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Causality between rheumatoid arthritis and the risk of cognitive impairment: a Mendelian randomization study

Arthritis Research & Therapy volume 26, Article number: 5 (2024) Cite this article

Abstract

Background

There is mounting proof that rheumatoid arthritis (RA) and cognitive decline are related. These studies, however, have not all been uniform, and others have not discovered such a correlation. It is essential to investigate the link between RA and cognitive decline.

Method

We conducted a Mendelian randomization analysis utilizing three different publicly accessible RA GWAS summary datasets and a variety of meticulously verified instrumental variables. We mostly used inverse variance weighting (IVW), as well as MR-Egger, weighted median, MR-PRESSO, and several sensitivity analyses, to figure out the link between RA and cognitive impairment (CI).

Results

Our MR study identified the causality between RA and declining cognitive performance (β = − 0.010, 95% CI of − 0.017 to − 0.003, P = 4.33E−03) and cognitive function (β = − 0.029, 95% CI of − 0.053 to − 0.005, P = 1.93E−02). The consistent direction of the connection is revealed by sensitivity analysis utilizing the weighted median and the MR-Egger method. Furthermore, we reproduced our findings across two additional RA datasets and found identical outcomes, strengthening the validity of our findings.

Conclusion

This study offers proof of causality between RA and an increased risk of CI. Our findings highlight the importance of examining RA patients for cognitive ability, which may open up fresh ideas for the prevention of CI.

Introduction

Rheumatoid arthritis (RA) is an autoimmune disease that affects more than one system [1]. Females and the elderly are more likely to be diagnosed with it, and it affects roughly 1% of the general population [2]. Along with bone degradation and cartilage loss, RA is characterized by peripheral inflammatory polyarthritis [3]. But RA does not just damage the joints; it is also linked to vasculitis, cancer, lung and cardiovascular disease, and mental health problems and, in many instances, can result in lifelong impairment [4]. The prevalence of RA’s extra-articular symptoms varies from 17.8 to 40.9% and can affect a variety of organs, including the nervous and mental systems [5, 6].

Cognitive impairment (CI), a prevalent chronic disorder linked to aging that is characterized by challenges with memory, acquiring new information, problem-solving, concentration, or decision-making, can eventually lead to dementia [7]. Globally, more than 10% of people who are 70 or older have a minor cognitive impairment [8]. However, because some studies have revealed inconsistent results [9,10,11,12,13,14,15,16,17,18], the link between RA and CI is still up for debate. Studies have revealed a probable connection between RA and CI, with even moderate CI interfering with everyday functioning in RA patients. While some studies, notably those looking at Alzheimer’s disease and other dementias, have discovered a connection between RA and CI, other studies have not. Largely unclear are the processes that underlie the association between RA and cognitive decline. Chronic inflammatory conditions [19, 20], immune system modifications [21], and ongoing pain and discomfort [22,23,24] are possible explanations. Mechanistic research should be scientifically developed and put into practice in the future since it is not yet sufficient to support any of these mechanisms.

Therefore, the relationship between RA and the emergence of CI is still unclear. Due to confounding bias, association inference from this earlier observational data may be constrained, and even findings may have reverse causality. Randomized controlled trials (RCTs) can show that RA and CI are causally related, but they are costly and time-consuming. Mendelian randomization (MR), a new method, examines whether observed correlations between exposure variables and outcomes are compatible with causal effects by utilizing genetic variation as an instrumental variable (IV) [25]. Confounding variables and reverse causal linkages may be avoided for correlation effects, and bias can be minimized since genetic variation is unaffected by external environmental, social, behavioral, or other factors [26]. Therefore, the goal of this study was to determine the causative link between RA and CI using a two-sample MR analysis.

Methods

Study design

The largest publicly accessible GWAS datasets were used in our two-sample MR analysis to investigate the causal link between RA and the risk of cognitive impairment. Three critical assumptions (Fig. 1) must be met by the effective instrumental variables (IVs) during the MR analysis process in order to obtain trustworthy results [27]: (i) the IVs and RA exhibit a strong correlation, (ii) there is no connection between the IVs and any confounding variables that might affect both RA and CI, and (iii) IVs cannot affect CI by any other means besides RA, but only via RA. The most recent recommendations (STROBE-MR) were followed in this investigation [28].

