Faculty Bibliography

STUDY OBJECTIVES/OBJECTIVE:To determine the effect of self-reported clinical diagnosis of Obstructive Sleep Apnea (OSA) on longitudinal changes in brain amyloid-PET and CSF-biomarkers (Aβ42, T-tau and P-tau) in cognitively normal (NL), mild cognitive impairment (MCI) and Alzheimer's Disease (AD) elderly. METHODS:Longitudinal study with mean follow-up time of 2.52±0.51 years. Data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Participants included 516 NL, 798 MCI and 325 AD elderly. Main Outcomes were annual rate-of-change in brain amyloid-burden (i.e. longitudinal increases in florbetapir-PET uptake or decreases in CSF-Aβ42 levels); and tau-protein aggregation (i.e. longitudinal increases in CSF total-tau (T-tau) and phosphorylated-tau (P-tau)). Adjusted multi-level mixed effects linear regression models with randomly varying intercepts and slopes was used to test whether the rate-of-biomarker-change differed between participants with and without OSA. RESULTS:In NL and MCI groups, OSA+ subjects experienced faster annual increase in florbetapir uptake (B=.06, 95% CI .02, .11 and B=.08, 95% CI .05, .12 respectively) and decrease in CSF-Aβ42 levels (B=-2.71, 95% CI -3.11, -2.35 and B=-2.62, 95% CI -3.23, -2.03, respectively); as well as increases in CSF T-tau (B=3.68, 95% CI 3.31, 4.07 and B=2.21, 95% CI 1.58, 2.86, respectively) and P-tau (B=1.221, 95% CI, 1.02, 1.42 and, B=1.74, 95% CI 1.22, 2.27, respectively); compared to OSA- participants. No significant variations in the biomarker changes over time were seen in the AD group. CONCLUSIONS:In both NL and MCI, elderly, clinical interventions aimed to treat OSA are needed to test if OSA treatment may affect the progression of cognitive impairment due to AD.

Introduction: We determined whether Obstructive Sleep Apnea (OSA) and beta-Amyloid Burden (Abeta) act additively or synergistically to promote conversion from cognitive normal (CN) to mild cognitive impairment (MCI) and from MCI to AD.
Method(s): In this longitudinal observational study, we examined CN (n=298) and MCI (n=418) older adults from the ADNI database (adni.loni.usc.edu). OSA was self-reported during a clinical interview. Brain Abeta was assessed using Florbetapir-PET imaging. The primary outcome of the analysis was conversion from CN to MCI (CN participants) and from MCI to AD (MCI participants). Participants were required to have a baseline and at least one follow-up clinical visit that identified their cognitive status. Logistic mixed-effects models with random intercept and slope were used to assess associations between OSA, Abeta, and risk of conversion from CN to MCI, and MCI to AD. All models included age at baseline, sex, APOE4 status, years of education, and their interactions with time.
Result(s): Of the 716 participants, 329 (46%) were women. The overall mean (SD) age was 74.7 (5.0) years, and the overall mean (SD) follow-up time was 5.5 (1.7) years (Range: 2.7 - 10.9 years). In CN participants at baseline, conversion to MCI was associated with both OSA (beta = 0.418; 95% CI, 0.133 to 0.703; P < .001) and higher Abeta-burden (beta = 0.554; 95% CI, 0.215 to 0.892; P < .001). The interaction of OSA and Abeta burden with time was significant (beta = 1.169, 95% CI, 0.776 to 1.562; P < .001), suggesting a synergistic effect. In MCI participants at baseline, conversion to AD was associated with both OSA (beta = 0.637; 95% CI, 0.291 to 0.982; P < .001) and higher Abeta-burden (beta = 1.061; 95% CI, 0.625 to 1.497; P < .001). The interaction of OSA and Abeta burden with time was significant (beta = 1.312, 95% CI, 0.952 to 1.671; P < .001), suggesting a synergistic effect.
Conclusion(s): In both CN and MCI elderly, Abeta modified the risk of progression to AD in OSA participants. OSA patients maybe more physiologically susceptible as Abeta load becomes increasingly abnormal

