Obstructive sleep apnea and CPAP therapy alter distinct transcriptional programs in subcutaneous fat tissue

Cross-sectional study of the association between triglyceride glucose-body mass index and obstructive sleep apnea risk

BACKGROUND

The triglyceride glucose-body mass index (TyG-BMI) is a novel indicator of insulin resistance (IR). Obstructive sleep apnea (OSA) is a prevalent disorder characterized by recurrent complete or partial collapse of the pharyngeal airway during sleep; however, the relationship between these two conditions remains unexplored. We hypothesized that a higher TyG-BMI is associated with the occurrence of OSA.

AIM

To assess the association between TyG-BMI and OSA in adults in the United States.

METHODS

A cross-sectional study was conducted utilizing data from the National Health and Nutrition Examination Surveys spanning from 2005-2008 to 2015-2018. TyG-BMI was calculated as Ln [triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2] × BMI. Restricted cubic splines were used to analyze the risk of TyG-BMI and OSA occurrence. To identify potential nonlinear relationships, we combined Cox proportional hazard regression with smooth curve fitting. We also conducted sensitivity and subgroup analyses to verify the robustness of our findings.

RESULTS

We included 16794 participants in the final analysis. Multivariate regression analysis showed that participants with a higher TyG-BMI had a higher OSA incidence. After adjusting for all covariates, TyG-BMI was positively correlated with the prevalence of OSA (odds ratio: 1.28; 95% confidence interval: 1.17, 1.40; P < 0.001); no significant nonlinear relationship was observed. Subgroup analysis showed no strong correlation between TyG-BMI and OSA in patients with diabetes. The correlation between TyG-BMI and OSA was influenced by age, sex, smoking status, marital status, hypertensive stratification, and obesity; these subgroups played a moderating role between TyG-BMI and OSA. Even after adjusting for all covariates, there was a positive association between TYG-BMI and OSA prevalence.

CONCLUSION

A higher TyG-BMI index is linked to higher chances of developing OSA. As TyG-BMI is an indicator of IR, managing IR may help reduce the risk of OSA.

Identification and Mendelian randomization validation of pathogenic gene biomarkers in obstructive sleep apnea

Background

By 2020, obstructive sleep apnea (OSA), a prevalent respiratory disorder, had affected 26.6-43.2% of males and 8.7-27.8% of females worldwide. OSA is associated with conditions such as hypertension, diabetes, and tumor progression; however, the precise underlying pathways remain elusive. This study aims to identify genetic markers and molecular mechanisms of OSA to improve understanding and treatment strategies.

Methods

The GSE135917 dataset related to OSA was obtained from the GEO database. Differentially expressed genes (DEGs) were subsequently identified. Weighted gene co-expression network analysis (WGCNA) was conducted to pinpoint disease-associated genes. The intersection of these data enabled the identification of potential diagnostic DEGs. Further analyses included Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment studies, exploration of protein-protein interactions based on these genes, and an examination of immune infiltration. Mendelian randomization was employed to validate core genes against the Genome-Wide Association Study database.

Results

A total of 194 DEGs were identified in this study. WGCNA network analysis highlighted 2,502 DEGs associated with OSA. By intersecting these datasets, 53 diagnostic DEGs primarily involved in metabolic pathways were identified. Significant alterations were observed in immune cell populations, including memory B cells, plasma cells, naive CD4 T cells, M0 macrophages, and activated dendritic cells. CETN3, EEF1E1, PMM2, GTF2A2, and RRM2 emerged as hub genes implicated in the pathogenesis. A line graph model provides diagnostic insights. Mendelian randomization analysis confirmed a causal link between CETN3 and GTF2A2 with OSA.

Conclusion

Through WGCNA, this analysis uncovered significant genetic foundations of OSA, identifying 2,502 DEGs and 194 genes associated with the disorder. Among these, CETN3 and GTF2A2 were found to have causal relationships with OSA.

An integrated bioinformatics analysis to identify the shared biomarkers in patients with obstructive sleep apnea syndrome and nonalcoholic fatty liver disease

Background

Obstructive sleep apnea (OSA) syndrome and nonalcoholic fatty liver disease (NAFLD) have been shown to have a close association in previous studies, but their pathogeneses are unclear. This study explores the molecular mechanisms associated with the pathogenesis of OSA and NAFLD and identifies key predictive genes.

