Objective To study the changes of receptor activator of nuclear factor-κB ligand (RANKL, an osteoclastogenesis-promoting factor) and osteoprotegerin (OPG, the decoy receptor for RANKL), oxidative stress and bone turnover markers in obstructive sleep apnea-hypopnea syndrome (OSAHS), in order to understand the potential mechanisms underlying bone loss in OSAHS patients. Methods Ninety-eight male patients with OSAHS, confirmed by polysomnography (PSG) study, were enrolled. The patients were divided into mild-moderate groups and severe groups. Forty-two male subjects who were confirmed as not having OSAHS served as the controls. The subjects’ bone mineral density (BMD) and T-score were assessed in lumbar spine and femoral neck using dual-energy X-ray absorptiometry. Blood samples were collected from all subjects for measurement of RANKL, OPG, the bone formation marker bone-specific alkaline phosphatase (BAP), the bone resorption marker tartrate-resistant acid phosphatase-5b (TRAP-5b), total antioxidant capacity (TAOC). Twenty-eight severe OSAHS patients accepted continuous positive airway pressure (CPAP) treatment voluntarily. After 6 months, PSG was conducted, and serum RANKL, OPG, TAOC, TRAP-5b, BAP was measured after six months treatment. Results The BMD, T-score of the femoral neck and the lumbar spine were significantly lower in OSAHS patients as compared to the control group. The level of BAP was significantly decreased in the OSAHS group as compared to the control group, and there was no significant difference in TRAP-5b level between two groups. As compared with the control group, levels of OPG, TAOC and the OPG/RANKL ratio decreased significantly. None of these parameters (BMD, T-score, RANKL, OPG, TRAP-5b, BAP) showed significant difference between patients with mild-moderate and severe OSAHS group. Correlation analysis showed that the apnea hypopnea index and oxygen desaturation index were correlated with TAOC. BAP level was positively correlated with TAOC and lowest pulse oxygen saturation. The serum level of TAOC was lower in the OSAHS group after CPAP therapy, but the levels of RANKL, OPG, TRAP-5b, BAP were not different. As compared with the OSAHS group before CPAP therapy, the BMD of the femoral neck and the lumbar spine were not significant difference. Conclusions In patients with OSAHS, the oxidative stress response is enhanced, and imbalance of OPG/RANKL is shifted, which participates in the occurrence of osteoporosis. The oxidative stress injury of severe OSAHS patients was relieved after non-invasive ventilation treatment, but the effect of oxidative stress response on bone metabolism still needs further evaluation.
Objective To explore the causal association between obstructive sleep apnea (OSA) and venous thromboembolism (VTE). Methods Using the summary statistical data from the FinnGen biological sample library and IEU OpenGWAS database, the relationship between OSA and VTE, including deep vein thrombosis (DVT) and pulmonary embolism, was explored through Mendelian randomization (MR) method, with inverse variance weighted (IVW) as the main analysis method. Results The results of univariate MR analysis using IVW method showed that OSA was associated with VTE and pulmonary embolism (P<0.05), with odds ratios and 95% confidence intervals of 1.204 (1.067, 1.351) and 1.352 (1.179, 1.544), respectively. There was no correlation with DVT (P>0.05). Multivariate MR analysis showed that after adjustment for confounding factors (smoking, diabetes, obesity and cancer), OSA was associated with VTE, DVT and pulmonary embolism (P<0.05), with odds ratios and 95% confidence intervals of 1.168 (1.053, 1.322), 1.247 (1.064, 1.491) and 1.158 (1.021, 1.326), respectively. Conclusion OSA increases the risk of VTE, DVT, and pulmonary embolism.
Objective To investigate the changes and clinical relationship of plasma adrenomedullin( ADM) , atrial natriuretic polypeptide( ANP) , and heart rate variability( HRV) in patients with obstructive sleep apnea-hypopnea syndrome ( OSAHS) . Methods Seventy-five inpatients with OSAHS were enrolled in this study. According to the apnea hypopnea index ( AHI) by polysomnography, the subjects were divided into a mild group, a moderate group, and a severe group. Meanwhile, HRV was screened bydynamic electrocardiogram in sleep laboratory. HRV parameters were obtained including LF ( low frequency power) , HF( high frequency power) , pNN50( percentage of NN50 in the total number of N-N intervals) ,SDNN( standard deviation of the N-N intervals) , rMSSD( square root of the mean squared differences of successive N-N intervals ) . Plasma levels of ADM/ANP were measured by radioimmunoassay. Results The levels of SDNN ( P lt;0. 05) , rMSSD, pNN50, LF ( P lt; 0. 05) and HF were gradually reduced, and the levels of ADM ( P lt;0. 05) and ANP ( P lt; 0. 05) were increased with increasing severity of OSAHS. Linear correlation analysis demonstrated that SDNN was negatively correlated with ADM( r = - 0. 423, P lt;0. 05)and ANP( r = - 0. 452, P lt; 0. 05) , and LF was also negatively correlated with ADM( r = - 0. 348, P lt;0. 05) . Conclusion Lower HRV is associated with more sever OSAHS, and it may be modulated neurohumorally by ADM and ANP.
