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Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history
  1. Jeffrey A Sparks1,
  2. Chia-Yen Chen2,
  3. Xia Jiang3,
  4. Johan Askling4,
  5. Linda T Hiraki5,
  6. Susan Malspeis1,
  7. Lars Klareskog4,
  8. Lars Alfredsson3,6,
  9. Karen H Costenbader1,
  10. Elizabeth W Karlson1
  1. 1Department of Medicine, Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA
  2. 2Harvard School of Public Health and Brigham and Women's Hospital, Boston, Massachusetts, USA
  3. 3Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
  4. 4Rheumatology Unit, Department of Medicine, Karolinska Institutet/Karolinska Hospital, Stockholm, Sweden
  5. 5Research Institute, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
  6. 6Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden
  1. Correspondence to Dr Jeffrey A Sparks, Department of Medicine, Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; jasparks{at}


Objective To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors and known genetic risk factors.

Methods 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.

Results 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.

Conclusions 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.

  • Rheumatoid Arthritis
  • Epidemiology
  • Gene Polymorphism
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