Assessing the repurposing potential of disease-modifying antirheumatic drug targets to reduce Alzheimer’s disease risk: a pQTL-based Mendelian randomization and colocalization analysis

Assessing the repurposing potential of disease-modifying antirheumatic drug targets to reduce Alzheimer’s disease risk: a pQTL-based Mendelian randomization and colocalization analysis | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Assessing the repurposing potential of disease-modifying antirheumatic drug targets to reduce Alzheimer’s disease risk: a pQTL-based Mendelian randomization and colocalization analysis View ORCID Profile Christina N. Kushnir , View ORCID Profile Victoria Taylor-Bateman , View ORCID Profile Neil M. Davies , View ORCID Profile Emma L. Anderson doi: https://doi.org/10.1101/2025.08.01.25332834 Christina N. Kushnir 1 Division of Psychiatry, University College London , London WC1E 6BT, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christina N. Kushnir For correspondence: christina.kushnir.23{at}alumni.ucl.ac.uk Victoria Taylor-Bateman 1 Division of Psychiatry, University College London , London WC1E 6BT, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Victoria Taylor-Bateman Neil M. Davies 1 Division of Psychiatry, University College London , London WC1E 6BT, UK 2 Department of Statistical Science, University College London , London WC1E 6BT, UK 3 Department of Public Health and Nursing, Norwegian University of Science and Technology , Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Neil M. Davies Emma L. Anderson 1 Division of Psychiatry, University College London , London WC1E 6BT, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emma L. Anderson Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Current literature has implicated systemic inflammation in the pathogenesis of Alzheimer’s disease (AD). However, the viability of anti-inflammatory drug targets as repurposed treatment candidates for AD remains unclear. We utilized two-sample Mendelian randomization (MR) and colocalization analysis to investigate whether disease-modifying antirheumatic drug (DMARD) targets reduce AD risk. Methods We investigated 9 DMARD targets, using blood protein quantitative trait loci (pQTLs) from the UK Biobank Pharma Proteomics Project ( n = 54,219). The associations of these variants and AD were extracted from the International Genomics of Alzheimer’s Project ( n cases = 21,982, n controls = 41,944). We used two-sample MR analyses to estimate the effects of perturbing DMARD targets on AD risk. We also examined the colocalization of target pQTLs and AD signals in the cis -regions of target-encoding genes. Results Our MR estimates suggested that a 1 SD increase in plasma protein concentration of the etanercept target, FCGR3B , increased the risk of AD by 10% (OR 1.10; 95% CI [1.02, 1.19]; p = 0.01). We found little evidence that the remaining DMARD targets reduced AD risk. Further colocalization analysis indicated no evidence of colocalization between encoding genes of DMARD targets and AD, including FCGR3B . Though, there was suggestive evidence of a non-colocalized association between TNF and AD on chromosome 6 via distinct causal variants (H 3 = 46.5%). Conclusions Our findings suggest that the majority of analyzed DMARD targets were unlikely to reduce AD risk, except for FCGR3B . Future research in AD drug targeting and repurposing should examine the therapeutic viability of drugs which lower FCGR3B protein levels in blood plasma. Background Over 50 million people worldwide suffer from dementia, with this number expected to increase nearly threefold by the mid-21st century [ 1 ]. Alzheimer’s disease (AD), the most common cause of dementia and a leading cause of death in the United States and the United Kingdom, imposes both immense pain on patients and their loved ones as well as a significant socioeconomic burden on healthcare systems, making it imperative to find treatment candidates that do not simply address quality of life but, more urgently, target pathological mechanisms that may prevent or halt disease progression [ 2 , 3 ]. In recent years, chronic and systemic inflammation has been recognized as a possible modifiable risk factor for AD. Elevated concentrations of cytokines in the central nervous system (CNS) lead to neuronal toxicity and tissue damage which