Objective To study the prevalence of snoring in adults aged over 35 yrs in Kelamayi city of Xinjiang province, and screen the snoring associated factors.Methods 2600 subjects( age≥35 yrs) were enrolled from a random sample of the population living in 5 streets, two districts in Kelamayi city. All subjects were required to answer questions about their snoring by himself or bed partner at home. The questionnaire included items concerning snoring, daytime sleepness ( Epworth sleep scale, ESS) , smoking and drinking habits, while height, weight, neck circumference, abdominal circumference, waistline, and hip circumference were meatured. The subjects were assigned to a high risk group and a low risk group according to the questionaire of scoring. Results 2590 subjects had completed the questionnaire and 2513 ( 97. 0% )were eligble for evaluation. The cases of ever snoring was 1312 ( 52. 2% ) , among which moderate to severe snoring accounted for 38. 9% . Before the age of 60 yrs, the risk of snoring increased with age, and higher in males than females( 61. 7% vs. 45. 1% ) . The prevalence of snoring increased with neck circumference( P lt;0. 05) . The body mass index ( BMI) [ ( 28. 2 ±3. 6) kg/m2 vs. ( 25. 1 ±3. 9) kg/m2 ] , neck circumference [ ( 37. 7 ±3. 6) cmvs. ( 35. 6 ±3. 6) cm] , abdominal circumference [ ( 96. 9 ±13. 6) cm vs. ( 88. 7 ±11. 1) cm] , waistline [ ( 92. 2 ±9. 8) cm vs. ( 84. 7 ±10. 2) cm] , hip circumference [ ( 102. 9 ±9. 6) cm vs.( 96. 4 ±9. 3) cm] , proportionlity of waistline to hip circumference ( 0. 90 ±0. 07 vs. 0. 88 ±0. 08) , systolic blood pressure [ ( 132. 0 ±17. 5) mm Hg vs. ( 125. 6 ±16. 8) mm Hg] , and diastolic blood pressure [ ( 83. 3 ±12. 8) mm Hg vs. ( 78. 3 ±12. 6) mm Hg] were significantly different bettween the high and the low risk groups ( P lt; 0. 01) . Logistic regression analysis revealed that age ( OR = 1. 519) , BMI ( OR =2. 549) , neck circumference (OR = 2. 473) , smoking (OR = 2. 765) , ESS(OR = 2. 575) , and postmenopause( OR=1. 806) were main risk factors for snoring( P lt; 0. 05) . Conclusions The prevalence of snoring in adults over 35 yrs is high in Kelamayi city. The high risk factors for snoring are age, BMI, neck circumference, smoking, ESS, and post-menopause.
ObjectiveTo establish a screening model for obstructive sleep apnea hypopnea syndrome (OSAHS) through data analysis, and explore the risk factors of OSAHS. MethodsA total of 558 patients who underwent polysomnography in the Sleep Monitoring Room of Zigong Fourth People’s Hospital were recruited in the study. Among them there were 163 cases in a snore group and 395 cases in an OSAHS group. Risk factors of OSAHS were screened by both univariate analysis and multivariate analysis, then the model was established by means of binary logistic regression analysis. Finally, the screening model was evaluated by receiver operating characteristic (ROC) curve of the combined predictive factor. ResultsThe screening model of OSAHS was established as: X=–10.286+0.280×body mass index+1.057×snoring degree+1.124×sex+0.085×Epworth score+0.036×age. In this equation, sex value was 1 for men and 0 for women. If the value of X is higher than 1.123, it is likely that OSAHS would occur, and the probability (P)=ex/(1+ex). The sensitivity of the screening model was 77.70%, the specificity was 85.89%, the area under the ROC curve was 0.890, and the 95% confidence interval ranged from 0.862 to 0.918. ConclusionThis study demonstrates that a screening model based on the snoring degree, Epworth score, and body measurement data is a valuable tool to predict and screen OSAHS in patients with snoring, and the screening model could be useful in clinical diagnosis of OSAHS.
Monitoring of bowel sounds is an important method to assess bowel motility during sleep, but it is seriously affected by snoring noise. In this paper, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was applied to remove snoring noise from bowel sounds during sleep. Specifically, the noisy bowel sounds were first band-pass filtered, then decomposed by the CEEMDAN method, and finally the appropriate components were selected to reconstruct the pure bowel sounds. The results of semi-simulated and real data showed that the CEEMDAN method was better than empirical mode decomposition and wavelet denoising method. The CEEMDAN method is used to remove snoring noise from bowel sounds during sleep, which lays an important foundation for using bowel sounds to assess the intestinal motility during sleep.
ObjectiveTo investigate the causal relationship between gastroesophageal reflux disease (GERD) and obstructive sleep apnea (OSA) with its typical symptoms (snoring and daytime sleepiness) by using Mendelian randomization (MR). MethodsThe inverse-variance weighted method was used as the main analysis method to assess the causal effect. Sensitivity and pleiotropy analyses were carried out using leave-one-out and MR-Egger analysis, and then heterogeneity tests were conducted. ResultsIn the MR analysis, genetically predicted GERD was associated with a greater risk of OSA (IVW: OR=1.528, 95%CI 1.374 to 1.699, P=5.315E‒15). Additional MR results were consistent with the IVW results, and no pleiotropy or heterogeneity was found. We also discovered a significant causal relationship between GRED and snoring (IVW: OR=0.959, 95%CI 0.949 to 0.969, P=1.507E‒15), and daytime sleepiness (IVW: OR=1.024, 95%CI 1.021 to 1.036, P=4.580E‒5), with no evidence of pleiotropy. ConclusionThe MR study supports a causal effect between GERD and OSA with its typical symptoms (daytime sleepiness and snoring).