Fig. 1
figure 1

Study design for our MR study

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Data sources

We used publicly accessible summary-level GWAS data; specifics are presented in Table 1. Specifically, a total of 74,823 controls and 22,350 RA patients made up the most recent summary dataset for this disease as the discovery sample [29], obtained from the GWAS catalog (https://www.ebi.ac.uk/gwas/). As replication samples, we also employed two additional RA datasets [30, 31] from the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/datasets/). For outcomes, the Within Family GWAS Consortium, which included 22,593 samples, provided the summary data on cognitive function [32]. A general cognitive function score was used to evaluate cognitive function; higher scores corresponded to greater cognitive functioning, and vice versa. Comprised of 257,841 participants of European ancestry, the biggest publicly available GWAS dataset offers a summary of information on cognitive performance [33]. It combines recently released findings from the COGENT consortium with earlier findings from a UKB investigation of cognitive ability. Finally, the features of exposure and outcome had no sample overlap, and all study participants were of European descent.

Table 1 The GWAS data source details in our study
Full size table

Instrumental selection

From the related GWAS pooled datasets, we first identified SNPs that were strongly linked with RA (P < 5.00E−08). We next used a reference set of 1000 genomes from a European population to rule out linkage disequilibrium between these SNPs (r2 < 0.001 and clump window > 10,000 kb). Palindromic SNPs were also manually eliminated. Following these procedures, the SNPs that were left over were utilized as instrument variables. To reduce weak instrumental bias (F > 10), we also employed the F-statistic (F = β2/se2) to determine the statistical efficacy of SNPs and to exclude SNPs with poor statistical efficacy [34].

Mendelian randomization analysis

We employed the IVW approach in our MR study as the main analysis, adding MR-Egger, weighted median, and MR-PRESSO as supplements and sensitivity analyses. While assuring that all IVs are valid, IVW can provide the most accurate assessment of causality [35]. Although not very efficient, the MR-Egger regression technique may yield estimates with a pleiotropy adjustment, and its intercept can be used to check if pleiotropy is present or not [36]. If half of the instrumental weights are derived from reliable instrumental variables, the weighted median method will yield unbiased causal estimates [37].

Sensitivity analysis

Further, several sensitivity analyses were carried out to evaluate the results’ robustness. First, we used Cochran’s Q test to measure the heterogeneity of the IVW, and funnel plots were used to display the findings. Additionally, we used radial_IVW to find and remove IVs that contribute more to heterogeneity [38]. Pleiotropy was examined using the MR-Egger intercept test, and the outcomes were shown using scatterplots. The MR-PRESSO test is also used to test for horizontal pleiotropy, identify outliers, and offer causal estimates when the corresponding outliers have been eliminated [39]. Furthermore, we carried out a leave-one-out analysis to see if a single SNP significantly changed the outcomes. In the end, we performed an MR-Steiger analysis to check the validity of our causal directions. The “TwoSampleMR” (version 0.5.6), “Radial MR” (version 1.0), and “MRPRESSO” (version 1.0) packages, together with RStudio (version 4.2.1), were used to conduct the whole analysis.

Results

Following thorough screening, we determined 46 and 47 SNPs to be IVs for the outcomes of cognitive performance and cognitive function, respectively (Additional file 2: Table S1). There is no mild instrumental bias, as shown by the F-statistics, which are all bigger than 10. The MR results of RA on CI are listed in Fig. 2.

Fig. 2
figure 2

Mendelian randomization for the association of RA on CI

Full size image

We employed a fixed-effects model using the IVW technique since Cochran’s Q test did not reveal heterogeneity (P > 0.05). Specifically, using the IVW approach, we found significant associations between RA and the risk of cognitive performance (β = − 0.010, 95% CI of − 0.017 to − 0.003, P = 4.33E−03) and cognitive function (β = − 0.029, 95% CI of − 0.053 to − 0.005, P = 1.93E−02) decline. The outcomes of the weighted median, MR-PRESSO, and MR-Egger analyses were similar to those of the IVW approach (Fig. 2). Additionally, we achieved identical results in duplicated samples, demonstrating the validity of the findings. Furthermore, we did not detect heterogeneity or pleiotropy of effects in our investigation using the MR-Egger intercept and Cochran’s Q test (Table 2). The MR-Steiger test also validates the correctness of all of our causal directions (Table 3).

Table 2 Sensitivity analysis of the associations between RA and CI
Full size table
Table 3 Results of MR Steiger direction test
Full size table

Discussion

This study is the first that we are aware of to evaluate the causal link between RA and CI using MR technology. The studies’ findings suggested that RA was associated with an increased risk of CI. Our work emphasizes the significance of understanding the effect of RA on cognitive function and addressing it in the treatment of the illness.

