Identifying predictors of reversible congenital hypogonadotropic hypogonadism

7.项目总结/摘要 罕见病患者面临的健康差异与对疾病自然史的了解不足有关， 获得专家护理的机会以及缺乏对孤儿状况的有效治疗。缺乏高质量的自然历史 数据以及地理上分散的小患者群体阻碍了研究和临床 罕见疾病的线索。越来越多的基于网络的平台将患者与临床专家、国际 利用结构化数据收集的跨境合作和罕见疾病网络已经建立了平台 来增进我们对罕见疾病的了解这些资源有望加速临床试验 新的诊断方法和新的治疗方法，以改善罕见疾病患者的健康和福祉。 罕见病通常被认为是慢性的、终身的疾病。这一教条的一个重要例外是 是先天性低促性腺激素性性腺功能减退症（CHH）。值得注意的是，一些CHH患者经历逆转， 有效地恢复正常健康-从“慢性到治愈”。逆转的情况下举行令人兴奋的承诺， 为治疗CHH开辟新的途径，改善患者的健康相关生活质量并降低成本。 目前，逆转病例的临床谱尚未被系统地绘制， 现象仍然未知。本R 03提案旨在更深入地了解 通过：（1）利用CHH的国际专业知识，（2）利用协调的疾病本体论， 和系统表型患者的共同数据元素，以及（3）阐明异质性， 通过应用新的统计方法来识别预测因子。我们将克服障碍， 通过与该领域国际公认的专家合作， 世界上最大的CHH队列。合作中心使用共享的疾病本体，并系统地 使用结构化的共同数据元素对他们的患者队列进行表型分析。首先，我们将使用现有的数据（de- 确定），以绘制最大逆转中的临床异质性 到目前为止，已经集结了一批人。其次，我们将应用潜在类混合模型来揭示反转的预测因子。 由此产生的发现将改变这种罕见疾病的护理和管理并推动临床试验 外地的发展。揭示逆转的模式和预测因素将产生重大的直接影响 在降低成本方面，临床护理和公共卫生效益。这项拟议中的研究是一个关键的下一步 这是改善临床实践的一步-自20世纪80年代以来几乎没有改变。此外，研究 研究结果可能会为未来对其他罕见疾病的调查提供信息。

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.