Huang Yu-Jyun, Kurniansyah Nuzulul, Levey Daniel F, Gelernter Joel, Huffman Jennifer E, Cho Kelly, Wilson Peter W F, Gottlieb Daniel J, Rice Kenneth M, Sofer Tamar
Department of Medicine, Harvard Medical School, Boston, MA, USA.
CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA.
medRxiv. 2025 Jan 2:2025.01.02.25319889. doi: 10.1101/2025.01.02.25319889.
Strong sex differences exist in sleep phenotypes and also cardiovascular diseases (CVDs). However, sex-specific causal effects of sleep phenotypes on CVD-related outcomes have not been thoroughly examined. Mendelian randomization (MR) analysis is a useful approach for estimating the causal effect of a risk factor on an outcome of interest when interventional studies are not available. We first conducted sex-specific genome-wide association studies (GWASs) for suboptimal-sleep phenotypes (insomnia, obstructive sleep apnea (OSA), short and long sleep durations, and excessive daytime sleepiness) utilizing the Million Veteran Program (MVP) dataset. We then developed a semi-empirical Bayesian framework that (i) calibrates variant-phenotype effect estimates by leveraging information across sex groups, and (ii) applies shrinkage sex-specific effect estimates in MR analysis, to alleviate weak instrumental bias when sex groups are analyzed in isolation. Simulation studies demonstrate that the causal effect estimates derived from our framework are substantially more efficient than those obtained through conventional methods. We estimated the causal effects of sleep phenotypes on CVD-related outcomes using sex-specific GWAS data from the MVP and All of Us. Significant sex differences in causal effects were observed, particularly between OSA and chronic kidney disease, as well as long sleep duration on several CVD-related outcomes. By applying shrinkage estimates for instrumental variable selection, we identified multiple sex-specific significant causal relationships between OSA and CVD-related phenotypes.
睡眠表型以及心血管疾病（CVD）中存在明显的性别差异。然而，睡眠表型对CVD相关结局的性别特异性因果效应尚未得到充分研究。当无法进行干预性研究时，孟德尔随机化（MR）分析是估计风险因素对感兴趣结局因果效应的一种有用方法。我们首先利用百万退伍军人计划（MVP）数据集，针对次优睡眠表型（失眠、阻塞性睡眠呼吸暂停（OSA）、短睡眠时长和长睡眠时长以及日间过度嗜睡）开展了性别特异性全基因组关联研究（GWAS）。然后，我们开发了一个半经验贝叶斯框架，该框架（i）通过利用跨性别群体的信息校准变异-表型效应估计值，以及（ii）在MR分析中应用收缩的性别特异性效应估计值，以减轻单独分析性别群体时的弱工具偏倚。模拟研究表明，我们框架得出的因果效应估计值比通过传统方法获得的估计值效率大幅提高。我们使用来自MVP和“我们所有人”计划的性别特异性GWAS数据，估计了睡眠表型对CVD相关结局的因果效应。观察到因果效应存在显著的性别差异，特别是在OSA与慢性肾病之间，以及长睡眠时长对多个CVD相关结局的影响方面。通过对工具变量选择应用收缩估计，我们确定了OSA与CVD相关表型之间多个性别特异性的显著因果关系。