Introduction: Sleep is related to socioeconomic status and impacts health. This study evaluated whether foregoing medical care due to cost impacts sleep and plays a role in sleep disparities and the sleep-obesity relationship.
Method(s): Data from the 2017 Behavioral Risk Factor Surveillance System (N=39,267 from 7 states). Sleep duration was assessed as hours/day. Participants were asked, "Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?" They were also asked for information about age, sex, race/ethnicity, education, income, employment, overall health, and access to health insurance. They were also asked for height/weight, which was used to compute body mass index (BMI).
Result(s): Access to health insurance was not associated with habitual sleep duration. However, foregoing medical care was associated with less sleep (B=-0.26, 95%CI[-0.35,-0.17], p<0.0005). There was an interaction with race/ethnicity; compared to non-Hispanic Whites, the effect was 115% larger among Blacks/African-Americans, 13% larger in Hispanics/Latinos, 101% larger and in the opposite direction for Asians, and non-significant for Multiracial. Race/ethnicity relationships to sleep duration were stratified by foregoing care. Among those who did not (90%), both short and long sleep duration were more likely among Blacks/African-Americans and other minority groups. Among those who did forego care (10%), these effects were dramatically reduced. Further, when sleep duration was evaluated as a predictor of obesity, this relationship was only seen among those who did not forego care.
Conclusion(s): Foregoing medical care due to cost is an independent risk factor for insufficient sleep, irrespective of income, employment, and access to insurance. It disproportionately affects Blacks/ African-Americans and may represent part of the reason why sleep disparities exist even after adjustment for most socioeconomic indices. Further, foregoing medical care may present such health risks that this subsumes the relationship between sleep and obesity

Introduction: Previous studies have shown that racial/ethnic minorities are more likely to be short and/or long sleepers, which may increase risk for morbidity/mortality. This analysis provides a more recent update from a very large national dataset, including representation of additional groups and examination of the role of poverty.
Method(s): Data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS, collected by the CDC) were used. N=464,671 adults >18yrs from all US states/territories provided data on sleep, demographics, and socioeconomics. Sleep duration was categorized as very short (<=4h), short (5-6h), normal (7-8h as reference), and long (>=9h). Race/ethnicity was self-reported as Non-Hispanic White, Black/ African-American, Hispanic/Latino, American-Indian/Alaskan-Native (AIAN), Native Hawaiian/Pacific-Islander (NHPI), or Multiracial/Other. Covariates included age, sex, relationship status, education, employment, and home ownership. Interactions were explored with poverty (income<$20,000) were explored. Multinomial logistic regressions were weighted using BRFSS-specific weights.
Result(s): A significant race-by-poverty interaction was seen (p<0.0005). Compared to non-poor Non-Hispanic White, increased very short sleep was seen among those who were non-poor Black/African-American (RRR=2.1, p<0.0005), Asian (RRR=1.6, p=0.001), AIAN (RRR=1.4, p=0.001), NHPI (RRR=2.0, p=0.002), and Multiracial/Other (RRR=2.2, p<0.0005), and poor Non-Hispanic White (RRR=1.8, p<0.0005), Black/African-American (RRR=1.8, p<0.0005), AIAN (RRR=1.5, p=0.007), NHPI (RRR=2.4, p=0.005), and Multiracial/Other (RRR=3.4, p<0.0005). Compared to non-poor White, increased short sleep was seen among non-poor Black/African-American (RRR=1.7, p<0.0005), Asian (RRR=1.3, p<0.0005), AIAN (RRR=1.2, p=0.02), NHPI (RRR=1.3, p=0.02), Multiracial/Other (RRR=1.3, p<0.0005), and poor Non-Hispanic White (RRR=1.3, p<0.0005), Black/African-American (RRR=1.4, p<0.0005), Asian (RRR=1.3, p=0.04), and Multiracial/Other (RRR=2.2, p<0.0005). Compared to non-poor Non-Hispanic White, increased long sleep was seen for Non-Poor Black/African-American (RRR=1.4, p<0.0005), Poor Non-Hispanic White (RRR=1.3, p<0.0005), Black/African-American (RRR=1.4, p<0.0005), and AIAN (RRR=1.3, p<0.05).
Conclusion(s): Established racial/ethnic sleep disparities are supported in this large national sample, with additional information on understudied vulnerable groups including AI/AN and NH/PI. Further, the this study as the contribution of poverty status