Disparities of Alzheimer's disease progression in sexual and gender minorities

摘要 性少数群体和性别少数群体(SGM)面临着独特的健康问题，但关于SGM健康的研究很少。在……里面 特别是，在SGM患者中可获得的与年龄相关的疾病如阿尔茨海默氏症的数据有限 阿尔茨海默病(AD)和相关痴呆。AD是一种具有多种危险因素的致命性退行性疾病， 范围从临床特征到健康的社会决定因素(SDoH)。AD患者通常从 认知未受损至(可能)轻度认知障碍(MCI)，随后严重程度增加 痴呆伴AD临床综合征。然而，有证据表明，在 通过多个中间阶段进展到AD。不同AD进展途径的特征 而相关的风险因素对于风险分层和预防至关重要。另一方面， 使用真实世界数据(RWD)的大型临床研究网络(CRN)激增，包括电子健康 记录(EHR)、索赔和帐单数据等提供了生成真实证据的机会 (RWE)将对SGM人群中的AD预防和护理产生直接的转化影响。 然而，在使用RWD研究AD方面存在一些关键研究和方法学上的空白 SGM，包括缺少(1)经过验证的可计算表型(CP)和自然语言处理(NLP) 可以准确定义SGM人群并提取关键患者特征和结果的工具 (例如，确定严重程度的MoCA评分)，(2)考虑AD及其进展的异质性 途径，和(3)考虑阿尔茨海默病在SGM人群中的差异，特别是在个体- 和上下文级SDoH。作为对NOT-AG-21-050的响应，我们建议分析大量的RWD集合 在One佛罗里达+和Insight网络中，两个CRN为国家以患者为中心的临床做出贡献 研究网络(PCORnet)，以：(1)创建可跟踪的真实世界纵向SGM和AD队列 通过常规的临床护理，(2)使用新型联邦机器对AD进展的异质性进行建模 学习方法，以及(3)检查阿尔茨海默病预后中的SGM差异(即，发病和进展途径) 以及阿尔茨海默病临床危险因素和SDoH影响这些AD结局的因果路径。我们的项目是 新颖，并将产生直接的翻译影响，因为它提供了具体的RWE来填补知识空白 检查SGM(和SGM亚组)和非SGM之间是否存在AD差异，并确定 潜在可操作的AD风险因素和SDoH对SGM及其差异具有重要意义。这件事的成功 该项目将填补我们在SGM人群中AD风险和进展途径方面的重要知识空白， 并建立了建立基于RWD的虚拟队列的框架，该框架可以为国家实用化试验提供信息 跨PCORnet进行未来的SGM老化临床研究。

ABSTRACT Sexual and gender minorities (SGM) face unique health issues, but studies on SGM health are scarce. In particular, limited data are available among SGM individuals on age-related conditions such as Alzheimer’s disease (AD) and related dementias. AD is a fatal degenerative disease with a diverse range of risk factors, ranging from clinical characteristics to social determinants of health (SDoH). AD patients often progress from cognitively unimpaired to (possible) mild cognitive impairment (MCI), followed by increasing severity of dementia with AD clinical syndrome. Nevertheless, evidence suggests there exists heterogeneity in the progression to AD through multiple intermediate stages. Characterizing the different AD progression pathways and the associated risk factors is crucial for risk stratification and prevention. On the other hand, the proliferation of large clinical research networks (CRNs) with real-world data (RWD), including electronic health records (EHRs), claims, and billing data among others, offers opportunities for generating real-world evidence (RWE) that will have direct translational impacts on AD prevention and care in the SGM populations. Nevertheless, there are a number of key research and methodological gaps in using RWD for studying AD in SGM, including the lack of (1) validated computable phenotypes (CP) and natural language processing (NLP) tools that can accurately define the SGM populations and extract key patient characteristics and outcomes (e.g., MoCA scores to determine severity), (2) consideration of the heterogeneity in AD and its progression pathways, and (3) consideration of AD disparities in SGM populations, especially structured on both individual- and contextual-level SDoH. Responding to NOT-AG-21-050, we propose to analyze large collections of RWD in the OneFlorida+ and INSIGHT networks, two CRNs contributing to the national Patient-Centered Clinical Research Network (PCORnet), to: (1) create real-world longitudinal SGM and AD cohorts that can be followed by virtue of routine clinical care, (2) model the heterogeneity in AD progression with novel federated machine learning methods, and (3) examine SGM disparities in AD outcomes (i.e., onset and progression pathways) and in the causal paths via which AD clinical risk factors and SDoH impact these AD outcomes. Our project is novel and will have direct translational impact as it provides concrete RWE to fill the knowledge gaps by examining whether AD disparities exist between SGM (and SGM subgroups) and non-SGM, and identifies potentially actionable AD risk factors and SDoH significant to SGM and their disparities. The success of this project will fill important gaps in our knowledge of AD risk and progression pathways in the SGM populations, and establish a framework for creating RWD-based virtual cohort, which can inform national pragmatic trials across PCORnet for future SGM aging clinical studies.