | <0.01 | ||||
| 0 | 503 (57.7) | 223 (66.4) | 176 (55.4) | 104 (52.2) | |
| 1 | 235 (27.0) | 79 (23.5) | 86 (27.0) | 70 (32.1) | |
| >1 | 134 (15.4) | 34 (10.1) | 56 (17.6) | 44 (20.2) | |
| Number of CVD risk factors, n (%) | |||||
| 0 | 336 (38.5) | 0 (0) | 0 (0) | 0 (0) | |
| 1 | 318 (36.5) | 0 (0) | 318 (100) | 0 (0) | |
| 2 | 147 (16.9) | 0 (0) | 0 (0) | 147 (67.4) | |
| 3 | 53 (6) | 0 (0) | 0 (0) | 53 (24.3) | |
| 4 | 18 (2) | 0 (0) | 0 (0) | 18 (8.2) | |
| 5 | 0 (0) | 0 (0) | 0 (0) | 0 (0) | |
| Type of CVD risk factors, n (%) | |||||
| Obesity | 219 (29.1) | 0 (0) | 85 (26.7) | 134 (61.5) | |
| Current smoking | 154 (18.3) | 0 (0) | 86 (27.0) | 68 (31.2) | |
| Hyperlipidemia | 81 (9.3) | 0 (0) | 20 (6.3) | 61 (28.0) | |
| Hypertension | 96 (11.0) | 0 (0) | 12 (3.8) | 84 (38.5) | |
| Diabetes | 293 (33.6) | 0 (0) | 115 (36.2) | 178 (81.7) | |
| bDMARDs, n (%) | 786 (90) | 301 (90) | 285 (90) | 200 (92) | <0.01 |
| Etanercept | 291 (33) | 105 (31) | 115 (36) | 71 (33) | |
| Adalimumab | 177 (20) | 62 (19) | 67 (21) | 48 (22) | |
| Abatacept | 37 (4) | 8 (2) | 14 (4) | 15 (7) | |
| Rituximab | 26 (3) | 10 (3) | 7 (2) | 9 (4) | |
| Golimumab | 92 (11) | 45 (13) | 32 (10) | 15 (7) | |
| Certolizumab | 93 (11) | 34 (10) | 33 (10) | 26 (12) | |
| Tocilizumab | 27 (3) | 16 (5) | 5 (2) | 6 (3) | |
| Infliximab | 38 (4) | 18 (5) | 10 (3) | 10 (3) | |
| Sarilumab | 1 (0.1) | 0 (0) | 1 (0.3) | 0 (0) | |
| tsDMARDs | 86 (10) | 35 (10) | 33 (10) | 18 (8) | >0.05 |
| Tofacitinib | 86 (10) | 35 (10) | 33 (10) | 18 (8) | |
| Upadacitinib | 4 (0.5) | 3 (0.9) | 1 (0.3) | 0 (0) | |
| csDMARDs, n (%) | 701 (80) | 258 (77) | 260 (82) | 183 (84) | >0.05 |
| NSAIDs, n (%) | 173 (20) | 75 (22) | 66 (21) | 32 (15) | 0.09 |
| Oral Corticosteroids | 110 (13) | 37 (11) | 42 (13) | 31 (14) | 0.08 |
| Reason for discontinuation, n (%) | 0.05 | ||||
| Primary failure | 72 (8) | 21 (6) | 31 (10) | 20 (9) | |
| Secondary failure | 125 (14) | 41 (12) | 45 (14) | 39 (18) | |
| Adverse events | 133 (15) | 45 (13) | 50 (16) | 38 (17) |
ACPA, anti‐citrullinated peptide antibody; bDMARD, biologic disease‐modifying anti‐rheumatic drug; CDAI, clinical disease activity index; CRP, C‐reactive protein; csDMARD, conventional synthetic disease‐modifying anti‐rheumatic drug; CVD, cardiovascular disease; DAS28, Disease Activity Score 28; ESR, erythrocyte sedimentation rate; HAQ, health assessment questionnaire; NSAID, nonsteroidal anti‐inflammatory drug; PhGA, physician global assessment; PtGA, patient global assessment; RA, rheumatoid arthritis; RF, rheumatoid factor; tDMARD, targeted‐synthetic‐modifying anti‐rheumatic drug.
