DISEASE-MODIFYING ANTIRHEUMATIC DRUGS AND DEMENTIA PREVENTION: A SYSTEMATIC REVIEW OF OBSERVATIONAL EVIDENCE IN RHEUMATOID ARTHRITIS

 

C.-Y. Wu^1,2, L.Y. Xiong^1,2, Y.Y. Wong^1,2, S. Noor^1,2, G. Bradley-Ridout³, W. Swardfager^1,2,4

 

1. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada; 2. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; 3. Gerstein Science Information Centre, University of Toronto Libraries, Toronto, Ontario, Canada; 4. KITE University Health Network Toronto Rehabilitation Institute, Toronto, Ontario, Canada

Corresponding Author: Walter Swardfager, Department of Pharmacology and Toxicology, University of Toronto, 1 King’s College Circle, Toronto, Ontario, M5S 1A8, Canada, w.swardfager@utoronto.ca

J Prev Alz Dis 2024;
Published online April 15, 2024, http://dx.doi.org/10.14283/jpad.2024.78

 

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Abstract

BACKGROUND: Many observational studies have examined the association of disease-modifying antirheumatic drugs (DMARDs) with dementia risk, but the evidence has been mixed, possibly due to methodological reasons. This systematic review (PROSPERO: CRD42023432122) aims to assess existing observational evidence and to suggest if repurposing DMARDs for dementia prevention merits further investigation.
METHODS: Four electronic databases up to October 26, 2023, were searched. Cohort or case-control studies that examined dementia risk associated with DMARDs in people with rheumatoid arthritis were included. Risk of bias was evaluated using the Cochrane Collaboration’s Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) criteria. Findings were summarized by individual drug classes and by risk of bias.
RESULTS: Of 12,180 unique records, 14 studies (4 case-control studies, 10 cohort studies) were included. According to the ROBINS-I criteria, there were 2 studies with low risk of bias, 1 study with moderate risk, and 11 studies with serious or critical risk. Among studies with low risk of bias, one study suggested that hydroxychloroquine versus methotrexate was associated with lower incident dementia, and the other study showed no associations of tumor necrosis factor (TNF) inhibitors, tocilizumab, and tofacitinib, compared to abatacept, with incident dementia.
CONCLUSION: Studies that adequately addressed important biases were limited. Studies with low risk of bias did not support repurposing TNF inhibitors, tocilizumab, abatacept or tofacitinib for dementia prevention, but hydroxychloroquine may be a potential candidate. Further studies that carefully mitigate important sources of biases are warranted, and long-term evidence will be preferred.

Key words: Dementia, rheumatoid arthritis, disease-modifying antirheumatic drugs, pharmacoepidemiology.

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Introduction

Alzheimer’s disease (AD) is a leading cause of dementia. People with AD have higher levels of several cytokines in the peripheral and cerebrospinal fluid compared to people without AD (1, 2). Likewise, elevated levels of inflammatory markers have been associated with increased dementia risk (3). These findings highlight the potential for inflammation as a therapeutic target for dementia treatment and prevention.
Rheumatoid arthritis is a chronic inflammatory autoimmune disease characterized by dysregulation of cytokines involved in its pathogenesis (4). Disease-modifying antirheumatic drugs (DMARDs) are a class of medications indicated for rheumatoid arthritis, and these medications can be further grouped into “conventional synthetic DMARDs”, “biologic DMARDs”, and “targeted synthetic DMARDs” (5). As these DMARDs target inflammation via different mechanisms, some may be particularly beneficial to the brain due to potential involvement of systemic inflammatory or neuroinflammatory pathways, and repurposing DMARDs for dementia treatment or prevention has been proposed (6).
Many observational studies have investigated the associations of DMARDs with dementia risk in people with rheumatoid arthritis, but conclusions have been inconsistent. The potential benefit of DMARDs, or of certain DMARDs, remains uncertain. There exist considerable methodological differences between those studies, which might account for heterogeneity in their findings. In addition, studies that inadequately mitigate important sources of bias may generate misleading conclusions. For example, strong associations between phosphodiesterase-5 inhibitors and a lower dementia risk were seen in several studies (7–9), but no association was found when confounding by indication was mitigated using an active-comparator new-user cohort design, bringing the positive findings into question (10). Associations between metformin and a lower dementia risk were also observed in several studies (11–13), but those results are contradicted by studies that addressed immortal time bias (14, 15). No study design is perfect, but the strength of the evidence in each should be weighed against its potential for influential bias.
As a result, this study systematically reviews the observational studies that have examined the association between DMARD use and dementia risk. To identify robust clinical evidence, we apply standardized assessment criteria to evaluate each study according to important pharmacoepidemiologic principles. This systematic review is intended to assess the extant evidence to inform future observational studies and randomized trials of DMARDs to lower the risk of dementia.

