From the Department of Nutrition (S.A.M., A.A.), Harvard T.H. Chan School of Public Health; Epidemiology (K.C.H.), Optum; Department of Neurology (M.A.S.), and MassGeneral Institute for Neurodegenerative Disease (M.A.S.), Massachusetts General Hospital; Department of Epidemiology (A.A.), Harvard T.H. Chan School of Public Health; and Channing Division of Network Medicine (A.A.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Neurology. 2022 Aug 16;99(7 Suppl 1):26-33. doi: 10.1212/WNL.0000000000200788.
Significant progress has been made in expanding our understanding of prodromal Parkinson disease (PD), particularly for recognition of early motor and nonmotor signs and symptoms. Although identification of these prodromal features may improve our understanding of the earliest stages of PD, they are individually insufficient for early disease detection and enrollment of participants in prevention trials in most cases because of low sensitivity, specificity, and positive predictive value. Composite cohorts, composed of individuals with multiple co-occurring prodromal features, are an important resource for conducting prodromal PD research and eventual prevention trials because they are more representative of the population at risk for PD, allow investigators to evaluate the efficacy of an intervention across individuals with varying prodromal feature patterns, are able to produce larger sample sizes, and capture individuals at different stages of prodromal PD. A key challenge in identifying individuals with prodromal disease for composite cohorts and prevention trial participation is that we know little about the natural history of prodromal PD. To move toward prevention trials, it is critical that we better understand common prodromal feature patterns and be able to predict the probability of progression and phenoconversion. Ongoing research in cohort studies and administrative databases is beginning to address these questions, but further longitudinal analyses in a large population-based sample are necessary to provide a convincing and definitive strategy for identifying individuals to be enrolled in a prevention trial.
在扩大我们对前驱期帕金森病(PD)的认识方面取得了重大进展,特别是对早期运动和非运动迹象和症状的认识。尽管识别这些前驱特征可能会提高我们对 PD 最早阶段的理解,但由于敏感性、特异性和阳性预测值低,它们在大多数情况下不足以早期发现疾病并招募参与者参加预防试验。由多个同时存在的前驱特征组成的复合队列是进行前驱 PD 研究和最终预防试验的重要资源,因为它们更能代表 PD 风险人群,允许研究人员评估干预措施在具有不同前驱特征模式的个体中的疗效,能够产生更大的样本量,并捕获处于前驱 PD 不同阶段的个体。为了确定复合队列和预防试验参与者的前驱疾病个体,一个关键挑战是我们对前驱 PD 的自然史知之甚少。为了推进预防试验,我们必须更好地了解常见的前驱特征模式,并能够预测进展和表型转化的概率。队列研究和管理数据库中的正在进行的研究开始解决这些问题,但在大型基于人群的样本中进行进一步的纵向分析是必要的,以提供一种有说服力和明确的策略,用于确定要招募参加预防试验的个体。