Patients with ≥2 CVD risk factors were significantly older, had higher disease severity indicators including patient global assessment, inflammatory marker elevation, DAS28‐ESR, CDAI, and HAQ‐DI scores (Table 1). In addition, they had a higher number of non‐CVD comorbidities compared to those with no CVD risk factors.
The initial advanced therapeutic was a bDMARD in the vast majority (89%) compared to tsDMARD (11%). The most commonly initiated bDMARD class was TNFi (etanercept followed by adalimumab). Tofacitinib was the most common tsDMARD (n = 86). A high proportion of patients started the advanced therapy in combination with csDMARDs (72%). Methotrexate was the most common csDMARD (n = 483), of which 51% combined methotrexate with another csDMARD.
The mean follow‐up period was 31 months. Discontinuations were most common in the first two years of therapy. Fifty‐eight percent (n = 508) of the study population discontinued their initial b/tsDMARD after a median of 13 months (interquartile range [IQR] 6–29 months) from time of initiation. The most common causes for treatment discontinuation were primary failure (n = 72), secondary failure (n = 125), and adverse events (n = 133). This collectively represented 65% of all‐cause discontinuation. The other reasons for discontinuation included reimbursement issues (3.5%), patient decision (6.9%), physician decision (9.4%), improvement (2.7%), completed treatment (1.9%), or other reasons (10.6%). There was no significant difference between the median (IQR) treatment retention for bDMARD of 36 months (29–43 months) compared to tsDMARD of 30 months (20–43 months; P = 0.95).
In the univariate analysis, compared to patients with no CVD risk factors, patients with ≥2 CVD risk factors had a significant increase in all‐cause discontinuation (sHR 1.32, 95% CI 1.07–1.65; P = 0.01). This was not seen in patients with only one CVD risk factor (sHR 1.19, 95% CI 0.97–1.46). No significant association was found between all‐cause treatment discontinuation and the number of CVD risk factors in fully adjusted analyses (sHR 1.09, 95% CI 0.87–1.37 and sHR 1.15, 95% CI 0.89–1.48 for patients with 1 and ≥2 CVD risk factors, respectively).
Focusing on the specific reasons of discontinuation due to treatment failure, we found a significant association for patients with 1 or ≥2 CVD risk factors (Table 2). There was a higher magnitude of risk for combined treatment failure and secondary failure among those with ≥2 CVD risk factors, but no differences were seen for treatment discontinuation due to adverse events. No significant associations were identified in sensitivity analyses for the individual risk factors (HTN, DLD, DM, smoking, or obesity [BMI >30]) and all‐cause discontinuation (Table 2).
Table 2.
Multivariate analyses of reasons for discontinuation of the initial bDMARD or tsDMARD based on baseline CVD RF status and individual CVD RFs*
| Reason for discontinuation | |||||
|---|---|---|---|---|---|
| Primary failure, sHR (95% CI) | Secondary failure, sHR (95% CI) | All treatment failure, sHR (95% CI) | Adverse events, sHR (95% CI) | All treatment failure and adverse events, sHR (95% CI) | |
| Number of CVD RFs a | |||||
| 1 | 1.71 (0.90–3.25) | 1.34 (0.80–2.22) | 1.53 (1.03–2.28) | 1.15 (0.74–1.77) | 1.36 (1.02–1.83) |
| ≥2 | 1.21 (0.60–2.44) | 1.79 (1.04–3.08) | 1.54 (1.01–2.35) | 1.16 (0.71–1.89) | 1.39 (1.01–1.92) |
| Individual CVD RFs | |||||
| Obesity | 1.14 (0.62–2.07) | 1.18 (0.75–1.85) | 1.15 (0.79–1.65) | 1.17 (0.75–1.84) | 1.15 (0.86–1.54) |
| Current smoking | 1.70 (0.97–2.97) | 0.95 (0.56–1.61) | 1.25 (0.85–1.84) | 0.87 (0.52–1.43) | 1.13 (0.83–1.53) |
| Dyslipidemia | 1.63 (0.74–3.55) | 1.17 (0.61–2.26) | 1.36 (0.82–2.27) | 1.13 (0.63–2.03) | 1.30 (0.90–1.89) |
| Hypertension | 0.75 (0.41–1.37) | 1.42 (0.91–2.22) | 1.11 (0.78–1.58) | 1.21 (0.81–1.80) | 1.15 (0.88–1.50) |
| Diabetes | 0.59 (0.24–1.45) | 1.58 (0.92–2.71) | 1.09 (0.69–1.73) | 1.11 (0.65–1.92) | 1.11 (0.79–1.58) |
The multivariate analysis was adjusted for age, sex, CDAI score, disease duration, HAQ disability index score, number of non‐CVD comorbidities, concurrent conventional DMARD use, or oral glucocorticoid use. CDAI, clinical disease activity index; CI, confidence interval; CVD, cardiovascular disease; DMARD, disease‐modifying anti‐rheumatic drug; HAQ, health assessment questionnaire; RF, risk factor; sHR, subdistribution hazard ratio.