Objective To investigate the correlation between obstructive sleep apnea hypopnea syndrome (OSAHS) and biochemical indexes in children. Methods Seventy-eight children with OSAHS in our hospital from January 2015 to February 2017 were recruited as an observation group, and 100 normal children who underwent physical examination were selected as a control group in the same period. The mean values and positive rates of biochemical markers were compared between two groups including alanine aminotransferase (ALT), blood urea nitrogen (BUN), total cholesterol (TC), triglyceride (TG), creatine kinase isoenzyme (CK-MB), cardiac troponin I (cTnI), fasting blood glucose (FPG) level. Results The mean values of biochemical indexes showed significant differences between the observation group and the control group except BUN and FPG [ALT, (52.1±26.2) U/L vs. (41.3±18.5) U/L; TC, (4.9±0.9) mmol/L vs. (4.3±0.8) mmol/L; TG, (1.4±0.7) mmol/L vs. (1.0±0.4) mmol/L; CK-MB, (24.3±9.5) U/L vs. (11.2±8.2) U/L; cTnI, (1.4±0.7) μg/L vs. (1.0±0.6) μg/L] (all P<0.05). The positive rates also showed significant differences between the observation group and the control group except BUN and FPG [ALT (48.7%vs. 14.0%), TC (24.4% vs. 8.0%), TG (23.1% vs. 8.0%), CK-MB (41.0% vs. 11.0%), cTnI (34.6% vs. 7.0%) (all P<0.05). Conclusions The cardiac function and liver function are significantly impaired in children with OSAHS, showing the disorder of lipid metabolism to some extent. These abnormal indexes may be the occurrence and development of OSAHS. More attention should be paid to the detection of biochemical indexes in children with OSAHS.
Objective To discuss the screening and diagnostic value of nocturnal oximetry saturation monitoring combined with clinical score (CS) for patients with obstructive sleep apnea hypopnea syndrome (OSAHS). Methods A total of 106 snorers were recruited in the analysis whose general information and medical history were collected respectively. All patients received polysomnography (PSG) and oximeter monitoring. The patients were divided into a non-OSAHS group and an OSAHS group according to apnea hypopnea index (AHI). A correlation analysis was made between PSG-AHI and oximeter-ODI to analyze the diagnostic sensitivity and specificity of different ODI combined with CS for OSAHS. Results The AHI, ODI, CS for the non-OSAHS group were 1.8±1.4 times/h, 2.6±3.5 times/h and 1.0±0.8; while for the OSAHS group they were correspondingly 37.3±23.9 times/h, 31.0±24.1 times/h, 2.6±1.1. There was a significant correlation between ODI and AHI (r=0.943, P<0.01). The sensitivity and specificity of ODI≥5 times/h combined with CS≥2 for diagnosis of OSAHS were 91.7% and 94.1% respectively, which had the value of preliminary screening. The sensitivity and specificity of ODI≥10 times/h combined with CS≥2 for diagnosis of OSAHS were 77.8% and 100.0% respectively, which would not result in misdiagnose for severe patients with AHI >30 times/h, so it could be an index of severe OSAHS screening. Conclusion Nocturnal oxyhemoglobin saturation monitoring combined with clinical score is of significant value for initial diagnosis of OSAHS.
Objective To explore effects of edaravone on apoptosis and expressions of apoptotic proteins Smac and XIAP in hippocampal CA1 pyramidal cell of rats under intermittent hypoxia. Methods A total of 96 adult male Wistar rats were randomly divided into control group, 5% intermittent hypoxic group and edaravone group, and each group was divided into 4 time groups at 7 d, 14 d, 21 d and 28 d, respectively, with 8 rats in each subgroup. The content of reactive oxygen species (ROS) in hippocampal tissues of the experimental rats was detected by the reactive oxygen species detection kit. Immunohistochemistry and Western blot were used to detect the expressions of Smac and XIAP protein in hippocampal CA1 region. The Tunel method detected the apoptosis of neurons. Results Compared with the control group, the content of ROS, the expressions of Smac and XIAP proteins and the neuronal apoptosis index in the hippocampus were increased in the 5% intermittent hypoxia group and the edaravone group at each time point (all P<0.05). The content of ROS, the Smac protein expression and the neuronal apoptosis index in the edaravone group were significantly lower than those in the 5% intermittent hypoxia group (all P<0.05). The expression of XIAP protein in the edaravone group was significantly higher than that in the 5% intermittent hypoxia group (P<0.05). Conclusion Edaravone may improve the antioxidant capacity of the body by scavenging oxygen free radicals and regulate Smac and XIAP- mediated apoptosis, thus playing a protective role on neurons.