 

Methods

This systematic review was conducted according to the Joanna Briggs Institute’s approach (16). The reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines (17). This systematic review is registered on PROSPERO (CRD42023432122), with the study protocol.

Eligibility Criteria

The exposures of interest were individual DMARDs or DMARDs as drug classes. Conventional synthetic DMARDs included methotrexate, hydroxychloroquine, sulfasalazine, and leflunomide. Biologic DMARDs included tumor necrosis factor (TNF) inhibitors (i.e., etanercept, adalimumab, infliximab, golimumab, certolizumab), the anti-CD20 antibody (i.e., rituximab), the T-cell costimulatory inhibitor (i.e., abatacept), and interleukin-6 (IL-6) receptor inhibitors (i.e., tocilizumab, sarilumab). Targeted synthetic DMARDs referred to Janus kinase (JAK) inhibitors (i.e., tofacitinib, baricitinib, upadacitinib). The outcome of interest was Alzheimer’s disease and related dementias (ADRD), defined as Alzheimer’s disease or dementia caused by any aetiologies as a composite or as cause-specific events. The population of interest was dementia-free people with rheumatoid arthritis.
Case-control studies or cohort studies examining the association of DMARDs with the risk of ADRD in people with rheumatoid arthritis were included. Diagnoses of ADRD and rheumatoid arthritis had to be ascertained based on established clinical diagnostic criteria, a confirmed diagnosis from a clinician, or diagnostic records from an administrative database. Studies with dementia defined based on a binary classification of a single cognitive assessment (e.g. Mini-Mental State Examination) were not included, as dementia diagnosis is often dependent on comprehensive neuropsychological, clinical, and functional assessments. Conference abstracts, letters to the editor, editorials, and commentaries were excluded.

Selection Process

Deduplication and screening took place in Covidence. Two independent reviewers assessed eligibility for each record, and disagreement was settled by consensus or a third person.

Search Strategy

Four electronic databases were searched up to October 26, 2023: Ovid Medline, Cochrane CENTRAL, Ovid Embase, and Ovid PsycINFO. The search strategies were translated into each database using that platform’s command language and controlled vocabulary. The search strategies were peer reviewed by a second librarian using PRESS peer review of electronic search strategies (18). No date or language restriction was applied. The full search strategies can be found in Supplementary Table 1. A manual search of reference lists and Google Scholar citation history was conducted for included studies to capture additional relevant records that were not captured by the database searches.

Data Extraction

Data from each included study were extracted by 2 independent reviewers and collected in structured spreadsheets, and disagreement was settled by consensus. The following key information were extracted from included studies: study design (i.e., cohort or case-control), data sources, population characteristics, exposures of interest, the comparator, exposure ascertainment method, outcome definition, and effect estimates. Effect measures included but were not limited to hazard ratios, risk ratios, odds ratios, incidence rate ratio, risk differences, or incidence rate differences. The corresponding authors were contacted for key information missing in included studies. Population age, sex proportions, sample size, follow-up time, and study period were also extracted.