facilitate the susceptibility and development of AD [ 4 , 5 ]. Additional dysregulation of key pro-inflammatory mediators, such as tumor necrosis factor (TNF-α) and interleukin-6 (IL-6), has been connected to hallmark molecular mechanisms contributing to the pathogenesis of AD [ 4 , 5 , 6 , 7 , 8 ]. Furthermore, genome-wide association studies (GWAS) have substantiated the role of immune function and inflammation in AD. The largest and most recent AD GWAS to date, conducted by Bellenguez et al. (2022), identified multiple genome-wide significant loci near genes involved in innate immunity and microglial function, such as TREM2, PLCG2 , and ABI3 , as well as loci near IL-34, TNIP1 , and GRN , all of which have roles in immune regulation or inflammatory signaling [ 9 ]. These findings provide converging genetic evidence that dysregulated immune responses may contribute to AD pathogenesis, highlighting inflammatory pathways as promising therapeutic targets. Disease-modifying antirheumatic drugs (DMARDs), a class of anti-inflammatory medications that modulate both cytokine activity and other pro-inflammatory targets, present as potentially viable drug repurposing candidates for AD prevention and treatment. Importantly, the disease-modifying component of DMARDs makes them encouraging therapeutic candidates to counter the progressive neurodegeneration that characterizes AD. However, observational studies examining the association between prescribed DMARD usage and the risk of AD have yielded mixed and contradictory results [ 10 , 11 , 12 , 13 , 14 , 15 ]. Furthermore, such studies investigate sample populations already impacted by autoimmune and/or rheumatic diseases, making confounding by indication highly likely. While randomized control trials (RCTs) may help clarify the existing research, only two RCTs have investigated the casual effects of DMARDs on the risk of AD, both with small sample sizes ( n = 41, n = 15) [ 16 , 17 ]. These RCTs examined the use of etanercept, a commonly prescribed DMARD, over the course of 6 months with results from both trials suggesting positive trends in favor of a protective effect on AD risk. However, the lack of statistical power limits inference from these studies. Mendelian randomization (MR) is a gene-based instrumental variable approach that can help to minimize confounding and reverse causation—which are pervasive in observational studies— without the additional logistic and financial constraints of clinical trials [ 18 ]. Due to the randomized nature of genetic inheritance, MR can assess the causal effects of risk factors via instrumentation of genetic variants [ 19 ]. Additionally, MR can be specifically employed to study the effects of perturbing drug targets [ 19 ]. Drug target MR estimates the effects of changing protein levels or expression under the rationale that these traits are likely to mimic the consequences of perturbing these proteins by drugs known to target these mechanisms. The genetic variants are selected based on their association with the drug targets of interest ( Fig. 1 ). Colocalization often accompanies MR studies as a form of sensitivity analysis to reduce the threat that linkage disequilibrium (LD) poses to the assumptions of MR. As neither MR nor colocalization are alone sufficient in establishing causality, the integration of both methods may provide strength to studies in statistical genetics and drug repurposing. Download figure Open in new tab Fig. 1. Diagrammatic illustration of the study. MR assumes that the genetic variant (1) is robustly associated with the exposure variable (relevance); (2) has no shared confounders with the outcome (independence); and (3) affects the outcome only through the exposure (exclusion restriction) [ 20 ]. Abbreviations: DMARD, disease-modifying antirheumatic drug; TNF , tumor necrosis factor; BMI, body mass index Here, we assess whether select DMARD targets are likely to be efficacious therapeutic targets for reducing AD risk, using two-sample MR and colocalization. Methods Selection of DMARD targets 11 DMARDs, totaling 33 target genes, were initially selected for analysis. The rationale for inclusion was based on findings from the current literature regarding associations between DMARD usage and incidence rates of dementia or AD [10, 11, 12, 13, 14, 15]. The drugs identified were