Methods

Using the Gene Expression Omnibus (GEO) database, we obtained gene expression profiles GSE38792 for OSA and GSE89632 for NAFLD and related clinical characteristics. Mitochondrial unfolded protein response-related genes (UPRmtRGs) were acquired by collating and collecting UPRmtRGs from the GeneCards database and relevant literature from PubMed. The differentially expressed genes (DEGs) associated with OSA and NAFLD were identified using differential expression analysis. Gene Set Enrichment Analysis (GSEA) was conducted for signaling pathway enrichment analysis of related disease genes. Based on the STRING database, protein-protein interaction (PPI) analysis was performed on differentially co-expressed genes (Co-DEGs), and the Cytoscape software (version 3.9.1) was used to visualize the PPI network model. In addition, the GeneMANIA website was used to predict and construct the functional similar genes of the selected Co-DEGs. Key predictor genes were analyzed using the receiver operating characteristic (ROC) curve.

Results

The intersection of differentially expressed genes shared between OSA and NAFLD-related gene expression profiles with UPRmtRGs yielded four Co-DEGs: ASS1, HDAC2, SIRT3, and VEGFA. GSEA obtained the relevant enrichment signaling pathways for OSA and NAFLD. PPI network results showed that all four Co-DEGs interacted (except for ASS1 and HDAC2). Ultimately, key predictor genes were selected in the ROC curve, including HDAC2 (OSA: AUC = 0.812; NAFLD: AUC = 0.729), SIRT3 (OSA: AUC = 0.775; NAFLD: AUC = 0.750), and VEGFA (OSA: AUC = 0.812; NAFLD: AUC = 0.861) (they have a high degree of accuracy in predicting whether a subject will develop two diseases).

Conclusion

In this study, four co-expression differential genes for OSA and NAFLD were obtained, and they can predict the occurrence of both diseases. Transcriptional mechanisms involved in OSA and NAFLD interactions may be better understood by exploring these key genes. Simultaneously, this study provides potential diagnostic and therapeutic markers for patients with OSA and NAFLD.

Ferroptosis-related genes are considered as potential targets for CPAP treatment of obstructive sleep apnea

Obstructive sleep apnea (OSA) is a common syndrome characterized by upper airway dysfunction during sleep. Continuous positive airway pressure (CPAP) is the most frequently utilized non-surgical treatment for OSA. Ferroptosis play a crucial role in the physiological diseases caused by chronic intermittent hypoxia, but its involvement in the development of OSA and the exact mechanisms have incompletely elucidated. GSE75097 microarray dataset was used to identify differentially expressed genes between OSA patients and CPAP-treated OSA patients. Subsequently, Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING database, and FerrDb database were conducted to analyze the biological functions of differentially expressed genes and screen ferroptosis-related genes. Finally, GSE135917 dataset employed for validation. There were 1,540 differentially expressed genes between OSA patients and CPAP-treated OSA patients. These differentially expressed genes were significantly enriched in the regulation of interleukin-1-mediated signaling pathway and ferroptosis-related signaling pathway. Subsequently, 13 ferroptosis-related genes (DRD5, TSC22D3, TFAP2A, STMN1, DDIT3, MYCN, ELAVL1, JUN, DUSP1, MIB1, PSAT1, LCE2C, and MIR27A) were identified from the interaction between differentially expressed genes and FerrDb database, which are regarded as the potential targets of CPAP-treated OSA. These ferroptosis-related genes were mainly involved in cell proliferation and apoptosis and MAPK signaling pathway. Furthermore, DRD5 and TFAP2A were downregulated in OSA patients, which showed good diagnostic properties for OSA, but these abnormal signatures are not reversed with short-term effective CPAP therapy. In summary, the identification of 13 ferroptosis-related genes as potential targets for the CPAP treatment of OSA provides valuable insights into the development of novel, reliable, and accurate therapeutic options.