Numerous earlier studies have also shown that RA patients have a considerably elevated risk of cognitive impairment, which is consistent with our findings. Meade et al. [10] carried out a systematic review of CI in RA patients. They came to the conclusion that, when compared to healthy controls, patients with RA performed badly on cognitive functioning tests, notably in the areas of verbal performance, memory, and focus. Our findings are in line with data from a meta-analysis of 16 trials that show RA patients are at risk for CI [11]. A study conducted on a cohort of 1449 individuals demonstrated a strong correlation between the occurrence of any joint ailment during midlife and subsequent cognitive decline over a period of 21 years [40]. Similar findings were seen in Thailand among 464 patients with RA [41], where higher RA disease activity was linked to a higher likelihood of CI, in support of another cross-sectional study [42]. Another prospective study found that those with arthritis had poorer cognitive functioning than those without it, as measured by lower scores on situational memory, mental status, and general cognition [43]. However, other studies have found that RA is not associated with CI [13,14,15,16,17] or even shows a negative correlation [18, 44, 45]. Two other studies using data from the US Health and Retirement Study (HRS) did not find an association between RA and CI13, 15]. A Korean nested case-control investigation also neglected to find a link between RA and dementia [17], even as another case-control study suggested a negative association between RA and AD [45]. Also, a Taiwanese case-control study found that people with RA were 37% less likely to get dementia than people without RA, and the risk was even lower for people with RA who were taking antirheumatic drugs [44], the same conclusions as those obtained in another cohort study [18]. Additionally, two other MR studies conducted recently have produced contradictory results about the causal link between RA and AD [46, 47].

Currently, there is limited understanding of the factors that contribute to the association between RA and CI. Potential processes that could be implicated include the presence of chronic inflammation, participation of the immune system, adverse effects of medications, genetic variables, and the presence of persistent pain and psychiatric illnesses. First, by analyzing the relationship between cognitive performance and peripheral lymphocyte subsets in RA patients, Petersen et al. [48] demonstrated that the linkage between RA and cognitive impairment may be due to premature immunological senescence. A number of autoantibodies and brain-derived proteins, such as anti-myelinating oligodendrocyte glycoprotein (MOG) IgG and S100 calcium-binding beta (S100), may also be linked to cognitive impairment in RA [49,50,51]. Cognitive decline may result from these causes, which may have a negative impact on the number of neurons and synapses as well as the rate of information processing. Additionally, chronic inflammation with high levels of chemokines (CXCL10, CXCR3) [21, 52] and pro-inflammatory cytokines (IL-1, IL-6, and TNF−) [21, 53, 54] has been identified as a significant contributor to CI. Furthermore, some regularly used RA medications (such as methotrexate and corticosteroids) may be linked to CI in RA patients [10, 12, 19, 55]. While the mechanisms that contribute to the association between RA and CI remain uncertain, it is recommended in professional practice to implement preventive strategies, such as regular evaluation of cognitive function in individuals diagnosed with RA.

The construction of MR involves the utilization of openly accessible GWAS summary statistics. It is feasible to mine trustworthy genetic data without incurring additional experimental expenses. Secondly, we confirmed our findings with other data sets, which increased the credibility of our conclusions. Additionally, a series of sensitivity studies were conducted to showcase the robustness of the findings.

There are also some limitations to our study. First, due to the limits of the data, stratification by age or sex was not done. Second, the exposure and outcome analyses limited the research population to Europeans alone. To what extent these findings hold true for other populations needs to be investigated. Significant GWAS data must support the study and include participants from other ethnic groups. Finally, due to restrictions in the data sources, we were unable to investigate other different subtypes of cognitive impairment. For future analysis, larger, more thorough datasets might be required.

Conclusion

In conclusion, the present study demonstrated a significantly increased risk of CI in RA patients. It emphasizes the need to monitor cognitive function in RA patients, and further studies on the mechanisms of the role between the two are needed in the future.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request.

References

  1. Smolen JS, Aletaha D, Barton A, et al. Rheumatoid arthritis. Nat Rev Dis Primers. 2018;4:18001. https://doi.org/10.1038/nrdp.2018.1. Published 2018 Feb 8.

