Yingjun Qian

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.

Obstructive sleep apnea predicts risk of metabolic syndrome independently of obesity: a meta-analysis

Introduction Obstructive sleep apnea (OSA) has been suggested to be associated with a high risk of metabolic syndrome (MS). However, results on whether the association between OSA and risk of MS is independent of obesity, and the effect of nocturnal intermittent hypoxia (IH) on MS, are conflicting. Our purpose was to estimate the magnitude of the independent association between OSA and risk of MS and further explore whether nocturnal IH in OSA plays a role in MS risk. Material and methods The PubMed and EMBASE databases were systematically searched (until January 21, 2015) for available observational evidence. Unadjusted and body mass index (BMI)-adjusted pooled odds ratios (ORs) for MS in OSA or higher nocturnal IH were calculated using fixed or random models. Tests of homogeneity, publication bias, and robustness of the results were performed. Results A total of 13 independent studies (involving 857 participants in 3 case-control studies and 7077 participants in 10 cross-sectional studies) were included. The OSA was significantly associated with an increased risk of MS in a meta-analysis of 10 studies (pooled OR = 1.72, 95% CI: 1.31–2.26, p < 0.001), with a BMI-adjusted pooled OR of 1.97 (95% CI: 1.34–2.88, p < 0.001). Pooled results from 3 studies on the oxygen desaturation index (ODI) and MS risk (OR = 1.96, 95% CI: 1.73–2.22, p < 0.001) and 3 studies on the cumulative percentage of sleep time with SpO2 below 90% (CT90) and MS risk (OR = 1.05, 95% CI: 1.02–1.07, p < 0.001) were also significant. Conclusions Our findings demonstrated a significant association between OSA and increased MS risk independent of BMI, and further indicated a role of nocturnal IH in this association.

Metabolomics Profiling for Obstructive Sleep Apnea and Simple Snorers

Few clinical studies have explored altered urinary metabolite levels in patients with obstructive sleep apnea (OSA). Thus, we applied a metabolomics approach to analyze urinary metabolites in three groups of participants: patients with polysomnography (PSG)-confirmed OSA, simple snorers (SS), and normal subjects. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and gas chromatography coupled with time-of-flight mass spectrometry were used. A total of 21 and 31 metabolites were differentially expressed in the SS and OSA groups, respectively. Patients with OSA had 18 metabolites different from those with SS. Of the 56 metabolites detected among the 3 groups, 24 were consistently higher or lower. A receiver operator curve analysis revealed that the combination of 4-hydroxypentenoic acid, arabinose, glycochenodeoxycholate-3-sulfate, isoleucine, serine, and xanthine produced a moderate diagnostic score with a sensitivity (specificity) of 75% (78%) for distinguishing OSA from those without OSA. The combination of 4-hydroxypentenoic acid, 5-dihydrotestosterone sulfate, serine, spermine, and xanthine distinguished OSA from SS with a sensitivity of 85% and specificity of 80%. Multiple metabolites and metabolic pathways associated with SS and OSA were identified using the metabolomics approach, and the altered metabolite signatures could potentially serve as an alternative diagnostic method to PSG.

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.

Joint interaction effect of metabolic syndrome and obstructive sleep apnea on hypertension

Numerous studies have observed a relationship between obstructive sleep apnea and hypertension, but the effects of metabolic syndrome on hypertension, and their interaction with obstructive sleep apnea, remain unclear. For this study, a total of 2972 patients were recruited from the Shanghai Sleep Health Study. Data from overnight polysomnography parameters, serum lipids, fasting blood glucose, blood pressure, and anthropometric measurements were collected. The authors then explored the independent associations and multiplicative and additive interactions of predictors of metabolic syndrome with hypertension. A positive dose–response relationship was observed between systolic blood pressure and diastolic blood pressure and quartiles of fasting glucose, triglyceride, low‐density lipoprotein cholesterol, body mass index, and apnea–hypopnea index. Furthermore, logistic regression analysis showed that, in men, a high triglyceride level, hyperglycemia, and overweight status (and their interaction effect on obstructive sleep apnea) were associated with hypertension. Being overweight and hyperglycemic may markedly augment the adverse effect of obstructive sleep apnea on hypertension in men. Therefore, hypertension therapy should be individualized based on the specific comorbidities of each patient.

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.

Patients with Obstructive Sleep Apnea Display Decreased Flow-Mediated Dilatation: Evidence from a Meta-Analysis

Background Endothelial dysfunction, which can be measured by flow-mediated dilatation (FMD), is an early clinical marker of atherosclerosis, which is considered to be the main cause of the observed cardiovascular complications in obstructive sleep apnea (OSA) patients. The association between OSA and endothelial dysfunction has been reported in a number of studies; however, the findings are not entirely consistent. Our aim was to meta-analytically synthesize the existing evidence to explore the association between OSA and endothelial dysfunction. Material/Methods Data from PubMed, EMBASE, the Cochrane library, and Google Scholar for all trials that investigated the relationship between endothelial dysfunction and OSA were systematically reviewed. The minimum inclusion criteria for the studies were reporting of the Apnea-Hypopnea Index (AHI) and FMD measurements (as an indicator of endothelial dysfunction) for both OSA and control groups. Data from case-control studies that met the inclusion criteria were extracted. Results Twenty-eight studies comprising a total of 1496 OSA patients and 1135 controls were included in the meta-analysis. A random-effects model was used. The weighted mean difference in the FMD measurements was −3.07 and the 95% confidence interval was −3.71 to −2.43 (P<0.01). Meta-regression analysis showed that age, sex, body mass index (BMI), blood pressure, glucose, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol did not explain the heterogeneity. Conclusions This meta-analysis showed that patients with OSA have decreased FMD, which may contribute to the development of atherosclerosis.

Pediatric Obstructive Sleep Apnea is Associated With Changes in the Oral Microbiome and Urinary Metabolomics Profile: A Pilot Study

STUDY OBJECTIVES Several cross-sectional studies have reported associations between oral diseases and obstructive sleep apnea (OSA). However, there have been no reports regarding the structure and composition of the oral microbiota with simultaneous evaluation of potential associations with perturbed metabolic profiles in pediatric OSA. METHODS An integrated approach, combining metagenomics based on high-throughput 16S rRNA gene sequencing, and metabolomics based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and gas chromatography coupled with time-of-flight mass spectrometry, was used to evaluate the oral microbiome and the urinary metabolome. RESULTS 16S rRNA gene sequencing indicated that the oral microbiome composition was significantly perturbed in pediatric OSA compared with normal controls, especially with regard to Firmicutes, Proteobacteria, Bacteroidetes, Fusobacteria, and Actinobacteria. Moreover, metabolomics profiling indicated that 57 metabolites, 5 of which were metabolites related to the microflora of the digestive tract, were differentially present in the urine of pediatric patients with OSA and controls. Co-inertia and correlation analyses revealed that several oral microbiome changes were correlated with urinary metabolite perturbations in pediatric OSA. However, this correlation relationship does not imply causality. CONCLUSIONS High-throughput sequencing revealed that the oral microbiome composition and function were significantly altered in pediatric OSA. Further studies are needed to confirm and determine the mechanisms underlying these findings.

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.