Posset Roland, Zielonka Matthias, Gleich Florian, Garbade Sven F, Hoffmann Georg F, Kölker Stefan
Division of Pediatric Neurology and Metabolic Medicine, Center for Pediatric and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany.
Heidelberg Research Center for Molecular Medicine (HRCMM), Heidelberg, Germany.
J Inherit Metab Dis. 2023 Nov;46(6):1007-1016. doi: 10.1002/jimd.12678. Epub 2023 Oct 10.
The Urea Cycle Disorders Consortium (UCDC) and the European registry and network for Intoxication type Metabolic Diseases (E-IMD) are the worldwide largest databases for individuals with urea cycle disorders (UCDs) comprising longitudinal data from more than 1100 individuals with an overall long-term follow-up of approximately 25 years. However, heterogeneity of the clinical phenotype as well as different diagnostic and therapeutic strategies hamper our understanding on the predictors of phenotypic diversity and the impact of disease-immanent and interventional variables (e.g., diagnostic and therapeutic interventions) on the long-term outcome. A new strategy using combined and comparative data analyses helped overcome this challenge. This review presents the mechanisms and relevant principles that are necessary for the identification of meaningful clinical associations by combining data from different data sources, and serves as a blueprint for future analyses of rare disease registries.
尿素循环障碍协会（UCDC）和欧洲中毒型代谢疾病登记与网络（E-IMD）是全球最大的尿素循环障碍（UCD）患者数据库，包含来自1100多名个体的纵向数据，总体长期随访约25年。然而，临床表型的异质性以及不同的诊断和治疗策略阻碍了我们对表型多样性预测因素以及疾病内在和干预变量（如诊断和治疗干预）对长期结局影响的理解。一种使用联合和比较数据分析的新策略有助于克服这一挑战。本综述介绍了通过整合来自不同数据源的数据来识别有意义的临床关联所必需的机制和相关原则，并为未来罕见病登记处的分析提供了蓝图。
