Background Metabolomics belongs to the family of “-omics” sciences, all of which share the advantage of a non-targeted approach for identifying biomarkers and profiling the patient. Metabolomic procedure has become feasible recently with the advent and accessibility of new high-throughput technologies, including mass spectrometry and 1H Nuclear Magnetic Resonance (1H NMR) and a few studies have been published in rheumatic disorders.
Objectives To evaluate whether a 1H NMR-based metabolomic analysis in serum from patients with rheumatoid arthritis (RA) could predict the response to etanercept evaluated at 6 months.
Methods Adult patients fulfilling the 1987 ACR revised criteria for the classification of RA and designated to start anti-TNF therapy were prospectively enrolled. The analysis was restricted to female patients with active disease starting etanercept as the first biological treatment and having a minimum of 6 months' follow-up. Each patient was evaluated by the same rheumatologist at baseline before starting etanercept and after 6 months following the onset of biological treatment. DAS28 was calculated and the clinical response (good, moderate, none) was evaluated according to the EULAR criteria, based on both erythrocyte sedimentation rate (EULAR-ESR) and C-reactive protein (EULAR-CRP). We merged good and moderate categories as response in comparison with no response. Sera collected prior to the onset of etanercept were analyzed via 1H NMR-based metabolomics. Discriminating metabolites were identified, and the relationship between metabolic profiles and clinical outcomes was assessed.
Results Twenty-seven patients were included (mean age 57.8 years, ± SD 12.5; mean disease duration 102.5 months, ± SD 78.05). Eighteen patients had a good/moderate response and 9 were non responders according to both EULAR-ESR and EULAR-CRP after 6 months of etanercept. Baseline serum metabolic profiles discriminated between RA patients who did or did not have a response to etanercept. Unsupervised Principal Component Analysis (PCA) results according to EULAR-ESR or EULAR-CRP response criteria highlighted significant differences between the metabolic profiles of responders and non responders (p<0.0001), but failed to discriminate between good and moderate responders. Comorbidity moderately influenced the clustering of metabolic profiles (p=0.041), as confirmed by partial correlation analysis results. Conversely, age, smoking, dietary habits, and therapy were not found to be confounding factors. Supervised model, Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA), enabled discrimination between good responders, moderate responders and non-responders according to EULAR-ESR criteria with a very good predictivity (index of predictivity Q2=0.68) and an excellent sensitivity (100%), specificity (100%), and accuracy (100%).
Conclusions Metabolomics approach is a potentially useful technique for predicting the response of RA patients to etanercept, as demonstrated by differences in serum metabolic profiles at baseline. This observation deserves further investigation in larger cohorts of patients to confirm the capability of predicting clinical response without the need for empirical treatment.
Disclosure of Interest : None declared
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