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

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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.