Abstract
In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients’ response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.
Highlights
Rheumatoid arthritis (RA) is a chronic, disabling disease that mainly affects the synovial joints
The number of rheumatoid factor (RF) positive patients and the swollen joint count (SJC) was significantly higher in the good responders, besides there were no significant differences in other baseline clinical variables between good responders and non-responders
Metabolomic profiling is a powerful technique, which can be applied to analyze a wide range of metabolites from small sample volumes
Summary
Rheumatoid arthritis (RA) is a chronic, disabling disease that mainly affects the synovial joints. Disease-modifyinganti-rheumatic drugs (DMARDs) are the cornerstone of anti-inflammatory therapy in RA and can be divided into two categories: conventional synthetic DMARDs (csDMARDs) and biological DMARDs (bDMARDs) [4]. At the initiation of TNFi therapy it is as yet impossible to distinguish future responders from non-responders, the only used treatment approach is by trial and error. This approach is inefficient because the clinical response can only be assessed after at least three months of treatment. Within this timeframe, non-responders might develop joint damage or may experience toxic side effects. Many approaches have been explored, mostly by evaluation of clinical parameters, proteins or mRNA biomarker profiles, but none were far successful in such a way that they can be implemented in clinical practice [8]
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