The Role of Sex Hormones in Stroke Risk: A Sex-Specific Integrative Omics Analysis in the NHLBI Trans-Omics for Precision Medicine Cohorts

项目总结 生命过程中中风风险的性别差异已经得到了很好的描述，但我们缺乏关于性别差异是如何发生的 利用这些差异来个性化中风预防，并实现较低的中风相关死亡负担 和残疾。我们关于性激素在中风风险中的作用的拟议工作不仅将改善我们的 中风中性别差异的机械性知识，但将使我们能够识别新的性别特异性靶点 用于中风的预测和预防。我们的首要目标是描述一组小说的特点 与性激素相关的表型、基因组、表观基因组和代谢相关靶点 使用来自NHLBI不同人群的现有流行病学队列的意外卒中 TRANS-OMICS for Precision Medicine(TOPMed)联盟，然后将这些变量与性别- 来自ELSA-Brasil的外部队列中用于验证的特定中风风险评分。我们的脑血管团队， 心血管和遗传流行病学家以及中风临床医生、生物统计学家、性别和性别 科学专家计划利用TOPMed组学联盟内现有的流行病学队列来确定 性激素和中风的潜在机制，并开发特定性别的预测模型。首先，使用 有针对性的基因组方法基于我们小组之前的工作，我们将阐明性激素的作用 (包括性激素结合球蛋白，SHBG)在中风风险的病因中的作用。为了实现这一点，我们将使用 单核苷酸多态(SNPs)和遗传靶点，这是我们之前进行性行为的工作- 性激素水平与发病关系的孟德尔随机分解分析 卒中。其次，在综合组学分析中，我们将阐明性激素相关的生物机制 意外中风风险的途径。使用现有的TOPMed组学数据，我们将识别DNA甲基化和 将激素水平的遗传决定因素联系起来的代谢组谱(单个SNPs和基于激素的 遗传风险分数)和一系列性激素水平对中风风险的生理变化-- 分类调解分析。最后，我们将开发新的、特定于性别的中风预测模型 TOPMed结合了基于性激素的遗传风险评分、性激素水平、特别是 甲基化和代谢组谱，以及确定的临床、生活方式和生殖风险的存在 各种因素。然后，我们将在ELSA参与者的嵌套病例队列辅助研究中验证模型。 巴西。综上所述，我们建议使用现有的前瞻性队列研究和综合组学方法 要了解内源性性激素和中风风险之间的关系，需要迈出的一步 利用生物性行为实现个性化卒中预防。寻找支持生物学的证据 性激素和中风之间关系的潜在机制可能有助于建立因果关系， 改进风险预测，并确定新的目标，以改进、更个性化的中风预防和 改善大脑健康。

PROJECT SUMMARY Sex differences in stroke risk over the life course have been well-described, but we lack the knowledge of how to use such differences to personalize stroke prevention and achieve a lower burden of stroke-related death and disability. Our proposed work on the role of sex hormones in stroke risk will not only improve our mechanistic knowledge of sex differences in stroke but will allow us to identify novel sex-specific targets to be used for stroke prediction and prevention. Our overarching objective is to characterize a set of novel phenotypic, genomic, epigenomic, and metabolomic sex hormone-related targets that are causally linked to incident stroke using existing epidemiologic cohorts from a diverse population of women and men in the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium and to then integrate these variables into sex- specific stroke risk scores for validation in an external cohort from ELSA-Brasil. Our team of cerebrovascular, cardiovascular, and genetic epidemiologists along with stroke clinicians, biostatisticians, and sex and gender science experts plan to use existing epidemiologic cohorts within the TOPMed omics consortium to identify the mechanisms underlying sex hormones and stroke and to develop sex-specific prediction models. First, using a targeted genomic approach based on our group’s prior work, we will elucidate the role of sex hormones (including sex hormone binding globulin, SHBG) in the etiology of stroke risk. To achieve this, we will use single nucleotide polymorphisms (SNPs) and genetic targets informed by our prior work to conduct sex- disaggregated mendelian randomization analyses of the relationship between sex hormone levels and incident stroke. Secondly, in an integrative omics analysis, we will elucidate sex hormone related biologic mechanistic pathways for risk of incident stroke. Using existing TOPMed omics data, we will identify DNA methylation and metabolomic profiles linking genetic determinants of hormone levels (individual SNPs and a hormone-based genetic risk score) and physiologic variations in measured sex hormone levels to stroke risk in a series of sex- disaggregated mediation analyses. Finally, we will develop novel, sex-specific stroke prediction models in TOPMed incorporating sex hormone-based genetic risk scores, sex hormone levels, the presence of particular methylation and metabolomic profiles, and the presence of established clinical, lifestyle, and reproductive risk factors. We will then validate the models in a nested case cohort ancillary study of participants from ELSA- Brasil. In summary, we propose to use existing prospective cohort studies with an integrative omics approach to understand the relationship between endogenous sex hormones and stroke risk, a needed step toward the use of biologic sex to achieve personalized stroke prevention. Finding evidence to support biologic mechanisms underlying the relationship between sex hormones and stroke could help to establish causality, improve risk prediction, and identify novel targets for improved, more personalized, stroke prevention and improved brain health.