Estimation of heritability of different outcomes for genetic studies of TNFi response in patients with rheumatoid arthritis

Does persistence to methotrexate treatment in early rheumatoid arthritis have a familial component?

Objectives

To assess whether persistence to treatment with methotrexate (MTX) in early rheumatoid arthritis (RA) is shared among first-degree relatives with RA and to estimate any underlying heritability.

Methods

First-degree relative pairs diagnosed with RA 1999–2018 and starting MTX (in monotherapy) as their first disease-modifying anti-rheumatic drug (DMARD) treatment were identified by linking the Swedish Rheumatology Quality Register to national registers. Short- and long-term persistence to MTX was defined as remaining on treatment at 1 and 3 years, respectively, with no additional DMARDs added. We assessed familial aggregation through relative risks (RR) using log-binomial regression with robust standard errors and estimated heritability using tetrachoric correlations. We also explored the familial aggregation of EULAR treatment response after 3 and 6 months. To mimic the clinical setting, we also tested the association between having a family history of MTX persistence and persistence within the index patient.

Results

Familial persistence was not associated with persistence at 1 (RR=1.02, 95% CI 0.87–1.20), only at 3 (RR=1.41, 95% CI 1.14–1.74) years. Heritability at 1 and 3 years was estimated to be 0.08 (95% CI 0–0.43) and 0.58 (95% CI 0.27–0.89), respectively. No significant associations were found between family history and EULAR response at 3 and 6 months, neither overall nor in the clinical setting analysis.

Conclusions

Our findings imply a familial component, including a possible genetic element, within the long-term persistence to MTX following RA diagnosis. Whether this component is reflective of characteristics of the underlying RA disease or determinants for sustained response to MTX in itself will require further investigation.

A genome-wide screen for variants influencing certolizumab pegol response in a moderate to severe rheumatoid arthritis population

Certolizumab pegol (CZP) is a PEGylated Fc-free tumor necrosis factor (TNF) inhibitor antibody approved for use in the treatment of rheumatoid arthritis (RA), Crohn’s disease, psoriatic arthritis, axial spondyloarthritis and psoriasis. In a clinical trial of patients with severe RA, CZP improved disease symptoms in approximately half of patients. However, variability in CZP efficacy remains a problem for clinicians, thus, the aim of this study was to identify genetic variants predictive of CZP response. We performed a genome-wide association study (GWAS) of 302 RA patients treated with CZP in the REALISTIC trial to identify common single nucleotide polymorphisms (SNPs) associated with treatment response. Whole-exome sequencing was also performed for 74 CZP extreme responders and non-responders within the same population, as well as 1546 population controls. No common SNPs or rare functional variants were significantly associated with CZP response, though a non-significant enrichment in the RA-implicated KCNK5 gene was observed. Two SNPs near spondin-1 and semaphorin-4G approached genome-wide significance. The results of the current study did not provide an unambiguous predictor of CZP response.

Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease

In recent years, with the advancement of next-generation sequencing (NGS) technology, gene panel tests have been approved in the field of cancer diseases, and approaches to prescribe optimal molecular target drugs to patients are being developed. In the field of rare diseases, whole-genome and whole-exome analysis has been used to identify the causative genes of undiagnosed diseases and to diagnose patients’ diseases, and further progress in personalized medicine is expected. In order to promote personalized medicine in the future, we investigated the current status and progress of personalized medicine in disease areas other than cancer and rare diseases, where personalized medicine is most advanced. We selected rheumatoid arthritis and psoriasis as the inflammatory disease, in addition to Alzheimer’s disease. These diseases have high unmet needs for personalized medicine from the viewpoints of disease mechanisms, diagnostic biomarkers, therapeutic drugs with diagnostic markers and treatment satisfaction. In rheumatoid arthritis and psoriasis, there are many therapeutic options; however, diagnostic methods have not been developed to select the best treatment for each patient. In addition, there are few effective therapeutic agents in Alzheimer’s disease, although clinical trials of many candidate drugs have been conducted. In rheumatoid arthritis and psoriasis, further elucidation of the disease mechanism is desired to enable the selection of appropriate therapeutic agents according to the patient profile. In the case of Alzheimer’s disease, progress in preventive medicine is desired through the establishment of an early diagnosis method as well as the research and development of innovative therapeutic agents. To this end, we hope for further research and development of diagnostic markers and new drugs through progress in comprehensive data analysis such as comprehensive genomic and transcriptomic information. Furthermore, new types of markers such as miRNAs and the gut microbiome are desired to be utilized in clinical diagnostics.