methotrexate, sulfasalazine, hydroxychloroquine, leflunomide, adalimumab, etanercept, infliximab, certolizumab, golimumab, tocilizumab, and tofacitinib. Methotrexate and tofacitinib had no available pQTL data and were therefore excluded from analysis. In later stages, the pQTL data for the leflunomide target, DHODH , did not meet the required significance threshold; as a result, leflunomide was also excluded from analysis. DMARDs and their corresponding encoding gene targets were identified using the DrugBank database ( https://go.drugbank.com ) and are listed in Table 1 . Note, this table does not encapsulate all targets of the respective DMARDs but rather the targets with available pQTL data. Additionally, some targets, such as TNF , are shared across multiple drugs in our analysis. View this table: View inline View popup Download powerpoint Table 1: Selected DMARDs and their analyzed targets pQTL instrument selection Exposure data for each selected DMARD target were sourced from a large-scale proteogenomic analysis as part of the UK Biobank Pharma Proteomics Project (UKB-PPP, n = 54,219), which provides detailed mapping of 2,923 proteins and their associations with genetic variants within blood plasma [ 21 ]. We searched for cis -acting single nucleotide polymorphisms (SNPs) known to be associated with circulating protein levels (pQTLs) of encoded DMARD targets ( Fig. 2 ). Lead pQTLs were identified to proxy the effects of select DMARD target perturbations, based on independence and strength of association (meeting the genome-wide significance threshold of p < 5 × 10 −8 ). The following selection criteria were applied for instrumentation: 1) pQTLs within ± 1000 kb of the gene region ( cis -acting); and 2) a genome-wide significance threshold of p < 5 × 10 −8 ). Download figure Open in new tab Fig. 2. Flow diagram of the study design. Abbreviations: DMARDs, disease-modifying antirheumatic drugs; UKB-PPP, UK Biobank Pharma Proteomics Project; pQTL, protein quantitative trait loci; AD, Alzheimer’s disease; IGAP, International Genomics of Alzheimer’s Project; GWAS, genome-wide association study; MR, Mendelian randomization Outcome data Effect estimates of instrumented pQTLs were extracted from a genome-wide association meta-analysis of clinically diagnosed AD cases within the International Genomics of Alzheimer’s Project (IGAP) [ 22 ]. Stage 1 of the GWAS included 21,982 cases and 41,944 controls, all of whom were of European ancestry. Risk of late-onset AD (LOAD) was defined as symptom onset at ≥ 65 years of age. Diagnostic assessment occurred either in clinical evaluation or postmortem autopsy. The mean age at onset for AD cases ranged from 71.1 to 82.6 years, and mean age at examination (or last follow-up) for healthy controls ranged from 51.0 to 78.9 years. Further information on quality control can be found in the original publication. Statistical analyses Mendelian randomization analysis MR analyses were conducted to examine the individual effects of each drug target on the risk of AD. Due to the outcome GWAS comprising only autosomal data, we did not include SNPs located within the X chromosome during analysis. To reduce impacts from LD, the instrumented pQTLs underwent a clumping procedure with a threshold of r 2 = 0.001 and a 10,000 kb window to ensure independent variants. The exposure data was coded using the most recent genome reference consortium human build 38 (GRCh38), assembly Hg20, while the IGAP GWAS outcome data used GRCh37 with assembly Hg19. Thus, any instrument pQTLs not found in the outcome dataset were replaced by proxy variants in high LD from the 1000 Genomes European reference panel ( r 2 > 0.8). During data harmonization, palindromic SNPs were excluded if the minor allele frequency was > 0.4. MR was performed using the inverse variance weighted (IVW) method. Upon completion of analysis, odds ratios (ORs) and 95% confidence intervals (CIs) for AD outcomes were presented per 1 standard deviation (SD) increase in blood plasma protein concentration. Colocalization analysis Colocalization was performed on cis -regions of the encoding target genes of interest (± 500 kb window) to assess if identified associations with AD resulted from shared causal variants. ACE2 was excluded from this portion of the analysis as it is located on the X chromosome and could not