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

摘要 尽管越来越多的研究表明，性和性别的独特健康问题 少数民族（SGM）的个人面临，以前的研究SGM的健康仍然有限，主要集中在 精神健康、药物使用和滥用以及性传播感染和疾病。特别是， SGM研究已经检查了与年龄相关的慢性疾病，如癌症和阿尔茨海默病（AD）， 分别是美国人的第二和第六大死因。尽管两者都与衰老有关， 癌症和AD并不经常同时发生;然而，它们有许多共同的风险因素，例如 肥胖，尤其是健康的社会和行为决定因素（SDoH和BDoH），在人群中更为普遍 SGMs。考虑到SGM与非SGM相比具有不同的健康风险特征，我们必须 研究这两个群体在癌症和AD风险方面的差异以及早期癌症的相关风险因素。 预防和更好的临床诊断。大型临床研究网络（CRN）的激增， 真实世界数据（RWD）包括电子健康记录（EHR）、索赔和账单数据等， 产生真实世界证据（RWE）的独特机会，将对 SGM人群的癌症和AD预防和护理。 在我们的父项目R21 CA 245858 - 01 A1中，我们提出：（1）开发可计算表型（CP） 算法来识别跨性别和性别歧视（TGNC）个体（SGM的一个亚组）， 来自OneFlorida的RWD-一个覆盖1500万佛罗里达人的大型临床数据网络， NLP管道提取癌症相关风险因素，以及（2）进行基于人群的队列分析， 估计和比较TGNC和非TGNC个体之间的癌症发病率和癌症风险因素。 在这份行政补充文件中，我们建议（1）扩大《TGNC CP》的适用范围，以包括性取向 (e.g.,女同性恋，男同性恋，双性恋等），和（2）进行回顾性研究，以检查是否SGMs 和非SGM分别在癌症和AD风险方面不同。我们的目标是：（1）开发表型分析算法 准确识别SGM个体并提取与癌症和AD相关的风险因素， 结构化和非结构化的EHR数据;（2）估计和比较癌症和AD的发病率和风险 SGM与非SGM个体的因素。这个管理补充将（1）填补空白，因为没有人口为基础的 存在关于SGM癌症和AD风险的研究;（2）创建一个可以纵向跟踪的大型SGM队列 凭借常规护理，以及（3）填补了我们对SGM风险和风险因素的理解的关键空白， 癌症和AD的交叉点-为我们未来确定适当预防的研究奠定基础 交叉癌症和AD的策略（例如，某些癌症筛查是否足以用于早期 认知障碍的迹象和AD的高风险）。

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).