Towards Precise Phenotype Discovery of Obstructive Sleep Apnea with a Data-Inclusive Multi-Study Analysis Using the National Sleep Research Resource (NSRR)

使用国家睡眠研究资源 (NSRR) 通过包含数据的多项研究分析来精确发现阻塞性睡眠呼吸暂停的表型

• 批准号: 10675011
• 负责人: Bing Si
• 金额: $ 11.43万
• 依托单位: STATE UNIVERSITY OF NY,BINGHAMTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10675011
• 关键词: Address; Affect; African American population; Algorithms; Apnea; Asian Americans; Benefits and Risks; Black American; Black Populations; Cardiac health; Cardiovascular Diseases; Cardiovascular system; Categories; Cessation of life; Classification; Clinical; Clinical Research; Clinical Treatment; Clinical Trials; Cohort Studies; Collection; Common Data Element; Data; Data Element; Data Set; Deposition; Drowsiness; Enrollment; Event; Foundations; Funding; Goals; Habits; Health; Heterogeneity; Hispanic Americans; Hispanic Community Health Study; Individual; Machine Learning; Maps; Measures; Medical History; Modeling; Multi-Ethnic Study of Atherosclerosis; Obstructive Sleep Apnea; Outcome; Patient-Focused Outcomes; Patients; Phenotype; Polysomnography; Population; Population Heterogeneity; Prognosis; Prognostic Factor; Quality of life; Reproducibility; Research; Research Personnel; Resources; Risk; Severities; Sleep; Sleep Apnea Syndromes; Stratification; Testing; Treatment Efficacy; United States National Institutes of Health; Woman; cardiovascular disorder risk; cardiovascular risk factor; caucasian American; clinical application; clinical phenotype; clinical practice; cohort; community health study; comorbidity; cost; demographics; design; empowerment; epidemiology study; improved; inclusion criteria; indexing; individual patient; insight; men; middle age; mortality; novel; outcome prediction; patient stratification; phenotypic data; pre-clinical; predict clinical outcome; prognostication; prospective; risk stratification; secondary analysis; sociodemographics; targeted treatment; treatment planning; treatment strategy; validation studies

Project Summary/Abstract Obstructive sleep apnea (OSA) is highly prevalent and associated with a spectrum of cardiovascular (CV) diseases and adverse health outcomes. However, OSA treatment strategies tend to show inconsistent treatment efficacy across individuals and little or no reduction in risk of CV diseases, events, or death. Phenotype discovery is critical for precise risk stratification and targeted treatment of OSA. Substantial heterogeneity among OSA patients is likely an important contributor to the suboptimal results of clinical trials. Thus, it is critical to delineate the OSA heterogeneity and stratify patients into high-vs low-risk clusters (i.e., “phenotypes”) associated with markedly different outcomes for precise risk stratification and targeted treatment. OSA data hold great promise to facilitate OSA phenotype discovery. Rigor of Prior Research: (1) We and others identified new prognostic factors in OSA data that are associated with one or more adverse CV outcomes. (2) Emerging OSA phenotypes were defined by machine learning and clustering algorithms from multi-faceted OSA data. (3) Newly identified OSA phenotypes, predictive of patients’ benefit from OSA treatments and risk for adverse CV outcomes, laid the foundation for OSA phenotypes’ clinical utility in targeted treatment and precise prognosis. However, significant gaps exist in fully leveraging the OSA data for phenotype discovery: There is a lack of “outcome-predictive”, “clinically-interpretable”, and “reproducible” phenotypes, defined from multi-domain OSA data in a large diverse U.S. population. To address these gaps, we propose a secondary multi-study analysis that seeks to develop new classification criteria and identify phenotypes in OSA by integrating multi-domain OSA-related sleep common data elements, including but not limited to patient socio-demographics, health habits, medical history, anthropometrics, polysomnography measures, daytime sleepiness, quality of life, and cardiovascular comorbidities and mortalities, combined across three of the largest epidemiological study cohorts deposited in the NIH-funded National Sleep Research Resource (NSRR). This includes Sleep Heart Health Study, Hispanic Community Health Study, and Multi-Ethnic Study of Atherosclerosis, with at least 5,336 OSA patients from a diverse population of African American, Caucasian, Hispanic, and Asian American men and women. Aim 1: Develop a novel sparse, outcome-predictive multi-domain Factor Mixture Model for OSA phenotype identification from multi-domain mixed-typed patient pre-clinical features and clinical features. Aim 2: Apply the developed model in Aim 1 to individual and pooled NSRR datasets to: (1) identify, characterize, and validate OSA phenotypes; (2) evaluate consistency and reproducibility in findings supported by individual and pooled analyses. Impact: We will identify, characterize, and validate OSA phenotypes that assist clinicians with determining how aggressive to be with the treatment plans and assist researchers with selecting appropriate patients to enroll in clinical trials of OSA treatment, eventually leading to precise prognosis and treatment of OSA.

项目总结/摘要 阻塞性睡眠呼吸暂停（OSA）是一种非常普遍的疾病，与一系列心血管疾病有关。 (CV)疾病和不良健康后果。然而，OSA的治疗策略往往表现出不一致的 个体间的治疗疗效，CV疾病、事件或死亡风险几乎没有或没有降低。 表型发现对于OSA的精确风险分层和靶向治疗至关重要。实质性 OSA患者之间的异质性可能是临床试验的次优结果的重要因素。 因此，描述阻塞性睡眠呼吸暂停综合征的异质性并将患者分层为高风险和低风险集群（即， 表型）与精确风险分层和靶向治疗的显著不同结果相关。 OSA数据对促进OSA表型发现具有很大的希望。先前研究的严谨性：（1）我们 其他人在OSA数据中发现了与一种或多种不良CV相关的新预后因素 结果。(2)新出现的OSA表型是通过机器学习和聚类算法定义的， 多方面的OSA数据。(3)新发现的OSA表型，可预测患者从OSA中获益 治疗和不良CV结局的风险，为OSA表型在靶向治疗中的临床应用奠定了基础。 治疗和准确的预后。然而，在充分利用OSA数据以 表型发现：缺乏“结果预测性”、“临床可解释性”和“可重现性” 表型，定义从多域OSA数据在一个大的不同的美国人口。 为了解决这些差距，我们提出了一个二级多研究分析，旨在开发新的 分类标准和确定表型在OSA通过整合多域OSA相关的睡眠共同 数据元素，包括但不限于患者社会人口统计学、健康习惯、病史， 人体测量、多导睡眠图测量、日间嗜睡、生活质量和心血管 合并症和死亡率，合并了三个最大的流行病学研究队列 国家睡眠研究资源（NSRR）这包括睡眠心脏健康研究，西班牙裔 社区健康研究和动脉粥样硬化的多种族研究，至少有5，336名OSA患者， 非裔美国人、高加索人、西班牙裔和亚裔美国人男性和女性的多元化人口。 目的1：建立一种新的稀疏的、可预测结果的OSA表型多域因子混合模型 从多领域混合型患者临床前特征和临床特征中识别。目标2：应用 在目标1中开发了针对单个和合并NSRR数据集的模型，以：（1）识别、表征和验证 OSA表型;（2）评价个体和汇总支持的结果的一致性和重现性 分析。影响：我们将识别，表征和验证OSA表型，帮助临床医生 确定如何积极与治疗计划，并协助研究人员选择适当的 患者参加OSA治疗的临床试验，最终导致OSA的精确预后和治疗。