Association between glycolipids and risk of obstructive sleep apnea: A population-based study

Zheng, Murui; Duan, Xueru; Zhou, Huanning; Sun, Weidi; Sun, Guoqiang; Chen, Jianying; Wu, Xiuyi; Rong, Sijing; Huang, Jun; Zhao, Wengjing; Deng, Hai; Li, Xudong

doi:10.3389/fnut.2023.974801

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Elevated TG levels were found to increase the OSA risk, while there was a negative correlation between HDL-C and the risk of OSA, which was consistent with the results of Zheng et al’s study. 42 OSA is closely related to the lipoprotein and lipid metabolisms. Among them, triglyceride/HDL-C, total cholesterol (TC)/HDL-C, and the atherogenic index are more predictive of severe OSA than the other non-traditional lipid indicators.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Huang,

Zhuang,

Zheng

et al. 2024

NSS

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Objective Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening. Methods The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were “Gaussian Naive Bayes (GNB)”, “Support Vector Machine (SVM)”, “K Neighbors Classifier (KNN)”, and “Logistic Regression (LR)”. Finally, the optimal algorithm was selected to construct the final prediction model. Results Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively. Conclusion We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Huang,

Zhuang,

Zheng

et al. 2024

NSS

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What can we do for obstructive sleep apnea patients in China?

Zhang,

2024

Sleep Research

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Obstructive sleep apnea (OSA) is a heterogeneous disease, having diversity in symptom, polysomnography parameter, etc. Studies have found that apnea–hypopnea index as a single measure cannot reflect its heterogeneity. More personalized diagnostic criteria and treatment measures are needed. But there are different kinds of results with different races and regions. It is essential to consider the characteristics of the local population when formulating individual treatment plan. There are significant discrepancies between Chinese and White race in terms of morphologic features, daily habits, religious beliefs and so on. However, the research based on the Chinese population still suffers from scarcity. This review sums up recent progress of studies on OSA from the Chinese population to provide useful references for related research and treatment in the future.

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The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and obstructive sleep apnea : a cross-sectional study

Chen,

Zou,

Lou

et al. 2024

Preprint

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Background Obstructive Sleep Apnea (OSA) is a common sleep disorder characterized by repeated instances of either partial or total blockage of the upper airway while sleeping. This leads to disrupted sleep and several related health issues. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) is a novel indicator that has the potential to provide more accurate assessment of the risk for cardiovascular and cerebrovascular illnesses. This study sought to explore the relationship between NHHR and OSA. Methods This research included cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2008, as well as between 2015 and 2018. The study included a total of 19,234 individuals. The NHHR was determined by dividing the non-high-density lipoprotein cholesterol by the high-density lipoprotein cholesterol. The participants were divided into quartiles according to their NHHR levels. OSA was characterized by symptoms described by the individuals themselves, such as extreme tiredness throughout the day, gasping or snorting during asleep, and frequent snoring. The link between NHHR and OSA was evaluated using multivariate logistic regression and segmented linear regression models. Results In this research, 19,234 individuals with a mean age of 41.88 ± 20.58 years were included. Among them, 50.46% were diagnosed with OSA. The mean and standard deviation for NHHR were measured as 3.08 ± 1.51.An elevated NHHR was shown to be substantially correlated with a higher risk of OSA, with an odds ratio (OR) of 1.113 and a 95% confidence interval (CI) ranging from 1.807 to 1.140 (p < 0.001). The observed correlation remained significant even after controlling for possible confounding variables such as age, gender, race, and other characteristics associated to health.An inverted U-shaped association was identified, which was nonlinear. The turning point of this relationship occurred at an NHHR value of 5.198.When the NHHR was below a certain level, each additional unit rise in NHHR resulted in a 17% increase in the risk of OSA. However, when the NHHR was beyond this level, the risk of OSA remained constant. Conclusions The presence of NHHR is a strong indicator of the probability of developing OSA. This highlights the crucial need to closely monitor and control lipid profiles in patients who have or are at risk of developing OSA. These findings provide fresh perspectives on the connection between lipid metabolism and OSA, indicating that NHHR might serve as a helpful indicator in clinical evaluations.

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Association between glycolipids and risk of obstructive sleep apnea: A population-based study

Cited by 4 publications

References 45 publications

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

What can we do for obstructive sleep apnea patients in China?

The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and obstructive sleep apnea : a cross-sectional study

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