Abstract Obstructive sleep apnea (OSA) and obesity are highly prevalent and bidirectionally associated. OSA is underrecognized, however, particularly in women. By mechanisms that overlap with those of obesity, OSA increases the risk of developing, or having poor outcomes from, comorbid chronic disorders and impairs quality of life. Using 2 illustrative cases, we discuss the relationships between OSA and obesity with type 2 diabetes, dyslipidemia, cardiovascular disease, cognitive disturbance, mood disorders, lower urinary tract symptoms, sexual function, and reproductive disorders. The differences in OSA between men and women, the phenotypic variability of OSA, and comorbid sleep disorders are highlighted. When the probability of OSA is high due to consistent symptoms, comorbidities, or both, a diagnostic sleep study is advisable. Continuous positive airway pressure or mandibular advancement splints improve symptoms. Benefits for comorbidities are variable depending on nightly duration of use. By contrast, weight loss and optimization of lifestyle behaviors are consistently beneficial.

Approach the patient with obstructive sleep apnea and obesity.

Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires.This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals.Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient.We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment.Retrospectively registered. 

Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults

https://www.mdpi.com/1648-9144/58/10/1499/pdf?version=1666345949

Evaluation of Blood Intercellular Adhesion Molecule-1 (ICAM-1) Level in Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis

Asthma and obstructive sleep apnea are highly prevalent conditions with a high cost burden. In addition to shared risk factors, existing data suggest a bidirectional relationship between asthma and OSA, where each condition can impact the other. Patients with asthma often complain of sleep fragmentation, nocturnal asthma symptoms, daytime sleepiness, and snoring. The prevalence of OSA increases with asthma severity, as evidenced by multiple large studies. Asthma may lower the threshold for arousal in OSA, resulting in the hypopnea with arousal phenotype. Epidemiologic studies in adults have shown that OSA is associated with worse asthma severity, increased frequency of exacerbation, and poor quality of life. The current literature assessing the relationship among OSA, asthma, and CPAP therapy is heavily dependent on observational studies. There is a need for randomized controlled trials to minimize the interference of confounding shared risk factors.

Revisiting Asthma Obstructive Sleep Apnea Overlap: Current Knowledge and Future Needs

The importance of ligand gated ion channels in sleep and sleep disorders.

There is a great association between the prevalence of obstructive sleep apnea (OSA) and asthma. Nonetheless, whether OSA impacts lung function, symptoms, and control in asthma and whether asthma increases the respiratory events in OSA are unknown. This meta-analysis aimed to examine the relationship between obstructive sleep apnea and asthma severity and vice versa.We carried out a systematic search of PubMed, EMBASE, and Scopus from inception to September 2022. Primary outcomes were lung function, parameters of polysomnography, the risk of OSA in more severe or difficult-to-control asthmatic patients, and the risk of asthma in patients with more severe OSA. Heterogeneity was examined with the Q test and I2 statistics. We also performed subgroup analysis, Meta-regression, and Egger's test for bias analysis.34 studies with 27,912 subjects were totally included. The results showed that the comorbidity of OSA aggravated lung function in asthmatic patients with a consequent decreased forced expiratory volume in one second %predicted (%FEV1) and the effect was particularly evident in children. %FEV1 tended to decrease in adult asthma patients complicated with OSA, but did not reach statistical significance. Interestingly, the risk of asthma seemed to be slightly lower in patients with more severe OSA (OR = 0.87, 95%CI 0.763-0.998). Asthma had no significant effect on polysomnography, but increased daytime sleepiness assessed by the Epworth Sleepiness Scale in OSA patients (WMD = 0.60, 95%CI 0.16-1.04). More severe asthma or difficult-to-control asthma was independently associated with OSA (odds ratio (OR) = 4.36, 95%CI 2.49-7.64).OSA was associated with more severe or difficult-to-control asthma with decreased %FEV1 in children. The effect of OSA on lung function in adult patients should be further confirmed. Asthma increased daytime sleepiness in OSA patients. More studies are warranted to investigate the effect of asthma on OSA severity and the impact of different OSA severity on the prevalence of asthma. It is strongly recommended that people with moderate-to-severe or difficult-to-control asthma screen for OSA and get the appropriate treatment. 

/pdf/the-relationship-between-obstructive-sleep-apnea-and-asthma-1gwjkov5.pdf

The relationship between obstructive sleep apnea and asthma severity and vice versa: a systematic review and meta-analysis

