Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in rheumatoid arthritis - 02/03/19
, Xavier Romand a, b, Candice Trocmé c, Anaïs Courtier k, Hubert Marotte d, e, Thierry Thomas f, g, Martin Soubrier h, Pierre Miossec i, Jacques Tébib j, Laurent Grange b, Bertrand Toussaint c, i, Thierry Lequerré j, Olivier Vittecoq j, Philippe Gaudin a, bBenvenuto su EM|consulte, il riferimento dei professionisti della salute.
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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.
Il testo completo di questo articolo è disponibile in PDF.Keywords : Biomarkers, Rheumatoid arthritis, TNFα inhibitor, Etanercept, Adalimumab, Infliximab, Prediction, Prealbumin, Platelet factor 4, S100A12
Mappa
Vol 86 - N° 2
P. 195-201 - marzo 2019 Ritorno al numero
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