Genetic risk score predicting risk of rheumatoid arthritis phenotypes and age of symptom onset

Background

Cumulative genetic profiles can help identify individuals at high-risk for developing RA. We examined the impact of 39 validated genetic risk alleles on the risk of RA phenotypes characterized by serologic and erosive status.

Methods/Principal Findings

We evaluated single nucleotide polymorphisms at 31 validated RA risk loci and 8 Human Leukocyte Antigen alleles among 542 Caucasian RA cases and 551 Caucasian controls from Nurses'' Health Study and Nurses'' Health Study II. We created a weighted genetic risk score (GRS) and evaluated it as 7 ordinal groups using logistic regression (adjusting for age and smoking) to assess the relationship between GRS group and odds of developing seronegative (RF− and CCP−), seropositive (RF+ or CCP+), erosive, and seropositive, erosive RA phenotypes. In separate case only analyses, we assessed the relationships between GRS and age of symptom onset.In 542 RA cases, 317 (58%) were seropositive, 163 (30%) had erosions and 105 (19%) were seropositive with erosions. Comparing the highest GRS risk group to the median group, we found an OR of 1.2 (95% CI = 0.8–2.1) for seronegative RA, 3.0 (95% CI = 1.9–4.7) for seropositive RA, 3.2 (95% CI = 1.8–5.6) for erosive RA, and 7.6 (95% CI = 3.6–16.3) for seropositive, erosive RA. No significant relationship was seen between GRS and age of onset.

Conclusions/Significance

Results suggest that seronegative and seropositive/erosive RA have different genetic architecture and support the importance of considering RA phenotypes in RA genetic studies.     

Cigarette smoking and risk of rheumatoid arthritis: a dose-response meta-analysis

Introduction

Although previous studies found that cigarette smoking is associated with risk of rheumatoid arthritis (RA), the dose-response relationship remains unclear. This meta-analysis quantitatively summarizes accumulated evidence regarding the association of lifelong exposure to cigarette smoking assessed as pack-years with the risk of RA.

Methods

Relevant studies were identified by a search of MEDLINE and EMBASE from 1966 to October 2013, with no restrictions. Reference lists from retrieved articles were also reviewed. Studies that reported relative risks (RR) or odds ratio (OR) estimates with 95% confidence intervals (CIs) for the association between pack-years of cigarette smoking and rheumatoid arthritis were included in a dose-response random-effects meta-regression analysis.

Results

We included 3 prospective cohorts and 7 case-control studies in the meta-analysis. They included a total of 4,552 RA cases. There was no indication of heterogeneity (P_{heterogeneity} = 0.32) and publication bias did not affect the results. Compared to never smokers, the risk of developing RA increased by 26% (RR = 1.26, 95% CI 1.14 to 1.39) among those who smoked 1 to 10 pack-years and doubled among those with more than 20 pack-years (RR for 21 to 30 pack years = 1.94, 95% CI 1.65 to 2.27). The risk of RA was not increasing further for higher exposure levels (RR for >40 pack-years = 2.07, 95% CI 1.15 to 3.73). The risk of RA was statistically significantly higher among rheumatoid factor (RF)-positive RA cases (RR = 2.47, 95% CI 2.02 to 3.02) compared to RF-negative (RR = 1.58, 95% CI 1.15 to 2.18) when comparing the highest versus lowest category of pack-years for the individual studies.

Conclusions

Lifelong cigarette smoking was positively associated with the risk of RA even among smokers with a low lifelong exposure. The risk of RA did not further increase with an exposure higher than 20 pack-years.     

Fish consumption and risk of rheumatoid arthritis: a dose-response meta-analysis

Introduction

The association between fish consumption and rheumatoid arthritis (RA) is unclear. The aim of this paper was to summarize the available evidence on the association between fish consumption and risk of RA using a dose-response meta-analysis.

Methods

Relevant studies were identified by a search of MEDLINE and EMBASE through December 2013, with no restrictions. A random-effects dose-response meta-analysis was conducted to combine study specific relative risks. Potential non-linear relation was investigated using restricted cubic splines. A stratified analysis was conducted by study design.

Results

Seven studies (four case-controls and three prospective cohorts) involving a total of 174 701 participants and 3346 cases were included in the meta-analysis. For each one serving per week increment in fish consumption, the relative risk (RR) of RA was 0.96 (95% confidence interval (CI) 0.91 to 1.01). Results did not change when stratifying by study design. No heterogeneity or publication bias was observed. When fish consumption was modeled using restricted cubic splines, the risk of RA was 20 to 24% lower for 1 up to 3 servings per week of fish (RR =0.76, 95% CI: 0.57 to 1.02) as compared to never consumption.

Conclusions

Results from this dose-response meta-analysis showed a non-statistically significant inverse association between fish consumption and RA.     

Genome-wide meta-analysis for rheumatoid arthritis

Meta-analysis is being increasingly used as a tool for integrating data from different studies of complex phenotypes, because the power of any one study to identify causal loci is limited. We applied a novel meta-analytical approach (Loesgen et al. in Genet Epidemiol 21(Suppl 1):S142–S147, 2001) in compiling results from four studies of rheumatoid arthritis in Caucasians including two studies from NARAC (Jawaheer et al. in Am J Hum Genet 68:927–936, 2001; Jawaheer et al. in Arthritis Rheum 48:906–916, 2003), one study from the UK (MacKay et al. in Arthritis Rheum 46:632–639, 2001) and one from France (Cornelis et al. in Proc Natl Acad Sci USA 95:10746–10750, 1998). For each study, we obtained NPL scores by performing interval mapping (2 cM intervals) using GeneHunter2 (Kruglyak et al. in Am J Hum Genet 58:1347–1363, 1996; Markianos et al. in Am J Hum Genet 68:963–977, 2001). The marker maps differed among the three consortium groups, therefore, the marker maps were aligned after the interval mapping was completed and the NPL scores that were within 1 cM of each other were combined using the method of Loesgen et al. (Genet Epidemiol 21(Suppl 1):S142–S147, 2001) by calculating the weighted average of the NPL score. This approach avoids some problems in analysis encountered by using GeneHunter2 when some markers in the sample are not genotyped. This procedure provided marginal evidence (P<0.05) of linkage on chromosome 1, 2, 5 and 18, strong evidence (P<0.01) on chromosomes 8 and 16, and overwhelming evidence in the HLA region of chromosome 6.     