Pharmacogenetics of inflammatory bowel disease

Patients with inflammatory bowel disease (IBD) show large variability in disease course, and also treatment response. The variability in treatment response has led to many initiatives in search of genetic markers to optimize treatment and avoid severe side effects. This has been very successful for thiopurines, one of the drugs used to induce and maintain remission in IBD. However, for the newer treatment options for IBD, like biologicals, the search for genetic predictors has not yielded any candidate biomarkers with clinical utility. In this review, a summary of recent advances in pharmacogenetics focusing on thiopurines and anti-TNF agents is given.

Pharmacogenetics of TNF inhibitor response in rheumatoid arthritis utilizing the two-component disease activity score

Aim:

TNF inhibitor drugs are a treatment option for rheumatoid arthritis, but response is not universal. Response is typically measured using the composite 4-component (4C) disease activity score 28 (DAS28) which contains more subjective measures. This study used a validated 2-component (2C) DAS28 score to determine whether SNPs associated with response were replicated in the UK population.

Materials & methods:

A literature review identified TNF inhibitor response SNPs. Linear regression was conducted to replicate associations with 4C or 2C-DAS28 response.

Results:

Eighteen independent SNPs were analyzed in 1828 patients. One and four associations with 4C and 2C-DAS28 response respectively were identified (p ≤ 0.05).

Conclusion:

Further genetic associations were replicated using the 2C-DAS28 which may reflect the objective nature of 2C-AS28.

Response to IL-17A inhibitors secukinumab and ixekizumab cannot be explained by genetic variation in the protein-coding and untranslated regions of the IL-17A gene: results from a multicentre study of four European psoriasis cohorts

BACKGROUND

Genetic predictors for treatment response could optimize allocation of biological treatment in patients with psoriasis. There is minimal knowledge about pharmacogenetics of anti-IL-17 agents.

OBJECTIVES

To assess whether genetic variants in the protein-coding region or untranslated regions of the IL-17A gene are associated with response to IL-17A inhibitors in patients with psoriasis.

METHODS

This was a multicenter European cohort study investigating pharmacogenetics of IL-17A inhibitors in patients with psoriasis. Patients with plaque psoriasis treated with secukinumab or ixekizumab in daily practice were included. For all participants, the protein-coding region and untranslated regions of the IL-17A gene were analysed using Sanger sequencing. Identified genetic variants were tested for association with response to secukinumab/ixekizumab, measured as ∆PASI, after 12 weeks (primary outcome) and after 24 weeks (secondary outcome). Association was tested using a linear regression model with correction for baseline PASI as a fixed covariate and for biological naivety and body mass index as additional covariates.

RESULTS

In total, 134 patients treated with secukinumab or ixekizumab were included. Genotyping of the cohort identified genetic variants present in untranslated regions and intronic DNA, but not in the protein-coding region of the IL-17A gene. Five genetic variants in non-coding DNA with a known or suspected functional effect on IL-17A expression were selected for association analyses: rs2275913, rs8193037, rs3819025, rs7747909 and rs3748067. After 12 weeks, 62% of patients achieved PASI75 and 39% achieved PASI90. At week 24, PASI75 and PASI90 response rates were 72% and 62%, respectively. No associations were found between the five genetic variants and ∆PASI, PASI75 or PASI90 after 12 and 24 weeks of anti-IL-17A treatment.

CONCLUSIONS

Response to IL-17A inhibitors secukinumab and ixekizumab cannot be explained by genetic variation in the protein-coding and untranslated regions of the IL-17A gene. Pharmacogenetics of IL-17A inhibitors in the treatment of psoriasis requires further exploration.