Risk of Bias

Risk of bias was evaluated using the Cochrane Collaboration’s Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) criteria (19). We further evaluated the risk of other important sources of bias in the context of dementia that were not explicitly listed as criteria in the ROBINS-I, including reverse causality (20, 21), immortal time bias (for cohort studies only) (22, 23), time-window bias (for case-control studies only) (24), prevalent-user bias (for cohort studies only) (25, 26), and overadjustment bias (27). Two independent reviewers assessed risk of bias for each included study, and disagreement was settled by consensus or a third person. Supplementary Table 2 provides definitions of important biases highlighted in the present review.

Data Synthesis

Data were summarized by individual drug classes of DMARDs and by the risk of bias. Forest plots were used to visualize effect estimates and were created with R 4.0.5 (28). As per the study protocol, meta-analyses were not performed, and quantitative summary estimates were not provided due to the anticipation of high heterogeneity between studies in their design elements. Certainty of evidence was assessed using criteria adapted from the GRADE guidelines (29). Findings from studies with low risk of bias were considered as robust evidence to suggest whether different DMARDs were associated with incident ADRD in dementia-free people with rheumatoid arthritis.

 

Results

There were 12,173 unique records identified from Ovid Medline, Cochrane CENTRAL, Ovid Embase, and Ovid PsycINFO. There were 7 additional records identified from the manual search. Following screening, a total of 14 eligible studies (4 case-control studies, 10 cohort studies) were included in this review (Figure 1) (30–43). Characteristics of included studies are presented in Supplementary Table 3.

 

Of the 4 studies that investigated conventional synthetic DMARDs as a drug class, 3 studies compared conventional synthetic DMARDs to non-users (30, 31, 36) and 1 study compared conventional synthetic DMARDs to other classes of DMARDs (37). Among studies that investigated individual conventional synthetic DMARDs, the exposure of interest was methotrexate in 4 studies (30, 36, 39, 40), hydroxychloroquine in 4 studies (36, 39, 41, 42), leflunomide in 1 study (36), and sulfasalazine in 3 studies (36, 40, 43). However, only 1 out of 7 studies investigating individual conventional synthetic DMARDs had an active comparator (42).
Biologic DMARDs as a class were compared to non-users in 2 studies (31, 36). One study compared cumulative doses of DMARDs regardless of subclassifications to non-users (38). Among studies that investigated subclassifications or individual biologic DMARDs, TNF inhibitors were investigated in 7 studies (32–35, 37, 39, 43), but only two of them compared TNF inhibitors to an active comparator (32, 33). There was 1 study comparing tocilizumab to abatacept (32). Furthermore, two studies examined tofacitinib, a targeted synthetic DMARD, but they had different active comparators (32, 37).

Risk of Bias Assessment

According to the ROBINS-I criteria (19), 2 studies were rated low risk of bias (32, 42), 1 study was rated with moderate risk (33), and 11 studies were rated with serious or critical risk (Figure 2) (30, 31, 34–41, 43). Confounding by indication and selection bias were common reasons for serious or critical risk of bias.

Most studies had a high potential for bias due methodology susceptible to at least one common source of bias, including immortal time bias, time-window bias, prevalent-user bias, overadjustment bias, and reverse causality. (Supplementary Table 4).

 

Synthesis of Data

Table 1 summarizes highlighted findings from all eligible studies. Serious or critical risk of bias was evident in all studies that investigated conventional synthetic DMARDs (Supplementary Figure 1) and biologic DMARDs (Supplementary Figure 2) as exposure groups of interest. Critical risk of bias, primarily due to immortal time bias, was identified in the cohort that found an association of cumulative doses of DMARDs with a lower dementia risk (38). Among studies that investigated individual conventional synthetic DMARDs as an exposure of interest (Figure 3 for hydroxychloroquine, Supplementary Figure 3 for methotrexate, Supplementary Figure 4 for sulfasalazine), only 1 study had low risk of bias. The finding from that study suggested that hydroxychloroquine was associated with a lower risk of dementia compared to methotrexate (42). Most studies that examined individual biologic DMARDs (Figure 4 for TNF inhibitors) were susceptible to substantial bias, and the only 1 study with low risk of bias showed no association between TNF inhibitors or tocilizumab (an IL-6 receptor inhibitor) compared to abatacept initiation and incident dementia (32). Tofacitinib, a JAK inhibitor, was examined in 2 studies, and 1 showed a low risk of bias (Supplementary Figure 5). The study with a low risk of bias found no association of tofacitinib versus abatacept with dementia risk (32).

