Sleep Apnea Indices Associated with Markers of Inflammation and Cardiovascular Disease: A Proteomic Study in the MUSTACHE Cohort

Moderate to Severe Obstructive Sleep Apnea Is a Risk Factor for Severe COVID-19-A Nationwide Cohort Study

The impact of obstructive sleep apnea (OSA) and positive airway pressure (PAP) treatment on COVID-19 severity is unclear. In this population-based, nationwide study using multi-register data, we aimed to assess if OSA is a risk factor for COVID-19 severity and how adherence to PAP treatment and clinical characteristics affect the risk. Swedish residents with COVID-19 infection January 2020-May 2022 were included. An exposed group of OSA (starting PAP treatment 2015-2019) was identified. COVID-19 severity outcome was defined as mild (non-hospitalised), severe (hospitalised) or critical (intensive care or death). Covariates included comorbidities and sociodemographics. Conditional odds ratios (COR) with 95% confidence intervals (95% CI) were estimated using multinomial logistic regression. Among 8,894,162 individuals in Sweden, 1,932,081 (21.7%) had registered COVID-19 January 2020-May 2022. OSA was identified in 11,407 (0.6%) and was associated with an increased risk of severe (COR 1.34; 95% CI 1.25-1.43) and critical (1.25; 1.11-1.42) COVID-19 after adjustment for age, sex, education and comorbidities. Stratified by PAP adherence, age and COVID-19 wave, OSA was a risk factor for more severe COVID-19 in PAP-adherent and non-adherent individuals, in people aged 40-60 but not > 60 years and not after June 2021. OSA severity, assessed with the oxygen desaturation index (ODI), was independently associated with COVID-19 severity, with the highest risks for severe (1.23; 1.01-1.52) and critical (1.76; 1.17-2.63) COVID-19 observed in ODI ≥ 30 (vs. ODI < 15). We conclude that patients with moderate to severe OSA have an increased risk of severe COVID-19, also when PAP-treated, with an independent dose-response relationship between the severity of intermittent hypoxia and COVID-19 severity.

Treatment effect of head extension by cervical collar on moderate obstructive sleep apnea-A randomized controlled trial

As available treatments in obstructive sleep apnea are all associated with side-effects or adherence problems, there is a need for alternative treatment options. In this randomized, open, parallel-group intervention study, the effect of head extension by cervical collar was evaluated in patients with moderate obstructive sleep apnea. One-hundred patients with moderate obstructive sleep apnea (apneas and hypopneas per estimated hours asleep = respiratory events index: 15-30) were randomized to either lifestyle intervention, or cervical collar in combination with lifestyle intervention. Both groups received lifestyle advice. In addition, the treatment group was treated with a cervical collar, which allows adjustment of head extension, during sleep. Assessment with questionnaires and polygraphy were performed at baseline and after 6 ± 2 weeks. A linear regression model was used to assess a total effect on respiratory events index, which was the primary endpoint. In the intention-to-treat analysis, the cervical collar in combination with lifestyle intervention group decreased their respiratory events index (p = 0.008) and oxygen desaturation index (p = 0.008) more than the lifestyle intervention group, with a mean difference of -4.5 and -4.3, respectively. In the sub-analysis, there was a clear effect on respiratory events index in the supine position (mean difference between the groups -9.1, p = 0.018) but not on non-supine apnea-hypopnea index (-2.3, p = 0.17). We conclude that head extension by cervical collar during sleep resulted in improved respiratory events index and oxygen desaturation index values in patients with moderate obstructive sleep apnea. Cervical collar can be a second-line treatment option in this group, especially in positional obstructive sleep apnea.

Obstructive sleep apnoea and lung function, and their association with nocturnal hypoxemia: results from the Swedish CArdioPulmonary bioimage Study (SCAPIS) - a cross-sectional study

Obstructive sleep apnoea (OSA) and its associations with lung function.

BACKGROUND

OSA is highly prevalent and characterised by abnormal respiration during sleep. This large, population-based study aimed to investigate the associations between OSA and lung function in subjects aged 50-64 years.

METHOD

The population-based Swedish CArdioPulmonary bioimage Study includes information on anthropometry, comorbidities and spirometry. The current analysis included data from three centres (Gothenburg, Umeå and Uppsala) on whole-night respiratory polygraphy as a meta-analysis examining the overall effect size of lung function on sleep apnoea severity, expressed as ß-coefficient after stratifying for sex and adjusting for age, waist circumference and smoking status.

RESULTS

Data from 9016 participants (54% women, age 58±4 years, body mass index 27±4 kg/m2 ) with sleep recordings of good quality were included in the final analysis. Forced expiratory volume during 1 s (FEV 1 ) (ß=-0.10 (95% CI -0.16 to -0.03)), forced vital capacity (FVC) (-0.15 (-0.21 to -0.10)) and diffusion capacity for carbon monoxide (DLCO ) (-0.08 (-0.10 to -0.05)) were all negatively associated with the oxygen desaturation index (ODI) and also with per cent of registration with nocturnal oxygen saturation <90% FVC (-0.44 (-0.87 to -0.01)), FEV 1 (-0.86 (-1.36 to -0.36)) and DL CO (-0.47 (-0.60 to -0.35)). Additionally, a positive association was observed between FEV 1 (0.13 (0.05 to 0.22)) and DL CO (0.07 (0.04 to 0.09)) with the mean nocturnal saturation. There was a negative association between DL CO and apnoea-hypopnoea index, AHI, (ß=-0.04 (95% CI-0.06 to -0.03)), while no associations were found between FEV 1 or FVC and AHI.

CONCLUSION

In OSA, lower lung function is more distinctly associated with the nocturnal hypoxic burden than AHI. Potential lung function impairment should be investigated in OSA patients with a high ODI relative to AHI.

Low-Grade Inflammation Associated with Major Depression Subtypes: A Cross-Sectional Study

Major depressive disorder (MDD) is associated with inflammation and a high level of comorbidities. Atypical depression (AD) is a MDD subtype based on DSM criteria, that could have specific underlying biological mechanisms. AD is associated with elevated cardiovascular (CVD) comorbidities, higher risk of suicide attempts, hypersomnia, and anxiety disorder. In this study, we aim to investigate if AD and polysomnographic parameters could be associated with low-grade inflammation (LGI). LGI is defined by a range from 3 to 10 mg/L of C-reactive protein levels. We carried out a retrospective cohort study in which 765 individuals with MDD were split into two groups: with and without LGI. Our results exhibit differences between the groups for the polysomnographic parameters, with the LGI group showing parameters already associated with inflammation such as reduced rapid eye movement sleep and elevated hypoxemia markers (identified as CVD risk factor). We found that AD is associated with LGI (OR 1.48; p = 0.047) after adjustment. Likewise, we found an LGI prevalence in AD higher (34.8%) than in MDD without atypical features (26.8%). Overall, these results confirm the low-grade inflammation feature of AD and highlight polysomnographic parameters associated with LGI that could also act as risk factors in this context.

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.