    Article  PubMed  Google Scholar 

  2. van der Woude D, van der Helm-van Mil AHM. Update on the epidemiology, risk factors, and disease outcomes of rheumatoid arthritis. Best Pract Res Clin Rheumatol. 2018;32(2):174–87. https://doi.org/10.1016/j.berh.2018.10.005.

    Article  PubMed  Google Scholar 

  3. McInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 2011;365(23):2205–19. https://doi.org/10.1056/NEJMra1004965.

    Article  PubMed  CAS  Google Scholar 

  4. Dougados M, Soubrier M, Antunez A, et al. Prevalence of comorbidities in rheumatoid arthritis and evaluation of their monitoring: results of an international, cross-sectional study (COMORA). Ann Rheum Dis. 2014;73(1):62–8. https://doi.org/10.1136/annrheumdis-2013-204223.

    Article  PubMed  Google Scholar 

  5. Joaquim AF, Appenzeller S. Neuropsychiatric manifestations in rheumatoid arthritis. Autoimmun Rev. 2015;14(12):1116–22. https://doi.org/10.1016/j.autrev.2015.07.015.

    Article  PubMed  Google Scholar 

  6. Marrie RA, Hitchon CA, Walld R, et al. Increased burden of psychiatric disorders in rheumatoid arthritis. Arthritis Care Res (Hoboken). 2018;70(7):970–8. https://doi.org/10.1002/acr.23539.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Arvanitakis Z, Shah RC, Bennett DA. Diagnosis and management of dementia: review. JAMA. 2019;322(16):1589–99. https://doi.org/10.1001/jama.2019.4782.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Petersen RC, Lopez O, Armstrong MJ, et al. Practice guideline update summary: mild cognitive impairment: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2018;90(3):126–35. https://doi.org/10.1212/WNL.0000000000004826.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Vitturi BK, Nascimento BAC, Alves BR, de Campos FSC, Torigoe DY. Cognitive impairment in patients with rheumatoid arthritis. J Clin Neurosci. 2019;69:81–7. https://doi.org/10.1016/j.jocn.2019.08.027.

    Article  PubMed  Google Scholar 

  10. Meade T, Manolios N, Cumming SR, Conaghan PG, Katz P. Cognitive impairment in rheumatoid arthritis: a systematic review. Arthritis Care Res (Hoboken). 2018;70(1):39–52. https://doi.org/10.1002/acr.23243.

    Article  PubMed  Google Scholar 

  11. Pankowski D, Wytrychiewicz-Pankowska K, Janowski K, Pisula E. Cognitive impairment in patients with rheumatoid arthritis: a systematic review and meta-analysis. Joint Bone Spine. 2022;89(3):105298. https://doi.org/10.1016/j.jbspin.2021.105298.

    Article  PubMed  Google Scholar 

  12. Shin SY, Katz P, Wallhagen M, Julian L. Cognitive impairment in persons with rheumatoid arthritis. Arthritis Care Res (Hoboken). 2012;64(8):1144–50. https://doi.org/10.1002/acr.21683.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Booth MJ, Janevic MR, Kobayashi LC, Clauw DJ, Piette JD. No association between rheumatoid arthritis and cognitive impairment in a cross-sectional national sample of older U.S. adults. BMC Rheumatol. 2021;5(1):24. https://doi.org/10.1186/s41927-021-00198-z. Published 2021 Aug 18.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Booth MJ, Kobayashi LC, Janevic MR, Clauw D, Piette JD. No increased risk of Alzheimer’s disease among people with immune-mediated inflammatory diseases: findings from a longitudinal cohort study of U.S. older adults. BMC Rheumatol. 2021;5(1):48. https://doi.org/10.1186/s41927-021-00219-x. Published 2021 Nov 12.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Baker NA, Barbour KE, Helmick CG, Zack M, Al Snih S. Arthritis and cognitive impairment in older adults. Rheumatol Int. 2017;37(6):955–61. https://doi.org/10.1007/s00296-017-3698-1.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Veeranki SP, Downer B, Jupiter D, Wong R. Arthritis and risk of cognitive and functional impairment in older Mexican adults. J Aging Health. 2017;29(3):454–73. https://doi.org/10.1177/0898264316636838.

    Article  PubMed  Google Scholar 

  17. Min C, Bang WJ, Kim M, Oh DJ, Choi HG. Rheumatoid arthritis and neurodegenerative dementia: a nested case-control study and a follow-up study using a national sample cohort. Clin Rheumatol. 2020;39(1):159–66. https://doi.org/10.1007/s10067-019-04769-x.

