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

摘要 性和性别少数群体（SGM）面临着独特的健康问题，但对SGM健康的研究很少。在 特别是，有限的数据是在SGM个人对年龄相关的条件，如阿尔茨海默氏症 疾病（AD）和相关痴呆。AD是一种致命的退行性疾病，具有多种风险因素， 从临床特征到健康的社会决定因素（SDoH）。AD患者通常会从 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临床综合征尽管如此，有证据表明， 通过多个中间阶段进展为AD。描述不同的AD进展途径 相关的危险因素对危险分层和预防至关重要。另一方面 具有真实世界数据（RWD）的大型临床研究网络（CRN）的激增，包括电子健康 电子病历（EHR）、索赔和账单数据等，为生成真实世界的证据提供了机会 (RWE)这将对SGM人群的AD预防和护理产生直接的转化影响。 然而，在使用RWD研究AD方面存在一些关键的研究和方法上的差距， SGM，包括缺乏（1）经验证的可计算表型（CP）和自然语言处理（NLP） 能够准确定义SGM人群并提取关键患者特征和结局的工具 (e.g.,莫卡评分以确定严重程度），（2）考虑AD的异质性及其进展 途径，以及（3）考虑SGM人群中的AD差异，特别是基于个体- 和上下文级SDoH。响应NOT-AG-21-050，我们建议分析大量RWD 在OneFlorida+和INSIGHT网络中，两个CRN为国家以患者为中心的临床 研究网络（PCORnet），以：（1）创建可跟踪的真实世界纵向SGM和AD队列 通过常规临床护理，（2）使用新型联邦机器对AD进展中的异质性进行建模 学习方法，和（3）检查AD结果中的SGM差异（即，发病和进展途径） 以及AD临床风险因素和SDoH影响这些AD结局的因果路径。我们的项目是 新颖，将产生直接的翻译影响，因为它提供了具体的RWE，以填补知识空白， 检查SGM（和SGM亚组）和非SGM之间是否存在AD差异，并确定 潜在的可采取行动的AD风险因素和SDoH对SGM及其差异有重要意义。的成功 该项目将填补我们在SGM人群中AD风险和进展途径知识的重要空白， 并建立一个框架，用于创建基于RWD的虚拟队列，这可以为国家实用试验提供信息 用于未来的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.