Gene therapy for rheumatoid arthritis

Rheumatoid arthritis (RA) is a severe autoimmune systemic disease. Chronic synovial inflammation results in destruction of the joints. No conventional treatment is efficient in RA. Gene therapy of RA targets mainly the players of inflammation or articular destruction: TNF-alpha or IL-1 blocking agents (such as anti-TNF-alpha monoclonal antibodies, soluble TNF-alpha receptor, type II soluble receptor of IL-1, IL-1 receptor antagonist), antiinflammatory cytokines (such as IL-4, IL-10, IL-1), and growth factors. In this polyarticular disease, the vector expressing the therapeutic protein can be administered as a local (intra-articular injection) or a systemic treatment (extra-articular injection). All the main vectors have been used in experimental models, including the more recent lentivirus and adeno-associated virus. Ex vivo gene transfer was performed with synovial cells, fibroblasts, T cells, dendritic cells, and different cells from xenogeneic origin. In vivo gene therapy is simpler, although a less controlled method. Clinical trials in human RA have started with ex vivo retrovirus-expressing IL-1 receptor antagonists and have demonstrated the feasibility of the strategy of gene therapy. The best target remains to be determined and extensive research has to be conducted in preclinical studies.     

Mouse models for rheumatoid arthritis

Rheumatoid arthritis (RA) affects millions of people world wide causing considerable human suffering and large socioeconomic costs. Increased knowledge of pathological pathways involved in RA will enable development of modern drugs, with reduced side effects. The mouse models offer an attractive approach to dissect the genetic and molecular mechanisms of RA.     

Influence of BLK polymorphisms on the risk of rheumatoid arthritis

B cell lymphocyte kinase (BLK) encodes a member of the Src kinase family and thus may influence the proliferation and differentiation of cells. A single nucleotide polymorphism (SNP) located in the first intron of BLK has shown that the risk C allele of rs2248932 is associated with lower levels of messenger RNA expression of BLK. We hypothesized that this polymorphism may contribute to rheumatoid arthritis (RA) susceptibility. We studied BLK rs2248932 T/C gene polymorphisms in 329 patients with RA and 697 controls in a Chinese population. Genotyping was done using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). When the BLK rs2248932 TT homozygote genotype was used as the reference group, the CC genotype was associated with a significantly increased risk of RA. In the recessive model, when the BLK rs2248932 TT/TC genotypes were used as the reference group, the CC homozygote genotype was associated with a significantly increased susceptibility to RA. In stratification analyses, a significantly increased risk for RA associated with the BLK rs2248932 CC genotype was evident among younger patients, CRP-negative patients and anti-CCP-positive patients compared with the BLK rs2248932 TT/TC genotype. The risk was also significantly evident among RF-positive patients, patients with lower ESR levels, patients with lower or higher DAS28 score and patients with a lower functional class. These findings suggested that the functional SNP BLK rs2248932 T/C variant allele was associated with RA development. However, our results were obtained from a moderate-sized sample, and therefore this is a preliminary conclusion. Validation in a larger study from a more diverse ethnic population is needed to confirm these findings.     

Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network

The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein–protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.     

Profiling microRNAs in individuals at risk of progression to rheumatoid arthritis

Background

Individuals at risk of rheumatoid arthritis (RA) demonstrate systemic autoimmunity in the form of anti-citrullinated peptide antibodies (ACPA). MicroRNAs (miRNAs) are implicated in established RA. This study aimed to (1) compare miRNA expression between healthy individuals and those at risk of and those that develop RA, (2) evaluate the change in expression of miRNA from “at-risk” to early RA and (3) explore whether these miRNAs could inform a signature predictive of progression from “at-risk” to RA.

Methods

We performed global profiling of 754 miRNAs per patient on a matched serum sample cohort of 12 anti-cyclic citrullinated peptide (CCP)?+?“at-risk” individuals that progressed to RA. Each individual had a serum sample from baseline and at time of detection of synovitis, forming the matched element. Healthy controls were also studied. miRNAs with a fold difference/fold change of four in expression level met our primary criterion for selection as candidate miRNAs. Validation of the miRNAs of interest was conducted using custom miRNA array cards on matched samples (baseline and follow up) in 24 CCP+ individuals; 12 RA progressors and 12 RA non-progressors.

Results

We report on the first study to use matched serum samples and a comprehensive miRNA array approach to identify in particular, three miRNAs (miR-22, miR-486-3p, and miR-382) associated with progression from systemic autoimmunity to RA inflammation. MiR-22 demonstrated significant fold difference between progressors and non-progressors indicating a potential biomarker role for at-risk individuals.

Conclusions

This first study using a cohort with matched serum samples provides important mechanistic insights in the transition from systemic autoimmunity to inflammatory disease for future investigation, and with further evaluation, might also serve as a predictive biomarker.