Compared to patients with no cardiovascular risk factors.
DISCUSSION
In this study of patients with active RA, a substantial proportion exhibited at least one CVD risk factor. The initial choice of advanced therapy favored bDMARD rather than tsDMARD in the total population, but the treatment type did not vary based on the number of CVD risk factors present at the time of initiation. The higher use of bDMARD may be explained by the lack of tsDMARD in Canada prior to 2014.
Notably, we observed a high rate of treatment discontinuation within three years, and this risk increased with the number of CVD risk factors (0, 1, ≥2). Our key finding was an observed association between CVD risk factors and the likelihood of treatment failure, particularly secondary failure, whereas no association was found for discontinuation due to adverse events. These findings imply that individuals with CVD comorbidity may have mechanistic or behavioral distinctions that impact their response to targeted therapies. These findings have important implications for clinical practice, highlighting the significance of distinguishing between types of treatment failure.
Primary failure is typically attributed to mechanistic shortcomings and often necessitates switching to a therapeutic class with a different mechanism of action. 6 In contrast, secondary failure may arise from immunogenicity due to production of neutralizing anti‐drug antibodies (ADAs). 8 This type of nonresponse may necessitate different therapeutic options including dose intensification to overcome ADAs, cycling within the therapeutic class given the initial good response, or class switching. 9
The precise mechanisms by which CVD risk factors may increase the chances of secondary treatment failure are not fully elucidated. It is hypothesized that risk factors such as DM, obesity, and smoking can induce immune dysregulation and disrupt the balance between pro‐inflammatory and anti‐inflammatory responses and increase the risk of developing ADAs. 10 CVD risk comorbidities can also impact organ function, leading to altered drug metabolism contributing to increased immunogenicity. 11 Furthermore, a higher burden of comorbidities has been associated with nonadherence or intermittent use of RA treatment. 12 This episodic treatment can increase the likelihood of ADA formation, similar to inflammatory bowel disease cohorts in which episodic infliximab treatments had the highest risk of ADA development. 13 Additionally, nonadherence to concomitant csDMARDs, which are known to mitigate ADA development risk, further elevates the probability of ADA formation.
The extent to which these factors contributed to increased treatment failure in our cohort cannot be determined solely from our data. An intriguing finding is the consistently high rates of concomitant csDMARD usage, regardless of therapeutic class (bDMARD vs tsDMARD) or the number of CVD risk factors present. Therefore, it is possible that csDMARDs may not be as effective in reducing ADAs in patients with CVD comorbidity. This may help practitioners identify a high‐risk group in whom therapeutic drug monitoring may be considered. This approach is in line with the recent EULAR task force that conditionally recommends examining drug levels and ADAs to help understand clinical nonresponse in RA. 9
It is also possible that secondary treatment failure was influenced by other mechanisms in our patient population. Our data did not include information on polypharmacy or treatment adherence, nor did we have serum samples to measure drug levels or detect ADAs. These limitations are noteworthy and can be explored in future analyses.
To the best of our knowledge, there have been no prior studies investigating the influence of the number of CVD risk factors on discontinuation of b/tsDMARDs for different types of treatment failure in patients with RA. Previous reports have suggested that certain individual risk factors, such as obesity and smoking, can diminish the response to targeted therapies. 10 , 14 However, it remains uncertain whether this association is specific to primary or secondary treatment failure or an increased likelihood of experiencing side effects. In our study, we did not find any association between the individually measured CVD risk factors (eg, hypertension) and treatment retention. This suggests that the cumulative burden of CVD risk factors may be more relevant in influencing drug retention than the specific risk factors.