Sleep-Disordered Breathing, Hypoxia, and Pulmonary Physiologic Influences in Atrial Fibrillation

BACKGROUND

We leverage a large clinical cohort to elucidate sleep-disordered breathing and sleep-related hypoxia in incident atrial fibrillation (AF) development given the yet unclear contributions of sleep-related hypoxia and pulmonary physiology in sleep-disordered breathing and AF.

METHODS AND RESULTS

Patients who underwent sleep studies at Cleveland Clinic January 2, 2000, to December 30, 2015, comprised this retrospective cohort. Cox proportional hazards models were used to examine apnea hypopnea index, percentage time oxygen saturation <90%, minimum and mean oxygen saturation, and maximum end-tidal carbon dioxide on incident AF adjusted for age, sex, race, body mass index, cardiopulmonary disease and risk factors, antiarrhythmic medications, and positive airway pressure. Those with spirometry were additionally adjusted for forced expiratory volume in 1 second, forced vital capacity, and forced expiratory volume in 1 second/forced vital capacity. This cohort (n=42 057) was 50.7±14.1 years, 51.3% men, 74.1% White individuals, had median body mass index 33.2 kg/m2, and 1947 (4.6%) developed AF over 5 years. A 10-unit apnea hypopnea index increase was associated with 2% higher AF risk (hazard ratio [HR], 1.02 [95% CI, 1.00-1.03]). A 10-unit increase in percentage time oxygen saturation <90% and 10-unit decreases in mean and minimum oxygen saturation were associated with 6% (HR, 1.06 [95% CI, 1.04-1.08]), 30% (HR, 1.30 [95% CI, 1.18-1.42]), and 9% (HR, 1.09 [95% CI, 1.03-1.15]) higher AF risk, respectively. After adjustment for spirometry (n=9683 with available data), only hypoxia remained significantly associated with incident AF, although all coefficients were stable.

CONCLUSIONS

Sleep-related hypoxia was associated with incident AF in this clinical cohort, consistent across 3 measures of hypoxia, persistent after adjustment for pulmonary physiologic impairment. Findings identify a strong role for sleep-related hypoxia in AF development without pulmonary physiologic interdependence.

Interleukin-10 is not associated with obstructive sleep apnea hypopnea syndrome: A meta-analysis and meta-regression

BACKGROUND

This study was conducted to explore the potential relationship between interleukin-10 (IL-10) and obstructive sleep apnea hypopnea syndrome (OSAHS).

METHODS

All the related research articles published before October 2022 were retrieved through the online database (EMBASE, VIP, Wan Fang, Web of Science, PubMed, and CNKI). Stata 11.0 software was used to calculate the standard mean difference (SMD) of the continuous variable and 95% confidence interval (CI). Expression profiles GSE38792 and GSE135917 were acquired from Gene Expression Omnibus (GEO) database, respectively. The expression of IL-10 mRNA in subcutaneous adipose tissue and visceral adipose tissue of OSAHS patients and healthy subjects was extracted by R software to verify the difference in IL-10 between the 2 groups.

RESULTS

The IL-10 level in the plasma of people with and without OSAHS (STD Mean Difference (SMD) = -0.68, 95% CI = -1.58 to 0.21, I2 = 94.3%, P = .136) was the same. There was also no difference in IL-10 levels in serum between people with and without OSAHS (SMD = -0.12, 95% CI = -0.55 to 0.32, I2 = 94.4%, P = .591). In addition, the subjects were divided into different subgroups for meta-analysis according to race, body mass index, age, study type, and disease severity. Based on the outcomes, no notable difference was observed in the plasma/serum IL-10 level between the OSAHS subgroups and the control group. The results of bioinformatics analysis indicated that there was no significant difference in the expression of IL-10 mRNA in subcutaneous adipose tissue and visceral adipose tissue between patients with OSAHS and those in the control group.

CONCLUSION

The current meta-analysis highlighted that IL-10 levels between patients with OSAHS and healthy people had no difference.

