Examine the risk of Alzheimer's disease in sexual and gender minorities

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ABSTRACT Although there is an increasing amount of research on the unique health issues that sexual and gender minority (SGM) individuals face, prior studies on SGM health are still limited and have primarily focused on mental health, substance use and abuse, and sexual transmitted infections and diseases. In particular, few SGM studies have examined age-related chronic conditions such as cancer and Alzheimer’s disease (AD), the 2nd and 6th leading causes of death for Americans, respectively. Despite both being associated with aging, cancer and AD do not often occur together; nevertheless, they share a number of common risk factors such as obesity, especially social & behavioral determinants of health (SDoH & BDoH) that are more prevalent among SGMs. Considering that SGMs have different health risk profiles compared to non-SGMs, it is crucial that we examine how these two groups differ in cancer and AD risks and the associated risk factors for early prevention and better clinical prognostication. The proliferation of large clinical research networks (CRNs) of real-world data (RWD) including electronic health records (EHRs), claims, and billing data among others, offer unique opportunities to generate real-world evidence (RWE) that will have direct translational impacts on cancer and AD prevention and care in SGM populations. In our parent award R21 CA245858-01A1, we proposed to: (1) develop computable phenotype (CP) algorithms to identify transgender and gender nonconforming (TGNC) individuals (a subgroup of SGM) using RWD from OneFlorida—a large clinical data network covering 15 millions of Floridians as well as develop a NLP pipeline to extract cancer-related risk factors, and (2) conduct a population-based cohort analysis to estimate and compare cancer incidence and cancer risk factors between TGNC and non-TGNC individuals. In this administrative supplement, we propose to (1) extend the TGNC CP to include sexual orientations (e.g., lesbian, gay, bisexual among others), and (2) conduct a retrospective study to examine whether SGMs and non-SGMs differ in cancer and AD risk, separately. Our aims are to: (1) develop phenotyping algorithms to accurately identify SGM individuals and extract risk factors associated with cancer and AD, leveraging both structured and unstructured EHR data; and (2) estimate and compare the cancer and AD incidences and risk factors in SGM versus non-SGM individuals. This admin supplement will (1) fill a gap as no population-based studies on SGMs’ cancer and AD risks exist; (2) create a large SGM cohort that can be tracked longitudinally by virtue of routine care, and (3) fill a critical gap in our understanding of SGMs’ risks and risk factors at the intersection of cancer and AD—setting the stage for our future studies on identifying the appropriate prevention strategies intersecting cancer and AD (e.g., whether certain cancer screening is adequate for SGMs with early signs of cognitive impairment and a high-risk of AD).