Epidemiological and pathophysiological evidence supporting links between obstructive sleep apnoea and Type 2 diabetes mellitus

Exploring the nexus: Sleep disorders, circadian dysregulation, and Alzheimer's disease

We reviewed the connections among Alzheimer's disease (AD), sleep deprivation, and circadian rhythm disorders. Evidence is mounting that disrupted sleep and abnormal circadian rhythms are not merely symptoms of AD, but are also involved in accelerating the disease. Amyloid-beta (Aβ) accumulates, a feature of AD, and worsens with sleep deprivation because glymphatic withdrawal is required to clear toxic proteins from the brain. In addition, disturbances in circadian rhythm can contribute to the induction of neuroinflammation and oxidative stress, thereby accelerating neurodegenerative processes. While these interactions are bidirectional, Alzheimer's pathology further disrupts sleep and circadian function in a vicious cycle that worsens cognitive decline, which is emphasized in the review. The evidence that targeting sleep and circadian mechanisms may serve as therapeutic strategies for AD was strengthened by this study through the analysis of the molecular and physiological pathways. Further work on this nexus could help unravel the neurobiological mechanisms common to the onset of Alzheimer's and disrupted sleep and circadian regulation, which could result in earlier intervention to slow or prevent the onset of the disease.

LncRNA NEAT1 aggravates human microvascular endothelial cell injury by inhibiting the Apelin/Nrf2/HO-1 signalling pathway in type 2 diabetes mellitus with obstructive sleep apnoea

Long noncoding RNAs (lncRNAs) regulate the progression of type 2 diabetes mellitus complicated with obstructive sleep apnoea (T2DM-OSA). However, the role of the lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) in T2DM-OSA remains unknown. This study aimed to reveal the function of NEAT1 in T2DM-OSA and the underlying mechanism. KKAy mice were exposed to intermittent hypoxia (IH) or intermittent normoxia to generate a T2DM-OSA mouse model. HMEC-1 cells were treated with high glucose (HG) and IH to construct a T2DM-OSA cell model. RNA expression was detected by qRT-PCR. The protein expression of Apelin, NF-E2-related factor 2 (Nrf2), haem oxygenase-1 (HO-1), and up-frameshift suppressor 1 (UPF1) was assessed using western blot. Cell injury was evaluated using flow cytometry, enzyme-linked immunosorbent assay, and oxidative stress kit assays. RIP, RNA pull-down, and actinomycin D assays were performed to determine the associations between NEAT1, UPF1, and Apelin. NEAT1 expression was upregulated in the aortic vascular tissues of mice with T2DM exposed to IH and HMEC-1 cells stimulated with HG and IH, whereas Apelin expression was downregulated. The absence of NEAT1 protected HMEC-1 cells from HG- and IH-induced damage. Furthermore, NEAT1 destabilized Apelin mRNA by recruiting UPF1. Apelin overexpression decreased HG- and IH-induced injury to HMEC-1 cells by activating the Nrf2/HO-1 pathway. Moreover, NEAT1 knockdown reduced HG- and IH-induced injury to HMEC-1 cells through Apelin. NEAT1 silencing reduced HMEC-1 cell injury through the Apelin/Nrf2/HO-1 signalling pathway in T2DM-OSA.Abbreviations: LncRNAs, long non-coding RNAs; T2DM, type 2 diabetes mellitus; OSA, obstructive sleep apnoea; NEAT1, nuclear paraspeckle assembly transcript 1; IH, intermittent hypoxia; HMEC-1, human microvascular endothelial cells; HG, high glucose; Nrf2, NF-E2-related factor 2; UPF1, up-frameshift suppressor 1; HO-1, haem oxygenase-1; qRT-PCR, quantitative real-time polymerase chain reaction; ELISA, enzyme-linked immunosorbent assay; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; TNF-α, tumour necrosis factor-α; CCK-8, Cell Counting Kit-8; IL-1β, interleukin-1β; ROS, reactive oxygen species; MDA, malondialdehyde; SOD, superoxide dismutase; RIP, RNA immunoprecipitation; SD, standard deviations; GSH, glutathione; AIS, acute ischaemic stroke; HMGB1, high mobility group box-1 protein; TLR4, toll-like receptor 4.

