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Second-generation assays for anti-cyclic citrullinated peptide (anti-CCP), a highly sensitive and specific marker for rheumatoid arthritis (RA), have redefined the epidemiology of RA. In the Women's Health Initiative (WHI) RA study (2009–2011), we evaluated the prevalence of anti-CCP positivity among 15,691 (10.2% of 161,808) WHI participants aged 50–79 years who reported RA. Using stored baseline specimens, we measured serum anti-CCP, rheumatoid factor (RF), and antinuclear antibody in a defined sample of 9,988 of black, white, and Hispanic women. In a subset of women, we measured plasma cytokine levels and number of copies of the human leukocyte antigen (HLA)-DRB1 (HLA-DRB1) shared epitope in DNA by means of Luminex polymerase chain reaction typing (Luminex Corporation, Austin, Texas). We validated classification of probable clinical RA in 2 clinics as anti-CCP positivity or self-reported validated use of disease-modifying antirheumatic drugs (DMARDs). The prevalence of anti-CCP positivity was 8.1%, and the prevalence of RF positivity was approximately 16.0%. DMARD use including prednisone was reported by 1,140 (11.4%) participants (841 excluding prednisone) but by 57.5% of anti-CCP-positive women. The prevalence of 2 shared epitopes was also much higher for anti-CCP-positive women (18.2%, as opposed to only 5.5% for women with anti-CCP-negative DMARD-positive RA and 6.6% for anti-CCP-negative, RF-negative DMARD nonusers). Median cytokine levels were much higher for anti-CCP-positive/RF-positive women. Women with anti-CCP-positive RA and anti-CCP-negative RA had different characteristics with regard to HLA shared epitope, cigarette smoking, and inflammation (cytokines). [ABSTRACT FROM AUTHOR]

The linear mixed model (LMM), which is routinely used to describe change in outcomes over time and its association with risk factors, assumes that a unit change in any predictor is associated with a constant change in the outcome. When it is used on psychometric tests, this assumption may not hold. Indeed, psychometric tests usually suffer from ceiling and/or floor effects and curvilinearity (i.e., varying sensitivity to change). The authors aimed to determine the consequences of such misspecification when evaluating predictors of cognitive decline. As an alternative to the LMM, they considered 2 mixed models based on latent processes that handle discrete and bounded outcomes. Model differences are illustrated here using data on 4 psychometric tests from the Personnes Agées QUID (PAQUID) Study (1989–2004). The type I error of the Wald test for risk-factor regression parameters was formally assessed in a simulation study. It demonstrated that type I errors in the LMM could be dramatically inflated for some tests, such that spurious associations with risk factors were found. In particular, confusion between effects on mean level and effects on change over time was highlighted. The authors recommend use of the alternative mixed models when studying psychometric tests and more generally quantitative scales (quality of life, activities of daily living). [ABSTRACT FROM AUTHOR]