Jianyin Zou

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.

An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study

Background Obstructive sleep apnea (OSA) causes high morbidity and mortality and is independently associated with an increased likelihood of multiple complications. The diagnosis of OSA is presently time-consuming, labor-intensive and inaccessible. Aim This study sought to develop a simple and efficient model for identifying OSA in Chinese adult population. Methods In this study, the efficiency of Epworth Sleepiness Scale (ESS) and a new established prediction model for screening OSA were evaluated in the test cohort (2,032 participants) and confirmed in an independent validation cohort (784 participants). Results In the test cohort, a high specificity (82.77%, 95% confidence interval [CI], 77.36–87.35) and a moderate sensitivity (61.65%, 95% CI, 59.35–63.91) were obtained at the threshold of nine for the ESS alone. Notably, sex-stratified analysis revealed different optimum cut-off points: nine for males and six for females. The new generated screening model, including age, waist circumference, ESS score, and minimum oxygen saturation (SaO2) as independent variables, revealed a higher sensitivity (89.13%, 95% CI, 87.60–90.53) and specificity (90.34%, 95% CI, 85.85–93.77) at the best cut-off point. Through receiver operating characteristics curve analysis, the area under the receiver operating characteristics curve of the model was found significantly larger than that of the ESS alone (0.955 vs. 0.774, P<0.0001). All these results were confirmed in the validation cohort. Conclusions A practical screening model comprising minimum SaO2 and other parameters could efficiently identify undiagnosed OSA from the high-risk patients. Additionally, a sex-specific difference should be considered if the ESS alone is used.

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.

Evaluation of a 2-Channel Portable Device and a Predictive Model to Screen for Obstructive Sleep Apnea in a Laboratory Environment

BACKGROUND: Various portable monitors for identifying obstructive sleep apnea (OSA) have been investigated and reported to enable accurate recording of OSA severity. However, more information is needed from different populations. This study was conducted to evaluate the efficiency of a portable 2-channel sleep apnea device (SleepView) for screening OSA in the Chinese population. METHODS: Ninety-three consecutive subjects underwent simultaneous SleepView testing and laboratory polysomnography (PSG) monitoring. Data were collected and blindly analyzed. The efficiency of the SleepView device and a newly established predictive model for identifying OSA was evaluated in comparison with PSG. RESULTS: Good agreement was evident between the SleepView and PSG based on the apnea-hypopnea index (AHI; r2 = 0.84, P < .01). The median AHI yielded by the SleepView was higher than that of PSG: 33.2 (interquartile range of 10.5–53.3) versus 19.2 (interquartile range of 5.2–53.6). The sensitivity and specificity of the SleepView for a PSG AHI of ≥ 5 were 80.28% and 95.45%, respectively, and the cutoff was 16.8. The area under the receiver operating curve for PSG AHIs of ≥ 5, > 15, and > 30 was 0.923, 0.924, and 0.979, respectively. When the AHI and oxygen desaturation index calculated by the SleepView were combined with waist circumference, the new predictive model showed a higher sensitivity of 92.96% and a specificity of 95.45% for a PSG AHI of ≥ 5, and the corresponding area under the receiver operating curve was 0.983. CONCLUSIONS: The SleepView device exhibited acceptable diagnostic accuracy for OSA in the Chinese population, especially in the severe OSA group. A practical predictive model comprising waist circumference, AHI, and oxygen desaturation index obtained with the SleepView was highly effective for screening even mild OSA. This simple and practical device may serve as a useful tool to screen for OSA. Further studies are required to validate the diagnostic efficiency of the SleepView in the home environment and in different populations.

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.

Treatment choice by patients with obstructive sleep apnea: data from two centers in China

Background Standard management has been recommended for obstructive sleep apnea (OSA) by several guidelines, but patient choice in the practical setting is unclear. Methods A survey nested in two prospective cohort studies of OSA (enrollment: 2001-2010) in China. The last interview was conducted between July 2014 and May 2015, using a comprehensive 10-point questionnaire administered in a face-to-face or telephone interview, and assessed (I) whether the participant had received any OSA treatment; (II) why he or she had decided for or against treatment; (III) what treatment was received; (IV) whether the participant used continuous positive airway pressure (CPAP) or OA daily; and (V) the perceived efficacy of therapy. Results A total of 4,097 subjects with a mean age of 45 years [37-55] responded to this survey, with a response rate of 79.4% (4,097/5,160); 2,779 subjects (67.8%) did not receive any treatment: 1,485 (53.4%) believed that their condition was not serious, despite severe OSA in 53.7% of the patients. A multivariate regression showed that the decision to receive treatment was associated with: age between 45-59 years [odds ratio (OR) 0.805, 95% CI: 0.691-0.936; P<0.001], female gender (OR 0.492, 95% CI: 0.383-0.631; P<0.001), severe OSA (OR 1.92, 95% CI: 1.01-3.64; P<0.001), hypertension (OR 1.414, 95% CI: 1.209-1.654; P<0.001) and diabetes (OR 1.760, 95% CI: 1.043-2.972; P=0.034). In subjects receiving treatment (n=1,318), 50.9% reported negative perceptions about the treatments. Conclusions Nearly two thirds of Chinese patients choose not to receive treatment after OSA diagnosis, and nearly half are negative about their treatments for OSA. This requires clinical attention, and warrants further study in different geographic settings.