Phenotypic and Molecular Signatures for Sleep Apnea and Related Morbidities

我在睡眠医学流行病学研究方面有超过25年的经验， 在关于遗传、社会和环境风险因素对睡眠障碍的影响的研究中， 睡眠对儿童和成人健康结果的影响，以及睡眠干预在改善睡眠中的作用 健康成果。我的合作者，学员和我已经确定，睡眠呼吸暂停（SA）是非常普遍的， 不成比例地影响亚洲和非洲裔美国儿童，并与显着增加 高血压、中风、心力衰竭、糖尿病和行为问题的风险。我们也有 通过性别、种族/民族、年龄和遗传背景确定这些结果的变异性。我们有 特征的遗传模式的几个SDB性状，并通过使用家庭为基础的和队列 研究（> 20，000人）已经确定了基因组范围内的遗传变异的显着关联， 生物学候选基因，以及性别和睡眠阶段特异性分析提供了对机制的深入了解 这可以解释SA严重程度的已知性别和REM/NREM差异。然而，尽管取得了这些进展， SA潜在的分子和生理机制还不清楚，这限制了我们的能力， 预测哪些SA患者最容易受到不良健康结果的影响， 反映SDB病理生理学个体差异的治疗。我们的新数据表明， 通过系统分析更大的多导睡眠图数据集， 反映特定睡眠和呼吸模式的精确SDB表型，并将这些表型与 基因组和临床数据。通过在多个国家联盟和多中心研究中的领导地位， 准备在了解睡眠呼吸暂停的表型变异性和遗传学方面取得变革性进展 和相关的特征。我们计划利用大量的数据，包括国家睡眠研究中的数据， 资源和遗传，基因组和临床数据可通过几个财团，包括跨- Omics in Precision Medicine和Partners HealthCare Biobank。我们将扩大我们的遗传学/流行病学 与复杂的呼吸表型的领导者团队，开发一个多学科的计划， 系统地提取SA表型的定量指标，并将其与遗传学、基因组学、特异性 治疗反应性以及心血管、代谢和认知结果。通过与 功能基因组学实验室，我们将帮助确定功能性遗传变异，并阐明功能， 与SA相关的基因和途径。我们将使用复杂的统计方法来推导和验证 基于这些数据流的个性化药物预测算法。这种增强的生物 对SA的理解将通过更好的临床试验转化为改善的临床护理。最后， 我们将创造一个环境，培养新的调查人员的发展，配备使用现代 技术和“大数据”来识别疾病易感性和结果的特征。

I have over 25 years of experience in sleep medicine epidemiological research and have played a leading role in studies that address the contributions of genetic, social and environmental risk factors to sleep disorders, the influences of sleep on health outcomes in children and adults, and the role of sleep interventions in improving health outcomes. My collaborators, mentees and I have identified that sleep apnea (SA) is highly prevalent, disproportionately affects Asians and African American children, and is associated with significantly increased risks for developing hypertension, stroke, heart failure, diabetes, and behavioral problems. We also have identified variability in these outcomes by sex, race/ethnicity, age, and genetic background. We have characterized the patterns of heritability for several SDB traits and through use of family-based and cohort studies (>20,000 individuals) have identified genome-wide significant associations for genetic variants in biological candidate genes, and sex- and sleep stage-specific analyses have provided insight into mechanisms that may explain the known sex and REM/NREM differences in SA severity. Despite this progress, however, the underlying molecular and physiological mechanisms for SA are not well understood, limiting both our ability to predict which patients with SA are most vulnerable to adverse health outcomes and our ability to develop treatments that reflect individual differences in SDB pathophysiology. Our emerging data suggest that these gaps may be overcome through systematic analysis of larger sets of polysomnography data, deriving more precise SDB phenotypes that reflect specific sleep and respiratory patterns, and linking these phenotypes to genomic and clinical data. Through leadership in multiple national consortia and multi-center studies we are poised to make transformative advances in understanding the phenotypic variability and genetics of sleep apnea and related traits. We plan to harness a critical mass of data, including those in the National Sleep Research Resource and genetic, genomic and clinical data available through several consortia, including the Trans- Omics in Precision Medicine and Partners HealthCare Biobank. We will expand our genetics/epidemiology team with leaders in sophisticated respiratory phenotyping, developing a multi-disciplinary program that will systematically extract quantitative metrics of SA phenotypes and link these to genetics, genomics, specific treatment responsiveness, and cardiovascular, metabolic and cognitive outcomes. Through collaborations with functional genomics laboratories, we will help identify functional genetic variants and clarify the function of genes and pathways associated with SA. We will use sophisticated statistical methods to derive and validate personalized medicine prediction algorithms based on these data streams. This enhanced biological understanding of SA will be translated into improved clinical care through better-informed clinical trials. Finally, we will create an environment that nurtures the development of new investigators equipped to use modern technologies and “big data” to identify signatures of disease susceptibility and outcomes.