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265. Identification of Potential Biomarker Panels Predictive of Non-Response in Rheumatoid Arthritis Patients Treated with Methotrexate: A Gene-Expression-Profiling Approach

2014· article· en· W2276538608 on OpenAlexaff
Samantha Smith, Darren Plant, James Sellu, Kimme L Hyrich, Anne Barton, Suzanne Verstappen

Bibliographic record

VenueLara D. Veeken · 2014
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsMedicineRheumatoid arthritisMethotrexateBiomarkerGene expression profilingProfiling (computer programming)Gene expressionOncologyInternal medicineImmunologyGeneComputational biologyGenetics

Abstract

fetched live from OpenAlex

Background: The high heritability rates of radiological progression in RA suggest genetic markers could provide prognostic information.Several observational studies have examined the hypothesis that RA genetic susceptibility variants associate with radiological progression; their findings are variable.Two recent meta-analyses have expanded the number of non-MHC loci, HLA-DRB1 alleles and HLA amino acids associated with RA susceptibility.Our aim was to establish if these variants associate with radiological progression in early active RA patients enrolled to a randomized controlled trial (RCT).Methods: The Combination Anti-Rheumatic Drugs in Early RA (CARDERA) trial randomized 467 patients to receive MTX with or without ciclosporin, corticosteroids or both treatments in a factorialdesign.We evaluated the 421 European ancestry patients with archived DNA (passing QC procedures) and Larsen score data available.Radiographs were evaluated 6-monthly.Genotyping was performed on the ImmunoChip.Classical HLA-DRB1 alleles/HLA amino acid polymorphisms were imputed using SNP2HLA.We evaluated 69 SNPs, 16 four-digit HLA-DRB1 alleles and amino acids polymorphisms at positions 11, 13, 71 and 74 in HLA-DRB1, position 9 in HLA-B and position 9 in HLA-DP1; these represent validated RA risk variants.Their association with log transformed Larsen scores over time was evaluated using a linear mixed-effects model.b-values were back-transformed to the original scale.A genotype (or amino acid)*time fixed-effects predictor variable was included, which provided information on the annual increase in Larsen score per copy of the risk allele (or amino acid) carried compared with a noncarrier.We also evaluated the association between a weighted genetic risk score (wGRS) incorporating 68 susceptibility SNPs and radiological progression; this was formed from the product of individuallocus odds ratios estimated from the reference meta-analysis.The wGRS replaced the genotype term in the mixed-model.All analyses were performed in R version 3.0.1.Ethical approval was obtained; all patients provided informed consent.Results: Two SNPs (rs660895 and rs10175798), one HLA-DRB1 allele (*04:01) and two HLA amino acid polymorphisms (histidine at position 13 and valine at position 11 in HLA-DRB1) had P-values < 0.05 (Table 1).None passed Bonferonni corrected P-value thresholds.No association was seen between the wGRS and Larsen score progression (P ¼ 0.2391; b ¼ 1.006).Restricting our analyses to ACPA-positive patients resulted in similar findings.Conclusion: Our study provides strong evidence that RA susceptibility loci do not have a clinically relevant association with radiological progression in European patients with early, active RA.We found no association between non-HLA susceptibility variants and radiological progression, evaluated individually or cumulatively as a wGRS.The HLA-DRB1*04:01 allele and the amino acids it encodes had a nominal association with radiological progression, although their effect sizes were small.These findings suggest the genetic architecture of RA susceptibility and severity differ.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.262
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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