    Article  PubMed  Google Scholar 

  18. Kronzer VL, Crowson CS, Davis JM 3rd, Vassilaki M, Mielke MM, Myasoedova E. Trends in incidence of dementia among patients with rheumatoid arthritis: a population-based cohort study. Semin Arthritis Rheum. 2021;51(4):853–7. https://doi.org/10.1016/j.semarthrit.2021.06.003.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Oláh C, Schwartz N, Denton C, Kardos Z, Putterman C, Szekanecz Z. Cognitive dysfunction in autoimmune rheumatic diseases. Arthritis Res Ther. 2020;22(1):78. https://doi.org/10.1186/s13075-020-02180-5. Published 2020 Apr 15.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sood A, Raji MA. Cognitive impairment in elderly patients with rheumatic disease and the effect of disease-modifying anti-rheumatic drugs. Clin Rheumatol. 2021;40(4):1221–31. https://doi.org/10.1007/s10067-020-05372-1.

    Article  PubMed  Google Scholar 

  21. Petersen LE, Baptista TSA, Molina JK, et al. Cognitive impairment in rheumatoid arthritis: role of lymphocyte subsets, cytokines and neurotrophic factors. Clin Rheumatol. 2018;37(5):1171–81. https://doi.org/10.1007/s10067-018-3990-9.

    Article  PubMed  Google Scholar 

  22. Abeare CA, Cohen JL, Axelrod BN, Leisen JC, Mosley-Williams A, Lumley MA. Pain, executive functioning, and affect in patients with rheumatoid arthritis. Clin J Pain. 2010;26(8):683–9.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Dick BD, Rashiq S. Disruption of attention and working memory traces in individuals with chronic pain. Anesth Analg. 2007;104(5):1223–9. https://doi.org/10.1213/01.ane.0000263280.49786.f5.

    Article  PubMed  Google Scholar 

  24. Ziarko M, Siemiątkowska K, Sieński M, Samborski W, Samborska J, Mojs E. Mental health and rheumatoid arthritis: toward understanding the emotional status of people with chronic disease. Biomed Res Int. 2019;2019:1473925. https://doi.org/10.1155/2019/1473925. Published 2019 Feb 11.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318(19):1925–6. https://doi.org/10.1001/jama.2017.17219.

    Article  PubMed  Google Scholar 

  26. Ebrahim S, Davey Smith G. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet. 2008;123(1):15–33. https://doi.org/10.1007/s00439-007-0448-6.

    Article  PubMed  Google Scholar 

  27. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601 Published 2018 Jul 12.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomization: the STROBE-MR Statement. JAMA. 2021;326(16):1614–21. https://doi.org/10.1001/jama.2021.18236.

    Article  PubMed  Google Scholar 

  29. Ishigaki K, Sakaue S, Terao C, et al. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat Genet. 2022;54(11):1640–51. https://doi.org/10.1038/s41588-022-01213-w.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Ha E, Bae SC, Kim K. Large-scale meta-analysis across East Asian and European populations updated genetic architecture and variant-driven biology of rheumatoid arthritis, identifying 11 novel susceptibility loci. Ann Rheum Dis. 2021;80(5):558–65. https://doi.org/10.1136/annrheumdis-2020-219065.

    Article  PubMed  CAS  Google Scholar 

  31. Eyre S, Bowes J, Diogo D, et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat Genet. 2012;44(12):1336–40. https://doi.org/10.1038/ng.2462.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, et al. Withinsibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet. 2022;54:581–92. https://doi.org/10.1038/s41588-022-01062-7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. (2018) 50:1112–21.  https://doi.org/10.1038/s41588-018-0147-3

  34. Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40(3):740–52. https://doi.org/10.1093/ije/dyq151.

    Article  PubMed  Google Scholar 

  35. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63. https://doi.org/10.1002/sim.3034.

    Article  PubMed  Google Scholar 

  36. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. https://doi.org/10.1093/ije/dyv080.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14. https://doi.org/10.1002/gepi.21965.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Bowden J, Spiller W, Del Greco M F, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the radial plot and radial regression [published correction appears in Int J Epidemiol. 2018 Dec 1;47(6):2100]. Int J Epidemiol. 2018;47(4):1264-1278. https://doi.org/10.1093/ije/dyy101

  39. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases [published correction appears in Nat Genet. 2018;50(8):1196]. Nat Genet. 2018;50(5):693-698. https://doi.org/10.1038/s41588-018-0099-7

  40. Wallin K, Solomon A, Kåreholt I, Tuomilehto J, Soininen H, Kivipelto M. Midlife rheumatoid arthritis increases the risk of cognitive impairment two decades later: a population-based study. J Alzheimers Dis. 2012;31(3):669–76. https://doi.org/10.3233/JAD-2012-111736IF:4.0Q2.

