• TRUST: Gene and Protein Expression Biomarkers for Frequent Gout Flaring
This project’s objective is to build a predictive multi-omics model for gout flares.

In this project, we propose to use transcriptomic, proteomic, and genetic data. This requires a quality longitudinal biobank in gout with excellent phenotypic information, clear timing of collections and gout flares, and well-processed biospecimens. We propose to leverage the NIH-sponsored Treat-to-Target Serum Urate vs. Treat-to-Avoid Symptoms in Gout (TRUST) clinical trial for this objective. The Biobank derived from the TRUST trial could be used as the basis for projects to develop biomarkers for various outcomes in gout and to identify high-risk gout patients for enrollment in clinical trials. The Biobank could also be used to understand the molecular pathogenesis of gout and gout flares, as well as to contribute to identification of new drug targets.

Goals

  • Aim 1: Identification of differentially expressed biomarkers for inclusion in a frequent flare prediction model.

  • Aim 2: Testing and validation of predictive model for frequent gout flares.

Problem

Gout flares can be challenging and expensive to treat and are complicated by cardiometabolic co-morbidities that include contra-indications to oral anti-inflammatories (e.g. NSAIDs, colchicine). Medications currently available for treating gout flares are inadequate. There is a transient increase in risk for major cardiovascular events following a gout flare [4], implicating the inflammatory component of gout flares in associated cardiometabolic consequences. Gout patients experience premature all-cause and cardiovascular mortality [6], independent of urate levels and traditional cardiovascular risk factors, further implicating an underlying role of gout-specific pathology, including gout inflammation. These data emphasize the need for better prediction and prevention of gout flares, both to reduce the direct impact of gout and to reduce the risk of cardiometabolic consequences.

Solution

There is a clinical need to predict which patients are more likely to flare so clinicians can offer more intensive management to those individuals.

Partners

Public Sector Partners
  • National Institute of Arthritis and Musculoskeletal and Skin Diseases
Private Sector Partners
  • TBD
Academic Partners
  • Harvard Medical School
  • University of Alabama at Birmingham

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Contact
Beth Kohl

Program Manager, Translational Science Inflammation and Immunity

[email protected]
Emma Shaw

Strategic Alliances Officer

[email protected]

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