Dataset Information

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-? Inhibitors in Rheumatoid Arthritis.

ABSTRACT: In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-? 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-?3/?6), 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.

SUBMITTER: Cuppen BV

PROVIDER: S-EPMC5025050 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

Cuppen Bart V J BV Fu Junzeng J van Wietmarschen Herman A HA Harms Amy C AC Koval Slavik S Marijnissen Anne C A AC Peeters Judith J W JJ Bijlsma Johannes W J JW Tekstra Janneke J van Laar Jacob M JM Hankemeier Thomas T Lafeber Floris P J G FP van der Greef Jan J

PloS one 20160915 9

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 st ...[more]

PMID: 27631111

Similar Datasets

Project description:Background: Rheumatoid arthritis (RA) is a chronic inflammatory disorder primarily targeting joints, significantly impacting patients' quality of life. The introduction of tumor necrosis factor-alpha (TNF-α) inhibitors has markedly improved RA management by reducing inflammation. However, these medications are associated with adverse skin reactions, which can vary greatly among patients due to genetic differences. Objectives: This study aimed to identify risk factors associated with skin adverse events by TNF-α in RA patients. Methods: A cohort study was conducted, encompassing patients with RA who were prescribed TNF-α inhibitors. This study utilized machine learning algorithms to analyze genetic data and identify markers associated with skin-related adverse events. Various machine learning algorithms were employed to predict skin and subcutaneous tissue-related outcomes, leading to the development of a risk-scoring system. Multivariable logistic regression analysis identified independent risk factors for skin and subcutaneous tissue-related complications. Results: After adjusting for covariates, individuals with the TT genotype of rs12551103, A allele carriers of rs13265933, and C allele carriers of rs73210737 exhibited approximately 20-, 14-, and 10-fold higher incidences of skin adverse events, respectively, compared to those with the C allele, GG genotype, and TT genotype. The machine learning algorithms used for risk prediction showed excellent performance. The risk of skin adverse events among patients receiving TNF-α inhibitors varied based on the risk score: 0 points, 0.6%; 2 points, 3.6%; 3 points, 8.5%; 4 points, 18.9%; 5 points, 36.7%; 6 points, 59.2%; 8 points, 90.0%; 9 points, 95.7%; and 10 points, 98.2%. Conclusions: These findings, emerging from this preliminary study, lay the groundwork for personalized intervention strategies to prevent TNF-α inhibitor-associated skin adverse events. This approach has the potential to improve patient outcomes by minimizing the risk of adverse effects while optimizing therapeutic efficacy.

Project description:BackgroundRheumatoid arthritis (RA) is a genetically predisposed, systemic, chronic, inflammatory disease. Immune system dysregulation and inherited susceptibility polymorphisms suggest that this type of variation is functional and may help predict disease susceptibility and develop new therapeutic strategies. Anti-TNF-alpha (TNF-α) drugs are highly effective RA treatments, but not all patients respond the same way. It's important to figure out whether RA risk alleles can identify and predict anti-TNF-α-responsiveness in RA patients.Aims of the studyExamine the function of the NLR family pyrin domain containing 3 (NLRP3) and caspase recruitment domain family member 8 (CARD8) genes polymorphisms and their morbid genotypes and alleles in RA patients and apparently healthy controls. In addition, their role in disease susceptibility, severity, and response to anti-TNF-α therapy. Also, examine how single nucleotide polymorphisms (SNPs) affect serum levels of pro-inflammatory cytokines like TNF-α and interleukin (IL)-1β.Materials and methods100 RA patients (88 females, 12 males) and 100 apparently healthy people (86 females, 14 males) were examined. To measure serum TNF-α and IL-1β, Elabscience sandwich ELISA kits were used. Iraq Biotech, Turkey DNA extraction kit was used to extract genomic DNA from whole blood. CARD8 (rs2043211) and NLRP3 (rs4612666) were genotyped using Agilent, AriaMx, USA, through Tri-Plex SYBR Green-based real-time PCR allelic discrimination assays. Geneious software, version 2019.2.2, used to design primers from published sequences (GenBank accession no. GCA 009914755.1). Primer specificity was determined by NCBI's BLAST.ResultsStudy found that there is association between cytokines serum level and 28-joints disease activity score (DAS-28). The level of TNF-α increases with the higher DAS-28 (r2 = 0.45, P < 0.0001). Also, IL- 1β level increases with higher DAS-28 (r2 = 0.51, P < 0.0001). There were no statistically significant variations between patients with RA and the control group in the distribution of CARD8 SNP rs2043211 and NLRP3 SNP rs4612666 genotypes (P = 0.17 and 0.08 respectively) as well their alleles (P = 0.059 and 0.879 respectively). CARD8 (rs2043211) TT genotype was more frequent in patients with higher DAS-28 (P < 0.0001) and higher TNF-α and IL-1β serum levels (P < 0.0001 for both). Also, NLRP3 (rs4612666) TT genotype was more frequent in patients with higher DAS-28 (P < 0.0001) and higher TNF-α and IL- 1β serum levels (P < 0.0001 for both). Interestingly, this study revealed that CARD8 (rs2043211) and NLRP3 (rs4612666) variant genotypes are associated with lower response to anti-TNF-α drugs.ConclusionsSerum TNF-α and IL-1β correlate with DAS-28 and disease activity. Non-responders have elevated TNF-α and IL-1β. CARD8 rs2043211 and NLRP3 rs4612666 variant polymorphisms are associated with high serum TNF-α and IL-1β, active disease course, poor disease outcomes, and low response to anti-TNF-α therapy.

Dataset Information

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-? Inhibitors in Rheumatoid Arthritis.

Publications

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets