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.

中文翻译：

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谁适合参加帕金森病预防试验？复合前驱队列的案例。


在扩大我们对前驱帕金森病 (PD) 的了解方面取得了重大进展，特别是在识别早期运动和非运动体征和症状方面。尽管识别这些前驱特征可能会提高我们对 PD 早期阶段的了解，但在大多数情况下，由于敏感性、特异性和阳性预测值较低，它们单独不足以用于早期疾病检测和预防试验参与者的招募。复合队列由具有多种同时发生的前驱特征的个体组成，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，使研究人员能够评估干预措施的有效性。具有不同前驱特征模式的个体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。在识别患有前驱性疾病的复合队列和参与预防试验的个体时，一个关键挑战是我们对前驱性 PD 的自然史知之甚少。为了进行预防试验，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变的可能性。正在进行的队列研究和管理数据库研究正在开始解决这些问题，但有必要对大量基于人群的样本进行进一步的纵向分析，以便为识别要参加预防试验的个体提供令人信服且明确的策略。