be matched with autosomal outcome data. The following priors were set: SNPs within the default window and exclusively associated with trait 1 had a probability of p 1 = 10 −4 ; SNPs within the default window and exclusively associated with trait 2 had a probability of p 2 = 10 −4 ; and SNPs within the default window and associated with both traits 1 and 2 had a probability of p 12 = 10 −5 . A posterior probability for shared causal variants (PP.H 4 ≥ 80%) was considered to demonstrate strong evidence of colocalization. A threshold of PP.H 4 ≥ 50% was considered suggestive evidence. Results Effects of DMARD targets on the risk of AD Overall, there was little evidence in our study that DMARD targets affected the risk of AD ( Fig. 3 ). There was little evidence that the sulfasalazine targets, PLA2G1B (OR 0.99; 95% CI [0.91, 1.08]; p = 0.80) and NFKB1 (OR 0.89; 95% CI [0.74, 1.07]; p = 0.22), affected AD risk. There was little evidence for the hydroxychloroquine target, ACE2 (OR 1.10; 95% CI [0.97, 1.24]; p = 0.14). There was additionally little evidence for the targets of tocilizumab, the TNF inhibitors, and etanercept, including LTA (OR 1.03; 95% CI [0.95, 1.12]; p = 0.43), FCGR2A (OR 1.02; 95% CI [0.94, 1.10]; p = 0.63), FCGR2B (OR 0.99; 95% CI [0.87, 1.13]; p = 0.86), TNF (OR 0.92; 95% CI [0.72, 1.16]; p = 0.47), and IL6R (OR 0.97; 95% CI [0.90, 1.03]; p = 0.28). However, there was evidence that increasing plasma levels of FCGR3B , an etanercept target, increased the risk of AD (OR 1.10; 95% CI [1.02, 1.19]; p = 0.01). Download figure Open in new tab Fig. 3. Estimates of the effects of selected DMARD targets on the risk of developing AD. Error bars correspond to the 95% confidence intervals. Abbreviations: DMARD, disease-modifying antirheumatic drug; SNP, single nucleotide polymorphism; OR, odds ratio; SD, standard deviation Results of colocalization analysis Colocalization analyses were consistent with the MR analyses above. There was little evidence of colocalization between any of the targets and AD (PP.H 4 < 10% across all proteins). Despite evidence of a causal effect in the main MR analysis, FCGR3B protein and AD did not colocalize in the flanked chromosome 1 region of FCGR3B (PP.H 4 = 6.21%). There was also very little evidence of pleiotropy (PP.H 3 = 10.3%). However, there was some evidence of a non-colocalized association between TNF protein and AD, potentially through pleiotropic causal variants (PP.H 3 = 46.5%) ( Fig. 4 ). Download figure Open in new tab Fig. 4. Regional genomic plots from colocalization analysis of the flanked TNF region on chromosome 6. The LD reference SNP (rs2229094) was used for plotting. Both traits ( TNF , AD) had to be compared separately because of the differing builds, GrCH37/38. No pQTLs in the TNF region met the threshold for colocalization with AD. Discussion We utilized cis -pQTL and GWAS summary statistics to evaluate the causal effects of genetically perturbed DMARD targets on the risk of AD. To our knowledge, this is the first study to specifically examine DMARD targets and AD risk using drug target MR and colocalization. We found limited evidence to support the repurposing potential of select DMARD targets for reduced AD risk. The MR analyses provided little evidence that 8 out of the 9 selected targets— ACE2, FCGR2A, FCGR2B, IL6R, LTA, NFKB1, PLA2G1B , and TNF —affected the risk of AD. With MR, we found that a per 1 SD unit increase in plasma FCGR3B concentration leads to a 10% greater risk of AD. Though, further analysis of genetic signaling in the flanked region on chromosome 1 revealed that FCGR3B did not colocalize with AD and are thus unlikely to share a single causal variant. A possible explanation for this discordance is that the findings from MR may have been driven by distinct variants in LD. Alternatively, the lack of evidence for colocalization may be due to tissue specificity. In support of this, a recent study found evidence of colocalization between FCGR3B protein, located in cerebrospinal fluid (CSF), and AD signals [ 23 ]. Interestingly, this study observed the opposite direction of effect for FCGR3B , with their MR analysis revealing an association between increased CSF FCGR3B concentration and a lower risk of AD—although it should be noted that their instruments for FCGR3B did not reach genome-wide significance. CSF