Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in rheumatoid arthritis - 02/03/19
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Iconographies | 3 |
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Highlights |
• | Prealbumin, PF4 and S100A12 were identified as relevant biomarkers to predict response to a class of bDMARDs such as TNFalpha inhibitors in RA patients. |
• | Low levels of prealbumin and S100A12 and high level of PF4 at baseline in RA patients are good predictors for response to TNFalpha inhibitors. |
• | Generation of a multivariate model combining prealbumin, platelet factor 4 and S100A12 that accurately predicts response to TNFalpha inhibitors in RA patients. |
• | The predictive model showed similar predictive performance to IFX, ADA and ETN taken individually. |
Abstract |
Objectives |
Tumour necrosis factor-alpha inhibitors (TNFi) are effective treatments for Rheumatoid Arthritis (RA). Responses to treatment are barely predictable. As these treatments are costly and may induce a number of side effects, we aimed at identifying a panel of protein biomarkers that could be used to predict clinical response to TNFi for RA patients.
Methods |
Baseline blood levels of C-reactive protein, platelet factor 4, apolipoprotein A1, prealbumin, α1-antitrypsin, haptoglobin, S100A8/A9 and S100A12 proteins in bDMARD naive patients at the time of TNFi treatment initiation were assessed in a multicentric prospective French cohort. Patients fulfilling good EULAR response at 6 months were considered as responders. Logistic regression was used to determine best biomarker set that could predict good clinical response to TNFi.
Results |
A combination of biomarkers (prealbumin, platelet factor 4 and S100A12) was identified and could predict response to TNFi in RA with sensitivity of 78%, specificity of 77%, positive predictive values (PPV) of 72%, negative predictive values (NPV) of 82%, positive likelihood ratio (LR+) of 3.35 and negative likelihood ratio (LR−) of 0.28. Lower levels of prealbumin and S100A12 and higher level of platelet factor 4 than the determined cutoff at baseline in RA patients are good predictors for response to TNFi treatment globally as well as to Infliximab, Etanercept and Adalimumab individually.
Conclusion |
A multivariate model combining 3 biomarkers (prealbumin, platelet factor 4 and S100A12) accurately predicted response of RA patients to TNFi and has potential in a daily practice personalized treatment.
Le texte complet de cet article est disponible en PDF.Keywords : Biomarkers, Rheumatoid arthritis, TNFα inhibitor, Etanercept, Adalimumab, Infliximab, Prediction, Prealbumin, Platelet factor 4, S100A12
Plan
Vol 86 - N° 2
P. 195-201 - mars 2019 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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