Identifying predictors of reversible congenital hypogonadotropic hypogonadism

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7. Project Summary/Abstract Rare disease patients face health disparities related to poor understanding of disease natural history, limited access to expert care and lack of effective treatments for orphan conditions. Lack of high quality natural history data as well as small and geographically dispersed patient populations have hampered research and clinical trails in rare diseases. Increasingly, web-based platforms connecting patients with clinical experts, international cross-border collaboration and rare disease networks using structured data collection have established platforms for advancing our understanding of rare conditions. Such resources hold promise for accelerating clinical trials for novel diagnostic approaches and new therapies to improve the health and wellbeing of rare disease patients. Rare diseases are classically considered to be chronic, lifelong conditions. An important exception to this dogma is congenital hypogonadotropic hypogonadism (CHH). Notably, some patients with CHH undergo a reversal and are effectively restored to normal health - from “chronic to cured”. Cases of reversal hold exciting promise for opening new avenues for treating CHH, improving patients' health-related quality of life and reducing costs. Currently, the clinical spectrum of reversal cases has yet to be systematically charted and predictors of this phenomenon remain unknown. This R03 proposal aims to gain a deeper understanding of the reversal phenomenon by: (1) harnessing international expertise in CHH, (2) leveraging harmonized disease ontologies and common data elements for systematically phenotyped patients and (3) elucidating heterogeneity and complexity by applying a novel statistical approach for identifying predictors. We will overcome barriers to rare disease research by collaborating with internationally recognized experts in the field who have amassed the largest CHH cohorts in the world. Collaborating centers use shared disease ontologies and have systematically phenotyped their patient cohorts using structured common data elements. First, we will use existing data (de- identified) on systematically characterized patients to chart the clinical heterogeneity in the largest reversal cohort assembled to date. Second, we will apply latent class mixture modeling to uncover predictors of reversal. Resulting discoveries will transform care and management of this rare disease and propel clinical trial development in the field. Uncovering patterns and predictors of reversal will have significant immediate impact on clinical care as well as public health benefit in terms of reduced costs. The proposed study is a critical next step for improving clinical practice - which has remained virtually unchanged since the 1980's. Moreover, study results will likely inform future inquiry into other rare diseases.