Integrative Analysis and Experimental Validation of Competing Endogenous RNAs in Obstructive Sleep Apnea

Background: Obstructive sleep apnea (OSA) is highly prevalent yet underdiagnosed. This study aimed to develop a predictive signature, as well as investigate competing endogenous RNAs (ceRNAs) and their potential functions in OSA. Methods: The GSE135917, GSE38792, and GSE75097 datasets were collected from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) and differential expression analysis were used to identify OSA-specific mRNAs. Machine learning methods were applied to establish a prediction signature for OSA. Furthermore, several online tools were used to establish the lncRNA-mediated ceRNAs in OSA. The hub ceRNAs were screened using the cytoHubba and validated by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Correlations between ceRNAs and the immune microenvironment of OSA were also investigated. Results: Two gene co-expression modules closely related to OSA and 30 OSA-specific mRNAs were obtained. They were significantly enriched in the antigen presentation and lipoprotein metabolic process categories. A signature that consisted of five mRNAs was established, which showed a good diagnostic performance in both independent datasets. A total of twelve lncRNA-mediated ceRNA regulatory pathways in OSA were proposed and validated, including three mRNAs, five miRNAs, and three lncRNAs. Of note, we found that upregulation of lncRNAs in ceRNAs could lead to activation of the nuclear factor kappa B (NF-κB) pathway. In addition, mRNAs in the ceRNAs were closely correlated to the increased infiltration level of effector memory of CD4 T cells and CD56bright natural killer cells in OSA. Conclusions: In conclusion, our research opens new possibilities for diagnosis of OSA. The newly discovered lncRNA-mediated ceRNA networks and their links to inflammation and immunity may provide potential research spots for future studies.

CD40LG and GZMB were correlated with adipose tissue macrophage infiltration and involved in obstructive sleep apnea related metabolic dysregulation: Evidence from bioinformatics analysis

Both obesity and obstructive sleep apnea (OSA) can lead to metabolic dysregulation and systemic inflammation. Similar to obesity, increasing evidence has revealed that immune infiltration in the visceral adipose tissue (VAT) is associated with obstructive sleep apnea-related morbidity. However, the pathological changes and potential molecular mechanisms in visceral adipose tissue of obstructive sleep apnea patients need to be further studied. Herein, by bioinformatics analysis and clinical validation methods, including the immune-related differentially expressed genes (IRDEGs) analysis, protein-protein interaction network (PPI), functional enrichment analysis, a devolution algorithm (CIBERSORT), spearman's correlation analysis, polymerase chain reaction (PCR), Enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC), we identified and validated 10 hub IRDEGs, the relative mRNA expression of four hub genes (CRP, CD40LG, CCL20, and GZMB), and the protein expression level of two hub genes (CD40LG and GZMB) were consistent with the bioinformatics analysis results. Immune infiltration results further revealed that obstructive sleep apnea patients contained a higher proportion of pro-inflammatory M1 macrophages and a lower proportion of M2 macrophages. Spearman's correlation analysis showed that CD40LG was positively correlated with M1 macrophages and GZMB was negatively correlated with M2 macrophages. CD40LG and GZMB might play a vital role in the visceral adipose tissue homeostasis of obstructive sleep apnea patients. Their interaction with macrophages and involved pathways not only provides new insights for understanding molecular mechanisms but also be of great significance in discovering novel small molecules or other promising candidates as immunotherapies of OSA-associated metabolic complications.

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.

Construction of a mitochondrial dysfunction related signature of diagnosed model to obstructive sleep apnea

Background: The molecular mechanisms underlying obstructive sleep apnea (OSA) and its comorbidities may involve mitochondrial dysfunction. However, very little is known about the relationships between mitochondrial dysfunction-related genes and OSA. Methods: Mitochondrial dysfunction-related differentially expressed genes (DEGs) between OSA and control adipose tissue samples were identified using data from the Gene Expression Omnibus database and information on mitochondrial dysfunction-related genes from the GeneCards database. A mitochondrial dysfunction-related signature of diagnostic model was established using least absolute shrinkage and selection operator Cox regression and then verified. Additionally, consensus clustering algorithms were used to conduct an unsupervised cluster analysis. A protein-protein interaction network of the DEGs between the mitochondrial dysfunction-related clusters was constructed using STRING database and the hub genes were identified. Functional analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA), were conducted to explore the mechanisms involved in mitochondrial dysfunction in OSA. Immune cell infiltration analyses were conducted using CIBERSORT and single-sample GSEA (ssGSEA). Results: we established mitochondrial dysfunction related four-gene signature of diagnostic model consisted of NPR3, PDIA3, SLPI, ERAP2, and which could easily distinguish between OSA patients and controls. In addition, based on mitochondrial dysfunction-related gene expression, we identified two clusters among all the samples and three clusters among the OSA samples. A total of 10 hub genes were selected from the PPI network of DEGs between the two mitochondrial dysfunction-related clusters. There were correlations between the 10 hub genes and the 4 diagnostic genes. Enrichment analyses suggested that autophagy, inflammation pathways, and immune pathways are crucial in mitochondrial dysfunction in OSA. Plasma cells and M0 and M1 macrophages were significantly different between the OSA and control samples, while several immune cell types, especially T cells (γ/δ T cells, natural killer T cells, regulatory T cells, and type 17 T helper cells), were significantly different among mitochondrial dysfunction-related clusters of OSA samples. Conclusion: A novel mitochondrial dysfunction-related four-gen signature of diagnostic model was built. The genes are potential biomarkers for OSA and may play important roles in the development of OSA complications.

