There are known anatomic and physiologic risk factors for sleep apnea (OSA). One strategy to discover how these factors together contribute to OSA risk is to examine patients with clinically defined extreme phenotypes, i.e., patients whose disease status is not explained by primary clinical risk factors. As obesity is the primary risk factor for OSA, two such phenotypes are OSA patients with BMI?25 kg/m2 and non-apneic controls with BMI?35 kg/m2. Apneics with BMI?25 have OSA despite not having the obesity risk factor. Non- apneic controls with BMI?35 are protected from OSA despite high obesity levels; the mechanisms protecting these individuals are unknown. Thus, the overall objectives of this proposal are directed towards understanding the anatomical and physiological factors determining these two extreme OSA phenotypes. Most individuals with a BMI?35 have OSA and most individuals with a BMI?25 do not. The primary question is, ?What protects individuals with a BMI?35 from OSA and why do some individuals with a BMI?25 have OSA?? Based on our preliminary data, the global hypotheses motivating this study are that: 1) in obese controls (BMI?35) tongue fat and parapharyngeal fat pad volumes will be reduced, pharyngeal muscle responsiveness will be enhanced and airway collapsibility will be less compromised compared to obese apneics, protecting these obese subjects from OSA (Aim 1); 2) in thin apneics (BMI?25) mandibular depth (a measure of retrognathia) will be smaller, UA total soft tissue volume will be larger, loop gain will be higher, and airway collapsibility will be more compromised compared to non-apneics, causing OSA in these thin subjects (Aim 2); and 3) using machine learning techniques, we will assess complex interactions between all anatomic and physiologic traits that predict OSA status in each extreme phenotype (Aim 3). We have the following Specific Aims:
Specific Aim 1 : To quantitatively assess upper airway anatomic phenotypes with magnetic resonance imaging (MRI) and physiologic phenotypes in gender, age, BMI and race matched apneics and non- apneic controls with a BMI ? 35 kg/m2.
Specific Aim 2 : To quantitatively assess UA anatomic phenotypes with MRI and physiologic phenotypes in gender, age, BMI and race matched apneics and controls with a BMI ? 25 kg/m2.
Specific Aim 3 : Utilize machine learning techniques (CART and Random Forest) to explore pathways to OSA using the anatomic and physiologic domains and their interactions, in both extreme phenotypes. This analysis will identify the most important combinations of anatomic and physiologic risk/protective factors for OSA, serving to both validate the hypotheses in Aims 1-2 and identify novel disease pathways. This proposal has multiple innovative features, but the unique innovation is the examination of two extreme OSA phenotypes. The project will lead to better understanding of the relative contribution of each anatomic/physiologic trait to the development of OSA within a given individual, which may allow targeting appropriate trait(s) with specific and novel treatments. This is a primary goal of P4 medicine, an overarching theme to the overall Program Project.

Public Health Relevance

Known risk factors for sleep apnea (OSA) include the size of upper airway and craniofacial structures, as well as airway collapsibility and other physiologic traits. This project will study the combined role of these anatomic and physiologic characteristics in determining two extreme OSA phenotypes: lean patients (BMI?25 kg/m2) with OSA and obese patients (BMI?35 kg/m2) without OSA. Studying extreme phenotypes is a unique approach to understanding factors that cause or protect against OSA, and may result in novel treatment strategies targeting specific traits within a given individual.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Program Projects (P01)
Project #
Application #
Study Section
Heart, Lung, and Blood Initial Review Group (HLBP)
Program Officer
Laposky, Aaron D
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pennsylvania
United States
Zip Code
Feng, Yuan; Keenan, Brendan T; Wang, Stephen et al. (2018) Dynamic Upper Airway Imaging during Wakefulness in Obese Subjects with and without Sleep Apnea. Am J Respir Crit Care Med 198:1435-1443
Sands, Scott A; Edwards, Bradley A; Terrill, Philip I et al. (2018) Phenotyping Pharyngeal Pathophysiology using Polysomnography in Patients with Obstructive Sleep Apnea. Am J Respir Crit Care Med 197:1187-1197
Pien, Grace W; Ye, Lichuan; Keenan, Brendan T et al. (2018) Changing Faces of Obstructive Sleep Apnea: Treatment Effects by Cluster Designation in the Icelandic Sleep Apnea Cohort. Sleep 41:
Mazzotti, Diego R; Lim, Diane C; Sutherland, Kate et al. (2018) Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity. Physiol Meas 39:09TR01
Sands, Scott A; Edwards, Bradley A; Terrill, Philip I et al. (2018) Identifying obstructive sleep apnoea patients responsive to supplemental oxygen therapy. Eur Respir J 52:
Keenan, Brendan T; Kim, Jinyoung; Singh, Bhajan et al. (2018) Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis. Sleep 41:
van Hees, Vincent Theodoor; Sabia, S; Jones, S E et al. (2018) Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep 8:12975
Shulman, Rachel; Cohen, Debbie L; Grandner, Michael A et al. (2018) Sleep duration and 24-hour ambulatory blood pressure in adults not on antihypertensive medications. J Clin Hypertens (Greenwich) 20:1712-1720
Loro, Emanuele; Wang, Stephen H; Schwab, Richard J et al. (2017) In Vivo Evaluation of the Mechanical and Viscoelastic Properties of the Rat Tongue. J Vis Exp :
Gao, Xiaoling; Azarbarzin, Ali; Keenan, Brendan T et al. (2017) Heritability of Heart Rate Response to Arousals in Twins. Sleep 40:

Showing the most recent 10 out of 51 publications