Bioinformatics

Project 1: Severity and Sex Differences of Sleep Disordered Breathing in Elderly Adults
Sleep disordered breathing includes sleep apnea and hypoventilation. Sleep apnea occurs when normal breathing during sleep is interrupted. Sleep-related hypoventilation describes breathing that is too slow or shallow during sleep to maintain normal oxygen saturation and carbon dioxide clearance. It is important to diagnose sleep disordered breathing since treatments are effective and can dramatically improve patients daytime functions and comorbidities.

Many factors influence sleep and sleep apnea including sex. Men have a higher prevalence of obstructive sleep apnea (OSA) and have higher apnea-hypopnea index (AHI) scores than women. OSA in women is often underdiagnosed as women are often considered at lower risk and are less likely to be referred to sleep center than men; it is also undertreated. Moreover, symptoms and treatment response of OSA may differ between men and women, for example, women are less likely to report snoring.

We analyzed more than 19,100 in-laboratory polysomnography studies of adults with diverse ethnic backgrounds from both rural and urban areas carried out from 2003 to 2021 at a tertiary medical center. We compared both baseline and sleep parameters between men and women within 12 non-overlapping age intervals. We found that women showed a different age trajectory in sleep disordered breathing compared to men. Women had lower apnea hypopnea index but higher body mass index. The gap in apnea hypopnea index between women and men narrowed after age 60. The respiratory disturbance index leveled off after age 65 in men but continued to increase in women. Among those aged 70 or older, women had higher sleep hypoxemia index than men, and it continued to increase with age. Women aged 50 or older had higher nocturnal end-tidal CO2 level than similarly aged men. In conclusion, elderly women are likely underdiagnosed thus undertreated for sleep disordered breathing, especially hypoventilation and hypoxemia. Special attention is needed for elderly women suspected to have sleep disordered breathing. Our findings should be applicable to the general population and help raise awareness for the need for a more careful evaluation and monitoring of the elderly, especially women.

Project 2: Sleep Apnea and Hypoventilation in Patients with Five Major Types of Muscular Dystrophy
Muscular dystrophy disorders are a heterogenous group of inherited genetic diseases characterized by progressive muscle weakness and wasting. There are several major types of muscular dystrophy including Duchenne Muscular Dystrophy (DMD), Becker Muscular Dystrophy (BMD), Limb-Girdle Muscular Dystrophy (LGMD), Congenital Muscular Dystrophy (CMD), and myotonic dystrophy (DM, Greek name dystrophia myotonica). DMD is a degenerative X-linked recessive muscle disease often caused by out-of-frame mutations in the DMD gene resulting in a complete loss of the functional dystrophin protein. BMD is also due to defective DMD gene, but in a milder clinical spectrum compared to DMD and is usually caused by a partial loss of the dystrophin protein. LGMDs are a heterogenous group of diseases caused often by mutations (repeat expansions) in genes coding for proteins involving in muscle structure formation, functions, and/or repair. DM is characterized by progressive muscle weakness, wasting, and extra muscular manifestations including those in the cardiac and central nervous systems. There are two major types of DM. DM1 is caused by mutations in the DMPK gene, whereas DM2 is caused by mutations in the CNBP gene. CMD refers to a heterogenous group of often severe muscular dystrophy that become apparent at or near birth.

We analyzed more than 30 baseline and sleep variables obtained from 97 in-laboratory polysomnography (PSG) studies, in each of which the patient was diagnosed with one of the five major types of muscular dystrophy. A retrospective in-laboratory diagnostic polysomnography (PSG) data review, approved by the Institutional Review Board of the University of North Carolina at Chapel Hill (UNC-CH) (IRB #21-1984), was conducted for patients with diagnosis of muscular dystrophy between January 2003 and December 2021 in an American Academy of Sleep Medicine (AASM) accredited sleep laboratory at UNC-CH.

Sleep disordered breathing is common among the muscular dystrophy patients with each type having its own unique characteristics and hypoventilation is a prominent feature in this population. Non-DMD patients with hypoventilation had nearly 4.5 times higher risk for sleep apnea. Older patients were more likely to have hypoventilation. Patients with CMD, DMD, or DM were had significantly higher risk for hypoventilation compared to patients with BMD. Sleep apnea was not associated with the risk for hypoventilation after adjusting for age, sex, subtype, while significant interaction between hypoventilation and sleep apnea may exist in patients with subtype other than DMD. Thus, CO2 retention may be an independent process from upper airway obstructive breathing in DMD patients. Early diagnosis of sleep related breathing disorder is critical for early intervention with non-invasive ventilation. Such will have a positive effect on the quality of a muscular dystrophy patients care, earlier diagnosis, and thus, life expectancy. Patients with CMD or DMD had higher heart rates than patients with other types.


Project 3:SSAVE: Sleep Cycle and Spectrogram Analysis and Visualization for Electroencephalography Data
Sleep cycles represent ultradian variation in human sleep architecture. The detection of sleep cycles using data from an overnight sleep study enables identification of macro and micro changes in sleep architecture associated with sleep disorders and other aspects of human mental and physical health. Overnight sleep studies record, in addition to electroencephalography (EEG) and other electro-physiological signals, a sequence of sleep-stage annotations. SSAVE, introduced here, is the first open-source software that takes sleep-stage annotations and EEG signals as input, identifies and characterizes the sleep cycles, and produces a hypnogram and its time-matched EEG spectrogram. SSAVE fills an important gap for the rapidly growing field of sleep medicine by providing an easy-to-use tool for sleep cycle identification and visualization. SSAVE can be used as a Python package, as a desktop standalone tool or through a web portal. SSAVE code and documentation are at https://manticore.niehs.nih.gov/ssave