Sleep disorder is related to many comorbidities, such as diabetes, obesity, cardiovascular diseases, and hypertension. Because of its increasing prevalence rate, it has become a global problem that seriously threatens people’s health. Various forms of sleep disorder can cause increased insulin resistance and/or decreased sensitivity, thus affecting the occurrence, development and prognosis of diabetes. However, sleep health has not been paid attention to in recent years. Therefore, this article summarizes the findings of the correlation between sleep disorder and diabetes mellitus in recent years, by elaborating the relationship between various types of sleep disorder (including sleep apnea syndrome) and diabetes mellitus, as well as their mechanisms and intervention measures, in order to enhance the attention of clinical workers to sleep health, and to provide basis for reducing the risk of diabetes.
Objective To investigate the risk factors of chronic obstructive pulmonary disease (COPD) combined with obstructive sleep apnea (OSA) and its relationship with apnea-hypopnea index (AHI). Methods Clinical data of 216 COPD patients with OSA were retrospectively chosen in the period from January 2016 to December 2019 in our hospital. All patients were divided into different groups according to with or without OSA and the clinical features of patients with and without OSA were compared. Multivariate analysis was used to analyze the influencing factors of COPD with OSA and the correlation between AHI and COPD with OSA was also evaluated. Results ① The age, body mass index (BMI), neck circumference, smoking index, forced expiratory volume in 1 second (FEV1), FEV1% predicted (FEV1pred), the ratio of FEV1 to the forced vital capacity of the lungs (FEV1/FVC), COPD assessment test (CAT) score, Epworth sleepiness scale (ESS) score, Charlson comorbidity index (CCI) score, sleep apnea clinical score (SACS) score and proportion of patients with essential hypertension in OSA group were significantly higher than non-OSA group (P<0.05). The course of disease and the proportion of severe COPD and GOLD grade 4 in OSA group were significantly less than non-OSA group (P<0.05). ② AHI was positively correlated with age, BMI, neck circumference, smoking index, FEV1%pred, FEV1%pred<50%, CAT score, ESS score, CCI score and SACS score (P<0.05); and negatively correlated with FEV1%pred<50% (P<0.05). ③ Multivariate analysis showed that BMI, FEV1%pred<50%, CAT score and ESS score were the independent factors of COPD patients with OSA (P<0.05). ④ The proportion of AHI<5 times/h in GOLD grade 4 was significantly higher than GOLD grade 1-3 (P<0.05). The proportion of AHI> 30 times/h in GOLD grade 4 was significantly lower than GOLD grade 1-3 (P<0.05). Conclusion The incidence of COPD with OSA was independently correlated with BMI, FEV1%pred, CAT score and ESS score; patients with severe COPD possess lower OSA risk.
Habitual snoring can occur in both children and adults. If it is physiological snoring, it usually does not require special intervention. If it is pathological snoring, such as snoring caused by central diseases and obstructive diseases, it needs to be treated as soon as possible. Habitual snoring has more harm to children, such as causing sleep structure disorders, slow growth and development. During the snoring process, children’s sleep fragmentation and hypoxia state lead to changes in the transmission of neurochemicals in the brain’s precortex, causing adverse effects on brain function and inducing attention deficit hyperactivity disorder. This article reviews relevant research in recent years to further elucidate the relationship between children’s habitual snoring and attention deficit hyperactivity disorder, and provide a basis for future clinical research and intervention.
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.
This study seeks to explore the early signs of cognitive impairment in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). According to polysomnography, twenty patients diagnosed with OSAHS and twenty normal controls underwent event-related potential (ERP) examination including mismatch negativity (MMN) and P300. Compared with normal controls, OSAHS patients showed significantly prolonged latency of MMN and P300 at Cz. After controlling age and body mass index (BMI), MMN latency positively correlated with apnea hypopnea index (AHI), oxygen reduction index, stage N1 sleep and arousal index, while MMN latency negatively correlated with stage N3 sleep and mean blood oxygen saturation; and P300 latency positively related to AHI and oxygen reduction index; no relationships were found among MMN latency, MMN amplitude, P300 latency and P300 amplitude. These results suggest that the brain function of automatic processing and controlled processing aere impaired in OSAHS patients, and these dysfunction are correlated with nocturnal repeatedly hypoxemia and sleep structure disturbance.
In China, chronic respiratory diseases (CRD) are characterized by high prevalence, disability rate, and mortality rate, imposing a severe disease burden. Home non-invasive ventilation (HNIV) therapy can improve ventilation, alleviate respiratory muscle fatigue, enhance oxygenation and carbon dioxide retention, delay the progression of various CRD, and even improve survival. However, there is currently a lack of long-term management standards and standardized guidance for patients receiving HNIV therapy in China. The Respiratory Therapy Group of the Chinese thoracic Society and Chinese Association of Rehabilitation Medicine, has summarized 11 questions related to HNIV for different diseases, answered various questions, and put forward modification suggestions. This consensus aims to provide references for frontline clinical staff, promote the standardization of HNIV application in China, and improve the level of treatment.Summary of recommendationsQuestion 1. For which patients is HNIV suitable?Recommendation: HNIV is recommended for patients with ventilatory dysfunction due to various causes, such as: obstructive sleep apnea syndrome [high-quality evidence, strong recommendation], chronic obstructive pulmonary disease [high/moderate-quality evidence, strong recommendation], obesity hypoventilation syndrome [moderate/low-quality evidence, strong recommendation], and neuromuscular diseases [low-quality evidence, strong recommendation].Question 2. When should HNIV be initiated?Recommendation: The timing for initiating HNIV therapy should be based on a comprehensive assessment of disease diagnosis, severity, symptoms, and comorbidities. Early standardized intervention is a crucial measure for improving prognosis and reducing long-term disease burden. Specific recommended indications are listed in Table 2. [high/moderate quality evidence, strong recommendation]Question 3. How should health education on HNIV be conducted?Recommendation: All HNIV patients should receive educational training. The five-step training method is recommended as the preferred approach for educating HNIV patients and their families. [Moderate-quality evidence, weak recommendation]Question 4. How to properly select a home non-invasive ventilator?Recommendations: When selecting a home non-invasive ventilator, patients should first consult a professional physician or respiratory therapist to obtain specialized advice based on their specific condition. Physicians should make decisions by comprehensively considering the patient’s disease type and severity, ventilator modes and parameters, synchrony, comfort, remote monitoring requirements, and financial circumstances. Refer to Table 3 for ventilation mode selection based on different diseases.Question 5. How should accessories for HNIV be selected?Recommendation: Mask selection should be based on disease type, dynamic assessment of the patient’s breathing pattern, and patient preference, with regular reassessment of fit during follow-up [High/moderate-quality evidence, strong recommendation]. Active heated humidifiers are recommended as the first choice for HNIV patients [Low-quality evidence, weak recommendation].Question 6. How should HNIV parameters be set and adjusted?Recommendation: Parameter adjustments should be performed in hospital and community settings. Long-term home use should only commence after confirming appropriate and safe settings. Avoid patients or caregivers making arbitrary adjustments that may cause adverse events. [Moderate-quality evidence, strong recommendation]Pressure settings for NIV should be tailored to the patient’s underlying disease and clinical objectives. Additional parameters including backup rate, inspiratory sensitivity, pressure rise time, and expiratory sensitivity must also be configured. The setup process is summarized in Figure 1. [Moderate-quality evidence, strong recommendation]Question 7. What is the recommended daily usage duration for HNIV?Recommendation: For patients using HNIV due to sleep apnea or sleep-related hypoventilation, it is recommended to use the device for at least 4 hours daily on more than 70% of nights, with usage duration covering sleep periods as much as possible. For patients using HNIV due to chronic hypercapnia, daily use of at least 5 - 6 hours is required, with priority given to nighttime use. [Low-quality evidence, weak recommendation]Question 8. When should respiratory support be adjusted during HNIV?Recommendation: Assess the efficacy of HNIV based on clinical and physiological criteria to determine whether to continue ventilatory support [Moderate-quality evidence, strong recommendation]. If disease progression or complications arise, and HNIV can no longer maintain effective ventilation, discontinue HNIV and seek hospital care promptly [Low-quality evidence, strong recommendation]. HNIV should not be discontinued in patients requiring intermittent or continuous HNIV during exercise [Moderate-quality evidence, strong recommendation].Question 9. How should complications associated with HNIV be managed?Recommendation: Common complications of noninvasive ventilation include skin pressure injury, air leak, patient-ventilator asynchrony, and thick sputum. These should be actively prevented and managed during HNIV. [Moderate-quality evidence, strong recommendation]Question 10. How should the effectiveness of HNIV be assessed and followed up?Recommendation: Close monitoring and follow-up are recommended for patients receiving home noninvasive ventilation. Monitoring indicators and follow-up frequency are summarized in Table 6. [Moderate-quality evidence, GPS]Question 11. How should the management pathway for HNIV be established and optimized?Recommendations: Establish a tiered, dynamic, and individualized HNIV management pathway based on patient condition characteristics, technical support availability, and home care capabilities: ① For high-risk acute exacerbation/unstable patients: Primarily use the traditional "hospital-community-home" model supplemented by self-management; for low-risk acute exacerbation/stable patients: Primarily use self-management with IoT-based remote monitoring where feasible. ② Dynamically adjust based on disease stage: intensify in-person training during the initial phase and gradually transition to remote monitoring during the stable phase; ③ Promote multidisciplinary collaboration, utilize smart devices for real-time monitoring, and ensure data security; ④ Enhance patient self-management capabilities through standardized education and regular follow-ups. [Low-quality evidence, GPS]