Update on the clinical applications of SGLTis: Insight to benefits beyond hypoglycemic and cardiorenal protection

Sodium glucose cotransporter inhibitor (SGLTi) drugs have been widely used in clinical practice. In addition to their benefits in hyperglycemia, heart failure (HF), and kidney disease, their effects on obesity, metabolic dysfunction-associated steatotic liver disease (MASLD, formerly named nonalcoholic fatty liver disease [NAFLD]), polycystic ovarian syndrome (PCOS), abnormal lipid metabolism, hyperuricemia, obstructive sleep apnea syndrome (OSAS), anemia, and syndrome of inappropriate antidiuresis (SIAD, formerly named syndrome of inappropriate antidiuretic hormone [SIADH]) have been explored. In this review, we searched the data of clinical randomized controlled trials (RCTs) and meta-analyses of SGLTis in patients with diabetes from the PubMed library between January 1, 2020, and February 1, 2024. According to our review, certain SGLTis exhibit relatively superior clinical safety and effectiveness for treating the abovementioned diseases. Proper utilization of SGLTis in these patients can provide additional medication options for patients with different disease scenarios. However, studies of SGLTis in these diseases are relatively rare, with shortcomings such as small sample sizes and short intervention periods. Therefore, further large-scale, long-term, well-designed studies are needed to clarify the findings.

Machine learning methods for adult OSAHS risk prediction

BACKGROUND

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict OSAHS.

MATERIALS AND METHODS

Clinical data of 2064 snoring patients who underwent physical examination in the Health Management Center of the First Affiliated Hospital of Shanxi Medical University from July 2018 to July 2023 were retrospectively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3. By analyzing the importance of these features, it was concluded that LDL-C, Cr, common carotid artery plaque, A1c and BMI made major contributions to OSAHS. Moreover, five kinds of machine learning algorithm models such as logistic regression, support vector machine, Boosting, Random Forest and MLP were further established, and cross validation was used to adjust the model hyperparameters to determine the final prediction model. We compared the accuracy, Precision, Recall rate, F1-score and AUC indexes of the model, and finally obtained that MLP was the optimal model with an accuracy of 85.80%, Precision of 0.89, Recall of 0.75, F1-score of 0.82, and AUC of 0.938.

CONCLUSION

We established the risk prediction model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models. This predictive model helps to identify patients with OSAHS and provide early, personalized diagnosis and treatment options.

Visceral Adipose Tissue Indices Independently Correlated with Obstructive Sleep Apnea in Patients with Type 2 Diabetes

AIMS

We aimed to explore whether visceral adiposity indices were significantly associated with obstructive sleep apnea (OSA) in type 2 diabetes (T2DM) patients.

METHODS

100 patients with T2DM who underwent overnight polysomnography were analyzed in this study. Anthropometric data, lipid profiles, and glycemic parameters were recorded. Body fat percentage (BFP) and visceral adipose tissue area (VAT area) were collected from a whole body scan using dual-energy X-ray absorptiometry (DXA). Multivariate logistic regression analysis was performed to explore the associations of AHI with BFP, VAT area, and CVAI.

RESULTS

The prevalence rate of OSA was 80%, and the mean (±SD) of age was 47.0 ± 13.6 years. Apnea-hypopnea index (AHI) was significantly and positively associated with either VAT area (r = 0.433, p ≤ 0.001) or Chinese visceral adiposity index (CVAI) (r = 0.355, p ≤ 0.001) but not for BFP (r = 0.107, p = 0.294). Multivariate logistic regression analyses showed that VAT area and CVAI were significantly associated with increased risk of OSA, and the adjusted ORs were (95% CI) 1.025 (1.003-1.047, p = 0.023) and 1.018 (1.002-1.034, p = 0.030), respectively. However, there was no significant association between BFP and increased risk of OSA.

CONCLUSIONS

VAT area and CVAI were independent risk factors of OSA in the patients with T2DM.

STOP-BANG QUESTIONNAIRE - AN EASY TOOL FOR IDENTIFYING OBSTRUCTIVE SLEEP APNEA SYNDROME IN PATIENTS WITH TYPE 2 DIABETES MELLITUS

BACKGROUND

Patients with type 2 diabetes mellitus (T2DM) have a higher risk of developing obstructive sleep apnea (OSA) compared to the general population. Our study aims to analyze the usefulness of the STOP-BANG score, tool which was not yet validated in patients with diabetets, as a tool that estimates the severity of OSA, in patients with T2DM.

