Alzheimer's disease (AD) is the most common cause of dementia. Several haemodynamic risk factors for AD have been identified, including ageing, increased arterial stiffness, high systolic blood pressure (BP) and brain hypoperfusion. We propose a novel approach for assessing haemodynamic risk factors by analysing arterial pulse waves (PWs). The aim of this feasibility study was to determine whether features extracted from PWs measured by wearable sensors might have utility for stratifying patients at risk of AD. A numerical model of PW propagation was used to simulate PWs for virtual subjects of each age decade from 25 to 75 years (16 subjects in total), with subjects at each age exhibiting normal variation in arterial stiffness. Several PW features were extracted, and their relationships with AD risk factors were investigated. PWs at the wrist were found to exhibit changes with age and arterial stiffness, indicating that it may be possible to identify changes in risk factors from smart wearables. Several candidate PW features were identified which changed significantly with age for future testing. This study demonstrates the potential feasibility of assessing haemodynamic risk factors for AD from non-invasive PWs. These factors could be assessed from the PPG PW, which can be acquired by smart watches and phones. If the findings are replicated in clinical studies, then this may provide opportunities for patients to assess their own risk and make lifestyle changes accordingly.
Keyphrases
- blood pressure
- risk factors
- heart rate
- hypertensive patients
- end stage renal disease
- chronic kidney disease
- newly diagnosed
- heart failure
- metabolic syndrome
- physical activity
- type diabetes
- prognostic factors
- risk assessment
- peritoneal dialysis
- mass spectrometry
- patient reported outcomes
- blood glucose
- cerebral ischemia
- glycemic control
- high speed