Abstract Introduction To systematically review the association between smoking behavior and obstructive sleep apnea (OSA). Aims and Methods PubMed, Medline, the Cochrane Library, EMBASE, and Scopus databases were used to conduct this review. The two researchers independently screened the literatures, conducted the quality assessment, and data extraction according to the inclusion and exclusion criteria. The RevMan 5.3 was used to analysis the apnea hypopnea index (AHI) index, min saturation of oxyhemoglobin (SaO2), Epworth Sleepiness Scale (ESS) score, and oxygen desaturation index (DOI) and publication bias analysis to assess the effect of smoking on OSA patients. Furthermore, we performed subgroup of the severity of OSA, different countries of sample origin (western countries or eastern countries), and pack-years (PYs < 10 or PYs ≥ 20) to analyze the heterogeneity. Results Thirteen studies were included in this analysis that conformed to inclusion criteria and exclusion criteria. Totally 3654 smokers and 9796 non-smokers have participated. The meta-analysis of 13 studies demonstrated that AHI levels were significantly higher in smoker group compared with non-smoker, ESS scores were also significantly higher in smoker group compared with non-smoker, min SaO2 levels were obviously lower in smoker group compared with non-smoker, however, DOI levels hadn’t significantly different between two groups. The subgroup analysis showed that there was an association between severe OSA, eastern countries, pack-years, and smoking. Conclusions Smoking behavior is a significant association with OSA. Heavy smokers with histories of more than 20 PYs were at a higher risk of OSA. Moreover, patient with severe OSA exhibited a significantly association with smoking compared with patients with mild or moderate OSA. Implications The relationship between smoking and OSA was controversial, especially, whether smoking increase or aggravate the risk of OSA. In our review and meta-analysis, we demonstrated that smoking behavior is a significant association with OSA. Heavy smokers with histories of more than 20 PYs were at a higher risk of OSA. Moreover, patient with severe OSA exhibited a significant association with smoking compared with patients with mild or moderate OSA. More prospective long-term follow-up studies about effect of quit smoking on OSA are recommended to establish the further relationship.

https://academic.oup.com/ntr/advance-article-pdf/doi/10.1093/ntr/ntac126/45230058/ntac126.pdf

Association Between Smoking Behavior and Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis

BACKGROUND
To screen for risk predictors of hypertension in patients with Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) and develop and validate a clinical model for individualized prediction of hypertension in consecutive patients with OSAHS.


METHODS
114 consecutive patients with OSAHS confirmed by PSG monitoring participated in this study. Those individuals were divided into two sets at a ratio of 7:3, using computer-generated random numbers: 82 individuals were assigned to the training set and 32 to the validation set. Important risk predictors of hypertension in individuals with OSAHS were confirmed using the LASSO method and a clinical nomogram constructed. The predictive accuracy was assessed by unadjusted concordance index (C-index) and calibration plot.


RESULTS
Univariate and multivariate regression analysis identified BMI, REM-AHI, REM-MSpO2 and T90% as predictive risk factors of OSAHS. Those risk factors were used to construct a clinical predictive nomogram. The calibration curves for hypertension in patients with OSAHS risk revealed excellent accuracy of the predictive nomogram model, internally and externally. The unadjusted concordance index (C-index) for the training and validation set was 0.897 [95% CI 0.795-0.912] and 0.894 [95% CI 0.788-0.820] respectively. The AUC of the training and validation set was 0.8175882 and 0.8031522, respectively. Decision curve analysis showed that the predictive model could be applied clinically when the threshold probability was 20 to 80%.


CONCLUSION
We constructed and validated a clinical nomogram to individually predict the occurrence of hypertension in patients with OSAHS. We determined that BMI, REM-AHI, REM-MSpO2 and T90% were independent risk predictors for hypertension in patients with OSAHS. This practical prognostic nomogram may help improve clinical decision making.

Development and validation of a clinical model to predict hypertension in consecutive patients with obstructive sleep apnea hypopnea syndrome: a hospital-based study and nomogram analysis.

Objective Obstructive sleep apnea (OSA) is a common sleep-disordered breathing disease. We aimed to establish an improved screening questionnaire without physical examinations for OSA named the CNCQ-OSA (Chinese community questionnaire for OSA). Methods A total of 2585 participants who visited sleep medicine center and underwent overnight polysomnography were grouped into two independent cohorts: derivation (n = 2180) and validation (n = 405). The CNCQ-OSA was designed according to the baseline of patients in derivation cohort. We comprehensively analyzed the data to evaluate the predictive value of the CNCQ-OSA, compared to the GOAL questionnaire, STOP-Bang questionnaire (SBQ) and NoSAS questionnaire. Results The CNCQ-OSA included seven variables: loud snoring, BMI ≥ 25 kg/m2, male gender, apnea, sleepiness, hypertension and age ≥30, with a total score ranging from 7 to 16.7 points (≥13.5 points indicating high risk of OSA, ≥14.5 points indicating extremely high risk). In the derivation and validation cohorts, the areas under the curve of the CNCQ-OSA were 0.761 and 0.767, respectively. In the validation cohort, the sensitivity and specificity of a CNCQ-OSA score ≥13.5 points for the apnea–hypopnea index (AHI) ≥5/h were 0.821 and 0.559, respectively (Youden index, 0.380), and the score ≥14.5 points were 0.494 and 0.887, respectively (Youden index, 0.375). The CNCQ-OSA had a better predictive value for AHI ≥ 5/h, AHI > 15/h and AHI > 30/h, with the highest Youden index, compared to the other questionnaires. Conclusion The CNCQ-OSA can effectively identify the risk of OSA, which is appropriate for self-screening at home without physical examinations.

Establishment and Application Evaluation of an Improved Obstructive Sleep Apnea Screening Questionnaire for Chinese Community: The CNCQ-OSA

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Papers

The relationship between obstructive sleep apnea and asthma severity and vice versa: a systematic review and meta-analysis

Association Between Smoking Behavior and Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis

Development and validation of a clinical model to predict hypertension in consecutive patients with obstructive sleep apnea hypopnea syndrome: a hospital-based study and nomogram analysis.

Establishment and Application Evaluation of an Improved Obstructive Sleep Apnea Screening Questionnaire for Chinese Community: The CNCQ-OSA