Title
Exploring the inflammatory metabolomic profile to predict response to TNF-alpha inhibitors in rheumatoid arthritis
Author
Cuppen, B.V.J.
Fu, J.
van Wietmarschen, H.A.
Harms, A.C.
Koval, S.
Marijnissen, A.C.A.
Peeters, J.J.W.
Bijlsma, J.W.J.
Tekstra, J.
van Laar, J.M.
Hankemeier, T.
Lafeber, F.P.J.G.
van der Greef, J.
Publication year
2016
Abstract
In clinical practice, approximately one-Third of patients with rheumatoid arthritis(RA) respond insufficiently to TNF-alpha 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, metaboliteswere 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 metaboliteswere associated with DAS28, ESR or CRP (p
Subject
ELSS - Earth, Life and Social Sciences
Life
Healthy Living
Biomedical Innovation
Biology
MSB - Microbiology and Systems Biology
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http://resolver.tudelft.nl/uuid:fd9d4a0c-52d3-4c16-a838-58b26ec94f73
DOI
https://doi.org/10.1371/journal.pone.0163087
TNO identifier
573515
ISSN
1932-6203
Source
PLoS ONE, 11 (11)
Document type
article