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Obstructive sleep apnea (OSA) is a prevalent sleep disorder linked to significant daytime sleepiness and mood disturbances. Continuous positive airway pressure (CPAP) therapy is the standard treatment for OSA, but its effects on mental health outcomes, are not well understood. This study aimed to evaluate the impact of CPAP on daytime sleepiness, depressive symptoms, and anxiety symptoms while assessing how improvements vary with age.

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Background: Obstructive sleep apnea (OSA) is a complex and heterogeneous condition associated with chronic physiological and neuropsychological disturbances (1-4). One notable neuropsychological effect observed in OSA patients is memory impairment (2,5). Additionally, some reports suggest that OSA may be associated with Alzheimer's Disease (AD) (4).

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Background: The present study examined OSA using an objective home sleep test in 81 adults with DS (aged 25 ∼ 61 years) and evaluated associations between sleep-disordered breathing problems and biomarkers of AD pathology (PET Aβ and tau) and symptomology (cognitive performance and depressed mood).

Method: As part of the ABC-DS study, participants completed a 2.5-hour battery of cognitive measures and underwent MRI and PET imaging scans and a blood draw.

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Background: Obstructive sleep apnea (OSA) is associated with hypoxia-induced neuronal impairment and dysfunction-key risk factors for the pathogeneses of age-related neurodegenerative diseases such as Alzheimer's disease (AD). This study examined longitudinal associations between OSA severity and CSF biomarkers associated with AD, synaptic dysfunction, and neuroinflammation in a sample of late-middle-aged adults with increased risk for AD.

Method: N=25 cognitively unimpaired adults (64% female, mean age 65.

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Background: Timely identification of mild cognitive impairment (MCI) is key to early intervention. While primary care providers are the most likely entry point to detect early signs of MCI, their detection rates are low. Building upon a published study, we used electronic health records (EHR) to develop a clinically enhanced MCI risk prediction algorithm.

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