 

Certainty of evidence was evaluated in all included studies (Supplementary Table 5). Studies that compared the treatment of interest to a clinically comparable alternative were considered to have minimal indirectness, and 2 out of 14 studies had minimal indirectness (32, 42). In Desai et al., the estimates for tofacitinib and tocilizumab were considered imprecise because of wide confidence intervals resulting from a low incidence of events (32).

DMARDs = Disease-modifying antirheumatic drugs; OR = Odds ratio; HR = Hazard ratio; AD = Alzheimer’s disease; VD = Vascular dementia; TNF = Tumour necrosis factor; DDD = daily defined dose; a. For case-control studies, this column refers to the length of exposure window.

 

Discussion

The present review systematically assessed eligible studies that investigated the associations of DMARDs with dementia risk, using standardized risk of bias criteria. Only 2 studies had a low risk of bias (32, 42). Those studies concluded that hydroxychloroquine was associated with a lower risk of dementia compared to methotrexate (42), and that TNF inhibitors, tocilizumab, or tofacitinib showed no association with dementia risk compared to abatacept (32). Overall, evidence was insufficient to conclude whether DMARD use is associated with dementia risk, although the present review could be limited by publication bias caused by underreporting of negative studies. Further robust observational evidence would be required on which to recommend large randomized controlled trials.
The cohort studies by Varma et al. and Desai et al. (32, 42), which were projects under the Drug Repurposing for Effective Alzheimer’s Medicines (DREAM) study (44), had low risk of bias. Both utilized an active-comparator new-user cohort design with a clinically comparable reference treatment group (5), which mitigates confounding by indication (45–47). The active-comparator new-user cohort design also minimized immortal time bias and prevalent-user bias (25, 26, 45, 46), which are common sources of bias in pharmacoepidemiologic studies. Furthermore, an active-comparator new-user cohort design emulates an active-controlled randomized trial (25, 26, 45, 46), so as-treated and as-started (i.e., intent-to-treat) analyses can be performed to investigate effects among people who adhere with baseline treatments and among people who initiate baseline treatments, respectively.
The use of entire follow-up data to define baseline exposures and inappropriate confounding adjustment were the most common reasons for serious or critical risk of bias in the present systematic review. The use of entire follow-up data to define baseline exposures in a cohort can introduce immortal time bias and selection bias (48). For example, Huang et al. considered cumulative DMARD dose as a baseline variable that was defined based on post-baseline data, and an 80% lower risk was observed in the long-term user group (38). Although that study attempted to establish a dose-response relationship, the observation was likely biased by misclassified immortal time, as people with a longer dementia-free period were more likely to have longer drug exposure during follow-up, and people with shorter time to dementia were more likely to be considered as non-users.
In some studies (30, 34), a time-varying exposure definition was used to address immortal time bias (23); nonetheless, selection bias might still be a concern, as the follow-up data were used to ascertain exposure groups for baseline propensity score calculation (48). In addition, some studies did not adjust for time-varying confounders for time-varying exposures (30, 34). One study adjusted for time-varying confounders as covariates in a time-dependent Cox regression model (39), but this method is prone to overadjustment bias (27). To address overadjustment bias, inverse probability of treatment weights derived from a marginal structural model can be used to adjust for time-varying confounders without over-adjusting for mediators (49). These are important considerations when treatment effects are studied in a real-world setting.
In this systematic review, 3 out of the 4 included case-control studies had a concern of time-window bias (35, 36, 40), because the studies did not attempt to balance lengths of exposure window between cases and controls. In case-control studies using electronic health records or insurance claims data, time-window bias results from a differential likelihood of detecting the exposures between cases and controls caused by differing lengths of exposure window (24). Additionally, in Chou et al. (43), there were concerns about substantial residual confounding due to inadequate covariate adjustment. In Zhou et al. (35), healthy user bias might have exaggerated the estimates, as the comparator was “no treatment” (50). All 4 eligible case-control studies had a potential of overadjustment bias (27), because covariates might have been defined based on post-exposure information in some people (35, 36, 40, 43). To properly mitigate confounding, new-user cohorts with covariates defined based on pre-exposure data are generally preferred over case-control studies, which might be considered to provide more robust evidence (25, 26, 45, 46).
