title: Prediction of functional impairment and remission in rheumatoid arthritis patients by biochemical variables and genetic polymorphisms.
abstract: OBJECTIVE
  To develop a model to predict RA outcome based on biochemical variables and single nucleotide polymorphisms (SNPs).
  
  
  METHODS
  We collected baseline data from RA patients. SNP genotyping was performed using an oligonucleotide microarray. Remission and severe disability were investigated as outcomes of the study. Logistic regression models and receiver operating characteristic (ROC) curves were used to determine sensitivity (S), specificity (Sp) and likelihood ratio (LR).
  
  
  RESULTS
  Six hundred and thirty-two patients (375 in the study and 257 in the validation) were included. Twenty-two out of 152, and 19 out of 208 patients had an HAQ > 2. The model obtained to predict disability included levels of the anti-cyclic citrullinated peptide (anti-CCP) antibodies, ESR and SNP rs2070874 in the IL-4 gene. Homozygous and heterozygous carriers of the IL-4 33T allele had a decreased risk of severe disability. The discriminative power had an area under the curve (AUC) of 0.792 (95% CI 0.694, 0.889), with S 41%, Sp 95% and LR +7.6. Twenty-one out of 268 and 17 out of 211 patients were in remission in the study and validation cohorts, respectively. The model included absence of anti-CCP antibodies and the SNP rs2476601 on the PTPN22 gene. Homozygous and heterozygous carriers of the PTPN22 1858T allele had a decreased probability of remission. The discriminative power had an AUC of 0.842 (95% CI 0.756, 0.928), with S 76%, Sp 86% and LR + 5.4. Predictive ability was confirmed on the validation cohort.
  
  
  CONCLUSIONS
  We have developed two models based on laboratory variables that are associated with relevant outcomes for RA patients at disease onset.
authors.affiliation: Hospital Universitario La Paz, Paseo de la Castellana, 261, 28046 Madrid, Spain. abalsa.hulp@salud.madrid.org
authors.forename: Alejandro
authors.initials: A
authors.lastname: Balsa
authors.forename: Jokin
authors.initials: J
authors.lastname: Del Amo
authors.forename: Francisco
authors.initials: F
authors.lastname: Blanco
authors.forename: Rafael
authors.initials: R
authors.lastname: Caliz
authors.forename: Lucía
authors.initials: L
authors.lastname: Silva
authors.forename: Raimon
authors.initials: R
authors.lastname: Sanmarti
authors.forename: Francisco G
authors.initials: FG
authors.lastname: Martínez
authors.forename: Diego
authors.initials: D
authors.lastname: Tejedor
authors.forename: Marta
authors.initials: M
authors.lastname: Artieda
authors.forename: Dora
authors.initials: D
authors.lastname: Pascual-Salcedo
authors.forename: Natividad
authors.initials: N
authors.lastname: Oreiro
authors.forename: Maria D
authors.initials: MD
authors.lastname: Collado
authors.forename: Jose L
authors.initials: JL
authors.lastname: Andreu
authors.forename: Eduard
authors.initials: E
authors.lastname: Graell
authors.forename: Laureano
authors.initials: L
authors.lastname: Simón
authors.forename: Antonio
authors.initials: A
authors.lastname: Martínez
authors.forename: Juan
authors.initials: J
authors.lastname: Mulero
mesh_terms: D000328:Adult; D000368:Aged; D001172:Arthritis, Rheumatoid / Q000175:diagnosis* / Q000235:genetics / Q000503:physiopathology / Q000628:therapy; D001323:Autoantibodies / Q000097:blood; D015415:Biomarkers / Q000097:blood; D004185:Disability Evaluation; D004812:Epidemiologic Methods; D005260:Female; D005819:Genetic Markers; D005838:Genotype; D006801:Humans; D015847:Interleukin-4 / Q000235:genetics; D008297:Male; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010456:Peptides, Cyclic / Q000276:immunology; D020641:Polymorphism, Single Nucleotide; D011379:Prognosis; D012074:Remission Induction
chemical_list: D001323:Autoantibodies; D015415:Biomarkers; D005819:Genetic Markers; D010456:Peptides, Cyclic; C487763:cyclic citrullinated peptide; D015847:Interleukin-4