Objective To study the correlation between smoking and obstructive sleep apnea (OSA). Methods A total of 454 patients from October 2015 to July 2021 were retrospectively collected for nocturnal polysomnography monitoring (no less than 7 hours). The patients were divided into an OSA group (n=405) and a control group (n=49, patients with primary snoring) according to the results of polysomnography monitoring. According to the apnea hypopnea index (AHI) and the lowest oxygen saturation during sleep, the severity of OSA was classified into a mild to moderate group (5 times/h ≤ AHI<30 times/h) and a severe group (AHI ≥30 times/h). The patients were inquired about their smoking history, then the patients diagnosed with OSA were further divided into a smoking group, a smoking cessation group, and a non-smoking group based on their smoking history. Results The smoking rate of the patients in the OSA group was higher than that in the control group (50.9% vs. 32.7%, P<0.05), while the smoking rate in the severe OSA group was higher than that in the mild to moderate group (55.7% vs. 39.8%, P<0.05). Smoking was positively correlated with AHI, cumulative percentages of time spent at oxygen saturation below 90% (Ts90%), and total apnea time (r value was 0.196, 0.197, 0.163, P<0.05), while negatively correlated with the lowest and average SpO2 during sleep (r value was –0.202, –0.214, P<0.05). The logistic regression analysis with severe OSA as the outcome variable showed that smoking (OR=1.781) and obesity (OR=1.930) were independent risk factors of severe OSA (P<0.05). The comparison between groups of the OSA patients with different smoking states showed that the proportion of severe OSA, AHI, Ts90%, and total apnea time (77.8%, 53.55 times/h, 18.35%, and 111.70 minutes, respectively) of the smoking group were higher than those of the non-smoking group (62.8%, 40.20 times/h, 8.40%, and 76.20 minutes, respectively, P<0.05). The lowest SpO2 and average SpO2 during sleep (69.50%, 93.00%, respectively) of the smoking group were lower than those of the non-smoking group (75.00%, 94.00%, respectively, both P<0.05). The average SpO2 of the smoking cessation group was higher than that of the smoking group (94.00% vs. 93.00%, P<0.05), and the Ts90% of the smoking cessation group was lower than that of the smoking group (6.75% vs. 18.35%, P<0.05). Conclusions Smoking significantly affects the degree of sleep-disordered breathing and may be an independent risk factor for severe OSA. Smoking can exacerbate the severity of OSA and the degree of hypoxia, while smoking cessation can improve the degree of hypoxia in OSA patients.
ObjectiveTo observes the postoperative transformation of subjective and objective sleep quality and related influencing factors in patients with rheumatic valvular heart disease (RVHD) who were found with central sleep apnea (CSA) after cardiac valve replacement.MethodsA total of 262 patients undergoing cardiac valve replacement were screened for CSA by overnight polysomnography and 21 patients with CSA were enrolled and followed up successfully in the 3th, 6th and 12th month of postoperation from April 2010 to January 2013. The Pittsburgh Sleep Quality Index, Epworth Sleepiness, polysomnography and cardiac function were evaluated in preoperation and postoperation (in the 3th, 6th and 12th month).ResultsNew York Heart Association class ［preoperation and postoperation: (3.7±0.5), (2.3±0.6), (1.7±0.6), (1.6±0.7), F=81.124, P<0.05］ continuously decreased, left ventricular ejection fraction ［preoperation and postoperation: (58.5±6.8)%, (60.0±7.4)%, (60.9±5.6)%, (64.4±4.0)%, F=7.182, P<0.05］ steadily increased, six-minute walk distance ［preoperation and postoperation: (271.5±76.6), (422.1±71.9), (445.1±56.2), (454.5± 63.5) m, F=67.134, P<0.05］ constantly increased. During postoperative follow-up, sleep apnea-hypopnea index ［preoperation and postoperation: (26.2±13.4), (12.0±11.5), (8.6±7.5), (7.4±5.5)/h, F=20.548, P<0.05, central sleep apnea index ［preoperation and postoperation: (19.6±10.3), (0.5±1.5), (0.3±1.3), (0.2±0.7)/h, F=72.926, P<0.05］ and oxygen desaturation index ［preoperation and postoperation: (20.1±16.6), (10.8±9.5), (8.5±7.2), (6.1±5.1)/h, F=9.646, P<0.05］ sustained improved. Aroual index ［preoperation and postoperation: (23.1±12.1), (2.7±3.8), (3.5±4.8), (2.2±2.1)/h, F=58.370, P<0.05］ presented overall downward trend. Pittsburgh Sleep Quality Index scale ［preoperation and postoperation: (11.1±3.2), (8.2±3.3), (6.0±3.8), (4.4±2.5), F=27.670, P<0.05］ were constantly improved. Epworth Sleepiness scale ［preoperation and postoperation: (13.3±5.7), (6.9±4.5), (8.2±4.8), (6.1±3.7), F=15.994, P<0.05］ showed overall reduction.ConclusionThe sleep quality of patients with RVHD and CSA is improved after cardiac valve replacement, of which the trend is in keep with postoperative recovery of cardiac function.
