I have over 25 years of experience in sleep medicine epidemiological research and have played a leading role in studies that address the contributions of genetic, social and environmental risk factors to sleep disorders, the influences of sleep on health outcomes in children and adults, and the role of sleep interventions in improving health outcomes. My collaborators, mentees and I have identified that sleep apnea (SA) is highly prevalent, disproportionately affects Asians and African American children, and is associated with significantly increased risks for developing hypertension, stroke, heart failure, diabetes, and behavioral problems. We also have identified variability in these outcomes by sex, race/ethnicity, age, and genetic background. We have characterized the patterns of heritability for several SDB traits and through use of family-based and cohort studies (>20,000 individuals) have identified genome-wide significant associations for genetic variants in biological candidate genes, and sex- and sleep stage-specific analyses have provided insight into mechanisms that may explain the known sex and REM/NREM differences in SA severity. Despite this progress, however, the underlying molecular and physiological mechanisms for SA are not well understood, limiting both our ability to predict which patients with SA are most vulnerable to adverse health outcomes and our ability to develop treatments that reflect individual differences in SDB pathophysiology. Our emerging data suggest that these gaps may be overcome through systematic analysis of larger sets of polysomnography data, deriving more precise SDB phenotypes that reflect specific sleep and respiratory patterns, and linking these phenotypes to genomic and clinical data. Through leadership in multiple national consortia and multi-center studies we are poised to make transformative advances in understanding the phenotypic variability and genetics of sleep apnea and related traits. We plan to harness a critical mass of data, including those in the National Sleep Research Resource and genetic, genomic and clinical data available through several consortia, including the Trans- Omics in Precision Medicine and Partners HealthCare Biobank. We will expand our genetics/epidemiology team with leaders in sophisticated respiratory phenotyping, developing a multi-disciplinary program that will systematically extract quantitative metrics of SA phenotypes and link these to genetics, genomics, specific treatment responsiveness, and cardiovascular, metabolic and cognitive outcomes. Through collaborations with functional genomics laboratories, we will help identify functional genetic variants and clarify the function of genes and pathways associated with SA. We will use sophisticated statistical methods to derive and validate personalized medicine prediction algorithms based on these data streams. This enhanced biological understanding of SA will be translated into improved clinical care through better-informed clinical trials. Finally, we will create an environment that nurtures the development of new investigators equipped to use modern technologies and ?big data? to identify signatures of disease susceptibility and outcomes.
Sleep apnea is a common disorder associated with many adverse health outcomes, but often unrecognized and under-treated. The usual measure of disease severity, the Apnea Hypopnea Index, does not adequately characterize disease subtypes reflective of differences in underlying anatomic and physiological risk factors, and thus poorly predicts which patients are most susceptible to adverse health outcomes and would benefit from treatment. The mainstay treatment is a CPAP machine, which stents the airway open without treating the underlying cause of the apnea. This program will systematically identify new measures of sleep apnea that reflect underlying mechanisms and allow subgroups of patients to be better characterized, facilitating the discovery of the molecular basis for sleep apnea and novel treatments.
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