Moreover, we found no significant correlation between the number of CVD risk factors and treatment discontinuation specifically attributed to adverse events. This outcome contradicts the findings from the BIOBADSER registry, which found an association between higher comorbidity index (including CVD) and discontinuation of b/tsDMARDs due to adverse events. 15 These differences may be attributed to variations in cohort characteristics or strategies employed to mitigate the risk of adverse events across different countries or regions. It is worth noting that our cohort comprised patients who were b/tsDMARD‐naïve, potentially resulting in a lower overall incidence of adverse events. Previous research has shown that treatment discontinuation tends to be higher among patients who have failed multiple prior advanced therapies. 16
It is important to acknowledge the limitations of our study. The observational design may have led to missing data or unmeasured confounding variables. There is also the potential for channeling bias because b/tsDMARDs may be preferentially avoided in older patients with a greater burden of CVD risk factors. We only assessed CVD risk factors at baseline and did not consider them as time‐varying factors throughout the study duration. Additionally, we lacked information regarding the severity or level of control of the CVD risk factors among participants. Adherence to concomitant csDMARDs or b/tsDMARDs was not ascertained, and we did not evaluate any treatment dose changes that may have influenced treatment retention during the study period. We also did not evaluate each b/tsDMARDs by their specific mechanism of action. Last, we did not investigate whether CVD status may have differential associations with early and late secondary treatment failure.
To summarize, the results of our study indicate that the presence of multiple CVD risk factors may have an impact on the loss of treatment response to targeted therapies in RA. Further research is warranted to enhance our understanding of the underlying mechanisms involved and to explore the potential for modifying this association. Initiatives such as early therapeutic drug monitoring in high‐risk individuals with CVD comorbidity could be an initial approach to investigate.
AUTHOR CONTRIBUTIONS
All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published.
Study conception and design
Aboulenain, Li, Movahedi, Bombardier, Kuriya.
Acquisition of data
Li.
Analysis and interpretation of data
Li, Movahedi.
Supporting information
Disclosure form
APPENDIX A. OBRI PARTICIPANTS AND COMMITTEE MEMBERS
We would like to thank the OBRI participants and the current OBRI Clinical Advisory Committee members (Drs. V. Ahluwalia, S. Aydin, E. Keystone, B. Kuriya, A. Lau, J. Pope, and C. Thorne) for the dedication, leadership, and clinical expertise they have provided to OBRI. We would also like to thank our Patient Advisory Committee members for providing their valuable patient perspective (current members: C. Hofstetter, D. Barker, J. Boyle, M. Forbes, L. Linderman, G. Major, E. McQueen, and D. Morrice). This work would not be possible without our participating rheumatologists: Drs. V. Ahluwalia, Z. Ahmad, P. Akhavan, L. Albert, C. Alderdice, T. Ali, M. Aubrey, S. Aydin, S. Bajaj, L. Barra, P. Basharat, M. Bell, W. Bensen, S. Bhavsar, R. Bobba, C. Bombardier, A. Bookman, J. Brophy, T. Boyd, A. Cabral, S. Carette, R. Carmona, A. Chow, G. Choy, P. Ciaschini, A. Cividino, D. Cohen, J. D'Aoust, R. Dhillon, S. Dixit, R. Faraawi, A. Ghelani, R. Gill, D. Haaland, S. Haig, B. Hanna, N. Haroon, J. Hochman, S. Humphrey‐Murto, C. Ivory, A. Jaroszynska, S. Johnson, R. Joshi, A. Kagal, A. Karasik, J. Karsh, E. Keystone, N. Khalidi, B. Kuriya, S. Lake, M. Larche, A. Lau, N. LeRiche, Fe. Leung, Fr. Leung, D. Mahendira, N. Maltez, M. Matsos, E. McConville, H. McDonald‐Blumer, E. McKeown, I. Midzic, N. Milman, S. Mittoo, A. Mody, A. Montgomery, M. Mulgund, E. Ng, T. Papneja, V. Pavlova, L. Perlin, J. Pope, J. Purvis, R. Rai, S. Rawn, G. Rohekar, S. Rohekar, T. Ruban, N. Samadi, S. Sandhu, D. Seguin, S. Sekhon, S. Shaikh, A. Shickh, R. Shupak, D. Smith, E. Soucy, J. Stein, A. Sterrett, J. Thomson, A. Thompson, C. Thorne, R. Tse, O. Wierzbicki, S. Wilkinson, and N. Zeiadin.
