Excessive daytime sleepiness, metabolic syndrome, and obstructive sleep apnea: two independent large cross-sectional studies and one interventional study

Unraveling the associations and causalities between glucose metabolism and multiple sleep traits

Purpose

The aim of our study is to estimate the associations and causalities of glucose metabolism traits of fasting blood glucose (FBG), fasting insulin (FINS), glycosylated hemoglobin (HbA1c), and 2-h glucose post-challenge (2hGlu) with sleep traits consisting of excessive daytime sleepiness (EDS), insomnia, and sleep duration.

Methods

We employed standard quantitative analysis procedures to assess the associations between sleep traits and glucose metabolism. Moreover, we acquired published genome-wide association studies (GWAS) summary statistics for these traits and conducted Mendelian randomization (MR) analyses to estimate their causal directions and effects. Inverse variance weighting (IVW) was employed as the primary approach, followed by sensitivity analyses.

Results

A total of 116 studies with over 840,000 participants were included in the quantitative analysis. Our results revealed that participants with abnormal glucose metabolism had higher risks for EDS (OR [95% CI] = 1.37 [1.10,1.69]), insomnia (OR [95% CI] = 1.65 [1.24,2.20]), and both short and long sleep duration (OR [95% CI] = 1.35 [1.12,1.63]; OR [95% CI] = 1.38 [1.13,1.67] respectively). In addition, individuals with these sleep traits exhibited alterations in several glycemic traits compared with non-affected controls. In MR analysis, the primary analysis demonstrated causal effects of 2hGlu on risks of EDS (OR [95% CI] = 1.022 [1.002,1.042]) and insomnia (OR [95% CI] = 1.020[1.001,1.039]). Furthermore, FINS was associated with short sleep duration (OR [95% CI] = 1.043 [1.018,1.068]), which reversely presented a causal influence on HbA1c (β [95% CI] = 0.131 [0.022,0.239]). These results were confirmed by sensitivity analysis.

Conclusion

Our results suggested mutual risk and causal associations between the sleep traits and glycemic traits, shedding new light on clinical strategies for preventing sleep disorders and regulating glucose metabolism. Future studies targeting these associations may hold a promising prospect for public health.

Poor sleep pattern is associated with metabolic disorder during transition from adolescence to adulthood

OBJECTIVE

The purpose of this study was to investigate whether sleep pattern is associated with metabolic disorders among young adults.

METHODS

We measured sleep patterns using multiple sleep behaviors in an ongoing prospective cohort among college students (n = 1,151). At baseline, 729 college students provided fasting blood samples and human body morphological measurements for quantification of metabolic parameters. Then, 340 participants continued to take metabolic parameters measurements at a 2-year follow-up. Sleep patterns were defined by chronotype, sleep duration, insomnia, snoring, and daytime sleepiness. Metabolic scores were derived for four metabolic parameters including body mass index (BMI), waist circumference (WC), fasting blood sugar (FBG), and insulin. Multivariate linear regression model was applied to analyze the association between sleep pattern types and metabolic parameters and metabolic scores.

RESULTS

In the baseline survey, we found that a total of 41 (4.1%) participants had poor sleep patterns. Then, metabolic scores were significantly higher among college students with poor sleep patterns, compared with those who with healthy sleep patterns at baseline (1.00 ± 0.96 vs. 0.78 ± 0.72, p < 0.05) and 2-year follow-up (0.34 ± 0.65 vs. 1.50 ± 1.64, p < 0.05). After covariates were adjusted, poor sleep pattern (β = 0.22, 95% CI: 0.06~2.53, p = 0.001) was associated with elevated metabolic scores at the 2-year follow-up.

CONCLUSIONS

The elevated metabolic burden observed in college students with poor sleep patterns highlights the need to identify and address sleep problems in order to minimize the long-term impact on disease vulnerability.

Comorbidities in Clinical and Polysomnographic Features of Obstructive Sleep Apnea: A Single Tertiary Care Center Experience

BACKGROUND

Research on obstructive sleep apnea (OSA) is inadequate in Saudi Arabia, particularly among patients with comorbidities. This study investigates comorbidities in patients with different severity of apnea based on the Apnea-Hypopnea Index (AHI).

METHODS

The retrospective charts review that included a cohort of 4391 patients who underwent polysomnography (PSG) between 2003 and 2019. The AHI is classified into four ordinal groups: normal, mild, moderate, and severe. Ordinal logistic regression was used to model proportional odds of a higher AHI category.