protein composition is more diverse than that of plasma and may perform as a more accurate biomarker for neurodegenerative diseases [ 24 ]. However, plasma, while sub-optimal in this case, has greater availability of proteomic data, rendering it a useful and more accessible tool for analysis. It is possible that the use of a less disease-relevant tissue such as blood plasma may explain the difference in results, though, both sets of findings are indicative of causal effects of FCGR3B on the risk of AD. The evidence in our study suggests that ACE2, LTA, FCGR2A, FCGR2B, and PLA2G1B are unlikely to be effective candidate targets for AD; these findings should be taken into consideration to reduce the high attrition rate in AD drug development by discouraging trials of irrelevant targets such as those listed above [ 25 ]. Additionally, we observed little evidence of an effect for IL6R , though our estimate shows a consistent direction of effect with that of a recent MR study reporting significant protective effects of IL6R variants on AD risk via downstream C-reactive protein serum levels [ 26 ]. As that study utilized data from larger-scale GWASs, including ‘proxy’ AD cases—as opposed to the present study which only examined clinically diagnosed AD cases—it is possible that with greater power, we may have similarly observed a significant effect of IL6R . Alternatively, it is plausible that they observed an effect of IL6R due to bias introduced by the inclusion of ‘proxy’ AD cases. Definitive conclusions cannot be drawn regarding the efficacy of NFKB1 and TNF due to the imprecision of estimates observed in this study. However, a previously conducted MR found little evidence of a causal effect of TNF serum levels on AD [ 27 ]. Their instrumentation of pQTLs derived from blood serum raises the concern of tissue relevancy as mentioned earlier in the discussion of FCGR3B . Our suggestive evidence of a non-colocalized association between TNF and AD signals at the flanked chromosome 6 region highlights the possibility of alternate tissue-specific molecular pathways in which TNF and AD associate. Nevertheless, a potential ∼28% reduction in disease risk through TNF plasma levels, as evidenced by this study, merits further exploration of this target in plasma tissue. Limitations Several limitations should be considered. First, this study was limited by the lack of proteomic data available for relevant drug targets. Among 33 initially selected targets from a total of 11 DMARDs, the final MR analyses could only include 9 targets from 8 DMARDs. Notably, methotrexate had to be excluded, which has recently accumulated promising observational evidence for an association with reduced AD risk [ 28 ]. Second, clinical evidence has demonstrated differential protein expression according to tissue specificity and it is plausible that this would have impacted our findings, in addition to the sub-optimal relevance of plasma as a biomarker for neurodegenerative processes [ 29 ]. Additional analyses that incorporate locus-specific instruments in tissues other than plasma (e.g., CSF) may yield different results. Third, there are several caveats to drug target MR that limit the ability to causally examine broader drug usage and disease outcome associations [ 30 ]. As we estimated the effects of perturbing individual drug targets, we cannot infer the likely aggregate effects of specific DMARDs. Furthermore, it is difficult to achieve a complete understanding of the pharmacodynamics of medications more broadly (e.g., understanding the exact molecular pathways in which etanercept affects FCGR3B and other targets). Thus, based on the genetic evidence alone, we cannot conclude how prescribed etanercept usage would affect AD risk via FCGR3B and other known or unknown targets. Further mechanistic studies and clinical trials are recommended for comprehensive evaluation of DMARD repurposing. Finally, it is worth noting possible biases which could have further attenuated our estimates. Sourcing instruments from the UK BioBank—which is prone to selective representation—may introduce collider bias [ 31 , 32 ]. Selected study participants tend to be healthier and may therefore have a lower genetic liability for disease traits, which in turn may potentially impact MR estimates [ 33 ]. That said, to date, there is little evidence