The Relationship between Disturbed Sleep, OSAS, and Metabolic Diseases

Identification and validation of ferroptosis-related hub genes in obstructive sleep apnea syndrome

Background

By 2020, the prevalence of Obstructive Sleep Apnea Syndrome (OSAS) in the US has reached 26. 6-43.2% in men and 8.7-27.8% in women. OSAS promotes hypertension, diabetes, and tumor growth through unknown means. Chronic intermittent hypoxia (CIH), sleep fragmentation, and increased pleural pressure are central mechanisms of OSAS complications. CIH exacerbates ferroptosis, which is closely related to malignancies. The mechanism of ferroptosis in OSAS disease progression remains unknown.

Methods

OSAS-related datasets (GSE135917 and GSE38792) were obtained from the GEO. Differentially expressed genes (DEGs) were screened using the R software and intersected with the ferroptosis database (FerrDb V2) to get ferroptosis-related DEGs (f-DEGs). GO, DO, KEGG, and GSEA enrichment were performed, a PPI network was constructed and hub genes were screened. The TCGA database was used to obtain the thyroid cancer (THCA) gene expression profile, and hub genes were analyzed for differential and survival analysis. The mechanism was investigated using GSEA and immune infiltration. The hub genes were validated with RT-qPCR, IHC, and other datasets. Sprague-Dawley rats were randomly separated into normoxia and CIH groups. ROS, MDA, and GSH methods were used to detect CIH-induced ferroptosis and oxidative stress.

Results

GSEA revealed a statistically significant difference in ferroptosis in OSAS (FDR < 0.05). HIF1A, ATM, HSPA5, MAPK8, MAPK14, TLR4, and CREB1 were identified as hub genes among 3,144 DEGs and 74 f-DEGs. HIF1A and ATM were the only two validated genes. F-DEGs were mainly enriched in THCA. HIF1A overexpression in THCA promotes its development. HIF1A is associated with CD8 T cells and macrophages, which may affect the immunological milieu. The result found CIH increased ROS and MDA while lowering GSH indicating that it could cause ferroptosis. In OSAS patients, non-invasive ventilation did not affect HIF1A and ATM expression. Carvedilol, hydralazine, and caffeine may be important in the treatment of OSAS since they suppress HIF1A and ATM.

Conclusions

Our findings revealed that the genes HIF1A and ATM are highly expressed in OSAS, and can serve as biomarkers and targets for OSAS.

Gut microbiota dysbiosis in 4- to 6-year-old children with obstructive sleep apnea-hypopnea syndrome

OBJECTIVE

Several experiments on animals have reported the relationship between obstructive sleep apnea-hypopnea syndrome (OSAHS) and gut microbiota. We investigated the gut microbiota composition of children aged 4-6 years with OSAHS to complement the pathogenesis and clinical screening methods of OSAHS.

METHODS

We collected feces from 43 children with OSAHS and 45 controls aged 4-6 years. We extracted total bacterial DNA from feces and analyzed the composition of gut microbiota through 16S ribosomal RNA sequencing.

RESULTS

There were significant differences in bacteria producing short-chain fatty acids (SCFAs) between OSAHS children and controls, including Faecalibacterium, Roseburia, and a member of Ruminococcaceae. A gut microbiota model for pediatric OSAHS screening showed that the receiver operating characteristic-area under the curve (ROC-AUC) was 0.794 with 79.1% and 80.0% sensitivity and specificity, respectively. Functional analysis of the gut microbiota revealed several alterations in metabolism.

CONCLUSION

The composition of gut microbiota in OSAHS children is partially changed. The altered intestinal flora may provide a new screening method for the diagnosis of children with OSAHS. The prediction of gut microbiota function suggests that intestinal metabolic function may be altered in OSAHS children.

Sleep Quality among Patients with Type 2 Diabetes: A Cross-Sectional Study in the East Coast Region of Peninsular Malaysia

Poor sleep is related to type 2 diabetes and adversely influences a person’s quality of life. This study aimed to evaluate sleep quality in patients with type 2 diabetes (T2DM), its associated factors, and its relationship with quality of life. A cross-sectional study was conducted at a primary care clinic in a tertiary hospital on the east coast of Malaysia. This study included 350 participants (175 men and 175 women). Data were collected using the Malay version of the Pittsburgh Sleep Quality Index (PSQI-M) with a cut-off point of >5 as poor sleep, the Malay version of Diabetes Distress Scale (MDDS-17) and the revised Malay version of T2DM-related quality of life (Rv-DQOL). Statistical analysis was conducted using the SPSS software version 26.0. The respondents’ median (interquartile range (IQR)) age was 62.0 (11.0) years, and poor sleep was reported in 32% (95% confidence interval (CI) = 27.1, 36.9) of the participants. Multivariate logistic regression analysis revealed that poor sleep quality was significantly associated with nocturia (odds ratio (OR) = 2.04; 95% CI = 1.24, 3.35), restless legs syndrome (OR = 2.17; 95% CI = 1.32−3.56) and emotional burden (OR = 2.37; 95% CI = 1.41−3.98). However, no statistically significant association was observed between sleep quality and quality of life among our participants.

Association between Obstructive Sleep Apnea and Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis

Materials and Methods

We screened four databases (PubMed, Embase, Cochran Library, and CNKI) for the observational studies about the OSA and T2DM. Studies were collected from database establishment to October 2020. We performed a traditional subgroup meta-analysis. What is more, linear and spline dose-response models were applied to assess the association between apnea-hypopnea index (AHI), an indicator to evaluate the severity of OSA, and the risk of T2DM. Review Manager, version 5.3, software and Stata 16.0 were used for the analysis.

Result

Seven observational studies were included in the research. We excluded a study in the conventional meta-analysis. In the subgroup analysis, mild-dose AHI increased the risk of T2DM (odds ratio = 1.23, 95% confidence interval = 1.06–1.41, P < 0.05). Moderate-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 1.35, 95% CI = 1.13–1.61, P < 0.05). Moderate-to-severe-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 2.14, 95% CI = 1.72–2.67, P < 0.05). Severe-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 2.19 95% CI = 1.30–3.68, P < 0.05). Furthermore, the spline and linear dose-response meta-analysis results revealed that the risk of T2DM increased with increasing AHI values.

Conclusion

Through the dose-response meta-analysis, we found a potential dose-response relationship existed between the severity of OSA and the risk of T2DM. This relationship in our passage should be considered in the prevention of T2DM in the future.