Identifying predictors of reversible congenital hypogonadotropic hypogonadism

7.项目摘要/摘要 罕见病患者面临的健康差距与对疾病自然病史的了解不足有关，有限 获得专家护理的机会和缺乏有效的孤儿治疗。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 在罕见疾病中处于领先地位。越来越多的基于网络的平台将患者与临床专家联系起来， 使用结构化数据收集的跨境协作和罕见病网络已经建立了平台 以促进我们对罕见情况的理解。这样的资源有望加速临床试验 寻求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。 罕见疾病通常被认为是一种慢性、终生疾病。这一教条的一个重要例外 是先天性促性腺激素减退症(CHH)。值得注意的是，一些CHH患者经历了逆转和 有效地恢复了正常健康--从“慢性到治愈”。逆转的案例令人振奋，有望 为治疗慢性肝炎开辟新途径，提高患者与健康相关的生活质量，降低成本。 目前，逆转病例的临床谱系尚未被系统地绘制出来，其预测因素也尚未确定。 现象仍然不为人知。这份R03提案旨在更深入地了解这一逆转 通过：(1)利用CHH方面的国际专业知识；(2)利用协调一致的疾病本体论 和系统表型患者的通用数据元素，以及(3)阐明异质性和 通过应用一种新的统计方法来识别预测因素，从而提高了预测的复杂性。我们将克服难得的障碍 通过与该领域的国际公认的专家合作进行疾病研究，这些专家积累了 世界上最大的CHH队列。协作中心使用共享的疾病本体，并系统地 使用结构化公共数据元素对他们的患者队列进行表型分析。首先，我们将使用现有数据(去掉 确定)对系统表征的患者绘制临床异质性的最大逆转 到目前为止集结的队列。其次，我们将应用潜在类混合建模来揭示逆转的预测因子。 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 该领域的发展。发现逆转的模式和预测因素将产生重大的直接影响 关于临床护理以及降低成本方面的公共卫生惠益。这项拟议的研究是下一步的关键 改进临床实践的步骤-自1980年S以来基本保持不变。此外，研究 结果可能会为未来对其他罕见疾病的调查提供参考。

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.