in the literature specifically linking proteomic data to selection bias. Conclusions The findings from our study do not support the majority of selected DMARD targets as efficacious treatment candidates for AD, with exception of the etanercept target, FCGR3B . The observed positive effect of increased plasma FCGR3B concentration on AD risk warrants further investigation into the viability of therapeutics which lower FCGR3B protein levels in blood plasma. Our findings should be considered as part of the current discourse regarding the targeting of inflammatory mechanisms for AD treatment and prevention. Larger-scale MR studies that instrument a greater number of DMARD targets across various tissues are additionally recommended. Data Availability All data produced in the present work are contained in the manuscript and available at https://github.com/tkush15/AD_DMARD_MRCOLOC Declarations Ethics approval and consent to participate The GWASs included in this study obtained written informed consent from all participants prior to data collection. Related study protocols were approved accordingly by the appropriate institutional review boards accordingly. Therefore, separate ethical approval was not required for the present study. Consent for publication Not applicable. Availability of data and materials Proteogenomic summary data from the UKB-PPP can be found at http://ukb-ppp.gwas.eu . Full AD GWAS data can be obtained by requesting access via the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) data repository, ID: NG00075. The codes used to generate the results of this study are located at https://github.com/tkush15/AD_DMARD_MRCOLOC . Competing interests The authors declare that they have no competing interests. Funding ELA is supported by a UKRI Future Leaders Fellowship (MR/W011581/2). NMD is supported via a Norwegian Research Council 295989. Authors’ contributions NMD, ELA, and CNK conceptualized the study. ELA provided coding. VTB provided coding and guidance for colocalization. CNK performed all statistical analyses, prepared figures, and drafted the initial manuscript. NMD and ELA guided the interpretation of results and provided written edits to the manuscript draft. All authors contributed to the revision and approval of the final manuscript. Acknowledgments Not applicable. Footnotes Figure 2 revised; author funding information updated; citation number modified; medRxiv abstract formatting revised. Abbreviations AD Alzheimer’s disease MR Mendelian randomization DMARD Disease-modifying antirheumatic drug pQTL Protein quantitative trait loci CNS Central nervous system TNF-α Tumor necrosis factor IL-6 Interleukin-6 GWAS Genome-wide association study RCT Randomized control trial LD Linkage disequilibrium BMI Body mass index UKB-PPP UK Biobank Pharma Proteomics Project SNP Single nucleotide polymorphism IGAP International Genomics of Alzheimer’s Project LOAD Late-onset Alzheimer’s disease GRCh Genome Reference Consortium Human IVW Inverse variance weighted OR Odds ratio CI Confidence interval SD Standard deviation References 1. ↵ Nichols E , Steinmetz JD , Vollset SE , Fukutaki K , Chalek J , Abd-Allah F et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019 . Lancet Public Health . 2022 ; 7 ( 2 ): 105 – 125 . doi: 10.1016/S2468-2667(21)00249-8 OpenUrl CrossRef 2. ↵ National Center for Health Statistics . Leading Causes of Death. [online] . Centers for Disease Control and Prevention : Hyattsville MD ; 2024 [Accessed 2 May 2024 ]. Available from: https://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm 3. ↵ Nandi A , Counts N , Bröker J , Malik S , Chen S , Han R et al. Cost of care for Alzheimer’s disease and related dementias in the United States: 2016 to 2060 . npj Aging . 2024 ; 10 ( 13 ). doi: 10.1038/s41514-024-00136-6 OpenUrl CrossRef PubMed 4. ↵ Swardfager W , Lanctôt K , Rothenburg L , Wong A , Cappell J , Herrmann N. A Meta-Analysis of Cytokines in Alzheimer’s Disease . Biological Psychiatry . 2010 ; 68 ( 10 ): 930 – 941 . doi: 10.1016/j.biopsych.2010.06.012 OpenUrl CrossRef PubMed Web of Science 5. ↵ Kinney JW , Bemiller SM , Murtishaw AS , Leisgang AM , Salazar AM , Lamb BT . Inflammation as a central mechanism in Alzheimer’s disease . Alzheimers Dement TRCI . 