Project Summary Prevalence of Alzheimer?s disease and related dementias (ADRD), currently affecting an estimated 47 million people worldwide, is expected to triple by 2050. A poor understanding of its etiology has hindered progress toward treatments and preventive strategies. Several lines of evidence suggest that cardiovascular diseases (CVD) and various CVD risk factors are strongly associated with incidence of dementia. Vascular risk factors are easily identifiable and often modifiable. Therefore, discovery of vascular risk factors that precede CVD and cognitive impairment or decline would significantly enhance our understanding of the vascular etiology of ADRD. Heart rate variability (HRV) and QT interval variability (QTV), derived from electrocardiogram (ECG) analysis, are well-known indices of autonomic control over the heart. Lower HRV and higher QTV, indicating worse cardiac autonomic function, are strongly predictive of future cardiovascular morbidity and mortality. Notably, lower HRV and QT abnormalities have been noted in ADRD and mild cognitive impairment (MCI). Thus, the study of HRV and QTV may provide valuable insight into the role of cardiac autonomic function in the development of dementia. However, in most studies HRV and QTV are derived from standard short-term ECG (ranging from 10 seconds to several minutes duration), which does not reflect real-world conditions due to the requirement of stationarity. Although wearable devices such as Zio XT Patch allow ECG recording of up to 14 days duration, methods used to analyze HRV and QTV are based on the stationarity assumption, resulting in measures that may be uninterpretable or misleading when applied to such extra long-term recordings. Thus, innovative signal analysis tools are required to utilize the full potential of these ECGs. Recently, a new signal processing method was developed specifically to derive novel measures of HRV and QTV from extra long-term ECG that better represent cardiac autonomic function in real-world settings. The primary goal of this K01 mentored career development award is to implement this innovative analysis tool in a large, ongoing cohort study of older adults to derive novel measures of HRV and QTV from recently collected 14-day Zio Patch recordings. In Aim 1, we will evaluate the association of these novel measures with cognitive performance and risk of prevalent or incident MCI or dementia. In Aim 2, we will determine if their diurnal variation is related to worse cognitive performance or risk of MCI or dementia. Finally, we will leverage recent neuroimaging data in a subset of participants to determine if extra long-term HRV or QTV is associated with neuroimaging biomarkers of ADRD or cerebrovascular disease. If successful, the proposed study may provide insight into the mechanisms linking vascular risk factors and CVD to ADRD, and potentially identify cardiac autonomic function as a modifiable intervention target. In addition, this K01 award will provide the resources necessary to support the Candidate?s transition to an independent research career in an area of growing public health importance.

K01AG073581

2021-08-15

2025-04-30