Huaming Zhu

Distinct severity stages of obstructive sleep apnoea are correlated with unique dyslipidaemia: large-scale observational study

Background Dyslipidaemia is an intermediary exacerbation factor for various diseases but the impact of obstructive sleep apnoea (OSA) on dyslipidaemia remains unclear. Methods A total of 3582 subjects with suspected OSA consecutively admitted to our hospital sleep centre were screened and 2983 (2422 with OSA) were included in the Shanghai Sleep Health Study. OSA severity was quantified using the apnoea–hypopnea index (AHI), the oxygen desaturation index and the arousal index. Biochemical indicators and anthropometric data were also collected. The relationship between OSA severity and the risk of dyslipidaemia was evaluated via ordinal logistic regression, restricted cubic spline (RCS) analysis and multivariate linear regressions. Results The RCS mapped a nonlinear dose–effect relationship between the risk of dyslipidaemia and OSA severity, and yielded knots of the AHI (9.4, 28.2, 54.4 and 80.2). After integrating the clinical definition and RCS-selected knots, all subjects were regrouped into four AHI severity stages. Following segmented multivariate linear modelling of each stage, distinguishable sets of OSA risk factors were quantified: low-density lipoprotein cholesterol (LDL-C), apolipoprotein E and high-density lipoprotein cholesterol (HDL-C); body mass index and/or waist to hip ratio; and HDL-C, LDL-C and triglycerides were specifically associated with stage I, stages II and III, and stages II–IV with different OSA indices. Conclusions Our study revealed the multistage and non-monotonic relationships between OSA and dyslipidaemia and quantified the relationships between OSA severity indexes and distinct risk factors for specific OSA severity stages. Our study suggests that a new interpretive and predictive strategy for dynamic assessment of the risk progression over the clinical course of OSA should be adopted.

Independent Association between Sleep Fragmentation and Dyslipidemia in Patients with Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is independently associated with dyslipidemia. Previous studies have demonstrated that sleep fragmentation can impair lipid metabolism. The present study aimed to identify whether sleep fragmentation is independently associated with dyslipidemia, in a large-scale, clinic-based consecutive OSA sample. This cross-sectional study was conducted among 2,686 patients who underwent polysomnography (PSG) for suspicion of OSA from January 2008 to January 2013 at the sleep laboratory. Multivariate regression analyses were performed to evaluate the independent associations between the microarousal index (MAI) and lipid profiles adjusting for potential confounders, including metabolic syndrome components and nocturnal intermittent hypoxia. The adjusted odds ratios (ORs) for various types of dyslipidemia according to MAI quartiles, as determined by logistic regression were also evaluated. MAI was found positively associated with low-density lipoprotein cholesterol (LDL-c) but not with total cholesterol (TC), triglyceride (TG) or high-density lipoprotein cholesterol (HDL-c). Furthermore, the adjusted ORs (95% confidence interval) for hyper-LDL cholesterolemia increased across MAI quartiles, as follows: 1 (reference), 1.3 (1.1–1.7), 1.6 (1.2–2.0), and 1.6 (1.2–2.1) (p = 0.001, linear trend). Sleep fragmentation in OSA is independently associated with hyper-LDL cholesterolemia, which may predispose patients with OSA to a higher risk of cardiovascular disease.

Independent relationships between cardinal features of obstructive sleep apnea and glycometabolism: a cross-sectional study

BACKGROUND Obstructive sleep apnea (OSA) is associated with abnormal glycometabolism; however, the cardinal features of OSA, such as sleep fragmentation (SF) and intermittent hypoxia (IH), have yet to show clear, independent associations with glycometabolism. METHODS We enrolled 1834 participants with suspected OSA from July 2008 to July 2013 to participate in this study. Polysomnographic variables, biochemical indicators, and physical measurements were collected for each participant. Multiple linear regression analyses were used to evaluate independent associations between cardinal features of OSA and glycometabolism. Logistic regressions were used to determine the odds ratios (ORs) for abnormal glucose metabolism across microarousal index (MAI) and oxygen desaturation index (ODI) quartiles. The effect of the interaction between MAI and ODI on glycometabolism was also evaluated. RESULTS The MAI was independently associated with fasting insulin levels (β = 0.024, p = 0.001) and the homeostasis model assessment of insulin resistance (HOMA-IR; β = 0.006, p = 0.002) after multiple adjustments of confounding factors. In addition, the ORs for hyperinsulinemia across higher MAI quartiles were 1.081, 1.349, and 1.656, compared with the lowest quartile (p = 0.015 for a linear trend). Similarly, the ODI was independently associated with fasting glucose levels (β = 0.003, p < 0.001), fasting insulin levels (β = 0.037, p < 0.001), and the HOMA-IR (β = 0.010, p < 0.001) after adjusting for multiple factors. The ORs for hyperglycemia across higher ODI quartiles were 1.362, 1.231, and 2.184, compared with the lowest quartile (p < 0.05 for a linear trend). In addition, the ORs for hyperinsulinemia and abnormal HOMA-IR across ODI quartiles had the same trends. There was no interaction between MAI and ODI with respect to glycometabolism. CONCLUSION SF was independently associated with hyperinsulinemia, and IH was independently associated with hyperglycemia, hyperinsulinemia, and an abnormal HOMA-IR. We found no interaction between SF and IH with respect to OSA-related abnormal glycometabolism.

