Machine Learning and Exosome Derived Biomarkers of Obstructive Sleep Apnea Induced Hypertension

机器学习和外泌体衍生的阻塞性睡眠呼吸暂停诱发高血压的生物标志物

• 批准号: 10683802
• 负责人: Bharati Prasad
• 金额: $ 67.69万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10683802
• 关键词: Acute; Affect; Aldosterone; Angiotensin II; Angiotensins; Apnea; Area Under Curve; Bioinformatics; Biological Assay; Biological Markers; Biology; Biometry; Blood; Blood Pressure; Blood Pressure Monitors; Blood specimen; C-reactive protein; CD69 antigen; Cardiovascular Diseases; Case-Control Studies; Catecholamines; Cell Hypoxia; Cells; Characteristics; Chronic; Clinical; Clinical Research; Communication; Constitution; Data; Data Analyses; Diastolic blood pressure; Enrollment; Fasting; Flow Cytometry; Future; Gene Expression; Genomics; Gold; Hour; Human Resources; Hypertension; Hypoxia; Image; Individual; Inflammation; Intervention Studies; Isoprostanes; Lead; Lipids; Machine Learning; Malondialdehyde; Mass Spectrum Analysis; Measurement; Measures; Mediating; MicroRNAs; Molecular; Neural Network Simulation; Newly Diagnosed; Nocturnal Hypertension; Obstructive Sleep Apnea; Outcome; Oxidative Stress; Pathway interactions; Patients; Performance; Phenotype; Physiological; Physiology; Pilot Projects; Plasma; Polymerase Chain Reaction; Polysomnography; Prognostic Marker; Proteins; Proteomics; Regulator Genes; Renin; Renin-Angiotensin-Aldosterone System; Reporting; Research; Research Personnel; Rest; Reverse Transcription; Sampling; Series; Signal Transduction; Sleep; Sleep Fragmentations; Source; Stream; Surface; TNF gene; Testing; Time; Training; Urine; Validation; Vesicle; Western Blotting; awake; biomarker development; biomarker panel; blood pressure control; blood pressure elevation; cardiovascular disorder risk; case control; clinical application; clinical practice; clinically significant; comparative effectiveness; comparative treatment; deep neural network; differential expression; digital; exosome; genomic biomarker; high risk; improved; indexing; instrumentation; intercellular communication; lipidomics; long short term memory; long short term memory network; mRNA sequencing; machine learning method; molecular marker; multidisciplinary; novel; personalized management; point of care; prevent; prospective; screening; support tools; targeted treatment; urinary

Obstructive sleep apnea (OSA) can lead to hypertension via chronic intermittent hypoxia and sleep fragmentation. There are no biomarkers of OSA induced hypertension to target blood pressure (BP) control with OSA treatments and prevent cardiovascular disease. The objective of this proposal is to develop prognostic biomarkers of OSA induced hypertension. We will conduct a prospective case-control study in patients with newly diagnosed moderate to severe OSA (cases) or no OSA (controls) to achieve this objective. We will define OSA induced hypertension by characteristically abnormal BP profiles during sleep: post-apnea acute surges in BP, non-dipping BP, and nocturnal hypertension. Our proof-of-concept study used machine learning methods to devise a novel type of Long-Short Term Memory (LSTM) Encoder- Decoder network to predict BP during sleep using physiological signals from polysomnography. The first aim of this proposal is to train and validate an LSTM network to predict BP profiles during sleep from clinical polysomnography signals compared to non-invasive beat-to-beat BP measurements (Finapres Nova) as reference. An LSTM network that predicts nocturnal BP profiles from polysomnography will provide a clinically applicable digital biomarker for OSA induced hypertension. Our pilot study showed that a small cluster of exosome micro RNAs (miRNA), implicated in cardiovascular disease, are differentially expressed in OSA patients with non-dipping BP. Exosome cargo is specifically sorted, expressed stably in individuals, and functions as an essential vehicle of targeted inter-cellular genomic communication. We will build on this study with the second aim to identify genomic biomarkers of OSA induced hypertension in exosome cargo by miRNA and mRNA sequencing, proteomics, and lipidomics, followed by bioinformatics for differential expression between cases and controls. The small studies examining physiological pathways that mediate OSA induced hypertension have not systematically included multiple dysregulated pathways: sympathetic and renin-angiotensin-aldosterone activation, oxidative stress, and inflammation. Our third aim is to develop a parsimonious and robust biomarker panel by combining molecular assays of physiological dysregulation (blood renin, angiotensin II, aldosterone, high sensitivity C-reactive protein, tumor necrosis factor-α, malondialdehyde, and urine catecholamines and isoprostane) and exosome- derived omics biomarkers with a retrained LSTM network. The optimal biomarker panel will be determined by network performance and clinical applicability. This proposal includes a multidisciplinary team of investigators with expertise in clinical OSA research, machine learning, exosome biology and omics, biostatistics, and bioinformatics. This study will have a high clinical impact by providing accessible prognostic biomarkers of OSA induced hypertension to guide pivotal management decisions and future interventional research in OSA to reduce cardiovascular disease.

阻塞性睡眠呼吸暂停（OSA）可通过慢性间歇性缺氧和睡眠导致高血压 分散。没有OSA诱导高血压的生物标志物对靶血压（BP）对照 使用OSA治疗并预防心血管疾病。该提议的目的是发展 OSA的预后生物标志物诱导高血压。我们将在 新诊断为中度至重度OSA（病例）或没有OSA（对照组）的患者以实现此目的 客观的。我们将通过在睡眠期间通过特征异常的BP特征来定义OSA诱导的高血压： BP，非浸泡BP和夜间高血压中的通知后急性潮流。我们的概念证明研究 使用的机器学习方法来设计一种新型的长期术语记忆（LSTM）编码类型 解码器网络使用来自多个多摄影的物理信号在睡眠过程中预测BP。第一个 该提案的目的是培训和验证LSTM网络以预测临床睡眠期间的BP轮廓 与非侵入性Beat-Beat BP测量（Finapres Nova）相比，多性仪信号 参考。一个预测夜间BP概况的LSTM网络将提供一个 OSA诱导高血压的临床适用数字生物标志物。我们的试点研究表明，一个很小的 在心血管疾病中暗示的外泌体微RNA（miRNA）的簇以不同的表达 在非浸泡BP的OSA患者中。外部货物是专门分类的，在个体中稳定表达， 并充当靶向细胞间基因组通信的必不可少的工具。我们将基础 这项研究的第二个目的是鉴定OSA诱导外泌体高血压的基因组生物标志物 miRNA和mRNA测序，蛋白质组学和脂肪组学的货物，然后是生物信息学 病例和对照之间的差异表达。研究物理途径的小型研究 介导OSA诱导的高血压并未系统地包括多个失调途径： 交感神经和肾素 - 血管紧张素 - 醛固酮激活，氧化应激和炎症。我们的第三个 目的是通过结合分子测定 生理失调（血肾素，血管紧张素II，醛固酮，高灵敏度C反应蛋白， 肿瘤坏死因子-α，丙二醛，尿液儿茶酚胺和异丙烷）和外泌体 - 带有再训练LSTM网络的衍生的Omics生物标志物。最佳生物标志物面板将确定 通过网络性能和临床适用性。该建议包括一个多学科团队 具有临床OSA研究，机器学习，外泌体生物学和OMICS专业知识的研究人员， 生物统计学和生物信息学。这项研究将通过提供可访问的临床影响很高 OSA的预后生物标志物引起的高血压指导关键管理决策和未来 OSA的介入研究以减少心血管疾病。