A Combined Transcriptomic and Genomic Analysis Identifies a Gene Signature Associated With the Response to Anti-TNF Therapy in Rheumatoid Arthritis

Background: Rheumatoid arthritis (RA) is the most frequent autoimmune disease involving the joints. Although anti-TNF therapies have proven effective in the management of RA, approximately one third of patients do not show a significant clinical response. The objective of this study was to identify new genetic variation associated with the clinical response to anti-TNF therapy in RA.

Methods: We performed a sequential multi-omic analysis integrating different sources of molecular information. First, we extracted the RNA from synovial biopsies of 11 RA patients starting anti-TNF therapy to identify gene coexpression modules (GCMs) in the RA synovium. Second, we analyzed the transcriptomic association between each GCM and the clinical response to anti-TNF therapy. The clinical response was determined at week 14 using the EULAR criteria. Third, we analyzed the association between the GCMs and anti-TNF response at the genetic level. For this objective, we used genome-wide data from a cohort of 348 anti-TNF treated patients from Spain. The GCMs that were significantly associated with the anti-TNF response were then tested for validation in an independent cohort of 2,706 anti-TNF treated patients. Finally, the functional implication of the validated GCMs was evaluated via pathway and cell type epigenetic enrichment analyses.

Results: A total of 149 GCMs were identified in the RA synovium. From these, 13 GCMs were found to be significantly associated with anti-TNF response (P < 0.05). At the genetic level, we detected two of the 13 GCMs to be significantly associated with the response to adalimumab (P = 0.0015) and infliximab (P = 0.021) in the Spain cohort. Using the independent cohort of RA patients, we replicated the association of the GCM associated with the response to adalimumab (P = 0.0019). The validated module was found to be significantly enriched for genes involved in the nucleotide metabolism (P = 2.41e-5) and epigenetic marks from immune cells, including CD4+ regulatory T cells (P = 0.041).

Conclusions: These findings show the existence of a drug-specific genetic basis for anti-TNF response, thereby supporting treatment stratification in the search for response biomarkers in RA.

Evaluation of 12 GWAS-drawn SNPs as biomarkers of rheumatoid arthritis response to TNF inhibitors. A potential SNP association with response to etanercept

Research in rheumatoid arthritis (RA) is increasingly focused on the discovery of biomarkers that could enable personalized treatments. The genetic biomarkers associated with the response to TNF inhibitors (TNFi) are among the most studied. They include 12 SNPs exhibiting promising results in the three largest genome-wide association studies (GWAS). However, they still require further validation. With this aim, we assessed their association with response to TNFi in a replication study, and a meta-analysis summarizing all non-redundant data. The replication involved 755 patients with RA that were treated for the first time with a biologic drug, which was either infliximab (n = 397), etanercept (n = 155) or adalimumab (n = 203). Their DNA samples were successfully genotyped with a single-base extension multiplex method. Lamentably, none of the 12 SNPs was associated with response to the TNFi in the replication study (p > 0.05). However, a drug-stratified exploratory analysis revealed a significant association of the NUBPL rs2378945 SNP with a poor response to etanercept (B = -0.50, 95% CI = -0.82, -0.17, p = 0.003). In addition, the meta-analysis reinforced the previous association of three SNPs: rs2378945, rs12142623, and rs4651370. In contrast, five of the remaining SNPs were less associated than before, and the other four SNPs were no longer associated with the response to treatment. In summary, our results highlight the complexity of the pharmacogenetics of TNFi in RA showing that it could involve a drug-specific component and clarifying the status of the 12 GWAS-drawn SNPs.

The genetic pathogenesis, diagnosis and therapeutic insight of rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease that causes chronic inflammation of the joints. RA is a heterogeneous disorder caused by an abnormal autoimmune response triggered by the complex interactions of genetic and environmental factors that contribute to RA etiology. However, its underlying pathogenic mechanisms are yet to be fully understood. In this review, I provide an overview of the pathogenesis, diagnosis and therapeutic insight in the clinical management of RA in light of the recent updates to classification criteria and recent discoveries of genetic loci associated with susceptibility for RA.

Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

Genome-wide association study of response to tumour necrosis factor inhibitor therapy in rheumatoid arthritis

Rheumatoid arthritis (RA) is characterised by chronic synovial joint inflammation. Treatment has been revolutionised by tumour necrosis factor alpha inhibitors (TNFi) but each available drug shows a significant non-response rate. We conducted a genome-wide association study of 1752 UK RA TNFi-treated patients to identify predictors of change in the Disease Activity Score 28 (DAS28) and subcomponents over 3–6 months. The rs7195994 variant at the FTO gene locus was associated with infliximab response when looking at a change in the swollen joint count (SJC28) subcomponent (p = 9.74 × 10⁻⁹). Capture Hi-C data show chromatin interactions in GM12878 cells between rs2540767, in high linkage disequilibrium with rs7195994 (R² = 0.9) and IRX3, a neighbouring gene of FTO. IRX3 encodes a transcription factor involved in adipocyte remodelling and is regarded as the obesity gene at the FTO locus. Importantly, the rs7195994 association remained significantly associated following adjustment for BMI. In addition, using capture Hi-C data we showed interactions between TNFi-response associated variants and 16 RA susceptibility variants.

Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients

Methotrexate (MTX) monotherapy is a common first treatment for rheumatoid arthritis (RA), but many patients do not respond adequately. In order to identify genetic predictors of response, we have combined data from two consortia to carry out a genome-wide study of response to MTX in 1424 early RA patients of European ancestry. Clinical endpoints were change from baseline to 6 months after starting treatment in swollen 28-joint count, tender 28-joint count, C-reactive protein and the overall 3-component disease activity score (DAS28). No single nucleotide polymorphism (SNP) reached genome-wide statistical significance for any outcome measure. The strongest evidence for association was with rs168201 in NRG3 (p = 10^-7 for change in DAS28). Some support was also seen for association with ZMIZ1, previously highlighted in a study of response to MTX in juvenile idiopathic arthritis. Follow-up in two smaller cohorts of 429 and 177 RA patients did not support these findings, although these cohorts were more heterogeneous.

Validation study of genetic biomarkers of response to TNF inhibitors in rheumatoid arthritis

Genetic biomarkers are sought to personalize treatment of patients with rheumatoid arthritis (RA), given their variable response to TNF inhibitors (TNFi). However, no genetic biomaker is yet sufficiently validated. Here, we report a validation study of 18 previously reported genetic biomarkers, including 11 from GWAS of response to TNFi. The validation was attempted in 581 patients with RA that had not been treated with biologic antirheumatic drugs previously. Their response to TNFi was evaluated at 3, 6 and 12 months in two ways: change in the DAS28 measure of disease activity, and according to the EULAR criteria for response to antirheumatic drugs. Association of these parameters with the genotypes, obtained by PCR amplification followed by single-base extension, was tested with regression analysis. These analyses were adjusted for baseline DAS28, sex, and the specific TNFi. However, none of the proposed biomarkers was validated, as none showed association with response to TNFi in our study, even at the time of assessment and with the outcome that showed the most significant result in previous studies. These negative results are notable because this was the first independent validation study for 12 of the biomarkers, and because they indicate that prudence is needed in the interpretation of the proposed biomarkers of response to TNFi even when they are supported by very low p values. The results also emphasize the requirement of independent replication for validation, and the need to search protocols that could increase reproducibility of the biomarkers of response to TNFi.

Precision Medicine in Rheumatoid Arthritis

Treatment of rheumatoid arthritis (RA) has substantially improved in recent years because of the development of novel drugs. However, response is not universal for any of the treatment options, and selection of an effective therapy is currently based on a trial-and-error approach. Delayed treatment response increases the risk of progressive joint damage and resultant disability and also has a significant impact on quality of life for patients. For many drugs, the patient's genetic background influences response to therapy, and understanding the genetics of response to therapy in RA may allow for targeted personalized health care.

Genetics of rheumatoid arthritis susceptibility, severity, and treatment response

A decade after the first genome-wide association study in rheumatoid arthritis (RA), a plethora of genetic association studies have been published on RA and its clinical or serological subtypes. We review the major milestones in the study of the genetic architecture of RA susceptibility, severity, and response to treatment. We set the scientific context necessary for non-geneticists to understand the potential clinical applications of human genetics and its significance for a stratified approach to the management of RA in the future.

Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies

BACKGROUND

An individual patient's response to a particular drug is influenced by multiple factors, which may include genetic predisposition. Pharmacogenetic studies attempt to discover and estimate the contributions of genetic variants to the variability in response to a drug treatment. The task of identifying the genetic contribution is often complicated by response phenotypes that are based on imprecise or subjective clinical observations. Because the success of a pharmacogenetic study depends on the analysis of a heritable phenotype, it is important to identify phenotypes with a significant heritable component to ensure reliable and reproducible results in subsequent genetic association studies.

METHODS

We retrospectively analyzed data collected from 436 rheumatoid arthritis patients treated with golimumab during the phase III GO-FURTHER study. We investigated the reliability of several potential response outcomes after golimumab treatment. Using whole-genome sequencing of the clinical trial cohort, we estimated the heritability of each potential outcome measure. We further performed a longitudinal analysis of the clinical data to estimate variability of outcome measures over time and the degree to which each response metric could be confounded by placebo response.

RESULTS

We determined that the high degree of within-patient variation over time makes a single follow-up visit insufficient to assess an individual patient's response to golimumab treatment. We found that different potential response outcomes had varying degrees of heritability and that averaging across multiple follow-up visits yielded higher heritability estimates than single follow-up estimates. Importantly, we found that the change in swollen and tender joint counts were the most heritable outcome metrics we tested; however, we showed that they are also more likely to be confounded by a placebo response than objective phenotypes like the change in C-reactive protein levels.

CONCLUSIONS

Our rigorous approach to finding robust and heritable response phenotypes could be beneficial to all pharmacogenetic studies and may lead to more reliable and reproducible results.

TRIAL REGISTRATION

Clinicaltrials.gov NCT00973479 . Registered 4 September 2009.

A genetic risk score composed of rheumatoid arthritis risk alleles, HLA-DRB1 haplotypes, and response to TNFi therapy - results from a Swedish cohort study

Background

To prevent debilitating and irreversible joint damage, rheumatoid arthritis (RA) is often treated with tumor necrosis factor inhibitor (TNFi), but many patients do not respond to this costly therapy. Few predictors for response are known, and it has been proposed that genetic factors which influence the development of RA may also influence disease severity and response to therapy. Several previous studies have attempted to confirm this but results remain inconclusive. We expand on previous studies by including more RA risk alleles, and maximize power by combining them into a genetic risk score.

Method

We linked genotyped RA patients from the Epidemiological Investigation of Rheumatoid Arthritis study to the Swedish Rheumatology Quality Register, identifying patients who started a TNFi as their first biological disease-modifying anti-rheumatic drug, with a return visit within 2–8 months after treatment start (N = 867). We calculated risk scores from 76 established RA risk SNPs, and four HLA-DRB1 amino acid positions, and tested whether risk scores or individual genetic risk factors could predict the European League Against Rheumatism (EULAR) response.

Results

We found no association between any of the risk scores or HLA-DRB1 haplotypes and EULAR response, neither overall nor stratified by anti-citrullinated protein/peptide antibody (ACPA) status. When evaluating each of the 76 SNPs, we found that the number of SNPs presenting significant associations was not higher than expected by chance (5/76 SNPs had p < 0.05 in ACPA-positive RA, 4/76 in ACPA-negative RA).

Conclusion

Overall, known RA risk SNPs do not predict response to TNFi, either individually or when combined into a risk score. This does not support the hypothesis that genes influencing RA onset would also influence its prognosis and treatment response.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-016-1174-z) contains supplementary material, which is available to authorized users.

Family history of rheumatoid arthritis: an old concept with new developments

Family history of rheumatoid arthritis (RA) is a proxy for an individual's genetic and, in part, environmental risk of developing RA, and is a well-recognized predictor of disease onset. Although family history of RA is an old concept, the degree of familial aggregation of RA, whether it differs by age, sex, or serology, and what value it has for clinical decisions once a diagnosis of RA has been made remain unclear. New data have been emerging in parallel to substantial progress made in genetic association studies. In this Review, we describe the various ways that familial aggregation has been measured, and how the findings from these studies, whether they are based on twins, cohorts of first-degree relatives, or genetic data, correspond to each other and aid understanding of the aetiology of RA. In addition, we review the potential usefulness of family history of RA from a clinical point of view, demonstrating that, in the era of big data and genomics, family history still has a role in directing clinical decision-making and research.

