Identification of biologically relevant subtypes of hidradenitis suppurativa

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Project Summary Hidradenitis suppurativa (HS) is a neglected, prevalent, chronic, stigmatizing, and debilitating disease that has recently been prioritized for study by NIAMS. Evidence suggests that some HS patients choose to self-manage symptoms remaining unconnected to healthcare, and some seek medical care for repeated outbreaks of boils but never receive a diagnosis. Such ‘hidden populations’ create challenges for designing research studies that are generalizable. Precision medicine initiatives and resources offer opportunities to rapidly increase our knowledge about biological causes of HS and to improve the care that HS patients receive. For example, the NIH has made considerable investments in the development of data repositories that link genetic data to EHR for hundreds of thousands of patients, including the NHGRI-funded eMERGE Network and the NIH-funded All of Us Research Program. Columbia University investigators are integral members of these nationwide programs, both as a recruitment site, as well as a data and research center (5U01HG008680, 1OT2OD026556). Engaging research participants who are willing to contribute longitudinal data is a major obstacle to precision medicine initiatives. The public’s use of the Internet and social media to obtain and exchange health-related information has created opportunities to rapidly and efficiently assemble large longitudinal cohorts, yet there are important differences from traditional research methods and best practice guidelines have yet to be developed. Columbia University is at the forefront of the development and application of these methods. A major challenge to implementing precision medicine arises from patients who share a diagnosis but have different biological causes of disease. HS patients have a high burden of comorbidities and we hypothesize that sets of comorbidities that tend to present together in individual patients can be used to identify biologically relevant disease subtypes. Here we will use three approaches to identify patterns of comorbidities within patients, to characterize the generalizability of the results from studies conducted in EHR, and to use genetic data to biologically validate comorbidities and resolve causality underlying disease associations. Training in biomedical informatics and Internet-based survey research will allow the applicant to use EHR data and Internet resources for assembling cohorts to conduct these studies, and complement her previous training in epidemiology, biostatistics, molecular biology and human genetics, providing fluency across several domains that are crucial for advancing precision medicine initiatives. Completion of this proposal will achieve the applicant’s long-term goal of obtaining advanced training aimed at implementing precision medicine in the treatment of skin disease.