Objective To investigate the clinical significance of changes in cardiopulmonary function, degree of hypoxia and inflammatory factors in obstructive sleep apnea hypopnea syndrome (OSAHS) patients combined chronic obstructive pulmonary disease (COPD). Methods A retrospective case-control study was conducted on 209 patients with OSAHS admitted from October 2015 to April 2022. The OSAHS patients were divided into an OSAHS-only group, an OSAHS combined with mild COPD group, an OSAHS combined with moderate COPD group, and an OSAHS combined with severe and very severe COPD group based on pulmonary function test. The characteristics of cardiopulmonary function [(pulmonary artery pressure, N terminal pro B type natriuretic peptide (NT-proBNP), forced expiratory volume in the first second to forced vital capacity (FEV1/FVC), percent predicted value of FEV1 (FEV1%pred)], hypoxia indexes [night lowest saturation of pulse oxygen (NL-SpO2), night medial saturation of pulse oxygen (NM-SpO2), saturation of pulse oxygen less than 85% of the time (TS85), diurnal lowest saturation of pulse oxygen (DL-SpO2)], inflammatory factor indicators [procalcitonin (PCT), interleukin-6 (IL-6), hypersensitive C-reactive protein (hs-CRP), neutrophil to lymphocyte ratio (NLR)], and other characteristics were compared separately. The partial correlation analysis and logistic regression were used to analyze the influencing factors of OSAHS with COPD. Results There were statistically significant differences in age, days of hospitalization, cardiopulmonary function indexes, hypoxia indexes and inflammatory factor indexes between the OSAHS combined with COPD group and the OSAHS-only group (all P<0.05). And pulmonary artery pressure, NT-proBNP, TS85, IL-6, and NLR were higher and DL-SpO2, NL-SpO2, and NM-SpO2 were lower in the OSAHS combined with severe and very severe COPD group compared with the OSAHS combined with mild COPD group (all P<0.05). In the partial correlation analysis, FEV1%pred was negatively correlated with pulmonary artery pressure, NT-proBNP, TS85, IL-6, hs-CRP and NLR, and positively correlated with DL-SpO2, NL-SpO2 and NM-SpO2 (all P<0.05). In regression analysis, NLR and TS85 were the main risk factors for OSAHS combined with COPD (all P<0.05). Conclusions OSAHS patients combined with COPD have longer hospital days, greater burden of hypoxia, cardiopulmonary function and inflammation compared with patients with OSAHS alone, especially more significant in patients with poorer pulmonary function, and higher incidence of pulmonary heart disease, atrial fibrillation, and lower limb edema. NLR and TS85 are the main risk factors in patients with OSAHS combined with severe and very severe COPD.
Sleep apnea (SA) detection method based on traditional machine learning needs a lot of efforts in feature engineering and classifier design. We constructed a one-dimensional convolutional neural network (CNN) model, which consists in four convolution layers, four pooling layers, two full connection layers and one classification layer. The automatic feature extraction and classification were realized by the structure of the proposed CNN model. The model was verified by the whole night single-channel sleep electrocardiogram (ECG) signals of 70 subjects from the Apnea-ECG dataset. Our results showed that the accuracy of per-segment SA detection was ranged from 80.1% to 88.0%, using the input signals of single-channel ECG signal, RR interval (RRI) sequence, R peak sequence and RRI sequence + R peak sequence respectively. These results indicated that the proposed CNN model was effective and can automatically extract and classify features from the original single-channel ECG signal or its derived signal RRI and R peak sequence. When the input signals were RRI sequence + R peak sequence, the CNN model achieved the best performance. The accuracy, sensitivity and specificity of per-segment SA detection were 88.0%, 85.1% and 89.9%, respectively. And the accuracy of per-recording SA diagnosis was 100%. These findings indicated that the proposed method can effectively improve the accuracy and robustness of SA detection and outperform the methods reported in recent years. The proposed CNN model can be applied to portable screening diagnosis equipment for SA with remote server.