Untargeted metabolomics identifies indole-3-propionic acid to relieve Ang II-induced endothelial dysfunction in aortic dissection.

Indole-3-propionic acid (IPA), a gut microbiota-derived metabolite of tryptophan, has been proven to fulfill an essential function in cardiovascular disease (CVD) and nerve regeneration disease. However, the role of IPA in aortic dissection (AD) has not been revealed. We aimed to investigate the role of IPA in the pathogenesis of AD and the underlying mechanisms of IPA in endothelial dysfunction. Untargeted metabolomics has been employed to screen the plasma metabolic profile of AD patients in comparison with healthy individuals. Network pharmacology provides insights into the potential molecular mechanisms underlying IPA. 3-aminopropionitrile fumarate (BAPN) and angiotensin II (Ang II) were administered to induce AD in mice, while human umbilical vein endothelial cells (HUVECs) were employed for in vitro validation of the signaling pathways predicted by network pharmacology. A total of 224 potentially differential plasma metabolites were identified in the AD patients, with 110 up-regulated metabolites and 114 down-regulated metabolites. IPA was the most significantly decreased metabolite involved in tryptophan metabolism. Bcl2, caspase3, and AKT1 were predicted as the target genes of IPA by network pharmacology and molecular docking. IPA suppressed Ang II-induced apoptosis, intracellular ROS generation, inflammation, and endothelial tight junction (TJ) loss. Animal experiments demonstrated that administration of IPA alleviated the occurrence and severity of AD in mice. Taken together, we identified a previously unexplored association between tryptophan metabolite IPA and AD, providing a novel perspective on the underlying mechanism through which IPA mitigates endothelial dysfunction to protect against AD.

Analysis of the relationship between sleep-related disorder and systemic immune-inflammation index in the US population.

BACKGROUND: The association between sleep-related disorders and inflammation has been demonstrated in previous studies. The systemic immune-inflammation index (SII) is a novel inflammatory index based on leukocytes, but its relationship with sleep-related disorder is unclear. We aimed to investigate the relationship between sleep-related disorder and SII in a nationally representative nonhospitalized sample. METHODS: Data were obtained from the 2005-2008 National Health and Nutrition Examination Survey (NHANES). Exposure variables included self-reported sleep-related disorders, such as sleep duration, sleep problems, high risk of OSA, and daytime sleepiness. SII and other traditional markers of inflammation were considered as outcome variables, including platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR). Multiple linear regression models were employed to examine the correlation between sleep-related disorders and inflammatory markers. Subgroup interactions were analyzed using likelihood ratio tests, and nonlinear relationships were explored by fitting restricted cubic splines. RESULTS: A total of 8,505 participants were enrolled in this study. Overall, sleep-related disorders were found to have a stronger association with SII compared to the PLR and NLR. The results of multiple linear regression analysis revealed that participants who experienced sleep problems (ß: 21.421; 95% CI 1.484, 41.358), had symptoms of OSA (ß: 23.088; 95% CI 0.441, 45.735), and reported daytime sleepiness (ß: 30.320; 95% CI 5.851, 54.789) exhibited a positive association with higher SII. For the analysis of other inflammatory markers, we only found that daytime sleepiness was associated with increased NLR levels (ß: 0.081; 95% CI 0.002, 0.159). CONCLUSION: Sleep problems, symptoms of OSA, and daytime sleepiness were found to have a positive association with the SII in US adults. However, further prospective studies are necessary to establish whether there is a causal relationship between these factors.

Targeting metabolism in aortic aneurysm and dissection: from basic research to clinical applications.

