Association Between Metabolic Score for Insulin Resistance (METS-IR) and Risk of Obstructive Sleep Apnea: Analysis of NHANES Database and a Chinese Cohort

代谢评分(METS-IR)与阻塞性睡眠呼吸暂停风险的关联:基于NHANES数据库和中国队列的研究分析

阅读:1

Abstract

PURPOSE: Insulin resistance (IR) plays a significant role in the development of obstructive sleep apnea (OSA). The metabolic score for insulin resistance (METS-IR) is a novel method for assessing IR. This study aims to explore the relationship between METS-IR and the risk of OSA. PATIENTS AND METHODS: This cross-sectional study included a total of 8297 subjects from NHANES (National Health and Nutrition Examination Survey) database, as well as 581 patients who underwent sleep monitoring in Renmin Hospital of Wuhan University. Logistic regression, subgroup analysis, and receiver operating characteristic (ROC) curve analysis were employed for evaluation. RESULTS: In the American population, a significant positive association was found between METS-IR and increased risk of OSA. For each unit increase in METS-IR, the risk of OSA increased by 4.4% (OR= 1.044; 95% CI: 1.037-1.059; P <0.001). A similar relationship was observed in the Chinese population. Multivariate Logistic regression model showed that for each unit increase in METS-IR, the prevalence of OSA increased by 6.7% (OR= 1.067; 95% CI: 1.035-1.103; P <0.001), and apnea-hypopnea index (AHI) increased by 0.732 (β= 0.732; 95% CI: 0.573-0.732; P <0.001). Gender subgroup analysis further showed that the association between METS-IR and OSA was particularly significant in male participants (OR= 1.111; 95% CI: 1.065-1.163; P <0.001). In the ROC analysis, the area under the curve (AUC) value of METS-IR for predicting OSA was 0.777, but it is not statistically significantly different from triglyceride glucose (TyG) (AUC = 0.749; P = 0.054), body mass index (BMI) (AUC = 0.769; P = 0.269), and triglyceride glucose-body mass index (TyG-BMI) (AUC = 0.777; P = 0.996). CONCLUSION: METS-IR is significantly associated with the risk of OSA and may serve as an effective predictive marker for identifying OSA.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。