Prevalence and Predictors of Atherogenic Serum Lipoprotein Dyslipidemia in Women with Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is associated with dyslipidemia. However, no study has focused on dyslipidemia in women with OSA. The aim of this study was to determine the prevalence and risk factors for dyslipidemia in women with OSA. Between 2007 and 2013, 570 eligible female patients with suspected OSA were consecutively recruited. The analyzed data consisted of polysomnography parameters, biochemical indicators, and anthropometric measurements. Serum lipid levels and dyslipidemia were compared. Binary logistic regression and multivariate linear regression models were used to determine the independent risk factors influencing serum lipids. After multivariate adjustment, there were essentially no major differences in serum lipid levels among patients with no to mild, moderate, and severe OSA nor did serum lipid levels change with OSA severity. Dyslipidemia in total cholesterol, triglycerides, low-density lipoprotein cholesterol, apolipoproteins(apo) B and apoE increased with OSA severity, but only in non-obese subjects and those <55 years of age. Age, body mass index, waist to hip ratio, glucose and insulin were major risk factors for most serum lipids after multivariate adjustments. Our results indicate that, in women with OSA, age, obesity/central obesity, and insulin resistance are major determinants of dyslipidemia.

Smoking, obstructive sleep apnea syndrome and their combined effects on metabolic parameters: Evidence from a large cross-sectional study

Metabolic disorders have been separately associated with obstructive sleep apnea syndrome (OSAS) and smoking. However, no study has examined their interactions with metabolic parameters, including insulin resistance and dyslipidemia. To investigate whether the combination of OSAS and smoking results in an additive detriment in metabolic disorder parameters, we enrolled consecutive adult men during 2014–2015. Fasted blood samples were taken to determine glucose, insulin, and lipid levels. A questionnaire including an item on smoking pack-year exposure was administered, and the Epworth Sleepiness Scale and overnight polysomnography were performed. Smokers showed higher levels of glucose, insulin, total cholesterol (TC), triglycerides (TG), and low density lipoprotein-cholesterol (LDL-C), but lower high-density lipoprotein cholesterol (HDL-C) levels, than did non-smokers. In addition, the risks for insulin resistance increased with OSAS severity without fully adjustment. An OSAS × smoking interaction was found in insulin resistance after adjusting for potential confounding factors (p = 0.025). Although the difference was not significant, cessation of cigarette smoking seems to have a little benefit for smoking patients with OSAS. A synergistic effect was observed between smoking and OSAS on metabolic disorder parameters. Cessation of cigarette smoking may experience minor benefit for insulin resistance and lipid metabolism in patients with OSAS.

Excessive daytime sleepiness and metabolic syndrome in men with obstructive sleep apnea: a large cross-sectional study

Purpose Excessive daytime sleepiness is a common symptom in obstructive sleep apnea (OSA). Previous studies have showed that excessive daytime sleepiness is associated with some individual components of metabolic syndrome. We performed a large cross-sectional study to explore the relationship between excessive daytime sleepiness and metabolic syndrome in male OSA patients. Methods A total of 2241 suspected male OSA patients were consecutively recruited from 2007 to 2013. Subjective daytime sleepiness was assessed using the Epworth sleepiness scale. Anthropometric, metabolic, and polysomnographic parameters were measured. Metabolic score was used to evaluate the severity of metabolic syndrome. Results Among the male OSA patients, most metabolic parameters varied by excessive daytime sleepiness. In the severe group, male OSA patients with excessive daytime sleepiness were more obese, with higher blood pressure, more severe insulin resistance and dyslipidemia than non-sleepy patients. Patients with metabolic syndrome also had a higher prevalence of excessive daytime sleepiness and scored higher on the Epworth sleepiness scale. Excessive daytime sleepiness was independently associated with an increased risk of metabolic syndrome (odds ratio =1.242, 95% confidence interval: 1.019-1.512). No substantial interaction was observed between excessive daytime sleepiness and OSA/ obesity. Conclusions Excessive daytime sleepiness was related to metabolic disorders and independently associated with an increased risk of metabolic syndrome in men with OSA. Excessive daytime sleepiness should be taken into consideration for OSA patients, as it may be a simple and useful clinical indicator for evaluating the risk of metabolic syndrome.