METHODS

120 patients, who answered the STOP-BANG questionnaire and underwent polysomnography, were included in the study. The patients were divided into 3 groups, depending on the severity of OSA, defined by the apnea/hypopnea index (AHI).

RESULTS

A significant percentage of participants (42.1%) had a severe form of OSA (AHI ≥30) and a high percentage of subjects had a STOP-BANG score ≥5 (58.7%), equivalent to a severe form of the disease. The STOP-BANG score increased proportionally with AHI (p<0.001). The area under the ROC curve for the STOP-Bang score indicated an optimal cut-off value of 4.5, with a sensitivity of 88.2% and a specificity of 62.9% (p <0.001), STOP-BANG score ≥5 being an independent predictor for severe OSA in patients with T2DM.

CONCLUSIONS

The STOP-BANG score can be used in patients with diabetes to detect severe OSA in order to establish appropriate therapeutic measures.

The association between obstructive sleep apnea and the risk of poor delivery events in women: A population-based nested case-control study

Purpose

To investigate the risk of poor delivery events (PDEs; premature delivery, abortion, and stillbirth) in female subjects with obstructive sleep apnea (OSA).

Methods

The study identified 8346 female subjects with PDEs as cases between January 1, 2000 and December 31, 2015 from the Longitudinal Health Insurance Database 2005 in Taiwan. A total of 33,384 controls were also identified based on frequency matching for age and year of index date. Diagnoses of OSA and PDEs were determined according to the International Classification of Diseases, 9th Revision. The risk of PDEs in female subjects with OSA was estimated with conditional logistic regression analyses.

Findings

The mean age of the 41,730 female subjects was 35.53 years. The overall incidence rate of PDEs was 506.22 per 100,000 person‐years for subjects with OSA, which was significantly higher than that for the controls (501.95 per 100,000 person‐years). The risk of PDEs was higher in subjects with OSA than in controls (adjusted odds ratio [AOR] = 1.19; 95% confidence interval [CI] [95% CI]: 1.08–1.43), including for premature delivery (AOR = 1.20; 95% CI: 1.16–1.50), and abortion (AOR = 1.19; 95% CI: 1.09–1.47). OSA showed no relation to stillbirth (AOR = 1.04; 95% CI: 0.99–1.31). The findings indicate that the longer a subject has been experiencing OSA, the higher the probability of PDEs.

Conclusions

The risk of PDEs associated with OSA was found in this study. In particular, the longer a subject has OSA, the higher the likelihood of PDEs, exhibiting a dose–response effect.

Clinical relevance

To effectively promote maternal health in clinical practice, health providers need to recognize OSA as a risk factor associated with negative pregnancy outcomes. Furthermore, OSA symptoms should be assessed and managed in all pregnant women to enable more comprehensive maternal care.

Predictive Value of Clinical and Questionnaire Based Screening Tools of Obstructive Sleep Apnea in Patients With Type 2 Diabetes Mellitus

Background

Obstructive sleep apnea (OSA) is a major health problem for people with type 2 diabetes mellitus (DM2) and is associated with poorer glycemic control. Early detection is critical to proper management. In this study, we planned to assess and compare the diagnostic accuracy of various OSA screening tools in patients with DM2.

Methods

In this cross-sectional study, we consecutively recruited 58 patients with DM2, presenting to the endocrinology department of Services Hospital Lahore between February 2020 to March 2021. Along with demographic and anthropometric measurements, including BMI and neck circumference, participants answered the snoring, tiredness, observed apnea, high blood pressure - BMI, age, neck circumference, and gender (STOP-BANG) questionnaire, Berlin questionnaire, and Epworth sleepiness scale. All participants then underwent an overnight, level 3 polysomnography.

Results

The overall prevalence of OSA, diagnosed by overnight polysomnography, was 65.5% in type 2 diabeticDM2 patients. The STOP-BANG questionnaire had the highest sensitivity for mild, moderate, and severe OSA i.e., 84.2%, 90.3%, and 100% respectively. Berlin questionnaire also had 100% sensitivity for severe OSA and was most specific for mild and moderate OSA (70% and 63% respectively) whereas the Epworth sleepiness scale had the highest specificity of 53.3% for severe OSA.

