Latent Class Methods to Explore the Heterogeneity of Neurodegenerative Diseases

项目摘要/摘要 对许多复杂疾病的异质性缺乏了解，妨碍了它们的准确早期诊断。 有针对性的干预侧重于病因学。尤其令人关注的是，许多亚型存在于 神经退行性疾病的早期阶段，每种亚型都有不同的致病原因和 表型。准确诊断和预测这些疾病的进展速度对于 任何治疗疾病的方法。为了克服这一障碍，我们认为至关重要的是发展和 应用一种创新的潜在类别分析方法--将以下两种方法结合起来：(1) 临床和生物标志物信息的高维收集，以及(2)特定结果的时间 这样的数据是可用的--以便得出与潜在病因相关的子分类 以及疾病进展的速度。与现有的潜在类分析方法不同，我们的新方法是 可伸缩，并且只需要最少的建模假设。在短期内，我们将针对 轻度认知障碍(MCI)，临床上第一个可检测到的中间阶段的表现 正常衰老和痴呆症。我们将在两个现有的人员纵向数据集中集成信息 与MCI：国家阿尔茨海默氏症协调中心的统一数据集(UDS)，一个独特的资源，有29 参与美国国立卫生研究院资助的阿尔茨海默病中心，为标准化临床和神经病理学做出贡献 超过6500个独特的MCI个体的变量；以及Emory神经学-认知数据集(NeuCog)，其中 除了全面的临床信息，还对1015个独特的MCI的标准化生物标志物做出了贡献 有529人接受了MRI扫描，529人接受了脑脊液(CSF)样本。这项研究的具体目的 目的是：(1)开发一种可扩展的潜在轨迹类分析方法，允许研究人员建模 只有纵向观测的均值、方差和时间相关性；(2)扩展了该方法 在目标1中开发，供研究人员将时间与特定的临床或神经病理结果结合起来， 针对复杂的生存特征，引入潜在类分析；(3)将我们的新的统计方法应用于 专业临床科学家的指导，使用UDS和NeuCog数据集中可用的信息，以 确定临床病理上相关的MCI亚型；以及(4)开发免费提供的软件来分析 数据使用我们新的统计方法。

Project Summary/Abstract A poor understanding of the heterogeneity of many complex diseases prevents their accurate early diagnosis and targeted interventions focused on etiology. Of particular concern, many subtypes exist among persons in the early stages of neurodegenerative diseases, each subtype with distinct contributing causes and phenotypes. Accurately diagnosing and predicting rates of progression for these illnesses will be essential for any disease-modifying treatments. To overcome this barrier, we believe it is critically important to develop and apply an innovative method of latent class analysis -- incorporating both (1) the longitudinal trajectories of a high-dimensional collection of clinical and biomarker information, and (2) the times to specific outcomes when such data are available -- in order to arrive at subclassifications that are relevant to the underlying etiologies and the rate of disease progression. Unlike current methods of latent class analysis, our new method is scalable and requires minimal modeling assumptions. Over the short-term, we will target the heterogeneity of mild cognitive impairment (MCI), the first clinically detectable manifestation of the intermediate stage between normal aging and dementia. We will integrate the information in two existing longitudinal data sets of persons with MCI: the National Alzheimer’s Coordinating Center’s Uniform Data Set (UDS), a unique resource with 29 participating NIH-funded Alzheimer’s Disease Centers contributing standardized clinical and neuropathological variables on over 6500 unique MCI individuals; and the Emory Neurology-Cognitive Data Set (NeuCog), which addition to comprehensive clinical information also contributes standardized biomarkers on 1015 unique MCI individuals with MRI scans and 529 with cerebral spinal fluid (CSF) specimens. The specific aims of this study are to: (1) Develop a scalable method of latent trajectory class analysis that allows the researcher to model only the means, variances, and temporal correlations of the longitudinal observations; (2) Extend the method developed in Aim 1 for researchers to incorporate the times to specific clinical or neuropathological outcomes, subject to complex survival features, into the latent class analysis; (3) Apply our new statistical methods under the guidance of expert clinical scientists, using the information available in the UDS and NeuCog data sets, to identify clinicopathologically relevant subtypes of MCI; and (4) Develop freely available software to analyze data using our new statistical methods.