A longitudinal genome-wide association study of anti-tumor necrosis factor response among Japanese patients with rheumatoid arthritis

Background

Studies of Caucasian patients with rheumatoid arthritis (RA) to identify genetic biomarkers of anti-tumor necrosis factor (TNF) response have used response at a single time point as the phenotype with which single nucleotide polymorphism (SNP) associations have been tested. The findings have been inconsistent across studies. Among Japanese patients, only a few SNPs have been investigated. We report here the first genome-wide association study (GWAS) to identify genetic biomarkers of anti-TNF response among Japanese RA patients, using response at 2 time-points for a more reliable clinical phenotype over time.

Methods

Disease Activity Scores based on 28 joint counts (DAS28) were assessed at baseline (before initial therapy), and after 3 and 6 months in 487 Japanese RA patients starting anti-TNF therapy for the first time or switching to a new anti-TNF agent. A genome-wide panel of SNPs was genotyped and additional SNPs were imputed. Using change in DAS28 scores from baseline at both 3 (ΔDAS-3) and 6 months (ΔDAS-6) as the response phenotype, a longitudinal genome-wide association analysis was conducted using generalized estimating equations (GEE) models, adjusting for baseline DAS28, treatment duration, type of anti-TNF agent and concomitant methotrexate. Cross-sectional analyses were performed using multivariate linear regression models, with response from a single time point (ΔDAS-3 or ΔDAS-6) as phenotype; all other variables were the same as in the GEE models.

Results

In the GEE models, borderline significant association was observed at 3 chromosomal regions (6q15: rs284515, p = 6.6x10⁻⁷; 6q27: rs75908454, p = 6.3x10⁻⁷ and 10q25.3: rs1679568, p = 8.1x10⁻⁷), extending to numerous SNPs in linkage disequilibrium (LD) across each region. Potential candidate genes in these regions include MAP3K7, BACH2 (6q15), GFRA1 (10q25.3), and WDR27 (6q27). The association at GFRA1 replicates a previous finding from a Caucasian dataset. In the cross-sectional analyses, ΔDAS-6 was significantly associated with the 6q15 locus (rs284511, p = 2.5x10⁻⁸). No other significant or borderline significant associations were identified.

Conclusion

Three genomic regions demonstrated significant or borderline significant associations with anti-TNF response in our dataset of Japanese RA patients, including a locus previously associated among Caucasians. Using repeated measures of response as phenotype enhanced the power to detect these associations.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-016-0920-6) contains supplementary material, which is available to authorized users.

Genetic Variations in Pattern Recognition Receptor Loci Are Associated with Anti-TNF Response in Patients with Rheumatoid Arthritis

OBJECTIVES

To determine whether genetic variation within genes related to the Toll-like receptor, inflammasome and interferon-γ pathways contributes to the differences in treatment response to tumour necrosis factor inhibitors (anti-TNF) in patients with rheumatoid arthritis (RA).

METHODS

In a retrospective case-case study, we assessed 23 functional single nucleotide polymorphisms (SNPs) in 15 genes. We included 538 anti-TNF naïve Danish RA patients from the nationwide DANBIO database. Multivariable logistic regression analyses were performed to detect associations (p-value<0.05) between genotypes and European League Against Rheumatism (EULAR) treatment responses. False Discovery Rate corrections for multiple testing (q-value) and stratified analyses were performed to investigate association with individual therapies and IgM-rheumatoid factor (RF) status.

