Development of novel polysomnography-based digital biomarkers to predict Alzheimer’s disease and Parkinson’s disease in real world settings

开发基于多导睡眠图的新型数字生物标志物，以预测现实世界中的阿尔茨海默病和帕金森病

• 批准号: 10807908
• 负责人: Yue Leng
• 金额: $ 46.92万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10807908
• 关键词: Address; Affect; Alzheimer disease detection; Alzheimer disease prevention; Alzheimer disease screening; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease model; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Artificial Intelligence; Asian; Biological Markers; Black race; Blood; Brain; Breathing; Classification; Clinical; Clinical Trials; Communities; Complex; Data; Dementia; Development; Device or Instrument Development; Devices; Diagnosis; Digital biomarker; Dimensions; Disease; Disease Progression; Early Diagnosis; Early Intervention; Economic Burden; Elderly; Electrocardiogram; Electroencephalography; Epidemiology; Foundations; Future; Goals; Health Benefit; Heart; High Prevalence; Hispanic; Home; Individual; Intervention; Knowledge; Link; Longitudinal cohort; Measures; Modality; Modeling; Monitor; Muscle; Nerve Degeneration; Neurodegenerative Disorders; Parkinson Disease; Participant; Pathogenesis; Pattern; Performance; Persons; Phase; Physiological; Polysomnography; Population; Positioning Attribute; Predictive Value; Prevention; Public Health; REM Sleep Behavior Disorder; Reporting; Research; Risk Factors; Scientist; Sensitivity and Specificity; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Architecture; Sleep Disorders; Sleep disturbances; Structure; Time; United States National Institutes of Health; Work; aged; artificial intelligence method; brain health; clinical diagnosis; cohort; community setting; computational neuroscience; cost; cost effective; data resource; digital; disease phenotype; disorder risk; experience; follow-up; high risk; high risk population; improved; innovation; men; monitoring device; multidisciplinary; multimodality; novel; osteoporosis with pathological fracture; prediction algorithm; predictive marker; predictive modeling; respiratory; risk prediction; screening; standard measure; targeted treatment; user-friendly

PROJECT SUMMARY/ ABSTRACT One of the greatest unmet challenges in the management of neurodegenerative diseases is the early diagnosis of Alzheimer's disease and related dementias (ADRD) and Parkinson's disease (PD). Given their high prevalence, long prodromal period, and lack of disease-modifying therapies, early detection of ADRD and PD is of critical importance. Facilitated by recent advances in artificial intelligence (AI) methods, this proposal will break new ground by developing novel data-driven screening biomarkers for ADRD and PD, using multimodal, multidimensional, real-time polysomnography (PSG) sleep signals. Despite the growing evidence that suggests a bi-directional relationship between sleep and ADRD/PD, little is known about the utility of the multimodal PSG sleep signals [e.g., electroencephalogram (EEG) for the brain, electrocardiogram (ECG) for the heart, electromyogram (EMG) for the muscle, and respiratory flow and effort for breathing] for identifying future ADRD and PD cases. As a multidisciplinary team with strong preliminary data and extensive experiences in research of sleep and neurodegeneration, we are uniquely positioned to address this gap. The goal of this proposal is to use data-driven AI approaches to generate cost-effective and user-friendly PSG-based digital biomarkers for the prediction of ADRD and PD in clinical and at-home settings. Our hypothesis is that PSG sleep signals could be used to develop prediction algorithms that identify ADRD and PD, years before clinical diagnoses, and that the prediction algorithms can generalize from clinical to community settings. We have an unprecedented opportunity to leverage data from three NIH-supported multicenter longitudinal cohorts: a diverse clinical sleep cohort, the Complete AI Sleep Report (CAISR) study, consisting of over 70K subjects aged 50 years and older with 15 years of follow-up, and two community-based cohorts, the Osteoporotic Fractures in Men (MrOS) Sleep Study and the Study of Osteoporotic Fractures (SOF), with over 3500 community-dwelling older adults followed for up to 13 years. Using state-of-the-art AI models, we will pursue two specific aims: 1) discover PSG biomarkers that identify current and future diagnoses of ADRD and PD in clinical settings; and 2) validate the performance and generalizability of the PSG biomarkers for detecting ADRD and PD using in-home PSG in community settings. This will be the first study to create cost-effective, non-invasive PSG-based screening biomarkers for identifying ADRD and PD in real-world settings. This will set the foundation for further studying whether non-invasive PSG biomarkers are predictive of ADRD/PD pathogenesis and may be integrated with other innovative biomarkers for improved characterization of ADRD and PD phenotypes. By identifying specific PSG modalities with the best predictive value, this work will directly inform the development of new user-friendly devices for long-term monitoring of sleep biomarkers and ADRD/PD risk in home settings. This offers a transformative public health impact, as screening ADRD and PD through daily sleep monitoring is scalable and will identify high-risk individuals for early diagnosis and early intervention.

