Autoantibodies against a novel citrullinated fibrinogen peptide related to smoking status, disease activity and therapeutic response to methotrexate in cuban patients with early rheumatoid arthritis

The effect of smoking on response to methotrexate in rheumatoid arthritis patients: A systematic review and meta-analysis

OBJECTIVES

In the current study, we aimed to investigate the effect of smoking on inadequate response to methotrexate (MTX-IR) in rheumatoid arthritis (RA) patients.

METHODS

We searched PubMed, Embase, and Web of Science until 6 June 2022. Observational or interventional studies investigating MTX-IR in RA patients based on smoking status were included. Two independent reviewers assessed the risk of bias and the certainty of the evidence using the Risk of Bias in Nonrandomized Studies-of Interventions and Grades of Recommendation, Assessment, Development, and Evaluation tools, respectively.

RESULTS

We included 23 studies in the systematic review and 13 in the meta-analysis. Of the 13 included studies, 6 had a moderate risk, 3 had a serious risk, and 4 had a critical risk of bias. The overall random-effect meta-analysis suggested that smokers were 58% more likely to be MTX-IR when compared with nonsmokers [odds ratio (OR) 1.58, 95% confidence interval 1.21-2.06; P = .001; I2 = 69.3%]. The common-effect meta-analysis of the adjusted ORs demonstrated an overall OR of 2.69 (1.88-3.83; P < .001; I2 = 27.1%).

CONCLUSIONS

The current study showed that smoking is a significant predictor of MTX-IR, especially in disease-modifying antirheumatic drug-naïve early RA patients, as most of the included studies in the meta-analysis consisted of this population.

Identification of SNPs associated with methotrexate treatment outcomes in patients with early rheumatoid arthritis

Methotrexate is one of the cornerstones of rheumatoid arthritis (RA) therapy. Genetic factors or single nucleotide polymorphisms (SNPs) are responsible for 15%-30% of the variation in drug response. Identification of clinically effective SNP biomarkers for predicting methotrexate (MTX) sensitivity has been a challenge. The aim of this study was to explore the association between the disease related outcome of MTX treatment and 23 SNPs in 8 genes of the MTX pathway, as well as one pro-inflammatory related gene in RA patients naïve to MTX. Categorical outcomes such as Disease Activity Score (DAS)-based European Alliance of Associations for Rheumatology (EULAR) non-response at 4 months, The American College of Rheumatology and EULAR (ACR/EULAR) non-remission at 6 months, and failure to sustain MTX monotherapy from 12 to 24 months were assessed, together with continuous outcomes of disease activity, joint pain and fatigue. We found that the SNPs rs1801394 in the MTRR gene, rs408626 in DHFR gene, and rs2259571 in AIF-1 gene were significantly associated with disease activity relevant continuous outcomes. Additionally, SNP rs1801133 in the MTHFR gene was identified to be associated with improved fatigue. Moreover, associations with p values at uncorrected significance level were found in SNPs and different categorical outcomes: 1) rs1476413 in the MTHFR gene and rs3784864 in ABCC1 gene are associated with ACR/EULAR non-remission; 2) rs1801133 in the MTHFR gene is associated with EULAR response; 3) rs246240 in the ABCC1 gene, rs2259571 in the AIF-1 gene, rs2274808 in the SLC19A1 gene and rs1476413 in the MTHFR gene are associated with failure to MTX monotherapy after 12-24 months. The results suggest that SNPs in genes associated with MTX activity may be used to predict MTX relevant-clinical outcomes in patients with RA.

Handling missing data in a rheumatoid arthritis registry using random forest approach

Missing data in clinical epidemiological research violate the intention-to-treat principle, reduce the power of statistical analysis, and can introduce bias if the cause of missing data is related to a patient's response to treatment. Multiple imputation provides a solution to predict the values of missing data. The main objective of this study is to estimate and impute missing values in patient records. The data from the Kuwait Registry for Rheumatic Diseases was used to deal with missing values among patient records. A number of methods were implemented to deal with missing data; however, choosing the best imputation method was judged by the lowest root mean square error (RMSE). Among 1735 rheumatoid arthritis patients, we found missing values vary from 5% to 65.5% of the total observations. The results show that sequential random forest method can estimate these missing values with a high level of accuracy. The RMSE varied between 2.5 and 5.0. missForest had the lowest imputation error for both continuous and categorical variables under each missing data rate (10%, 20%, and 30%) and had the smallest prediction error difference when the models used the imputed laboratory values.