Understanding ethno-racial differences in AT(N)-defined heterogeneity profiles

项目摘要 阿尔茨海默病(AD)是一种高度异质性的疾病，其内部和跨领域的表现各不相同 不同的社区和背景。在神经解剖学上，在局部观察到了异质性。 用于AD分期的淀粉样蛋白(A)、tau(T)和神经变性(N)标记物的模式。例如，模式 Tau的积聚与脑萎缩高度相关，并已通过高级多变量进行亚型划分 和机器学习方法到涉及典型AD区域的那些(例如，内侧-颞侧、侧向 顶叶)，保留海马区，边缘优势区或轻度萎缩，而淀粉样蛋白 积聚可分为额区、顶区和枕区。值得注意的是，空间子类型是 与不同的认知、遗传(例如APOE e4基因)和流体生物标志物特征相关联，因此 提示临床的异质性可能部分源于神经解剖学的异质性。虽然 阿尔茨海默病的神经解剖异质性对认知和功能结果也有重要影响 作为针对患者的治疗，关于AD生物标记物的地形学方面的文献有很大的差距 种族群体中的模式。同样，结构性和社会性在多大程度上也不得而知。 健康决定因素(SSDOH)影响异质性。目前的研究填补了文献中的一个关键空白 将代表不足的少数群体和相关的SSDOH因素纳入#年异质性调查 在(N)个成像标记物。 使用种族和人种多样化的健康和老龄化大脑研究的数据- 健康差异(HABS-HD)，这项研究将 A)确定AD异质性分布(即空间亚型和 A、T和N神经成像标志物(即磁共振成像[MRI]、淀粉样蛋白和tau)的大小 正电子发射断层扫描[PET])使用机器学习方法；B)在组内和组间进行评估 墨西哥裔美国人、黑人和非西班牙裔白人之间的异质性特征差异(NHW)；以及 C)评估SSDOH的总体影响(例如，地区剥夺指数、文化适应、教育等)关于A、T和N 种族群体内部和群体之间的异质性概况。生物标记物的切点和组水平 在研究和临床环境中用于对个人进行分类的组合通常通过 识别AD特有的或易受AD影响的脑区。然而，已确定的AD敏感区和 相关的切割点通常来自一组(即，NHW)并应用于所有组。刻画 因此，使用多样化和代表性样本的AT(N)成像标记物的异质性对于 通知AD敏感区在不同个体之间是否相似，从而通知当前切点是否 恰如其分。

PROJECT ABSTRACT Alzheimer disease (AD) is a highly heterogeneous disorder which varies in presentation within and across diverse communities and backgrounds. Neuroanatomically, heterogeneity is observed in the topographical patterns of amyloid (A), tau (T), and neurodegeneration (N) markers used to stage AD. For example, patterns of tau accumulation and brain atrophy are highly correlated and have been subtyped by advanced multivariate and machine learning methods into those involving typical AD regions (e.g., medial-temporal, lateral temporoparietal), hippocampal-sparing, limbic-predominant regions or minimal atrophy, while amyloid accumulation can be subtyped into frontal, parietal, and occipital regions. Notably, spatial subtypes are associated with distinct cognitive, genetic (e.g., APOE e4 genotype), and fluid biomarker profiles, thus suggesting that clinical heterogeneity may in part stem from neuroanatomical heterogeneity. Although neuroanatomical heterogeneity in AD has important implications for cognitive and functional outcomes as well as patient-specific treatments, there is a large gap in the literature regarding AD biomarker topographical patterns in ethno-racial groups. Similarly, it is largely unknown to what extent structural and social determinants of health (SSDOH) affect heterogeneity. The present study fills a critical gap in the literature by including under-represented minorities and relevant SSDOH factors in the investigation of heterogeneity in AT(N) imaging markers. Using data from the racially and ethnically diverse Health and Aging Brain Study – Health Disparities (HABS-HD), this study will A) determine AD heterogeneity profiles (i.e., spatial subtypes and magnitude) for A, T, and N neuroimaging markers (i.e., magnetic resonance imaging [MRI], amyloid and tau positron emission tomography [PET]) using a machine learning approach; B) assess within and between group differences in heterogeneity profiles across Mexican-Americans, Blacks, and non-Hispanic Whites (NHW); and C) assess overall effects of SSDOH (e.g., area deprivation index, acculturation, education etc.) on A, T, and N heterogeneity profiles within and between ethno-racial groups. Biomarker cut-points and group-level composites used to classify individuals in research and clinical settings are often informed through the identification of AD-specific or AD-vulnerable brain regions. However, the identified AD-sensitive regions and associated cut-points are typically derived from one group (i.e., NHWs) and applied to all. Characterizing heterogeneity in AT(N) imaging markers using a diverse and representative sample is therefore crucial to informing whether AD-sensitive regions are similar across individuals and thus whether current cut-points are appropriate.