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-a, 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.