    Article  PubMed  Google Scholar 

  41. Katchamart W, Narongroeknawin P, Phutthinart N, Srinonprasert V, Muangpaisan W, Chaiamnauy S. Disease activity is associated with cognitive impairment in patients with rheumatoid arthritis. Clin Rheumatol. 2019;38(7):1851–6. https://doi.org/10.1007/s10067-019-04488-3.

    Article  PubMed  Google Scholar 

  42. McDowell B, Marr C, Holmes C, et al. Prevalence of cognitive impairment in patients with rheumatoid arthritis: a cross sectional study. BMC Psychiatry. 2022;22(1):777. https://doi.org/10.1186/s12888-022-04417-w. Published 2022 Dec 9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Liu W, Yang X, Jin X, et al. Prospective evaluation of the association between arthritis and cognitive functions in middle-aged and elderly Chinese. Front Aging Neurosci. 2021;13:687780.

  44. Huang LC, Chang YH, Yang YH. Can disease-modifying anti-rheumatic drugs reduce the risk of developing dementia in patients with rheumatoid arthritis? Neurotherapeutics. 2019;16(3):703–9. https://doi.org/10.1007/s13311-019-00715-6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Kao LT, Kang JH, Lin HC, Huang CC, Lee HC, Chung SD. Rheumatoid arthritis was negatively associated with Alzheimer’s disease: a population-based case-control study. PLoS One. 2016;11(12) Published 2016 Dec 20.

  46. Policicchio S, Ahmad AN, Powell JF, Proitsi P. Rheumatoid arthritis and risk for Alzheimer’s disease: a systematic review and meta-analysis and a Mendelian randomization study. Sci Rep. 2017;7(1):12861. https://doi.org/10.1038/s41598-017-13168-8.

  47. Bae SC, Lee YH. Causal association between rheumatoid arthritis and a decreased risk of Alzheimer’s disease: a Mendelian randomization study. Kausalzusammenhang zwischen rheumatoider Arthritis und vermindertem Risiko für M. Alzheimer : Eine Mendel-Randomisierungsstudie. Z Rheumatol. 2019;78(4):359–64. https://doi.org/10.1007/s00393-018-0504-8.

    Article  PubMed  CAS  Google Scholar 

  48. Petersen LE, Grassi-Oliveira R, Siara T, et al. Premature immunosenescence is associated with memory dysfunction in rheumatoid arthritis. Neuroimmunomodulation. 2015;22(3):130–7. https://doi.org/10.1159/000358437.

    Article  PubMed  CAS  Google Scholar 

  49. Baptista TSA, Petersen LE, Molina JK, et al. Autoantibodies against myelin sheath and S100β are associated with cognitive dysfunction in patients with rheumatoid arthritis. Clin Rheumatol. 2017;36(9):1959–68. https://doi.org/10.1007/s10067-017-3724-4.

    Article  PubMed  Google Scholar 

  50. Hamed SA, Selim ZI, Elattar AM, Elserogy YM, Ahmed EA, Mohamed HO. Assessment of biocorrelates for brain involvement in female patients with rheumatoid arthritis. Clin Rheumatol. 2012;31(1):123–32. https://doi.org/10.1007/s10067-011-1795-1.

    Article  PubMed  Google Scholar 

  51. Sağ S, Sağ MS, Tekeoğlu I, Kamanlı A, Nas K, Acar BA. Central nervous system involvement in rheumatoid arthritis: possible role of chronic inflammation and tnf blocker therapy. Acta Neurol Belg. 2020;120(1):25–31. https://doi.org/10.1007/s13760-017-0879-3.

    Article  PubMed  Google Scholar 

  52. Tran PB, Miller RJ. Chemokine receptors: signposts to brain development and disease. Nat Rev Neurosci. 2003;4(6):444–55. https://doi.org/10.1038/nrn1116.

