Novel Biomarkers and Prediction Models for Rheumatoid Arthritis

$785,824R01FY2017ARNIH

Brigham And Women'S Hospital, Boston MA

Investigators

Elizabeth W Karlsoncontact Karen H Costenbader

Linked publications & trials

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Abstract

DESCRIPTION (provided by applicant): In Novel biomarkers and Risk Prediction Modeling in RA we propose to study metabolites and metabolic profiles as new biomarkers of RA risk. The hypothesis underlying this proposal is that concentrations of certain metabolites involving the activation of multiple enzymatic pathways are highly discriminatory for a population at high risk for developing RA. We have validated RA risk prediction models and demonstrated the improvement in accuracy with addition of genetic risk scores and gene-environment interactions to environmental factors. We have demonstrated associations for novel autoantibodies and cytokines with RA risk. Our team has expertise in epidemiology, genetics, predictive modeling, biomarker analysis, and network and pathway analysis. Specific Aims are to: 1) identify individual metabolites and metabolic profiles associated with RA risk in both untargeted and candidate approaches with discovery analyses in the Nurses' Health Study cohorts and replication analyses in the Department of Defense military cohort; we will use advanced biostatistical techniques to identify novel RA risk metabolic patterns, as well as to investigate whether validated metabolic profiles, previously associated with the gastrointestinal microbiota and inflammation, atherosclerosis and cardiovascular disease are also associated with RA risk; 2) examine whether intermediate biomarkers of RA (IL-6, TNFR2, MCP-1, anti-citrullinated protein antibodies) are associated with distinct metabolic profiles and whether biomarkers mediate the relationship of metabolites to RA risk; 3) investigate whether relationships between both lifestyle factors and genetic risk scores and RA risk are mediated by individual metabolites and metabolic profiles. We will use our epidemiologic expertise to build comprehensive models that include environmental factors, cytokines, autoantibodies, metabolic profiles, and genetic predictors that can be used to identify high risk populations for targeted prevention therapy. The discovery of novel metabolic biomarkers associated with a greater likelihood of RA would provide an important public health benefit to subjects at high risk of RA due to family history and genetics.

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