连接阻塞性睡眠呼吸暂停的疾病严重程度与死亡率：内皮激活和应激指数的作用。
Linking disease severity and mortality in obstructive sleep apnea: the role of the endothelial activation and stress index.
文献信息
| PMID | 42704465 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Shudan Deng |
| 作者单位 | Second College of Clinical Medicine, Shanxi Medical University, Taiyuan, Shanxi Province, China. |
| 期刊 | European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery |
| SCI 分区 | Q1 |
| IF | 2.3 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 鼻科 |
中文摘要
背景： 系统性炎症和内皮功能障碍与阻塞性睡眠呼吸暂停（OSA）的不良结局有关，但将疾病严重程度与长期风险联系起来的可及生物标志物仍然有限。内皮激活和应激指数（EASIX）已在多种情况下与死亡率相关，但其在OSA中的作用尚不清楚。
方法： 我们分析了国家健康与营养调查（NHANES）中9,797名参与者的数据，使用加权Cox回归和限制性立方样条模型研究EASIX与全因死亡率和心血管死亡率之间的关联。进一步开发机器学习模型以评估预测性能，并使用SHAP解释特征重要性。此外，使用基于医院的横断面临床数据集（n = 258）来检验EASIX与OSA严重程度指标之间的关系，包括呼吸暂停低通气指数（AHI）和氧减饱和度指数（ODI）。
结果： 较高的EASIX水平与全因死亡率和心血管死亡率风险增加独立相关。观察到不同的剂量反应模式，全因死亡率呈非线性关联，而心血管死亡率呈线性关系。机器学习模型表现出强大的预测性能，SHAP分析将EASIX确定为关键贡献因素。在临床数据集中，EASIX与AHI和ODI均显著相关，表明其与OSA严重程度密切相关。
结论： EASIX与OSA中的死亡率和疾病严重程度均相关，表明其作为风险分层和综合临床评估的实用生物标志物的潜力。
英文摘要
BACKGROUND: Systemic inflammation and endothelial dysfunction are implicated in adverse outcomes in obstructive sleep apnea (OSA), yet accessible biomarkers linking disease severity with long-term risk remain limited. The Endothelial Activation and Stress Index (EASIX) has been associated with mortality in various settings, but its role in OSA is unclear.
METHODS: We analyzed data from 9,797 participants in the National Health and Nutrition Examination Survey (NHANES) to investigate the association between EASIX and all-cause and cardiovascular mortality using weighted Cox regression and restricted cubic spline models. Machine learning models were further developed to assess predictive performance and interpret feature importance using SHAP. In addition, a hospital-based cross-sectional clinical dataset (n = 258) was used to examine the relationship between EASIX and OSA severity indices, including the apnea-hypopnea index (AHI) and oxygen desaturation index (ODI).
RESULTS: Higher EASIX levels were independently associated with increased risks of all-cause and cardiovascular mortality. Distinct dose-response patterns were observed, with a nonlinear association for all-cause mortality and a linear relationship for cardiovascular mortality. Machine learning models demonstrated strong predictive performance, with EASIX identified as a key contributor by SHAP analysis. In the clinical dataset, EASIX was significantly associated with both AHI and ODI, indicating a close relationship with OSA severity.
CONCLUSIONS: EASIX is associated with both mortality and disease severity in OSA, suggesting its potential as a practical biomarker for risk stratification and integrated clinical assessment.