    Article  PubMed  CAS  Google Scholar 

  53. Lampa J, Westman M, Kadetoff D, et al. Peripheral inflammatory disease associated with centrally activated IL-1 system in humans and mice. Proc Natl Acad Sci U S A. 2012;109(31):12728–33. https://doi.org/10.1073/pnas.1118748109.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Vezzani A, Viviani B. Neuromodulatory properties of inflammatory cytokines and their impact on neuronal excitability. Neuropharmacology. 2015;96(Pt A):70–82. https://doi.org/10.1016/j.neuropharm.2014.10.027.

    Article  PubMed  CAS  Google Scholar 

  55. Coluccia D, Wolf OT, Kollias S, Roozendaal B, Forster A, de Quervain DJ. Glucocorticoid therapy-induced memory deficits: acute versus chronic effects. J Neurosci. 2008;28(13):3474–8. https://doi.org/10.1523/JNEUROSCI.4893-07.2008.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

The referenced studies or consortiums are gratefully acknowledged by the authors for contributing open-access datasets for the analysis.

Funding

Not applicable

Author information

Authors and Affiliations

  1. Chengdu University of Traditional Chinese Medicine, Chengdu, China

    Lincheng Duan, Shiyin Li, Haoming Li, Yue Shi & Yue Feng

  2. Meishan Hospital of Traditional Chinese Medicine, Affiliated Meishan Hospital of Chengdu University of Traditional Chinese Medicine, Meishan, China

    Xiaolong Xie

Authors
  1. Lincheng Duan
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  2. Shiyin Li
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  3. Haoming Li
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  4. Yue Shi
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  5. Xiaolong Xie
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  6. Yue Feng
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Contributions

LD, YS: conception, technique, formal analysis, data collection, first draft preparation, and visualization; SL, HL: preparation of the first draft preparation, visualization, and data curation in writing; XX, YF: editing, monitoring, and writing evaluation. The final draft of the manuscript was approved by all writers, who also made contributions to the work.

Corresponding authors

Correspondence to Xiaolong Xie or Yue Feng.

Ethics declarations

Ethics approval and consent to participate

The data used in our MR analysis is entirely from previously reported summary data. Therefore, neither patient consent nor ethical approval was necessary for the study.

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Not applicable

Competing interests

The authors declare no competing interests.

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Supplementary Information

Additional file 1:

Fig. S1. MR plots for the causal association of RA (Ishigaki K et al.) on cognitive performance. (A) Forest plot: each dot and its corresponding line represent the effect size and 95%CI. Each dot and its corresponding line represent the pooled estimates after the removal of the corresponding SNP. (B)Funnel plot: the x-axis represents β, and the y-axis represents 1/SE (standard error). (C) The leave-one-out sensitivity analysis: each dot and its corresponding line represent the pooled estimates after the removal of corresponding SNP. (D) Scatter plots: the estimate of intercept can be interpreted as an estimate of the average pleiotropy of all single-nucleotide polymorphisms (SNPs), and the slope coefficient provides an estimate of the bias of the causal effect. Fig. S2. MR plots for the causal association of RA (Ishigaki K et al.) on cognitive function. (A) Forest plot. (B) Funnel plot. (C) The leave-one-out sensitivity analysis. (D) Scatter plot. Fig. S3. MR plots for the causal association of RA (Ha E et al.) on cognitive performance. (A) Forest plot. (B) Funnel plot. (C) The leave-one-out sensitivity analysis. (D) Scatter plot. Fig. S4. MR plots for the causal association of RA (Ha E et al.) on cognitive function. (A) Forest plot. (B) Funnel plot. (C) The leave-one-out sensitivity analysis. (D) Scatter plot. Fig. S5. MR plots for the causal association of RA (Ha E et al.) on cognitive performance. (A) Forest plot. (B) Funnel plot. (C) The leave-one-out sensitivity analysis. (D) Scatter plot. Fig. S6. MR plots for the causal association of RA (Ha E et al.) on cognitive function. (A) Forest plot. (B) Funnel plot. (C) The leave-one-out sensitivity analysis. (D) Scatter plot.

Additional file 2:

 Table S1. Detailed information of instrumental variables used in the MR analysis.

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Duan, L., Li, S., Li, H. et al. Causality between rheumatoid arthritis and the risk of cognitive impairment: a Mendelian randomization study. Arthritis Res Ther 26, 5 (2024). https://doi.org/10.1186/s13075-023-03245-x

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Keywords

Arthritis Research & Therapy

ISSN: 1478-6362

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