高血压是我国重点防治的心血管疾病, 血压的控制率备受关注。在一些血压控制不良的患者中睡眠呼吸暂停是导致顽固性高血压的重要原因。以睡眠过程中反复、频繁出现呼吸暂停和低通气为特点的睡眠呼吸暂停低通气综合征( sleep apneahypopnea syndrome, SAHS) 自20 世纪80 年代以来也受到广泛关注, 临床和基础研究取得了迅速发展。目前, 多项临床、流行病学和基础研究证实SAHS可以导致和/ 或加重高血压, 与高血压的发生发展密切相关。
ObjectiveTo investigate the renal impairment and the risk factors of renal impairment in patients with OSA. MethodsData from patients who underwent polysomnography (PSG) in our department from July 2022 to January 2023 were collected, totaling 178 cases. Based on the results of the polysomnography, the patients were divided into an OSA group (145 cases) and a non-OSA group (33 cases). According to the severity of the condition, the OSA group was further divided into mild OSA (21 cases), moderate OSA (28 cases), and severe OSA (96 cases). The Pearson correlation analysis was further conducted to analyze the relationships between serum urea nitrogen (BUN), serum cystatin C (Cys-C) concentrations, and estimated Glomerular Filtration Rate (eGFR) with various risk factors that may influence renal impairment. Moreover, multiple linear regression analysis was used to identify the risk factors affecting BUN, Cys-C, and eGFR. ResultsWhen comparing the two groups, there were statistically significant differences in age, weight, BMI, neck circumference, waist circumference, eGFR、Cys-C、BUN, LSaO2, CT90% (all P<0.05). Univariate analysis of variance was used to compare differences in BUN, Serum creatinine (SCr), Cys-C, and eGFR among patients with mild, moderate, and severe OSA, indicating that differences in eGFR and Cys-C among OSA patients of varying severities were statistically significant. Further analysis with Pearson correlation was conducted to explore the associations between eGFR, BUN, and Cys-C with potential risk factors that may affect renal function. Subsequently, multiple linear regression was utilized, taking these three indices as dependent variables to evaluate risk factors potentially influencing renal dysfunction. The results demonstrated that eGFR was negatively correlated with age, BMI, and CT90% (β=−0.95, P<0.001; β=−1.36, P=0.01; β=−32.64, P<0.001); BUN was positively correlated with CT90% (β=0.22, P=0.01); Cys-C was positively correlated with CT90% (β=0.58, P<0.001. Conclusion Chronic intermittent hypoxia, age, and obesity are risk factors for renal dysfunction in patients with OSA.
ObjectiveThe aim of this study was to investigate the changes in peripheral blood metabolites and transcriptomes in patients with obstructive sleep apnea (OSA) and to assess their diagnostic value as biomarkers. MethodsIn this study, we utilized liquid chromatography-tandem mass spectrometry (LC-MS/MS) lipid-targeted metabolomics to compare the metabolic profiles of 30 OSA patients with those of 30 healthy controls, identifying differential lipid metabolites. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we determined that the glycerolipid metabolism pathway was significantly different. Furthermore, we conducted transcriptome analysis on peripheral blood mononuclear cells (PBMCs) from six OSA patients and six healthy controls to evaluate the expression of molecules related to the pathway. ResultsA total of 168 differential lipid metabolites were identified, with significant differences in the glycerolipid metabolism pathway between OSA patients and healthy controls. Transcriptome analysis revealed that glycerolipid metabolism-related molecules GPAT, AGPAT, and LPIN were under expressed in OSA patient PBMCs, suggesting that the glycerolipid metabolism pathway is suppressed in OSA patients. Additionally, diagnostic value analysis showed that GPAT and AGPAT had high AUC values, indicating their potential as biomarkers for OSA. ConclusionThe suppression of the glycerolipid metabolism pathway is closely related to the development of OSA, and the under expression of key genes in this pathway, such as GPAT, AGPAT, and LPIN, may be involved in the pathophysiological process of OSA. These findings not only provide a new perspective for understanding the pathogenesis of OSA but also offer new scientific evidence for the treatment of OSA from the perspective of glycerolipid metabolism regulation.
ObjectiveThe aim of this study was to investigate the value of Artificial Neural Networks (ANNs) in predicting the occurrence of Venous Thromboembolism (VTE) in patients with Obstructive Sleep Apnea (OSA), and to compare it with traditional Logistic regression models to assess its predictive efficacy, providing theoretical basis for the prediction of VTE risk in OSA patients. MethodsA retrospective analysis was conducted on patients diagnosed with OSA and hospitalized in the Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Kunming Medical University, from January 2018 to August 2023. Patients were divided into OSA combined with VTE group (n=128) and pure OSA control group (n=680). The dataset was randomly divided into a training set (n=646) and an independent validation set (n=162). The Synthetic Minority Oversampling Technique (SMOTE) was employed to address the issue of data imbalance. Artificial Neural Networks and Logistic regression models were then built on training sets with and without SMOTE. Finally, the performance of each model was evaluated using accuracy, sensitivity, specificity, Youden's index, and Area Under the Receiver Operating Characteristic Curve (AUC). Results When oversampling was conducted using SMOTE on the training set, both the Artificial Neural Network and Logistic regression models showed improved AUC. The Artificial Neural Network model with SMOTE performed the best with an AUC value of 0.935 (95%CI: 0.898–0.961), achieving an accuracy of 90.15%, specificity of 87.32%, sensitivity of 93.44%, and Youden’s index of 0.808 at the optimal cutoff point. The Logistic regression model with SMOTE yielded an AUC value of 0.817 (95%CI: 0.765–0.861), with an accuracy of 77.27%, specificity of 83.80%, sensitivity of 69.67%, and Youden's index of 0.535. The difference in AUC between the Artificial Neural Network model and Logistic regression model was statistically significant after employing SMOTE (P<0.05). Conclusions The Artificial Neural Network model demonstrates high effectiveness in predicting VTE formation in OSA patients, particularly with the further improvement in predictive performance when utilizing SMOTE oversampling technique, rendering it more accurate and stable compared to the traditional Logistic regression model.