title: Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.
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.
authors.affiliation: Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Bart V J
authors.identifier: http://orcid.org/0000-0002-7592-0915
authors.initials: BV
authors.lastname: Cuppen
authors.affiliation: Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.|Sino-Dutch center for Preventive and Personalized Medicine, Zeist, The Netherlands.
authors.forename: Junzeng
authors.initials: J
authors.lastname: Fu
authors.affiliation: Sino-Dutch center for Preventive and Personalized Medicine, Zeist, The Netherlands.|TNO, Netherlands Organization for Applied Scientific Research, Microbiology & Systems Biology, Zeist, The Netherlands.
authors.forename: Herman A
authors.initials: HA
authors.lastname: van Wietmarschen
authors.affiliation: Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.|Netherlands Metabolomics Center, Leiden, The Netherlands.
authors.forename: Amy C
authors.initials: AC
authors.lastname: Harms
authors.affiliation: Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.|Netherlands Metabolomics Center, Leiden, The Netherlands.
authors.forename: Slavik
authors.initials: S
authors.lastname: Koval
authors.affiliation: Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Anne C A
authors.initials: AC
authors.lastname: Marijnissen
authors.affiliation: St Jansdal hospital, Harderwijk, The Netherlands.
authors.forename: Judith J W
authors.initials: JJ
authors.lastname: Peeters
authors.affiliation: Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Johannes W J
authors.initials: JW
authors.lastname: Bijlsma
authors.affiliation: Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Janneke
authors.initials: J
authors.lastname: Tekstra
authors.affiliation: Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Jacob M
authors.initials: JM
authors.lastname: van Laar
authors.affiliation: Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.|Netherlands Metabolomics Center, Leiden, The Netherlands.
authors.forename: Thomas
authors.initials: T
authors.lastname: Hankemeier
authors.affiliation: Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Floris P J G
authors.initials: FP
authors.lastname: Lafeber
authors.affiliation: Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.|Sino-Dutch center for Preventive and Personalized Medicine, Zeist, The Netherlands.|TNO, Netherlands Organization for Applied Scientific Research, Microbiology & Systems Biology, Zeist, The Netherlands.|Netherlands Metabolomics Center, Leiden, The Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: van der Greef
mesh_terms: D000328:Adult; D000368:Aged; D018501:Antirheumatic Agents / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000188:drug therapy*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007249:Inflammation / Q000378:metabolism*; D008297:Male; D055432:Metabolomics; D008875:Middle Aged; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors*
chemical_list: D018501:Antirheumatic Agents; D014409:Tumor Necrosis Factor-alpha