4.3 Article

Hereditary, socio-behavioural, and immuno-hormonal predictors of incident rheumatoid arthritis and therapy response influences on survival versus matched control subjects using a generalised structural equation model

Journal

CLINICAL AND EXPERIMENTAL RHEUMATOLOGY
Volume 38, Issue 4, Pages 640-648

Publisher

CLINICAL & EXPER RHEUMATOLOGY

Keywords

Generalized structural equation model; risk factors; rheumatoid arthritis; incident onset; survival outcome

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Objectives Incident onset and survival outcomes involve multiple risk factors and complex interactions preferably investigated in a single study. A generalized structural equation model (GSEM) was used to build an integrative framework to analyse multiple risk factors for incident rheumatoid arthritis (RA) and factors affecting long-term survival outcome. Methods Incident RA cases (n=54) had onsets between 1977 and 1994, after cohort entry in 1974. Four cohort control (CN) subjects (n=216) were matched on entry to each case in the community-based CLUE cohort and 270 subjects were followed from 1995 through 2017. Baseline variables included demographic, RA family history, behavioural factors and z-score levels of serum immunological, cytokine, isotype rheumatoid factors (RFs), adrenal steroids, luteinising hormone, prolactin and sex steroids. Four numerical integration methods of GSEM were performed in Stata 15. Results Cohort entry factors predicting RA onset included family history of RA, cigarette smoking and IgM RF. Total survival time from cohort entry was associated with incident RA and baseline variables of age, years of completed education, cigarette smoking, immunoreactive proteins and androgenic-anabolic steroids. Mortality of RA was significantly greater than CN subjects for cases having less than good therapy responses in 1995 and only for RA onset before age 60 years. Androgenic-anabolic steroid z-scores significantly correlated with improved survival only in CN subjects with assigned onset before the age of 60. Conclusions Successful use of GSEM is feasible in analyses of prospective incident and subsequent survival data and promises to advance understanding of risk factors, survival, and casual pathways.

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