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

性少数群体中阿尔茨海默病进展的差异

• 批准号: 10590413
• 负责人: Jiang Bian
• 金额: $ 80.96万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590413
• 关键词: Affect; Aging; Alabama; Algorithms; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s Disease Pathway; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Alzheimer' s disease risk; American; Caring; Cause of Death; Characteristics; Clinical; Clinical Medicine; Clinical Research; Cognitive; Collection; Consumption; Data; Data Science; Degenerative Disorder; Dementia; Disease Outcome; Disease Progression; Disparity; Electronic Health Record; Face; Florida; Future; Goals; Grain; Health; Heterogeneity; Individual; Knowledge; Machine Learning; Mediation; Methodology; Methods; Modeling; Natural Language Processing; New York; Onset of illness; Outcome; Pathway interactions; Patients; Pattern; Phenotype; Population; Prevention; Proliferating; Research; Research Priority; Risk Factors; Severities; Severity of illness; Sex Differences; Sexual and Gender Minorities; Standardization; Structure; Subgroup; Syndrome; Testing; Time; age related; billing data; clinical care; clinical risk; cohort; computable phenotypes; cost; disease disparity; ethnic minority; federated learning; gender difference; gender minority group; gender minority health; machine learning method; mild cognitive impairment; minority disparity; neighborhood safety; novel; patient oriented; phenotyping algorithm; pragmatic trial; racial minority; risk stratification; rural underserved; social cohesion; social health determinants; socioeconomic disadvantage; success; tool; translational impact; virtual

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.

抽象的 性少数群体（SGM）面临独特的健康问题，但关于 SGM 健康的研究很少。 特别是，SGM 个体中有关阿尔茨海默氏症等年龄相关疾病的数据有限 疾病（AD）和相关的痴呆症是一种致命的退行性疾病，具有多种危险因素， 从临床特征到健康的社会决定因素 (SDoH)，AD 患者通常会从以下方面发展而来。 认知能力未受损到（可能）轻度认知障碍（MCI），随后严重程度增加 然而，有证据表明，AD 临床综合征中的痴呆症存在异质性。 通过多个中间阶段进展为 AD 表征不同的 AD 进展途径。 另一方面，相关的风险因素对于风险分层和预防至关重要。 具有真实世界数据 (RWD) 的大型临床研究网络 (CRN) 的激增，包括电子医疗 记录 (EHR)、索赔和计费数据等，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 方面存在许多关键研究和方法上的差距 SGM，包括缺乏 (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，MoCA 评分来确定严重程度），(2) 考虑 AD 的异质性及其进展 (3) 考虑 SGM 人群中的 AD 差异，特别是在个人- 和上下文级别的 SDoH 响应 NOT-AG-21-050，我们建议分析大量 RWD 集合。 在 OneFlorida+ 和 INSIGHT 网络中，两个 CRN 为国家以患者为中心的临床做出了贡献 研究网络 (PCORnet)，旨在：(1) 创建可跟踪的真实世界纵向 SGM 和 AD 队列 凭借常规临床护理，(2) 使用新型联合机器对 AD 进展的异质性进行建模 学习方法，以及 (3) 检查 SGM 在 AD 结果方面的差异（即发病和进展途径） 以及 AD 临床风险因素和 SDoH 影响这些 AD 结果的因果路径。 新颖，并将产生直接的转化影响，因为它提供了具体的 RWE 来填补知识空白 检查 SGM（和 SGM 亚组）与非 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 及其差异具有重要意义。 该项目将填补我们对 SGM 人群 AD 风险和进展途径知识的重要空白， 建立一个框架来创建基于 RWD 的虚拟队列，为国家实用试验提供信息 跨 PCORnet 用于未来 SGM 衰老临床研究。