Conclusion

This study shows that OSA is highly prevalent in DM2 patients in Pakistan. The STOP-BANG and Berlin questionnaire proved to be effective screening tools, especially for severe OSA. Results of our study should encourage the routine use of these questionnaires in clinical practice, to help in the early identification of OSA in diabetics.

Sleep disorders in Alzheimer's disease: the predictive roles and potential mechanisms

Sleep disorders are common in patients with Alzheimer's disease, and can even occur in patients with amnestic mild cognitive impairment, which appears before Alzheimer's disease. Sleep disorders further impair cognitive function and accelerate the accumulation of amyloid-β and tau in patients with Alzheimer's disease. At present, sleep disorders are considered as a risk factor for, and may be a predictor of, Alzheimer's disease development. Given that sleep disorders are encountered in other types of dementia and psychiatric conditions, sleep-related biomarkers to predict Alzheimer's disease need to have high specificity and sensitivity. Here, we summarize the major Alzheimer's disease-specific sleep changes, including abnormal non-rapid eye movement sleep, sleep fragmentation, and sleep-disordered breathing, and describe their ability to predict the onset of Alzheimer's disease at its earliest stages. Understanding the mechanisms underlying these sleep changes is also crucial if we are to clarify the role of sleep in Alzheimer's disease. This paper therefore explores some potential mechanisms that may contribute to sleep disorders, including dysregulation of the orexinergic, glutamatergic, and γ-aminobutyric acid systems and the circadian rhythm, together with amyloid-β accumulation. This review could provide a theoretical basis for the development of drugs to treat Alzheimer's disease based on sleep disorders in future work.

Does non-invasive ventilation change metabolic markers in children with obstructive sleep apnoea? A systematic review and meta-analysis study protocol

INTRODUCTION

Obstructive sleep apnoea (OSA) is not only common within paediatrics but is associated with critical childhood metabolic morbidity such as obesity, cardiovascular disease and glucose tolerance impairment. Increasing evidence suggests an association between childhood OSA and metabolic syndrome such as markers of cardiovascular disease, systemic hypertension, glucose intoleranceand increased lipid profile. Recent studies have targeted changes in metabolic markers in children using non-invasive ventilation (NIV) but no systematic reviews are available to summarise this emerging evidence. The purpose of this systematic review is to provide systematic synthesis of the evidence on the effect of NIV use on metabolic markers in children with OSA.

METHODS AND ANALYSIS

A systematic search of electronic databases and grey literature will include paediatric interventional studies (random controlled trials, cohort studies) with and without a comparison group. Two reviewers will independently undertake the two step process of title/abstract and full-text screening. Data will be extracted and assessed, with aggregate data being reported. When the data allow, meta-analysis will be performed.

ETHICS AND DISSEMINATION

There are no ethical concerns with this systematic review, as data have previously been published. This review will inform clinicians taking care of children with OSA and obesity/metabolic syndrome about the potential effects of NIV therapies on metabolic markers and has the potential to change the approach to childhood OSA and obesity. Results of this systematic review will be submitted for dissemination in abstract and manuscript form.

Effects of different obesity-related adipokines on the occurrence of obstructive sleep apnea

Obstructive sleep apnea (OSA), characterized by recurrent episodes of apnea during sleep and daytime sleepiness, seriously affects human health and may lead to systemic organ dysfunction. The pathogenesis of OSA is complex and still uncertain, but multiple surveys have shown that obesity is an important factor, and the incidence of OSA in people with obesity is as high as 30%. Adipokines are a group of proteins secreted from adipocytes, which are dysregulated in obesity and may contribute to OSA. Here, we review the most important and representative research results regarding the correlation between obesity-related adipokines including leptin, adiponectin, omentin-1, chemerin, and resistin and OSA in the past 5 years, provide an overview of these key adipokines, and analyze possible intrinsic mechanisms and influencing factors. The existing research shows that OSA is associated with an increase in the serum levels of leptin, chemerin, and resistin and a decrease in the levels of adiponectin and omentin-1; the findings presented here can be used to monitor the development of OSA and obesity, prevent future comorbidities, and identify risk factors for cardiovascular and other diseases, while different adipokines can be linked to OSA through different pathways such as insulin resistance, intermittent hypoxia, and inflammation, among others. We hope our review leads to a deeper and more comprehensive understanding of OSA based on the relevant literature, which will also provide directions for future clinical research.