Hydroxychloroquine as a treatment to slow cognitive decline was studied in a prior randomized controlled trial of people with AD, but no significant difference was seen (51). Those results contradict the findings from Varma et al., which had low risk of bias and found an association of hydroxychloroquine use with lower dementia risk (42). Several methodological differences might explain the discrepancies. First, the cumulative incidence curves did not diverge over the first 20 months, but a delayed effect was seen afterwards (42), implying that the 18-month follow-up time in the trial may not have been long enough to detect a difference in cognition (51). Second, the cohort included a rheumatoid arthritis population, whereas the trial was not limited to people with rheumatoid arthritis. It is possible that the presence of rheumatoid arthritis was effect-modifying (i.e., required for the benefit of the treatment). Third, despite a lack of robust large-scale evidence on higher dementia risk associated with methotrexate (52), it might not be a suitably neutral comparator for dementia outcomes; nonetheless, the sensitivity analysis with leflunomide as an alternative comparator in the cohort exhibited a larger association of hydroxychloroquine with reduced dementia risk (42). Lastly, the sample size of the trial might have been too small to detect a difference.
The hazard ratios were comparable for hydroxychloroquine (0.92, 95% confidence interval = 0.83-1.00) and for TNF inhibitors (0.93, 95% confidence interval = 0.72-1.20) in the as-treated analyses conducted by Varma et al. and Desai et al. (32, 42); however, TNF inhibitors versus abatacept exhibited intertwining cumulative incidence curves, and the estimates were inconsistent across other supporting analyses (32), which weakened confidence that TNF inhibitors might have had neuroprotective effects. Previously, etanercept, a TNF inhibitor, was found to have a neutral effect on cognition in a small randomized controlled trial of people with AD (53). The reasons for no observed treatment effect are not clear. TNF inhibitors do not cross the blood-brain barrier, and it is possible that peripheral TNF inhibition by TNF inhibitors might not generate meaningful effects on the brain (54, 55), even if peripheral and central immune responses are linked to each other (56). Further, a multi-database cohort conducted by Kern et al. did not identify consistent differences in dementia risk between TNF inhibitors and methotrexate (33), which might further strengthen that TNF inhibitors are not associated with dementia risk reduction. It should be noted that Kern et al. (2021) had moderate risk of bias, because a suboptimal comparison between first-line and second-line DMARDs might not adequately mitigate confounding bias in an active-comparator new-user cohort (5, 45-47). To solidify the conclusion that TNF inhibitors are not associated with dementia risk reduction, study designs that allow a proper comparison between first-line and second-line treatments (e.g. prevalent new-user cohort comparing continuous methotrexate to TNF inhibitor initiation) may be considered (57).
More observational evidence should be sought to replicate the positive hydroxychloroquine and negative TNF inhibitor findings, since they could have important implications. Further evidence will inform whether hydroxychloroquine merits an opportunity to be tested in randomized trials for dementia prevention or treatment. If efficacy can be established, the unique mechanisms of action of hydroxychloroquine might help to pinpoint pathways most relevant in the pathophysiology of dementia. Efficacy of hydroxychloroquine but not of other DMARDs could inform the development of more specifically targeted drugs that avoid the potential off-target effects of hydroxychloroquine. Hydroxychloroquine crosses the blood brain barrier and it is thought to act via multiple direct and indirect anti-inflammatory mechanisms, including inhibition of Toll-like receptor 7 and 9 signalling, cytokine production, and T-cell CD154 expression (42, 58, 59). Hydroxychloroquine is also thought to inhibit cyclic GMP-AMP synthase affecting intracellular signalling pathways, and to impair lysosome and autophagosomes previously implicated in Alzheimer’s disease (58, 60).
Despite low risk of bias for Varma et al. and Desai et al. (32, 42), the two cohort studies were limited by short follow-up times in adults older than 65 years. Due to long latency between dementia pathogenesis and symptoms (61), the results from those cohort studies may not generalize to younger initiators followed over decades, and a short follow-up time weakened the biological plausibility. New-user cohorts with lifetime data will help to examine mid-life initiation over longer follow-up time. Further, most eligible studies did not consider a sufficiently long lag time to mitigate potential reverse causality (20). Undiagnosed dementia or early dementia symptoms before drug initiation or drug discontinuation might influence the choice of prescription, which could be a potential source of unmeasured confounding or differential outcome misclassifications (21). Future cohort studies might consider implementing a lag time between initiation and the start of the follow-up window to exclude early incident dementia cases that were likely unrelated to the treatments. Lastly, it is usually challenging to ascertain dementia subtypes based on single diagnostic codes in electronic health records and insurance claims databases, because of the nature of data. Effect-modifying factors (e.g. different susceptibilities to different dementia aetiologies) are likely to exist. It is possible that treatment does not protect against all dementia aetiologies, so some but not all patients may have benefited from treatment. Although several included studies explored the association of DMARDs with different dementia subtypes, the degree of misclassification bias was unknown, and the results should be interpreted with caution, as the outcome definition was not previously validated. Validation in different databases for all-cause dementia and dementia subtypes, will be informative for researchers interested in using administrative data to pinpoint possible candidates repurposed for dementia treatment.