项目概要/摘要 神经退行性疾病治疗中尚未解决的最大挑战之一是早期诊断 阿尔茨海默病及相关痴呆症（ADRD）和帕金森病（PD）。鉴于他们的高 由于 ADRD 和 PD 的患病率较高、前驱期较长且缺乏疾病缓解疗法，因此早期发现 ADRD 和 PD 至关重要。在人工智能（AI）方法的最新进展的推动下，该提案将打破 通过使用多模式开发新的数据驱动的 ADRD 和 PD 筛选生物标志物， 多维实时多导睡眠图 (PSG) 睡眠信号。尽管越来越多的证据表明 睡眠与 ADRD/PD 之间存在双向关系，但对于多模式 PSG 的实用性知之甚少 睡眠信号[例如，大脑的脑电图 (EEG)、心脏的心电图 (ECG)、 肌肉肌电图 (EMG)、呼吸流量和呼吸努力] 用于识别未来的 ADRD 和PD病例。作为一个多学科团队，拥有强大的前期数据和丰富的研究经验 在睡眠和神经退行性疾病方面，我们具有独特的优势来解决这一差距。该提案的目标是 使用数据驱动的人工智能方法来生成具有成本效益且用户友好的基于 PSG 的数字生物标记物 在临床和家庭环境中预测 ADRD 和 PD。我们的假设是 PSG 睡眠信号可能是 用于开发预测算法，在临床诊断前数年识别 ADRD 和 PD，并且 预测算法可以从临床环境推广到社区环境。我们有一个前所未有的机会 利用来自 NIH 支持的三个多中心纵向队列的数据：多样化的临床睡眠队列、 完整的人工智能睡眠报告 (CAISR) 研究，由超过 70,000 名 50 岁及以上 15 岁受试者组成 随访，以及两个基于社区的队列，男性骨质疏松性骨折 (MrOS) 睡眠研究和 骨质疏松性骨折 (SOF) 研究对 3500 多名社区居住的老年人进行了长达 13 次的随访 年。使用最先进的人工智能模型，我们将追求两个具体目标：1）发现 PSG 生物标志物 确定临床环境中 ADRD 和 PD 当前和未来的诊断； 2）验证性能和 PSG 生物标志物在社区环境中使用家庭 PSG 检测 ADRD 和 PD 的普遍性。 这将是第一项创建具有成本效益、非侵入性的基于 PSG 的筛选生物标志物的研究，用于识别 现实环境中的 ADRD 和 PD。这将为进一步研究非侵入性PSG是否有效奠定了基础。 生物标志物可预测 ADRD/PD 发病机制，并可与其他创新生物标志物整合 改善 ADRD 和 PD 表型的表征。通过确定最佳的具体 PSG 模式 预测价值，这项工作将直接为新的用户友好设备的长期开发提供信息 监测家庭环境中的睡眠生物标志物和 ADRD/PD 风险。这提供了变革性的公共卫生 影响，因为通过日常睡眠监测筛查 ADRD 和 PD 是可扩展的，并且可以识别高风险 个人进行早期诊断和早期干预。