Biopsychosocial Phenotypes and Potential Mechanisms in CHARTER

中枢神经系统（CNS）并发症在HIV感染者（PWH）中很常见，即使是那些 正在接受抗逆转录病毒治疗（ART）。中枢神经系统并发症的范围很广，从轻度认知功能障碍， 从不影响日常功能的缺陷到危及生命的脑炎。认知和情绪障碍是 在影响PWH的最常见的CNS疾病中，有一个共同的风险因素，即炎症。 在有效治疗的PWH中，炎症持续存在，原因有多种，包括HIV RNA的产生， 蛋白质和肠道生态失调和微生物易位。CHARTER是一个多站点，美国-基于神经HIV队列 一项由NIH资助的研究，近二十年来一直在研究PWH的CNS疾病， 它已经完成了6,000多项评估，产生了1000多万个数据点。宪章 为了解这些疾病的发生频率、危险因素和发病机制做出了重要贡献。 紊乱近年来，出现了关于异质性、生物学基础、临床影响、 和CNS疾病的管理。这一争论强调了建立新的CNS分类的必要性 PWH中的疾病更多是基于生物学的。我们建议使用机器学习和 不可知论的方法来分类CHARTER的高维神经行为，神经医学，精神病学， 物质使用和成像数据。这样的分析不仅会产生认知表型， (BPS)表型，可以确定新的机制，导致临床上有用的诊断测试，新的 治疗，以及更好地管理PWH中的CNS疾病。 我们的首要目标是利用CHARTER的数据和储存的标本， a）更好地理解PWH中的认知和BPS表型和B）将它们与生物学机制联系起来。到 为了实现这一目标，我们将使用无监督和有监督的机器学习方法来分析来自 CHARTER的全面评估旨在识别新的认知和BPS表型（Aim 1）。然后，我们将比较这些新的表型的高维数据，从宪章的完成， 全基因组关联研究以及我们将生成的炎症新数据（45重珠基阵列） 以及血液和CSF中微生物组和代谢组的高度新颖的测定（Aim 2）。我们的分析将 包括具体关注性如何影响观察到的关系。为了确定这种新方法是否与 比以前的方法更强烈的生物学，我们还将比较新表型的性能， 根据2007年HAND标准定义。这一高度创新的应用得到了初步的有力支持。 数据，很好地响应了艾滋病研究办公室的优先事项，并将解决该领域的关键差距，包括 需要更好地了解共病的发病机制。

Central nervous system (CNS) complications are common among people with HIV (PWH), even those who are taking antiretroviral therapy (ART). The spectrum of CNS complications is broad, ranging from mild cognitive deficits that do not affect daily functioning to life-threatening encephalitis. Cognitive and mood disorders are among the most common CNS diseases that affect PWH and share a common risk factor, inflammation. Inflammation persists in effectively treated PWH for multiple reasons, including production of HIV RNA and proteins and gut dysbiosis and microbial translocation. CHARTER is a multisite, U.S.-based, neuroHIV cohort study that is funded by NIH and that has investigated CNS disorders in PWH for nearly two decades, during which it has completed more than 6,000 assessments generating more than 10 million data points. CHARTER has made important contributions to understanding the frequency, risk factors, and pathogenesis of these disorders. In recent years, new questions have arisen about the heterogeneity, biological basis, clinical impact, and management of CNS disorders. This debate has highlighted the need to create new classifications of CNS disorders in PWH that are more biology-based. We propose to use methods such as machine learning and an agnostic approach to categorize CHARTER’s high-dimensional neurobehavioral, neuromedical, psychiatric, substance use, and imaging data. Such analyses would yield not just cognitive phenotypes but biopsychosocial (BPS) phenotypes that could identify new mechanisms that lead to clinically useful diagnostic tests, new therapies, and better management of CNS disorders in PWH. Our overarching goal is to leverage prior investment in CHARTER by using its data and stored specimens to a) better understand cognitive and BPS phenotypes in PWH and b) relate them to biological mechanisms. To accomplish this, we will use unsupervised and supervised machine learning methods to analyze data from CHARTER’s comprehensive assessments with the goal of identifying new cognitive and BPS phenotypes (Aim 1). We will then compare these new phenotypes to high-dimensional data from CHARTER’s completed genomewide association study as well as new data we will generate on inflammation (45-plex bead-based array) and highly novel assays of the microbiome and the metabolome in blood and CSF (Aim 2). Our analyses will include a specific focus how sex affects the observed relationships. To determine if this novel approach relates more strongly to biology than prior methods, we will also compare the performance of the new phenotypes to those defined by the 2007 HAND criteria. This highly innovative application is supported by strong preliminary data, responds well to Office of AIDS Research priorities, and will address key gaps in the field, including the need to better understand the pathogenesis of comorbid disease.