 

Conclusion

Using standardized risk of bias criteria, we identified two studies that used robust methodology. In those studies, hydroxychloroquine as a candidate for drug repurposing for dementia prevention was suggested, but biologic DMARDs as candidates were not supported. Despite robust methodology, those studies were limited by short follow-up times, which might not well support the biological plausibility. Few studies adequately addressed important potential sources of bias, including confounding by indication, selection bias, and reverse causality. More studies that carefully mitigate these important biases will be needed to provide sufficient evidence, and long-term evidence will be preferred.

 

Acknowledgement: The authors would like to thank Patricia Ayala for their PRESS-peer review of the Ovid Medline search strategy.

Funding Sources: Che-Yuan Wu gratefully acknowledges financial support from the Canadian Institutes of Health Research (Doctoral Research Award: Canadian Graduate Scholarships; 202111FBD-47623-75801). Lisa Y Xiong gratefully acknowledges support from the Canadian Institutes of Health Research (Doctoral Research Award: Canadian Graduate Scholarships; 202111FBD-476226). Walter Swardfager gratefully acknowledges financial support from the Alzheimer’s Drug Discovery Foundation, Canada Research Chairs Program (Award Number: CRC-2020-00353) and Ontario Ministry of Colleges and Universities (Award Number: ER21-16-141).
Conflict of Interest: The funders did not participate in the conduct or reporting of the study. The authors declare no conflict of interest.

Author Contributions: Che-Yuan Wu and Walter Swardfager conceptualized the project. Che-Yuan Wu and Glyneva Bradley-Ridout had significant contributions to methodology. Glyneva Bradley-Ridout conducted literature search in electronic databases. Che-Yuan Wu, Lisa Xiong, Yuen Yan Wong, Shiropa Noor, Walter Swardfager were involved in the investigation process. Che-Yuan Wu, Lisa Xiong, and Walter Swardfager drafted the manuscript. All authors had inputs into the review/editing of the manuscript.

 

SUPPLEMENTARY MATERIAL

 

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