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

阻塞性睡眠呼吸暂停(OSA)可通过慢性间歇性低氧和睡眠导致高血压 碎片化。目前尚无阻塞性睡眠呼吸暂停低密度脂蛋白所致高血压的生物标志物用于靶向血压控制 阻塞性睡眠呼吸暂停综合症的治疗和预防心血管疾病。这项提议的目标是发展 阻塞性睡眠呼吸暂停低密度脂蛋白所致高血压的预后生物标志物。我们将在#年进行一项前瞻性病例对照研究 新诊断为中到重度OSA的患者(病例)或没有OSA的患者(对照组)实现这一目标 目标。我们将通过睡眠期间典型的血压异常来定义OSA诱发的高血压： 呼吸暂停后血压、非低血压和夜间高血压的急性激增。我们的概念验证研究 利用机器学习方法设计了一种新型的长-短期记忆(LSTM)编码器-- 利用多导睡眠图中的生理信号预测睡眠期间血压的译码网络。第一 这项建议的目的是训练和验证LSTM网络，以预测临床睡眠期间的BP曲线 多导睡眠监测信号与无创血压测量(Finapres Nova)的比较 参考资料。从多导睡眠图预测夜间血压曲线的LSTM网络将提供 阻塞性睡眠呼吸暂停综合征高血压的数字化生物标志物的临床应用。我们的初步研究表明，一小部分 与心血管疾病有关的外切体微小RNA簇(MiRNA)的差异表达 在非降血压的阻塞性睡眠呼吸暂停综合征患者。外显体货物是专门分类的，在个体中稳定表达， 是靶向细胞间基因组通讯的重要载体。我们将在此基础上 本研究的第二个目的是确定OSA引起的高血压的基因组生物标志物。 Cargo通过miRNA和mRNA测序、蛋白质组学和脂质组学，随后是生物信息学 病例和对照之间的差异表达。检查生理途径的小规模研究 介导性阻塞性睡眠呼吸暂停低密度脂蛋白引起的高血压并没有系统地包括多条失调的通路： 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 目的是开发一种节俭和强大的生物标志物小组通过结合分子检测 生理失调(血液肾素、血管紧张素II、醛固酮、超敏C反应蛋白、 肿瘤坏死因子-α、丙二醛、尿儿茶酚胺和异前列腺素)和外切体- 用重新训练的LSTM网络衍生组学生物标记物。将确定最佳生物标志物组合 由网络性能和临床适用性决定。这项提议包括一个多学科团队， 在临床OSA研究、机器学习、外显体生物学和组学方面具有专业知识的研究人员， 生物统计学和生物信息学。这项研究将产生高度的临床影响，通过提供 阻塞性睡眠呼吸暂停低密度脂蛋白诱发高血压的预后生物标志物用于指导关键的管理决策和未来 阻塞性睡眠呼吸暂停综合征的干预研究以减少心血管疾病。

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.