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Cognitive deficits and accelerated brain ageing in older adults at risk for undiagnosed sleep apnea

Principal Supervisor: Dr Magdalena Chechlacz

Secondary Supervisor(s): Professor Andrew Bagshaw  

University of Registration: University of Birmingham

BBSRC Research Themes: Understanding the Rules of Life (Neuroscience and Behaviour)

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Deadline: 4 January, 2024

Project Outline

Cognitive ageing is inevitable but numerous studies indicate a large heterogeneity in the rate of cognitive deterioration, ranging from a gradual drop in cognitive functioning to dementia. With an increasingly ageing UK population, one of the key research challenges is understanding factors associated with a risk of hastened cognitive decline. The proposed project stems from the hypothesis that undetected obstructive sleep apnea (OSA) in older adults leads to accelerated brain and cognitive ageing, and subsequently aims to expand understanding of the mechanisms of OSA-mediated cognitive decline in elderly population.

OSA is one of the leading causes of poor cardiovascular and cerebrovascular health and has been associated with increased risk of hypertension, coronary heart disease, and stroke. The links between OSA and cognitive deficits are poorly understood and often controversial. However, in the last few years OSA has emerged as a potentially modifiable risk factor for cognitive decline and dementia in the elderly population. It has been further suggested that OSA and dementia might share several common pathomechanisms, such as cerebrovascular changes, inflammation and neurodegeneration. Despite the increasing awareness of the negative health and cognitive consequences of OSA, current statistics indicate that only a relatively small number of older adults are tested and treated for OSA. In middle-aged individuals who are overweight or obese, loud snoring, waking up during the night, mental fatigue associated with concentration problems and memory lapses, excessive daytime sleepiness and napping are considered symptoms of OSA and trigger medical referral. By contrast, the same symptoms in older individuals are frequently attributed to normal ageing and not medically investigated. In one recent study (n=1,052), 56% of community-dwelling older adults, were classified as being at high risk of OSA based on detailed questionnaire screening, and in 94% of these cases OSA was medically confirmed. Strikingly, only 8% of these adults were previously referred to be tested for OSA.

The research addressing the mechanistic processes underlying the link between OSA and cognitive decline in older adults is only beginning to emerge. While recently published studies firmly indicate that robust structural brain changes contribute to cognitive deficits in older adults diagnosed with OSA, there is a paucity of studies exploring mechanisms underpinning the observed brain changes. Crucially, there is a lack of research specifically asking whether and to what extent the undiagnosed and untreated OSA not only hastens brain ageing and cognitive decline in the elderly, but also contributes to diminished quality of life and mental health problems. The proposed project aims to address these gaps. Specifically, the proposed project is based on the hypothesis that cerebrovascular changes and inflammation in older adults with undiagnosed OSA result in brain atrophy underpinning accelerated brain ageing and cognitive deficits. By combining detailed characterization of sleep fragmentation, airflow/breathing patterns, oxygen saturation and cerebrovascular health with advanced neuroimaging methods, inflammation markers and comprehensive cognitive testing, the project aims (1) to examine markers of inflammation and cerebrovascular health in older adults with poor sleep quality and/or excessive daytime sleepiness who has not been previously tested for OSA, and (2) to link these markers to measures of cognitive dysfunction, and accelerated brain ageing (brain changes exceeding these expected for chronological age of an individual).


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Programming Skills (e.g., Bash, Python, Matlab, R)

Advances statistical and machine learning analyses

Structural and perfusion/arterial spin labelling MRI

Physiological data modelling