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已发表论文
新型睡眠脑电图生物标志物与大型社区队列全因死亡率的关联
Received 5 August 2025
Accepted for publication 15 November 2025
Published 22 November 2025 Volume 2025:17 Pages 3015—3032
DOI https://doi.org/10.2147/NSS.S557087
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Prof. Dr. Ahmed BaHammam
Jinhuan Huang,1 Longlong Wang2
1Department of Pulmonary and Critical Care Medicine, People’s Hospital of Chenghai, Shantou Overseas Chinese Hospital, Shantou, People’s Republic of China; 2Division I, Department of Geriatric Respiratory, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Provincial Geriatrics Institute, Guangzhou, People’s Republic of China
Correspondence: Longlong Wang, Division I, Department of Geriatric Respiratory, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Provincial Geriatrics Institute, No. 106, Zhongshan 2 nd Road, Yuexiu District, Guangzhou, People’s Republic of China, Tel +86 15521227561, Email wanglonglong@gdph.org.cn
Background: The prognostic value of sleep depth remains poorly understood. The odds ratio product (ORP) is a novel electroencephalogram-based biomarker of sleep depth. We investigated the association between ORP-derived biomarkers and all-cause mortality in a large community-based cohort.
Methods: We analyzed 5802 Sleep Heart Health Study participants. A suite of ORP biomarkers was derived from baseline polysomnography, including mean ORP values across sleep stages, change in ORP across the night (ΔORP), interhemispheric sleep depth coherence (ORP Icc R/L), and ORP architecture phenotypes. Cox proportional hazards models with false discovery rate (FDR) correction estimated mortality associations. Prognostic nomograms were constructed based on variables selected through least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression.
Results: During 11.0 years of follow-up, 1305 deaths occurred. After multivariable adjustment and FDR correction, higher ORPW (HR: 0.54, 95% CI: 0.39– 0.73), ORPREM (HR: 0.81, 95% CI: 0.69– 0.95), ORPN1 (HR: 0.71, 95% CI: 0.59– 0.87), ORP ICC R/L (HR: 0.49, 95% CI: 0.29– 0.81), and ΔORP (HR: 0.70, 95% CI: 0.56– 0.87) were associated with lower mortality risk, while higher ORPN3 (HR: 1.38, 95% CI: 1.06– 1.81) predicted increased risk. ORP architecture phenotypes 1,2 (HR: 1.28, 95% CI: 1.06– 1.56), 1,3 (HR: 1.27, 95% CI: 1.05– 1.54), and 3,1 (HR: 1.48, 95% CI: 1.19– 1.84) conferred higher mortality risk compared to phenotype 2,2. Non-linear associations and threshold effects were identified for ORPN1, ORP ICC R/L, and ΔORP. Among ORP parameters examined, ΔORP and ORP architecture phenotypes were identified as the most important predictors through LASSO and multivariable Cox regression. Prognostic nomograms integrating these selected ORP metrics with traditional risk factors demonstrated excellent discrimination (C-index: 0.81).
Conclusion: ORP-derived biomarkers are independently associated with all-cause mortality and complement conventional sleep metrics in refining mortality risk stratification. Identified threshold effects for several ORP parameters may provide potential cutoff points for clinical intervention.
Keywords: EEG biomarkers, sleep depth, odds ratio product, all-cause mortality
1Department of Pulmonary and Critical Care Medicine, People’s Hospital of Chenghai, Shantou Overseas Chinese Hospital, Shantou, People’s Republic of China; 2Division I, Department of Geriatric Respiratory, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Provincial Geriatrics Institute, Guangzhou, People’s Republic of China
Correspondence: Longlong Wang, Division I, Department of Geriatric Respiratory, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Provincial Geriatrics Institute, No. 106, Zhongshan 2 nd Road, Yuexiu District, Guangzhou, People’s Republic of China, Tel +86 15521227561, Email wanglonglong@gdph.org.cn
Background: The prognostic value of sleep depth remains poorly understood. The odds ratio product (ORP) is a novel electroencephalogram-based biomarker of sleep depth. We investigated the association between ORP-derived biomarkers and all-cause mortality in a large community-based cohort.
Methods: We analyzed 5802 Sleep Heart Health Study participants. A suite of ORP biomarkers was derived from baseline polysomnography, including mean ORP values across sleep stages, change in ORP across the night (ΔORP), interhemispheric sleep depth coherence (ORP Icc R/L), and ORP architecture phenotypes. Cox proportional hazards models with false discovery rate (FDR) correction estimated mortality associations. Prognostic nomograms were constructed based on variables selected through least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression.
Results: During 11.0 years of follow-up, 1305 deaths occurred. After multivariable adjustment and FDR correction, higher ORPW (HR: 0.54, 95% CI: 0.39– 0.73), ORPREM (HR: 0.81, 95% CI: 0.69– 0.95), ORPN1 (HR: 0.71, 95% CI: 0.59– 0.87), ORP ICC R/L (HR: 0.49, 95% CI: 0.29– 0.81), and ΔORP (HR: 0.70, 95% CI: 0.56– 0.87) were associated with lower mortality risk, while higher ORPN3 (HR: 1.38, 95% CI: 1.06– 1.81) predicted increased risk. ORP architecture phenotypes 1,2 (HR: 1.28, 95% CI: 1.06– 1.56), 1,3 (HR: 1.27, 95% CI: 1.05– 1.54), and 3,1 (HR: 1.48, 95% CI: 1.19– 1.84) conferred higher mortality risk compared to phenotype 2,2. Non-linear associations and threshold effects were identified for ORPN1, ORP ICC R/L, and ΔORP. Among ORP parameters examined, ΔORP and ORP architecture phenotypes were identified as the most important predictors through LASSO and multivariable Cox regression. Prognostic nomograms integrating these selected ORP metrics with traditional risk factors demonstrated excellent discrimination (C-index: 0.81).
Conclusion: ORP-derived biomarkers are independently associated with all-cause mortality and complement conventional sleep metrics in refining mortality risk stratification. Identified threshold effects for several ORP parameters may provide potential cutoff points for clinical intervention.
Keywords: EEG biomarkers, sleep depth, odds ratio product, all-cause mortality