RESULTS

Gender was distributed equally in the study sample. The average age was 49.6 ± 14.8 years and the average AHI was 16.1 ± 22 per hour. Hypertension (43.2%) and diabetes mellitus (37.3%) were the most common comorbidities: Mild OSA 28.9%, Moderate OSA 15.6%, and severe 16.4%. The severity of apnea increased with age and BMI classes. The prevalence of hypertension increased with the severity of apnea: 42.9% in mild, 47.4% in moderate, and 54.6% in severe AHI. The prevalence of coronary artery disease (CAD), congestive heart failure (CHF), and diabetes mellitus (DM) increased with the severity of apnea. Comorbidities was more among OSA patients with excessive sleepiness. After adjustment for age and gender, greater proportional odds of severe AHI were observed in males (aOR = 1.8), 30-59 years (aOR = 2.064), 60 years or above (aOR = 2.873), obese class II (aOR = 2.016), obese class III (aOR = 2.527), and in patients with hypertension (aOR = 1.272).

CONCLUSION

Hypertension and obesity were highly prevalent in the study cohort and were associated with greater proportional odds of severe AHI.

The relationship between metabolic syndrome and obstructive sleep apnea syndrome: a nationwide population-based study

There has been a need for research on the association between metabolic syndrome (MetS) and obstructive sleep apnea syndrome (OSAS) using large data such as nationwide population-based data that adjusts important confounding factors. Therefore, we investigated the relationship between MetS and OSAS. The data source we used was the National Health Insurance Service claims database managed by the Republic of Korea government, in which 10,113,560 individuals were enrolled in 2009 and followed up until 2018. The independent association of MetS with the risk of OSAS was determined using a Cox proportional hazards model with adjustment for age, sex, smoking status, alcohol consumption, regular physical exercise, and body mass index. Our results showed that MetS were strongly associated to OSAS which was adjusted for several confounding factors. Also, we found men, increased waist circumference and increased triglyceride are important risk factors for OSAS.

The Associations Between Sleep Architecture and Metabolic Parameters in Patients With Obstructive Sleep Apnea: A Hospital-Based Cohort Study

Background and Objectives: The associations between objective sleep architecture and metabolic parameters have been rarely studied in patients with obstructive sleep apnea (OSA). Here, we evaluated the associations between objective sleep measures derived via polysomnography (PSG) and metabolic parameters.

Methods: A total of 2,308 subjects with suspected OSA were included. We measured common metabolic parameters such as body mass index (BMI) and glucose, insulin, blood pressure, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels. All subjects underwent full-night PSG. PSG sleep parameters included total sleep time (TST), time spent in slow-wave sleep (SWS) and rapid eye movement (REM) sleep, sleep efficiency, and the microarousal index (MAI).

Results: The TST correlated with the BMI, glucose level, and systolic blood pressure. The SWS/TST ratio correlated with BMI and glucose, TC, and TG levels. The REM/TST ratio correlated with BMI, glucose, insulin, and TG levels, and diastolic blood pressure. We found significant relationships between sleep efficiency and BMI, glucose levels, and TG levels. The MAI was significantly correlated with all metabolic parameters. After adjustment for age, gender, smoking status, alcohol use, apnea hypopnea index, and oxygen desaturation index (ODI), multiple linear regression analysis showed that the MAI was independently associated with glucose level, TC, HDL, and LDL. REM/TST ratio was positively associated with diastolic blood pressure but negatively associated with glucose metabolism.

Conclusions: Though some independent correlation between sleep and metabolic parameters was confirmed, only weak associations were observed, suggesting a clinically negligible influence of sleep structure. Further prospective studies are warranted to confirm our findings.

Excessive Daytime Sleepiness in Depression and Obstructive Sleep Apnea: More Than Just an Overlapping Symptom

Excessive daytime sleepiness (EDS) is a significant public health concern, with obstructive sleep apnea (OSA) being a common cause, and a particular relationship exists with the severity of depression. A literature search on OSA, depression, and EDS was performed in PubMed. The chosen evidence was limited to human studies. Available evidence was systematically reviewed to ascertain the association of EDS with depression and OSA according to the general population and some specific population subgroups. In addition, effectiveness of continuous positive airway pressure (CPAP) was analyzed as a standard therapy for improving EDS and depression in patients with OSA. In the general population, patients with OSA, and some other subpopulations, the review contributed to: (1) delineating the prevalence of EDS; (2) substantiating the relationship of EDS and depression; (3) presenting the relationship between EDS and OSA; and (4) revealing that the duration of CPAP is crucial for its therapeutic effects in improving EDS and depressive symptoms in patients with OSA.