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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. Longitudinal study with mean follow-up time of 2.52 ± 0.51 years. Data were 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 multilevel 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. 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 with OSA- participants. No significant variations in the biomarker changes over time were seen in the AD group. 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.

Abstract Study Objectives: Mounting evidence implicates disturbed sleep or lack of sleep as one of the risk factors for Alzheimer’s disease (AD), but the extent of the risk is uncertain. We conducted a broad systematic review and meta-analysis to quantify the effect of sleep problems/disorders on cognitive impairment and AD. Methods: Original published literature assessing any association of sleep problems or disorders with cognitive impairment or AD was identified by searching PubMed, Embase, Web of Science, and the Cochrane library. Effect estimates of individual studies were pooled and relative risks (RR) and 95% confidence intervals (CI) were calculated using random effects models. We also estimated the population attributable risk. Results: Twenty-seven observational studies (n = 69216 participants) that provided 52 RR estimates were included in the meta-analysis. Individuals with sleep problems had a 1.55 (95% CI: 1.25–1.93), 1.65 (95% CI: 1.45–1.86), and 3.78 (95% CI: 2.27–6.30) times higher risk of AD, cognitive impairment, and preclinical AD than individuals without sleep problems, respectively. The overall meta-analysis revealed that individuals with sleep problems had a 1.68 (95% CI: 1.51–1.87) times higher risk for the combined outcome of cognitive impairment and/or AD. Approximately 15% of AD in the population may be attributed to sleep problems. Conclusion: This meta-analysis confirmed the association between sleep and cognitive impairment or AD and, for the first time, consolidated the evidence to provide an “average” magnitude of effect. As sleep problems are of a growing concern in the population, these findings are of interest for potential prevention of AD.

Background: To determine whether sleep disturbance (SD) and vascular-risk interact to promote Alzheimer’s disease (AD) stage-progression in normal, community-dwelling older adults and evaluate their combined risk beyond that of established AD biomarkers. Methods: Longitudinal data from the National Alzheimer’s Coordinating Center Uniform-Dataset. SD data (i.e., SD+ vs. SD-), as characterized by the Neuropsychiatric Inventory-Questionnaire, were derived from 10,600 participants at baseline, with at-least one follow-up visit. A subset ( n = 361) had baseline cerebrospinal fluid (CSF) biomarkers and MRI data. The Framingham heart study general cardiovascular disease (FHS-CVD) risk-score was used to quantify vascular risk. Amnestic mild cognitive impairment (aMCI) diagnosis during follow-up characterized AD stage-progression. Logistic mixed-effects models with random intercept and slope examined the interaction of SD and vascular risk on prospective aMCI diagnosis. Results: Of the 10,600 participants, 1,017 (9.6%) reported SD and 6,572 (62%) were female. The overall mean (SD) age was 70.5 (6.5), and follow-up time was 5.1 (2.7) years. SD and the FHS-CVD risk-score were each associated with incident aMCI (aOR: 1.42 and aOR: 2.11, p < 0.01 for both). The interaction of SD and FHS-CVD risk-score with time was significant (aOR: 2.87, p < 0.01), suggesting a synergistic effect. SD and FHS-CVD risk-score estimates remained significantly associated with incident aMCI even after adjusting for CSF (Aβ, T-tau, P-tau) and hippocampal volume ( n = 361) (aOR: 2.55, p < 0.01), and approximated risk-estimates of each biomarker in the sample where data was available. Conclusions: Clinical measures of sleep and vascular risk may complement current AD biomarkers in assessing risk of cognitive decline in older adults.

Introduction Recent evidence suggests novel plasma Alzheimer’s Disease (AD) pathology biomarkers have high potential for AD risk prediction. We determined whether obstructive sleep apnea (OSA) severity is associated with plasma levels of Aβ40, Aβ42, Aβ42/Aβ40, Tau, tau/Aβ42 and NfL and whether this relationship is dependent of amyloid burden. Methods Cross-sectional analysis of baseline data from 120 community-dwelling, cognitively normal older-adults, selected from ongoing NYU prospective longitudinal studies on memory, sleep and aging. Of the 120 participants, 70 had baseline CSF-Aβ42 (measured using ELISA). OSA-severity was defined using AHI4% criteria. Levels of plasma Aβ40, Aβ42, Tau and NfL were determined using single molecule array technology ultra-sensitive assays. Associations of OSA-severity and plasma AD-biomarker levels (n=120) were assessed using Pearson correlation analysis. The association of OSA-severity and AD plasma biomarkers dependent on CSF-Aβ42 levels (n=70) was assessed using generalized linear models. Analyses were adjusted for age, sex, BMI, race, education and APOE4. Results Of the 120 participants, 80 (67%) were women. Mean (SD) age was 69.1 (7.2) years. Mean (SD) AHI was 14.3/hr. (16.3) {48 (40%) had AHI <5, 30 (25%) had AHI: 5 to ≤ 15, 18 (15%) had AHI: 15 to ≤30, and 22 (18%) had AHI >30}. Independent of amyloid-burden, OSA-severity was associated with higher levels of plasma Aβ40 (r=.21, p-value=.02), plasma Aβ42 (r=.26, p-value=.01), plasma Aβ42/Aβ40 (r=.20, p-value=.05), but not plasma Tau, plasma tau/Aβ42 or plasma NfL. The association of OSA-severity and plasma levels of Tau, Tau/Aβ42 or NfL dependent on CSF-Aβ42 levels revealed significant interactions between CSF-Aβ42 levels and AHI (p-value <.05 for all), with β-estimates suggesting that with combined increases in AHI and decreases in CSF-Aβ42 levels, there were corresponding increases in plasma levels of Tau, plasma Tau/Aβ42 or plasma NfL. The analysis was not powered for generating dichotomized strata specific (i.e. OSA+/Aβ+, OSA+/Aβ-, OSA-/Aβ+ and OSA-/Aβ-) estimates. Conclusion In this sample of cognitively-normal older- adults, OSA-severity was associated with levels of plasma Aβ40, Aβ42, Aβ42/Aβ40 and showed a synergistic effect with CSF Aβ42 on plasma levels of tau and NfL. Larger cohorts are necessary to delineate mechanisms and examine for OSA/Aβ strata-specific estimates. Support (if any) NIH/NIA/NHLBI (L30-AG064670, CIRAD-P30AG059303-Pilot, NYU-ADRC-P30AG066512-Developmental-Grant, AASM#231-BS-20)

Introduction We determined whether Obstructive Sleep Apnea (OSA) and β-Amyloid Burden (Aβ) act additively or synergistically to promote conversion from cognitive normal (CN) to mild cognitive impairment (MCI) and from MCI to AD. Methods 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 Aβ 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, Aβ, 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. Results 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 (β=0.418; 95% CI, 0.133 to 0.703; P<.001) and higher Aβ-burden (β=0.554; 95% CI, 0.215 to 0.892; P<.001). The interaction of OSA and Aβ burden with time was significant (β=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 (β=0.637; 95% CI, 0.291 to 0.982; P<.001) and higher Aβ-burden (β=1.061; 95% CI, 0.625 to 1.497; P<.001). The interaction of OSA and Aβ burden with time was significant (β=1.312, 95% CI, 0.952 to 1.671; P<.001), suggesting a synergistic effect. Conclusion In both CN and MCI elderly, Aβ modified the risk of progression to AD in OSA participants. OSA patients maybe more physiologically susceptible as Aβ load becomes increasingly abnormal Support (If Any) NIH/NIA/NHLBI-T32HL129953, RO1AG056031, R01HL118624, K07AG052685