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INTRODUCTION We examined obstructive sleep apnea (OSA) severity's association with Alzheimer's disease (AD) plasma biomarkers, independent or synergistic with cerebrospinal fluid (CSF) amyloid, and as a proof of concept, whether plasma amyloid beta (Aβ)42/Aβ40 with OSA severity improves detection of amyloidosis and tau pathology. METHODS In 120 cognitively normal older adults (70 with CSF data) from New York University sleep and aging studies (2013–2021), OSA severity was measured using apnea/hypopnea index with 4% desaturation; plasma Aβ40, Aβ42, tau, and neurofilament light chain (NfL) via single molecule array; CSF amyloid and tau via enzyme‐linked immunosorbent assay. Associations evaluated adjusted correlations and generalized models; receiver operating characteristic analyses evaluated diagnostic accuracy. RESULTS OSA severity correlated with plasma Aβ40 (r = 0.21), Aβ42 (r = 0.26), and Aβ42/Aβ40 (r = 0.20). Plasma tau and NfL associations depended on CSF–Aβ42. OSA severity with Aβ42/Aβ40 improved CSF amyloidosis (area under the curve [AUC] = 0.78) and tau pathology (AUC = 0.71) detection. DISCUSSION OSA severity relates to elevated plasma Aβ and, with CSF amyloid, to tau/NfL. Combined plasma and OSA measures aid non‐invasive AD associations’ detection. Highlights Obstructive sleep apnea (OSA) is associated with plasma amyloid beta (Aβ)40, Aβ42, and Aβ42/Aβ40. OSA in synergism with brain amyloid levels is associated with plasma tau and neurofilament light chain. Combined with plasma Aβ42/Aβ40, OSA enhances brain amyloidosis and tau pathology detection.

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.

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 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