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: 10516409
• 负责人: Bing Si
• 金额: $ 12.85万
• 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/10516409
• 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; 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; base; cardiovascular disorder risk; cardiovascular risk factor; caucasian American; clinical application; clinical phenotype; clinical practice; cohort; comorbidity; cost; demographics; design; epidemiology study; improved; inclusion criteria; indexing; individual patient; insight; men; middle age; mortality; novel; outcome prediction; patient stratification; phenotypic data; pre-clinical; prognostication; prospective; risk stratification; secondary analysis; sociodemographics; targeted treatment; treatment planning; treatment risk; 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.

项目概要/摘要 阻塞性睡眠呼吸暂停 (OSA) 非常普遍，并且与一系列心血管疾病相关 (CV) 疾病和不良健康结果 然而，OSA 治疗策略往往表现出不一致。 个体间的治疗效果，并且对心血管疾病、事件或死亡的风险几乎没有或没有降低。 表型发现对于 OSA 的精确风险分层和针对性治疗至关重要。 OSA 患者之间的异质性可能是导致临床试验结果不理想的一个重要因素。 因此，描绘 OSA 异质性并将患者分为高风险组和低风险组（即， “表型”）与精确风险分层和针对性治疗的明显不同结果相关。 OSA 数据对于促进 OSA 表型发现有着巨大的希望：(1) 我们。 等人在 OSA 数据中发现了与一种或多种不良心血管相关的新预后因素 (2) 新兴 OSA 表型由机器学习和聚类算法定义。 (3) 新发现的 OSA 表型，可预测患者从 OSA 中获益。 治疗和不良心血管结果的风险，为 OSA 表型在靶向治疗中的临床应用奠定了基础 然而，在充分利用 OSA 数据进行治疗和精确预后方面存在重大差距。 表型发现：缺乏“结果预测”、“临床可解释”和“可重复” 表型，根据大量不同美国人群的多域 OSA 数据定义。 为了解决这些差距，我们提出了一项二次多项研究分析，旨在开发新的 通过整合与 OSA 相关的多域睡眠常见问题，制定分类标准并识别 OSA 的表型 数据元素，包括但不限于患者的社会人口统计、健康习惯、病史、 人体测量学、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 NIH 资助的国家睡眠研究资源 (NSRR)，其中包括西班牙裔睡眠心脏健康研究。 社区健康研究和动脉粥样硬化多种族研究，至少有 5,336 名 OSA 患者来自 非洲裔美国人、白人、西班牙裔和亚裔美国人的不同人群。 目标 1：针对 OSA 表型开发一种新颖的稀疏、结果预测多域因子混合模型 从多域混合类型患者的临床前特征和临床特征中进行识别目标 2：应用 在目标 1 中针对单独和汇总的 NSRR 数据集开发了模型，以：(1) 识别、表征和验证 OSA 表型；(2) 评估个体和汇总结果支持的一致性和可重复性 影响：我们将识别、表征和验证有助于同情的 OSA 表型。 确定治疗计划的积极程度并协助研究人员选择合适的治疗方案 患者参加 OSA 治疗的临床试验，最终实现 OSA 的精确预后和治疗。