Comprehensive Analysis of N6-Methyladenosine Regulators in the Subcluster Classification and Drug Candidates Prediction of Severe Obstructive Sleep Apnea

Background: Obstructive sleep apnea (OSA) is the most common type of sleep apnea that impacts the development or progression of many other disorders. Abnormal expression of N6-methyladenosine (m6A) RNA modification regulators have been found relating to a variety of human diseases. However, it is not yet known if m6A regulators are involved in the occurrence and development of OSA. Herein, we aim to explore the impact of m6A modification in severe OSA.

Methods: We detected the differentially expressed m6A regulators in severe OSA microarray dataset GSE135917. The least absolute shrinkage and selection operator (LASSO) and support vector machines (SVM) were used to identify the severe OSA-related m6A regulators. Receiver operating characteristic (ROC) curves were performed to screen and verify the diagnostic markers. Consensus clustering algorithm was used to identify m6A patterns. And then, we explored the character of immune microenvironment, molecular functionals, protein-protein interaction networks and miRNA-TF coregulatory networks for each subcluster. Finally, the Connectivity Map (CMap) tools were used to tailor customized treatment strategies for different severe OSA subclusters. An independent dataset GSE38792 was used for validation.

Results: We found that HNRNPA2B1, KIAA1429, ALKBH5, YTHDF2, FMR1, IGF2BP1 and IGF2BP3 were dysregulated in severe OSA patients. Among them, IGF2BP3 has a high diagnostic value in both independent datasets. Furthermore, severe OSA patients can be accurately classified into three m6A patterns (subcluster1, subcluster2, subcluster3). The immune response in subcluster3 was more active because it has high M0 Macrophages and M2 Macrophages infiltration and up-regulated human leukocyte antigens (HLAs) expression. Functional analysis showed that representative genes for each subcluster in severe OSA were assigned to histone methyltransferase, ATP synthesis coupled electron transport, virus replication, RNA catabolic, multiple neurodegeneration diseases pathway, et al. Moreover, our finding demonstrated cyclooxygenase inhibitors, several of adrenergic receptor antagonists and histamine receptor antagonists might have a therapeutic effect on severe OSA.

Conclusion: Our study presents an overview of the expression pattern and crucial role of m6A regulators in severe OSA, which may provide critical insights for future research and help guide appropriate prevention and treatment options.

Identification and Validation of Prognostic Factors of Lipid Metabolism in Obstructive Sleep Apnea

Background: Obstructive sleep apnea (OSA) is considered to be an independent factor affecting lipid metabolism. This study explored the relationship between immune genes and lipid metabolism in OSA.

Methods: Immune-related Differentially Expressed Genes (DEGs) were identified by analyzing microarray data sets from the Gene Expression Omnibus (GEO) database. Subsequently, we conducted protein-protein interaction (PPI) network analysis and calculated their Gene Ontology (GO) semantic similarity. The GO, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Disease Ontology (DO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were employed for functional enrichment analyses and to determine the most significant functional terms. Combined with the results of boruta and random forest, we selected predictors to build a prognostic model, along with seeking out the potential TFs and target drugs for the predictive genes.