RESULTS

Six of twenty successfully genotyped polymorphisms were nominally associated with EULAR treatment response. Three of these were in weak to moderate linkage disequilibrium with polymorphisms previously reported associated with anti-TNF treatment response. TLR5(rs5744174) variant allele carriers (odds ratio(OR) = 1.7(1.1-2.5),p = 0.010,q = 0.46) and TLR1(rs4833095) homozygous variant carriers (OR = 2.8(1.1-7.4),p = 0.037,q = 0.46) had higher odds for a positive treatment response. NLRP3(rs10754558) variant allele carriers (odds ratio(OR) = 0.6(0.4-1.0),p = 0.045,q = 0.46) were more likely to have a negative treatment response. The association in TLR5(rs5744174) remained significant after correction for multiple comparisons among patients negative for RF (OR = 6.2(2.4-16.3),p = 0.0002,q = 0.024). No other association withstood correction for multiple testing. Post hoc analyses showed that change in Patient Global score on a visual analogue scale (VAS) and change in pain VAS were the main factors responsible for the association.

CONCLUSIONS

We reproduced previously reported associations between genetic variation in the TLR10/1/6 gene cluster, TLR5, and NLRP3 loci and response to anti-TNF treatment in RA. Changes in VAS pain and patient global scores were the main contributors to the association found for TLR5. Furthermore, we identified other candidate genes that require replication in independent cohorts.

The -308 TNFα and the -174 IL-6 promoter polymorphisms associate with effective anti-TNFα treatment in seronegative spondyloarthritis

The genetic predisposition to a long-term efficacy of anti-tumor necrosis factor (TNF)α treatment in seronegative spondyloarthritis (SpA) was investigated by analysing the possible correlation between several single nucleotide gene polymorphisms and the retention rate of anti-TNFα therapies. We compared patients needing to switch the first anti-TNFα (Sw, No. 64) within at least 12 months of follow-up with patients not needing to switch (NSw, No. 123), observing at least 6 months of treatment to establish anti-TNFα failure, leading to treatment change. Response to treatment was evaluated by standardised criteria (BASDAI for axial involvement, DAS28-EULAR for peripheral involvement). The TNFα -308 A allele and the interleukin (IL)-6 -174GG homozygosis resulted as independent biomarkers predicting survival of the first anti-TNFα therapy in SpA patients (P=0.007, odds ratio (OR): 4.4, 95% confidence interval (CI)=1.5-13.1 and P=0.035, OR: 2.1, 95% CI=1.1-4.4). Also, the male gender (P=0.001, OR: 3.4, 95% CI=1.6-7.1) associated with the NSw phenotype, whereas no association was found either with the specific diagnosis or the predominant joint involvement.

Does a family history of RA influence the clinical presentation and treatment response in RA?

OBJECTIVES

To assess whether family history of rheumatoid arthritis (RA), among the strongest risk factors for developing RA, also carries information on the clinical presentation and treatment response.

METHODS

The prospective Swedish Rheumatology register was linked to family history of RA, defined as diagnosed RA in any first-degree relative, ascertained through the Swedish Multi-Generation and Patient registers. Clinical presentation was examined among patients with early RA 2000-2011 (symptom onset <12 months before inclusion, N=6869), and response to methotrexate (MTX) monotherapy in the subset starting this treatment (N=4630). Response to tumour necrosis factor inhibitors (TNFi) was examined among all patients with RA starting a TNFi as the first biological disease-modifying antirheumatic drug 2000-2011 (N=9249). Association of family history with clinical characteristics, drug survival, European League Against Rheumatism (EULAR) response and change in disease activity at 3 and 6 months was estimated using linear and generalised logistic regression models. Correlation in relatives' response measures was also assessed.

RESULTS

Patients with early RA with family history of RA were more often rheumatoid factor positive, but with no other clinically meaningful differences in their clinical presentation. Family history of RA did not predict response to MTX or TNFi, with the possible exception of no versus good EULAR response to TNFi at 6 months (OR=1.4, 95% CI 1.1 to 1.7). Having a relative who discontinued TNFi within a year increased the odds of doing the same (OR=3.7, 95% CI 1.8 to 7.5), although we found no significant familial correlations in change in disease activity measures.

CONCLUSIONS

Family history of RA did not modify the clinical presentation of RA or predict response to standard treatment with MTX or TNFi. Treatment response, particularly drug survival, may itself be familial.