Dataset Information

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history.

ABSTRACT: To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors and known genetic risk factors.We developed and validated models for RA based on known RA risk factors, among women in two cohorts: the Nurses' Health Study (NHS, 381 RA cases and 410 controls) and the Epidemiological Investigation of RA (EIRA, 1244 RA cases and 971 controls). Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC) in logistic regression models for the study population and for those with positive family history. The joint effect of family history with genetics, smoking and body mass index (BMI) was evaluated using logistic regression models to estimate ORs for RA.The complete model including family history, epidemiologic risk factors and genetics demonstrated AUCs of 0.74 for seropositive RA in NHS and 0.77 for anti-citrullinated protein antibody (ACPA)-positive RA in EIRA. Among women with positive family history, discrimination was excellent for complete models for seropositive RA in NHS (AUC 0.82) and ACPA-positive RA in EIRA (AUC 0.83). Positive family history, high genetic susceptibility, smoking and increased BMI had an OR of 21.73 for ACPA-positive RA.We developed models for seropositive and seronegative RA phenotypes based on family history, epidemiological and genetic factors. Among those with positive family history, models using epidemiologic and genetic factors were highly discriminatory for seropositive and seronegative RA. Assessing epidemiological and genetic factors among those with positive family history may identify individuals suitable for RA prevention strategies.

SUBMITTER: Sparks JA

PROVIDER: S-EPMC4262726 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history.

Sparks Jeffrey A JA Chen Chia-Yen CY Jiang Xia X Askling Johan J Hiraki Linda T LT Malspeis Susan S Klareskog Lars L Alfredsson Lars L Costenbader Karen H KH Karlson Elizabeth W EW

Annals of the rheumatic diseases 20140331 8

<h4>Objective</h4>To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors and known genetic risk factors.<h4>Methods</h4>We developed and validated models for RA based on known RA risk factors, among women in two cohorts: the Nurses' Health Study (NHS, 381 RA cases and 410 controls) and the Epidemiological Investigation of RA (EIRA, 1244 RA cases and 971 controls). Model discrimination was evaluated using the area under the receiver operating ...[more]

PMID: 24685909

Similar Datasets

Project description:IntroductionRheumatoid arthritis (RA) is a complex and clinically heterogeneous autoimmune disease. Currently, the relationship between pathogenic molecular drivers of disease in RA and therapeutic response is poorly understood.MethodsWe analyzed synovial tissue samples from two RA cohorts of 49 and 20 patients using a combination of global gene expression, histologic and cellular analyses, and analysis of gene expression data from two further publicly available RA cohorts. To identify candidate serum biomarkers that correspond to differential synovial biology and clinical response to targeted therapies, we performed pre-treatment biomarker analysis compared with therapeutic outcome at week 24 in serum samples from 198 patients from the ADACTA (ADalimumab ACTemrA) phase 4 trial of tocilizumab (anti-IL-6R) monotherapy versus adalimumab (anti-TNFα) monotherapy.ResultsWe documented evidence for four major phenotypes of RA synovium - lymphoid, myeloid, low inflammatory, and fibroid - each with distinct underlying gene expression signatures. We observed that baseline synovial myeloid, but not lymphoid, gene signature expression was higher in patients with good compared with poor European league against rheumatism (EULAR) clinical response to anti-TNFα therapy at week 16 (P =0.011). We observed that high baseline serum soluble intercellular adhesion molecule 1 (sICAM1), associated with the myeloid phenotype, and high serum C-X-C motif chemokine 13 (CXCL13), associated with the lymphoid phenotype, had differential relationships with clinical response to anti-TNFα compared with anti-IL6R treatment. sICAM1-high/CXCL13-low patients showed the highest week 24 American College of Rheumatology (ACR) 50 response rate to anti-TNFα treatment as compared with sICAM1-low/CXCL13-high patients (42% versus 13%, respectively, P =0.05) while anti-IL-6R patients showed the opposite relationship with these biomarker subgroups (ACR50 20% versus 69%, P =0.004).ConclusionsThese data demonstrate that underlying molecular and cellular heterogeneity in RA impacts clinical outcome to therapies targeting different biological pathways, with patients with the myeloid phenotype exhibiting the most robust response to anti-TNFα. These data suggest a path to identify and validate serum biomarkers that predict response to targeted therapies in rheumatoid arthritis and possibly other autoimmune diseases.Trial registrationClinicalTrials.gov NCT01119859

Dataset Information

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history.

Publications

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets