A Comparison of the Reliability of Five Sleep Questionnaires for the Detection of Obstructive Sleep Apnea

The impact of obesity, age, and gender on plasmatic levels of selected glycoprotein biomarkers and miRNA-499 in OSA patients

BACKGROUND

The current obstructive sleep apnea (OSA) diagnostic uses polysomnography or limited polygraphy and requires specialized personnel and technical equipment. Glycoprotein biomarkers and microRNAs are being explored as a possible new method for screening. We aimed to evaluate whether certain biomarkers and microRNA, previously identified as related to OSA, could be influenced by factors such as gender, age, and obesity level in patients with OSA.

METHODS

In this retrospective analytical study, patients with moderate to severe OSA (n = 130) were compared with the control group. Serum levels of selected biomarkers and microRNA were taken from both groups. The group of OSA patients was then stratified by gender, obesity level, and age to see the possible influence of those variables on biomarker levels.

RESULTS

Levels of all studied biomarkers - C-reactive protein (CRP), high-sensitivity troponin I (hsTnI), pentraxin-3 (PTX-3), and microRNA-499 were significantly higher in patients with OSA compared to the control group. In the OSA group only hsTnI showed a statistically significant relationship with gender. Levels of CRP and hsTnI showed a significant dependence on the level of obesity. Dependency on age was proven for hsTnI. CRP, PTX-3, and microRNA-499 did not have any statistically significant relationship with age.

CONCLUSION

We found that serum levels of pentraxin-3 and microRNA-499 in patients with moderate to severe obstructive sleep apnoea are independent of gender, obesity, and age. CRP was affected by the level of obesity and hsTnI was influenced by all 3 variables. We consider these findings important for further research of OSA biomarkers.

Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review

Obstructive sleep apnea (OSA) is a chronic condition affecting up to 1 billion people, globally. Despite this spread, OSA is still thought to be underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake (wakefulness) is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs.

Stop-bang questionnaire for screening obstructive sleep apnea syndrome among hypertensive patients in Kenya

INTRODUCTION

Obstructive sleep apnea (OSA) is a type of breathing problem during sleep caused by the blockage of the upper airway, which can cause cessation of airflow. There is limited research on the prevalence of OSA in hypertensive patients in sub-Saharan Africa (SSA). The study aimed to describe the prevalence and clinical characteristics of OSA among hypertensive patients at a tertiary hospital in Nairobi, Kenya.

METHODS

This cross-sectional study was conducted at the Aga Khan University Hospital in Nairobi, Kenya. Two hundred and fifty-one hypertensive patients were screened for OSA risk using the STOP-Bang questionnaire (SBQ). Patients with a SBQ score of ≥ 4 were categorized as high risk for OSA. Descriptive statistics were employed to describe both categorical and continuous variables and binary logistic regression to assess factors associated with the high risk of OSA.

RESULTS

The study reported that 78.5% of the participants had high-risk OSA. The median age and body mass index (BMI) were 57.0 years (IQR: 50.0-64.0) and 28.3 kg/m2, respectively. Age, neck circumference, gender, and BMI were significantly higher in the high-risk OSA group as compared to the low-risk group.

CONCLUSION

The study highlights the importance of screening hypertensive patients for OSA using the SBQ in clinical settings, particularly in low-and middle-income countries (LMICs). Healthcare providers can use patient characteristics such as age, gender, neck circumference, and BMI to identify those at greater risk of developing OSA. Further research could focus on developing effective OSA prevention and treatment interventions in hypertensive patients.

Prevalence and Risk Factors for Poor Sleep Quality in People Living with HIV: Preliminary Observations from an HIV Outpatient Clinic

INTRODUCTION

Sleep disorders have been reported in individuals living with HIV (PLWH), with a prevalence rate of over 50%. The main risk factors contributing to the development of sleep disturbances are not yet fully understood. We investigate the prevalence and risk factors associated with poor sleep quality in a population of PLWH who are receiving antiretroviral therapy (ART).

METHODS

The Pittsburgh Sleep Quality Index (PSQI) was used to evaluate sleep quality in PLWH attending our HIV Outpatient Clinic between October 2022 and April 2023. All subjects with a PSQI score > 5 were considered bad sleepers. A logistic regression analysis was carried out to assess risk factors associated with a PSQI score > 5.

RESULTS

A total of 132 PLWH (78% males) who received ART for at least one month were included in this observational study. The median age was 56 (IQR 47-61). Among all, 41 (31%) had a history of AIDS, and 95 (72%) were receiving an INSTI-based ART. The study population was divided into two groups: PSQI ≤ 5 (90; 68.2%) and PSQI > 5 (42; 31.8%). A lower BMI and the use of bictegravir in the current ART were associated with a PSQI score ≤ 5. In the multivariate analysis, the use of a bictegravir-based ART remained the only factor associated with better sleep quality (OR 0.17; p = 0.0222). No further associations between sleep disturbances and other epidemiological and clinical features were found.

CONCLUSION

In this real-life scenario, poor sleep quality was observed in 31% of the cases, primarily among individuals with higher BMI. In addition, bictegravir users might seem to have a lower likelihood of experiencing poor sleep quality.

No Difference in Sleep Desaturations Severity between Patients with Wake-Up and Non-Wake-Up Stroke: A PRESS Study Results

BACKGROUND

Wake-up stroke (WUS) is a certain type of ischemic stroke in which a patient wakes up with a new neurological deficit due to cerebral ischemia. Sleep-disordered breathing is an independent risk factor for stroke, but the role of nocturnal oxygen desaturation in the pathophysiology of WUS is still insufficiently explored. According to several studies, patients with WUS have a significantly more severe sleep apnea syndrome and lower mean blood oxygen saturation. This study aimed to assess the severity of nocturnal desaturations in acute WUS and non-WUS patients using nocturnal pulse oximetry.

MATERIAL AND METHODS

The cohort of 225 consecutive patients with neuroimaging-verified acute cerebral ischemia was prospectively enrolled. For further analyses, 213 subjects with known WUS/non-WUS status were selected (111 males and 102 females, average age 70.4 ±12.9, median baseline NIHSS = 5, median baseline mRS = 3). Patients were divided into the WUS group (n = 45) and the non-WUS group (n = 168). Overnight pulse oximetry was performed within 7 days of the stroke onset and data of both of the studied groups were compared.

RESULTS

We found oxygen desaturation index (ODI) in the WUS group was 14.5 vs. 16.6 (p = 0.728) in the non-WUS group, basal O2 saturation was 92.2% vs. 92.5% (p = 0.475), average low O2 saturation was 90.3% vs. 89.6% (p = 0.375), minimal O2 saturation was 79.5% vs. 80.6% (p = 0.563), and time with O2 saturation <90% (T90) was 4.4% vs. 4.7% (p = 0.729).

CONCLUSIONS

In the studied sample, monitored respiratory parameters including ODI, basal O2 saturation, average low O2 saturation, minimal O2 saturation, and T90 did not significantly differ between groups of WUS and non-WUS patients.