The Ontario Best Practices Research Initiative was funded by peer‐reviewed grants from the Canadian Institute for Health Research, the Ontario Ministry of Health and Long‐Term Care, the Canadian Arthritis Network, and unrestricted grants from Abbvie, Amgen, Aurora, Bristol‐Meyers Squibb, Celgene, Gilead, Hospira, Janssen, Lilly, Medexus, Merck, Novartis, Pfizer, Roche, Sandoz, Sanofi, and UCB.
Author disclosures are available at https://onlinelibrary.wiley.com/doi/10.1002/acr2.11629.
REFERENCES
- 1. Taylor PC, Atzeni F, Balsa A, et al. The key comorbidities in patients with rheumatoid arthritis: a narrative review. J Clin Med 2021;10(3):509. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Cui K, Movahedi M, Bombardier C, et al. Cardiovascular risk factors are negatively associated with rheumatoid arthritis disease outcomes. Ther Adv Musculoskelet Dis 2021;13:1759720X20981217. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. de Resende Guimarães MFB, Rodrigues CEM, Gomes KWP, et al. High prevalence of obesity in rheumatoid arthritis patients: association with disease activity, hypertension, dyslipidemia and diabetes, a multi‐center study. Adv Rheumatol 2019;59(1):44. [DOI] [PubMed] [Google Scholar]
- 4. Alfredsson L, Klareskog L, Hedström AK. Influence of smoking on disease activity and quality of life in patients with rheumatoid arthritis: results from a Swedish case‐control study with longitudinal follow‐up. Arthritis Care Res (Hoboken) 2023;75(6):1269–1277. [DOI] [PubMed] [Google Scholar]
- 5. Vicente GNS, Pereira IA, de Castro GRW, et al. Cardiovascular risk comorbidities in rheumatoid arthritis patients and the use of anti‐rheumatic drugs: a cross‐sectional real‐life study. Adv Rheumatol 2021;61(1):38. [DOI] [PubMed] [Google Scholar]
- 6. Vallejo‐Yagüe E, Keystone EC, Kandhasamy S, et al. Primary and secondary non‐response: in need of operational definitions in observational studies. Ann Rheum Dis 2021;80:961–964. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Aletaha D, Neogi T, Silman AJ, et al. Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum 2010;62(9):2569–2581. [DOI] [PubMed] [Google Scholar]
- 8. Kalden JR, Schulze‐Koops H. Immunogenicity and loss of response to TNF inhibitors: implications for rheumatoid arthritis treatment. Nat Rev Rheumatol 2017;13(12):707–718. [DOI] [PubMed] [Google Scholar]
- 9. Krieckaert CL, van Tubergen A, Gehin JE, et al. EULAR points to consider for therapeutic drug monitoring of biopharmaceuticals in inflammatory rheumatic and musculoskeletal diseases. Ann Rheum Dis 2023;82:65–73. [DOI] [PubMed] [Google Scholar]
- 10. Michelsen B, Berget KT, Kavanaugh A, et al. Association between TNFi anti‐drug antibodies, smoking, and disease activity in patients with inflammatory arthritis: Results from a Norwegian cross‐sectional observational study. Rheumatol Ther 2022;9(4):1171–1179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Gehin JE, Goll GL, Brun MK, et al. Assessing immunogenicity of biologic drugs in inflammatory joint diseases: progress towards personalized medicine. BioDrugs 2022;36(6):731–748. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. van den Bemt BJ, Zwikker HE, van den Ende CH. Medication adherence in patients with rheumatoid arthritis: a critical appraisal of the existing literature. Expert Rev Clin Immunol 2012;8(4):337–351. [DOI] [PubMed] [Google Scholar]
- 13. Bots SJ, Parker CE, Brandse JF, et al. Anti‐Drug antibody formation against biologic agents in inflammatory bowel disease: a systematic review and meta‐analysis. BioDrugs 2021;35(6):715–733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Baker JF, Reed G, Poudel DR, et al. Obesity and response to advanced therapies in rheumatoid arthritis. Arthritis Care Res 2022;74(11):1909–1916. [DOI] [PubMed] [Google Scholar]
- 15. Prior‐Español A, Sánchez‐Piedra C, Campos J, et al. Clinical factors associated with discontinuation of ts/bDMARDs in rheumatic patients from the BIOBADASER III registry. Sci Rep 2021;11(1):11091. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Novella‐Navarro M, Plasencia C, Tornero C, et al. Clinical predictors of multiple failure to biological therapy in patients with rheumatoid arthritis. Arthritis Res Ther 2020;22(1):284. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Disclosure form