Sleep spindle density and sleep depth as predictors of cardiovascular outcomes: A prospective EEG study

Abstract

Objectives

The objective of this study was to investigate the relationship between sleep EEG features and cardiovascular outcomes in a large prospective cohort. We aimed to identify key EEG markers that could serve as indicators of cardiovascular risk.

Methods

This study utilized baseline polysomnography (PSG) data from Sleep Heart Health Study Visit 1 (SHHS1), including 5782 participants aged 40 and older. PSG recorded EEG features including sleep spindle density, power, and the odds ratio product (ORP), a measure of sleep depth. Cardiovascular outcomes, including CHD and CVD incidence and mortality, were assessed during the follow-up visit (SHHS2). Statistical analysis included logistic regression and receiver operating characteristic (ROC) curves to examine associations between EEG features and CHD/CVD risk.

Results

Among 5782 participants (median age: 63 years; 47.6 % male), 15.7 % had CHD, and 23.7 % had CVD. CHD- and CVD-related deaths occurred in 4.6 % and 7.1 % of participants, respectively. Higher ORP, indicating shallower sleep, was associated with a 78.2 % increased risk of CHD and a 63.8 % increased risk of CVD. Short REM latency was also linked to increased cardiovascular risk. In contrast, higher sleep spindle density and frequency and greater REM sleep proportion were protective, reducing odds of CHD, CVD, and mortality. Elevated ORP in non-REM sleep was associated with a 133.8 % increase in CHD mortality and 63.7 % increase in CVD mortality.

Conclusions

Sleep spindle density and sleep depth are key EEG features associated with cardiovascular outcomes. EEG patterns from routine sleep studies may offer valuable biomarkers for identifying individuals at elevated cardiovascular risk, enabling earlier preventive interventions.