{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T05:47:25Z","timestamp":1792388845348,"version":"4.4.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Cardiovascular risk assessment in obstructive sleep apnoea (OSA) has traditionally relied on the Apnea\u2013Hypopnea Index (AHI). However, AHI primarily reflects respiratory event burden and may not adequately capture inter-individual heterogeneity in sleep-related cardiorespiratory regulation. This study aimed to identify sleep stage-specific heart rate variability (HRV) phenotypes and evaluate their associations with long-term cardiovascular outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>Data were obtained from the Sleep Heart Health Study (SHHS). After exclusion of participants with missing clinical covariates or outcome data, 4029 individuals were included. Linear and non-linear HRV features were extracted from stage-specific electrocardiographic segments during N2, N3, and rapid eye movement (REM) sleep. K-means clustering was performed independently for each sleep stage after Z-score standardization, and the optimal cluster number was determined using silhouette coefficients and the elbow method. Multivariable Cox proportional hazards models and Kaplan\u2013Meier analyses were used to evaluate associations between HRV-informed phenotypes and all-cause mortality, coronary heart disease, and cardiovascular disease (CVD). 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 the Stable phenotype, the High-risk phenotype was consistently associated with increased cardiovascular risk, with the strongest association observed for incident CVD in the N2 stage [hazard ratio (HR) 1.468, 95% confidence interval (CI) 1.280\u20131.684, P &amp;lt; 0.001]. Similar associations were observed in the N3 (HR 1.426, 95% CI 1.249\u20131.629, P &amp;lt; 0.001) and REM (HR 1.394, 95% CI 1.216\u20131.598, P &amp;lt; 0.001) 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 severity measures. In contrast, the Burden phenotype was consistently associated with lower all-cause mortality across all three sleep stages (HR 0.83\u20130.85, all P &amp;lt; 0.001). Subgroup analyses stratified by OSA severity demonstrated broadly consistent prognostic associations across AHI strata.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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 OSA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag144","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T11:32:36Z","timestamp":1788867156000},"source":"Crossref","is-referenced-by-count":0,"title":["Sleep stage-specific heart rate variability feature-driven clustering analysis: identifying cardiac autonomic endotypes with long-term cardiovascular predictive value"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4604-0002","authenticated-orcid":false,"given":"Xiao","family":"Chen","sequence":"first","affiliation":[{"name":"Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University , Beijing 102218 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luqi","family":"Bai","sequence":"additional","affiliation":[{"name":"Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University , Beijing 102218 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Tsinghua University , Beijing 100084 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8207-7367","authenticated-orcid":false,"given":"Boda","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University , Beijing 102218 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"2026101901214474700_ztag144-B1","article-title":"The interplay between sleep disorders and cardiovascular diseases: a systematic 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