Is there a link between heart rate variability and cognitive decline? A cross-sectional study on patients with mild cognitive impairment and cognitively healthy controls

Abstract

Background Given that, up to date, there is no effective strategy to treat dementia, a timely start of interventions in a prodromal stage such as mild cognitive impairment (MCI) is considered an important option to lower the overall societal burden. Although autonomic functions have been related to cognitive performance, both aspects have rarely been studied simultaneously in MCI. Objective The aim of the present study was to investigate cardiac autonomic control in older adults with and without MCI. Methods Cardiac autonomic control was assessed by means of heart rate variability (HRV) at resting state and during cognitive tasks in 22 older adults with MCI and 29 healthy controls (HCs). Resting HRV measurement was performed for 5 minutes during a sitting position. Afterwards, participants performed three PC-based tasks to probe performance in executive functions and language abilities (i.e., Stroop, N-back, and a verbal fluency task). Results Participants with MCI showed a significant reduction of HRV in the frequency-domain (high frequency power) and nonlinear indices (SD2, D2, and DFA1) during resting state compared to HCs. Older individuals with MCI exhibited decreases in RMSSD and increases in DFA1 from resting state to Stroop and N-back tasks, reflecting strong vagal withdrawal, while this parameter remained stable in HCs. Conclusion The results support the presence of autonomic dysfunction at the early stage of cognitive impairment. Heart rate variability could help in the prediction of cognitive decline as a noninvasive biomarker or as a tool to monitor the effectiveness of therapy and prevention of neurodegenerative diseases.

possible scalable solution for Early detection of prodromal phase of dementia based on EEG complexity

“…Some good examples mentioned here are using nonlinear measures for a proper characterization of EEG 94 , and with a good choice of ML model (with proper internal and external validation, plus taking care of dimensionality problem), they could offer a solution for clinical recognition of prodromal phase of AD. Besides, it is already emerging that MCI (and particularly aMCI) could be detected based on another electrophysiological signal, namely electrocardiogram (ECG) from which heart rate variability (HRV) can be extracted and is a marker of autonomic nervous system 14,15 .…”

“…Amnestic MCI (aMCI) is considered to be a prodromal phase for AD 3,5 , the other one is correlated to different forms of dementias, like vascular dementia or levy body dementias 14 . Half of the people who exhibit aMCI are converted to AD (on average 3 years after the first MCI identification, as in 15 ). Consequently, aMCI is considered to be a risk for AD and sometimes is called MCI-prodromal-to-dementia 3,17,18 .…”