2018 ; 4 ( 1 ): 575 – 590 . doi: 10.1016/j.trci.2018.06.014 OpenUrl CrossRef PubMed 6. ↵ Jayaraman A , Htike TT , James R , Picon C , Reynolds R. TNF-mediated neuroinflammation is linked to neuronal necroptosis in Alzheimer’s disease hippocampus . BMC ANC . 2021 ; 9 ( 159 ) doi: 10.1186/s40478-021-01264-w OpenUrl CrossRef PubMed 7. ↵ Erta M , Quintana A , Hidalgo J. Interleukin-6, a major cytokine in the central nervous system . Int J Biol Sci . 2012 ; 8 ( 9 ): 1254 – 1266 . doi: 10.7150/ijbs.4679 OpenUrl CrossRef PubMed 8. ↵ Chang R , Yee K-L , Sumbria RK . Tumor necrosis factor α Inhibition for Alzheimer’s Disease . J Cent Nerv Syst Dis . 2017 ; 9 . doi: 10.1177/117957351770927 OpenUrl CrossRef 9. ↵ Bellenguez C , Küçükali F , Jansen IE , Kleineidam L , Moreno-Grau S , Amin N et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias . Nat Genet . 2022 ; 54 : 412 – 436 . doi: 10.1038/s41588-022-01024-z OpenUrl CrossRef PubMed 10. ↵ Chou R , Kane M , Ghimire S , Gautam S , Gui J. Treatment for Rheumatoid Arthritis and Risk of Alzheimer’s Disease: A Nested Case-Control Analysis . CNS Drugs . 2016 ; 30 : 1111 – 20 . doi: 10.1007/s40263-016-0374-z OpenUrl CrossRef PubMed 11. ↵ Huang L-C , Chang Y-H , Yang Y-H. Can disease-modifying anti-rheumatic drugs reduce the risk of developing dementia in patients with rheumatoid arthritis? Neurotherapeutics . 2019 ; 16 ( 3 ): 703 – 9 . doi: 10.1007/s13311-019-00715-6 OpenUrl CrossRef PubMed 12. ↵ Xie W , Hou Y , Xiao S , Zhang X , Zhang Z. Association between disease-modifying antirheumatic drugs for rheumatoid arthritis and risk of incident dementia: a systematic review with meta-analysis . RMD Open . 2024 ; 10 ( 1 ). doi: 10.1136/rmdopen-2023-004016 OpenUrl Abstract / FREE Full Text 13. ↵ Sattui S , Navarro-Millan I , Xie F , Rajan M , Yun H , Curtis JR . Incidence of dementia in patients with rheumatoid arthritis and association with disease modifying anti-rheumatic drugs – Analysis of a national claims database . Semin Arthritis Rheum . 2022 ; 57 : 152083 . doi: 10.1016/j.semarthrit.2022.152083 OpenUrl CrossRef PubMed 14. ↵ Watad A , McGonagle D , Anis S , Carmeli R , Cohen AD , Tsur AM et al. TNF inhibitors have a protective role in the risk of dementia in patients with ankylosing spondylitis: Results from a nationwide study . Pharmacol Res . 2022 ; 182 : 106325 . doi: 10.1016/j.phrs.2022.106325 OpenUrl CrossRef PubMed 15. ↵ Zhou M , Xu R , Kaelber DC , Gurney ME . Tumor Necrosis Factor (TNF) blocking agents are associated with lower risk for Alzheimer’s disease in patients with rheumatoid arthritis and psoriasis . PLOS One . 2020 ; 15 ( 3 ). doi: 10.1371/journal.pone.0229819 OpenUrl CrossRef PubMed 16. ↵ Butchart J , Brook L , Hopkins V , Teeling J , Pütener U , Culliford D et al. Etanercept in Alzheimer disease: A randomized, placebo-controlled, double-blind, phase 2 trial . Neurology . 2015 ; 84 ( 21 ): 2161 – 8 . doi: 10.1212/WNL.0000000000001617 OpenUrl CrossRef PubMed 17. ↵ Tobinick E , Gross H , Weinberger A , Cohen H. TNF-alpha modulation for treatment of Alzheimer’s disease: a 6-month pilot study . Med Gen Med . 2006 ; 8 ( 2 ): 25 . https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/16926764/ OpenUrl 18. ↵ Smith GD , Ebrahim S. Mendelian randomization: prospects, potentials, and limitations . Int J Epidemiol . 2004 ; 33 ( 1 ): 30 – 42 . doi: 10.1093/ije/dyh132 OpenUrl CrossRef PubMed Web of Science 19. ↵ Gill D , Georgakis MK , Walker VM , Schmidt AF , Gkatzionis A , Freitag DF et al. Mendelian randomization for studying the effects of perturbing drug targets . Wellcome Open Res . 2021 ; 6 : 16 . doi: 10.12688/wellcomeopenres.16544.2 OpenUrl CrossRef PubMed 20. ↵ Davies NM , Holmes MV , Smith GD . Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians . BMJ . 2018 ; 362 : k601 . doi: 10.1136/bmj.k601 OpenUrl FREE Full Text 21. ↵ Sun BB , Chiou J , Traylor M , Benner C , Hsu Y-H , Richardson TG . Plasma proteomic associations with genetics and health in the UK Biobank . Nature . 2023 ; 622 ( 7982 ): 329 – 338 . doi: 10.1038/s41586-023-06592-6 OpenUrl CrossRef 22. ↵ Kunkle BW , Grenier-Boley B , Sims R , Bis JC , Damotte V , Naj AC et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing . Nat Genet . 2019 ; 51 ( 3 ): 414 – 430 . doi: 10.1038/s41588-019-0358-2 OpenUrl CrossRef PubMed 23. ↵ Cruchaga C , Western D , Timsina J , Wang L , Wang C , Yang C et al. Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer’s disease . Res Sq . 