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Longitudinal normative standards for cognitive tests and composites using harmonized data from two Wisconsin AD‐risk‐enriched cohorts
Alzheimer's & Dementia ( IF 14.0 ) Pub Date : 2024-03-28 , DOI: 10.1002/alz.13774
Erin M. Jonaitis 1, 2 , Bruce P. Hermann 3 , Kimberly D. Mueller 4, 5 , Lindsay R. Clark 5, 6 , Lianlian Du 1 , Tobey J. Betthauser 2, 7 , Karly Cody 2 , Carey E. Gleason 2, 5, 6, 7 , Bradley T. Christian 2, 8, 9 , Sanjay Asthana 2, 7 , Richard J. Chappell 10, 11 , Nathaniel A. Chin 2, 5 , Sterling C. Johnson 1, 2, 6, 7 , Rebecca E. Langhough 1, 2, 7
Affiliation  

INTRODUCTIONPublished norms are typically cross‐sectional and often are not sensitive to preclinical cognitive changes due to dementia. We developed and validated demographically adjusted cross‐sectional and longitudinal normative standards using harmonized outcomes from two Alzheimer's disease (AD) risk‐enriched cohorts.METHODSData from the Wisconsin Registry for Alzheimer's Prevention and the Wisconsin Alzheimer's Disease Research Center were combined. Quantile regression was used to develop unconditional (cross‐sectional) and conditional (longitudinal) normative standards for 18 outcomes using data from cognitively unimpaired participants (N = 1390; mean follow‐up = 9.25 years). Validity analyses (N = 2456) examined relationships between percentile scores (centiles), consensus‐based cognitive statuses, and AD biomarker levels.RESULTSUnconditional and conditional centiles were lower in those with consensus‐based impairment or biomarker positivity. Similarly, quantitative biomarker levels were higher in those whose centiles suggested decline.DISCUSSIONThis study presents normative standards for cognitive measures sensitive to pre‐clinical changes. Future directions will investigate potential clinical applications of longitudinal normative standards.Highlights Quantile regression was used to construct longitudinal norms for cognitive tests. Poorer percentile scores were related to concurrent diagnosis and Alzheimer's disease biomarkers. A ShinyApp was built to display test scores and norms and flag low performance.

中文翻译:

使用威斯康星州两个 AD 风险丰富队列的统一数据进行认知测试和复合的纵向规范标准

简介 已发布的规范通常是跨部门的,并且通常对痴呆症引起的临床前认知变化不敏感。我们使用两个阿尔茨海默病 (AD) 风险丰富队列的统一结果,制定并验证了人口统计调整的横断面和纵向规范标准。方法将威斯康星州阿尔茨海默病预防登记处和威斯康星州阿尔茨海默病研究中心的数据合并起来。使用来自认知未受损参与者的数据,使用分位数回归为 18 个结果制定无条件(横断面)和条件(纵向)规范标准(= 1390;平均随访时间 = 9.25 年)。有效性分析(= 2456)检查了百分位分数(百分位数)、基于共识的认知状态和 AD 生物标志物水平之间的关系。结果 在基于共识的损伤或生物标志物呈阳性的患者中,无条件和条件百分位数较低。同样,那些百分位数表明下降的人的定量生物标志物水平较高。讨论本研究提出了对临床前变化敏感的认知测量的规范标准。未来的方向将调查纵向规范标准的潜在临床应用。亮点 分位数回归用于构建认知测试的纵向规范。 较差的百分位数分数与同时诊断和阿尔茨海默病生物标志物有关。 ShinyApp 旨在显示测试分数和标准并标记低性能。
更新日期:2024-03-28
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