Results: Immune-related DEGs included 64 genes upregulated and 98 genes downregulated. The enrichment analysis might closely associate with cell adhesion and T cell-mediated immunity pathways and there were many DEGs involved in lipid and atherosclerosis signaling pathways. The highest-ranking hub gene in PPI network have been reported lowly expressed in OSA. In line with the enrichment analysis, DO analysis reveal that respiratory diseases may be associated with OSA besides immune system disorders. Consistent with the result of the KEGG pathway, the analysis of GSVA revealed that the pro-inflammation pathways are associated with OSA. Monocytes and CD8 T cells were the predominant immune cells in adipose tissue. We built a prognostic model with the top six genes, and the prognostic genes were involved in the polarization of macrophage and differentiation of T lymphocyte subsets. In vivo experimental verification revealed that EPGN, LGR5, NCK1 and VIP were significantly down-regulated while PGRMC2 was significantly up-regulated in mouse model of OSA.

Conclusions: Our study demonstrated strong associations between immune genes and the development of dyslipidemia in OSA. This work promoted the molecular mechanisms and potential targets for the regulation of lipid metabolism in OSA.

Identification of key genes and immune infiltration modulated by CPAP in obstructive sleep apnea by integrated bioinformatics analysis

Patients with obstructive sleep apnea (OSA) experience partial or complete upper airway collapses during sleep resulting in nocturnal hypoxia-normoxia cycling, and continuous positive airway pressure (CPAP) is the golden treatment for OSA. Nevertheless, the exact mechanisms of action, especially the transcriptome effect of CPAP on OSA patients, remain elusive. The goal of this study was to evaluate the longitudinal alterations in peripheral blood mononuclear cells transcriptome profiles of OSA patients in order to identify the hub gene and immune response. GSE133601 was downloaded from Gene Expression Omnibus (GEO). We identified black module via weighted gene co-expression network analysis (WGCNA), the genes in which were correlated significantly with the clinical trait of CPAP treatment. Finally, eleven hub genes (TRAV10, SNORA36A, RPL10, OBP2B, IGLV1-40, H2BC8, ESAM, DNASE1L3, CD22, ANK3, ACP3) were traced and used to construct a random forest model to predict therapeutic efficacy of CPAP in OSA with a good performance with AUC of 0.92. We further studied the immune cells infiltration in OSA patients with CIBERSORT, and monocytes were found to be related with the remission of OSA and partially correlated with the hub genes identified. In conclusion, these key genes and immune infiltration may be of great importance in the remission of OSA and related research of these genes may provide a new therapeutic target for OSA in the future.

Functional evaluation of the diaphragm with a noninvasive test

Cardiac surgery with median sternotomy causes iatrogenic damage to the function of the diaphragm muscle that is both temporary and permanent. Myocardial infarction itself causes diaphragmatic genetic alterations, which lead the muscle to nonphysiological adaptation. The respiratory muscle area plays several roles in maintaining both physical and mental health, as well as in maximizing recovery after a cardiac event. The evaluation of the diaphragm is a fundamental step in the therapeutic process, including the use of instruments such as ultrasound, magnetic resonance imaging (MRI), and computed axial tomography (CT). This article reviews the neurophysiological relationships of the diaphragm muscle and the symptoms of diaphragmatic contractile dysfunction. The authors discuss a scientific basis for the use of a new noninstrumental diaphragmatic test in the hope of stimulating research.

Glial Cell-Derived Neurotrophic Factor Functions as a Potential Candidate Gene in Obstructive Sleep Apnea Based on a Combination of Bioinformatics and Targeted Capture Sequencing Analyses

Background

Obstructive sleep apnea (OSA) is a prevalent chronic disease that increases the risk of cardiovascular disease and metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. The present study was aimed at identifying potential key genes influencing the mechanisms and consequences of OSA.

Methods

Gene expression profiles associated with OSA were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from patients with OSA and normal tissues were screened using R software, followed by gene ontology and pathway enrichment analyses. Subsequently, a protein-protein interaction (PPI) network was constructed and hub genes were extracted using Cytoscape plugins. The intersected core genes derived from different topological algorithms were considered hub genes, and the potential candidate gene was selected from them for further analyses of expression variations using another GEO dataset and targeted capture sequencing in 100 subjects (50 with severe OSA and 50 without OSA).