2023 . doi: 10.21203/rs.3.rs-2814616/v1 OpenUrl CrossRef 24. ↵ Karlsson L , Vogel J , Arvidsson I , Åström K , Janelidze S , Blennow K et al. Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases . Nat Commun . 2024 ; 15 . doi: 10.1038/s41467-024-47971-5 OpenUrl CrossRef PubMed 25. ↵ Cummings J , Morstorf T , Zhong K. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures . Alz Res Therapy . 2014 ; 6 ( 37 ). doi: 10.1186/alzrt269 OpenUrl CrossRef PubMed 26. ↵ Myserlis EP , Anushree R , Anderson CD , Georgakis MK . Genetically proxied IL-6 signaling and risk of Alzheimer’s disease and lobar intracerebral hemorrhage: A drug target Mendelian randomization study . Alzheimers Dement TRCI . 2024 ; 10 ( 3 ). doi: 10.1002/trc2.70000 OpenUrl CrossRef 27. ↵ Andrews SJ , Goate A. Mendelian randomization indicates that TNF is not causally associated with Alzheimer’s disease . Neurobiol Aging . 2019 ; 84 . doi: 10.1016/j.neurobiolaging.2019.09.003 OpenUrl CrossRef 28. ↵ Newby D , Prieto-Alhambra D , Duarte-Salles T , Ansell D , Pedersen L , van der Lei J et al. Methotrexate and relative risk of dementia amongst patients with rheumatoid arthritis: a multi-national multi-database case-control study . Alzheimers Res Ther . 2020 ; 12 ( 38 ). doi: 10.1186/s13195-020-00606-5 OpenUrl CrossRef 29. ↵ Whitehead A , Crawford DL . Variation in tissue-specific gene expression among natural populations . Genome Biol . 2005 ; 6 ( R13 ). doi: 10.1186/gb-2005-6-2-r13 OpenUrl CrossRef PubMed 30. ↵ Anderson EL , Williams DM . Drug target Mendelian randomization: are we really instrumenting drug use? Diabetologia . 2023 ; 66 ( 6 ): 1156 – 8 . doi: 10.1007/s00125-023-05875-x OpenUrl CrossRef PubMed 31. ↵ Gkatzionis A , Burgess S. Contextualizing selection bias in Mendelian randomization: how bad is it likely to be? Int J Epidemiol . 2019 ; 48 ( 3 ): 691 – 701 . doi: 10.1093/ije/dyy202 OpenUrl CrossRef PubMed 32. ↵ Schoeler T , Speed D , Porcu E , Pirastu N , Pingault JB , Kutalik Z. Participation bias in the UK Biobank distorts genetic associations and downstream analyses . Nat Human Behav . 2023 ; 7 ( 7 ): 1216 – 27 . doi: 10.1038/s41562-023-01579-9 OpenUrl CrossRef 33. ↵ Munafò MR , Tilling K , Taylor AE , Evans DM , Smith GD . Collider scope: when selection bias can substantially influence observed associations . Int J Epidemiol . 2018 ; 47 ( 1 ): 226 – 35 . doi: 10.1093/ije/dyx206 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 07, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Assessing the repurposing potential of disease-modifying antirheumatic drug targets to reduce Alzheimer’s disease risk: a pQTL-based Mendelian randomization and colocalization analysis Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Assessing the repurposing potential of disease-modifying antirheumatic drug targets to reduce Alzheimer’s disease risk: a pQTL-based Mendelian randomization and colocalization analysis Christina N. Kushnir , Victoria Taylor-Bateman , Neil M. Davies , Emma L. Anderson medRxiv 2025.08.01.25332834; doi: https://doi.org/10.1101/2025.08.01.25332834 Share This Article: Copy Citation Tools Assessing the repurposing potential of disease-modifying antirheumatic drug targets to reduce Alzheimer’s disease risk: a pQTL-based Mendelian randomization and colocalization analysis Christina N. Kushnir , Victoria Taylor-Bateman , Neil M. Davies , Emma L. Anderson medRxiv 2025.08.01.25332834; doi: https://doi.org/10.1101/2025.08.01.25332834 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4440) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1510) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6605) Geriatric Medicine (668) Health Economics (998) Health Informatics (4541) Health Policy (1369) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1265) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6604) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1145) Occupational and Environmental Health (957) Oncology (3334) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (711) Psychiatry and Clinical Psychology (5448) Public and Global Health (9235) Radiology and Imaging (2199) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (594) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a016c7236c838650',t:'MTc3OTczNTU3Mw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();