Secondary Analysis of Cognitive Data from the Multi-Center AIDS Cohort Study (MACS) to Identify Longitudinal Change Phenotypes in HIV+ Individuals

项目摘要 抗逆转录病毒疗法（ART）已将人类免疫缺陷病毒（HIV）感染从几乎 从一种普遍的致命疾病到一种可控制的慢性疾病， (CI)持续存在的比率高于可比的未感染对照人群。这些缺陷是 在呈现（例如认知域）和轨迹方面是异质的。鉴于特定于域的 损伤可能是由于不同的病理变化，研究CI的基础上，全球 减值指标可能会忽略这一点。先进分析方法的应用，包括机器 学习探究复杂的疾病和生物过程，开始改变我们对 导致复杂疾病的潜在机制，包括艾滋病毒感染背景下的CI。 为了识别和描述导致认知变化的非线性模式的因素，我们 建议利用多中心艾滋病队列研究（MACS）20多年的数据。MACS是 这是对男性艾滋病毒感染者进行的规模最大、持续时间最长的研究之一，其中包括一项一年两次的子研究。 神经认知测试始于1988年。一套完整的纵向神经认知测试数据是 通过这个队列，以及包括人口统计学，临床， 精神病学生活方式和生化数据该数据集由超过3500个主题和超过50万个数据组成 点在目标1中，我们建议使用数据驱动的方法（用于关节纵向轨迹的k均值聚类 和动态时间扭曲）来识别具有不同领域特定认知模式的个体群体。 变化在目标2中，我们将使用从这些受试者中收集的大量数据来识别亚型特异性 有助于或预测群体成员资格的变量。通过使用先进的机器学习方法， 不受预设的统计学或生物学假设的约束，我们处于独特的地位，可以识别 有助于特定认知功能的纵向变化模式。

PROJECT SUMMARY Antiretroviral therapies (ART) have modified Human Immunodeficiency Virus (HIV) infection from a nearly universal fatal disease to a manageable chronic condition, yet associated symptoms like cognitive impairments (CI) persist at a higher rate than in comparable uninfected control populations. These impairments are heterogeneous in terms of presentation (e.g. cognitive domains) and trajectory. Given that domain-specific impairments are likely due to different pathological changes, studies that investigate CI based on a global metric of impairment are likely to miss this. The application of advanced analysis methods, including machine learning to interrogate complex diseases and biological processes, is beginning to transform our understanding of the underlying mechanisms that contribute to complex diseases, including CI in the setting of HIV infection. In order to identify and characterize factors that contribute to nonlinear patterns of cognitive change, we propose to capitalize on over 20 years of data from the Multi-Center AIDS Cohort Study (MACS). MACS is one of the largest and longest running studies of men infected with HIV, that includes a substudy with biannual neurocognitive testing that began in 1988. A complete battery of longitudinal neurocognitive testing data is available to us through this cohort, as well as concurrent datasets that include demographic, clinical, psychiatric, lifestyle and biochemical data. The data set consists of over 3500 subjects with over 500,000 data points. In Aim 1, we propose to use data-driven methods (k-means clustering for joint longitudinal trajectories and dynamic time warping) to identify groups of individuals with distinct domain-specific patterns of cognitive change. In Aim 2, we will use the vast amount of data collected on these subjects to identify subtype-specific variables that contribute or predict group membership. By using advanced machine learning methods that are unconstrained by preset statistical or biological assumptions, we are uniquely positioned to identify factors that contribute to longitudinal patterns of change in specific cognitive functions.