Results

A total of 373 DEGs were identified in OSA samples relative to normal controls, which were primarily associated with olfactory transduction and neuroactive ligand-receptor interaction. Upon analyses of nine topological algorithms and available literature, we finally focused on glial cell-derived neurotrophic factor (GDNF) as the candidate gene and validated its low expression in OSA samples. Two rare nonsynonymous variants (p.D56N and p.R93Q) were identified among the 100 cases through targeted sequencing of GDNF, which could be potentially deleterious based on pathogenicity prediction programs; however, no significant association was detected in single nucleotide polymorphisms.

Conclusion

The present study identified GDNF as a promising candidate gene for OSA and its two rare and potentially deleterious mutations through a combination of bioinformatics and targeted capture sequencing analyses.

Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis

Obstructive sleep apnea (OSA) is a common chronic disease and increases the risk of cardiovascular disease, metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. This study aimed to identify potential key genes influence the mechanisms and consequences of OSA.Gene expression profiles related to OSA were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from OSA compared with normal tissues were screened using R software, followed by gene ontology (GO) and pathway enrichment analyses. Subsequently, a protein-protein interaction (PPI) network for these DEGs was constructed by STRING, and key hub genes were extracted from the network with plugins in Cytoscape. The hub genes were further validated in another GEO dataset and assessed by receiver operating characteristic (ROC) analysis and Pearson correlation analysis.There were 373 DEGs in OSA samples in relative to normal controls, which were mainly associated with olfactory receptor activity and olfactory transduction. Upon analyses of the PPI network, GDNF, SLC2A2, PRL, and SST were identified as key hub genes. Decreased expression of the hub genes was association with OSA occurrence, and exhibited good performance in distinguishing OSA from normal samples based on ROC analysis. Besides, the Pearson method revealed a strong correlation between hub genes, which indicates that they may act in synergy, contributing to OSA and related disorders.This bioinformatics research identified 4 hub genes, including GDNF, SLC2A2, PRL, and SST which may be new potential biomarkers for OSA and related disorders.

Sleep Quality in Obesity: Does Adherence to the Mediterranean Diet Matter?

Obesity and unhealthy eating habits have been associated with sleep disturbances (SD). The Mediterranean diet (MD) is a healthy nutritional pattern that has been reported to be associated with better health and sleep quality. Thus, the aim of the study was to investigate whether adherence to the MD is associated with sleep quality in a population of middle-aged Italian adults. This cross-sectional study included 172 middle-aged adults (71.5% females; 51.8 ± 15.7 years) that were consecutively enrolled in a campaign to prevent obesity called the OPERA (Obesity, Programs of Nutrition, Education, Research and Assessment of the best treatment) prevention project that was held in Naples on 11–13 October 2019. Anthropometric parameters, adherence to the MD and sleep quality were studied. Overall, 50.6% of the subjects were good sleepers (the Pittsburgh Sleep Quality Index (PSQI) < 5) while 49.4% were poor sleepers (PSQI ≥ 5). Our results demonstrated that good sleepers, when compared to poor sleepers (p < 0.001) had significantly higher adherence to the MD as assessed by PREDIMED (Prevención con Dieta Mediterránea) score, lower BMI (body mass index) and waist circumference (WC). The higher PSQI, the higher the BMI (p < 0.001) and WC values (p < 0.001), thus suggesting that poor sleep was more common in subjects with obesity. In addition, a negative correlation between PSQI and the PREDIMED score (p < 0.001) was found. to the intake of the cluster of foods enclosed in the MD, rather than the intake of the single food, predicted PSQI. By performing a receiver operator characteristic (ROC) curve analysis, we determined a cut-off value at a PREDIMED score < 9 as the threshold for screening poor sleepers. In conclusion, good sleepers had lower BMI and WC and higher adherence to the MD than poor sleepers. PSQI was positively associated to BMI and WC while it was negatively associated to adherence to the MD. The consumption of the MD dietary pattern rather than the intake of a single nutrient has a beneficial effect on sleep quality. Hence, the assessment of sleep should be taken into account in the management of obesity and promoting adherence to the MD could be a tool to improve SD.