Sleep Stage-Specific HRV Feature-Driven Clustering Analysis: Identifying Cardiac Autonomic Endotypes with Long-term Cardiovascular Predictive Value

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ID: 328310
2026
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Abstract
Abstract Aim To identify sleep stage-specific heart rate variability (HRV) phenotypes and evaluate their associations with long-term cardiovascular outcomes. Methods Data were obtained from the Sleep Heart Health Study (SHHS). After exclusion of participants with missing clinical covariates or outcome data, 4,029 individuals were included. Linear and non-linear HRV features were extracted from stage-specific electrocardiographic segments during N2, N3, and REM sleep. Results Three HRV-informed phenotypes were identified within the selected clustering framework across N2, N3, and REM sleep stages: Stable (Cluster 0), Burden (Cluster 1), and High-risk (Cluster 2). Compared with Stable phenotype, High-risk phenotype was consistently associated with increased cardiovascular risk, with strongest association observed for incident CVD in N2 stage (HR 1.468, 95% CI 1.280–1.684, P < 0.001). Similar associations were observed in N3 and REM stages. Sequential adjustment for AHI, oxygen desaturation index, arousal index, and sleep efficiency resulted in only minimal attenuation of hazard estimates, indicating that the prognostic associations were largely independent of conventional respiratory measures. In contrast, Burden phenotype was consistently associated with lower all-cause mortality across all three sleep stages (HR range 0.83–0.85, all P < 0.001). Subgroup analyses stratified by OSA severity demonstrated broadly consistent prognostic associations across AHI strata. Conclusions Incorporating sleep stage-specific HRV features into conventional clinical phenotyping provides complementary prognostic information beyond traditional respiratory severity metrics. HRV-informed phenotyping may improve long-term cardiovascular risk stratification in patients with obstructive sleep apnea.
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Authors Xiao Chen, Luqi Bai, Chong Li, Boda Zhou
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Journal European Heart Journal - Digital Health
Year 2026
DOI
10.1093/ehjdh/ztag144
URL
https://doi.org/10.1093/ehjdh/ztag144
Keywords Keywords not found

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