Aortic aneurysm and dissection (AAD) are a group of insidious and lethal cardiovascular diseases that characterized by seriously threatening the life and health of people, but lack effective nonsurgical interventions. Alterations in metabolites are increasingly recognized as universal features of AAD because metabolic abnormalities have been identified not only in arterial tissue but also in blood and vascular cells from both patients and animal models with this disease. Over the past few decades, studies have further supported this notion by linking AAD to various types of metabolites such as those derived from gut microbiota or involved in TCA cycle or lipid metabolism. Many of these altered metabolites may contribute to the pathogenesis of AAD. This review aims to illustrate the close association between body metabolism and the occurrence and development of AAD, as well as summarize the significance of metabolites correlated with the pathological process of AAD. This provides valuable insight for developing new therapeutic agents for AAD. Therefore, we present a brief overview of metabolism in AAD biology, including signaling pathways involved in these processes and current clinical studies targeting AAD metabolisms. It is necessary to understand the metabolic mechanisms underlying AAD to provides significant knowledge for AAD diagnosis and new therapeutics for treatment.

The diagnostic and prognostic value of SAA1 as a novel biomarker for acute aortic dissection.

BACKGROUND AND AIMS: Acute aortic dissection (AAD) is a serious life-threatening cardiovascular condition. It is necessary to find rapid and accurate biomarkers for the diagnosis of AAD. This study aimed to determine the efficacy of serum amyloid A1 (SAA1) in the diagnosis and prediction of long-term adverse events in AAD. MATERIALS AND METHODS: Four-dimensional label-free quantification (4D-LFQ) technique was used to identify the differentially expressed proteins (DEPs) in aortic tissues of AAD. After comprehensive analysis, SAA1 was identified as a potential biomarker of AAD. ELISA was used to confirm the expression of SAA1 in serum of AAD patients. Moreover, the source of SAA1 in serum was explored by constructing AAD mouse model. RESULTS: A total of 247 DEPs were identified, of which 139 were upregulated while 108 were downregulated. SAA1 was nearly 6.4-fold and 4.5-fold upregulated in AAD tissue and serum. ROC curve and Kaplan-Meier survival curve confirmed the good efficacy of SAA1 for the diagnosis and prediction of long-term adverse events in AAD. In vivo experiments revealed that SAA1 was mainly derived from the liver when AAD occurred. CONCLUSION: SAA1 can be used as a potential biomarker for AAD with effective diagnostic and prognostic value. SIGNIFICANCE: Despite the advances in medical technology in recent years, the mortality rate of acute aortic dissection (AAD) is still high. It is still challenging for clinicians to diagnose AAD patients on time and reduce the mortality rate. In this study, 4D-LFQ technology was used to identify serum amyloid A1 (SAA1) as a potential biomarker of AAD and was verified in subsequent work. The results of this study determined the efficacy of SAA1 in the diagnosis and prediction of long-term adverse events in patients with AAD.

A prediction model to predict in-hospital mortality in patients with acute type B aortic dissection.

BACKGROUND: Acute type B aortic dissection (ABAD) is a life-threatening cardiovascular disease. A practicable and effective prediction model to predict and evaluate the risk of in-hospital death for ABAD is required. The present study aimed to construct a prediction model to predict the risk of in-hospital death in ABAD patients. METHODS: A total of 715 patients with ABAD were recruited in the first affiliated hospital of Xinjiang medical university from April 2012 to May 2021. The information on the demographic and clinical characteristics of all subjects was collected. The logistic regression analysis, receiver operating characteristic (ROC) curve analysis, and nomogram were applied to screen the appropriate predictors and to establish a prediction model for the risk of in-hospital mortality in ABAD. The receiver operator characteristic curve and calibration plot were applied to validate the performance of the prediction model. RESULTS: Of 53 (7.41%) subjects occurred in-hospital death in 715 ABAD patients. The variables including diastolic blood pressure (DBP), platelets, heart rate, neutrophil-lymphocyte ratio, D-dimer, C-reactive protein (CRP), white blood cell (WBC), hemoglobin, lactate dehydrogenase (LDH), procalcitonin, and left ventricular ejection fraction (LVEF) were shown a significant difference between the in-hospital death group and the in-hospital survival group (all P < 0.05). Furthermore, all these factors which existed differences, except CRP, were associated with in-hospital deaths in ABAD patients (all P < 0.05). Then, parameters containing LVEF, WBC, hemoglobin, LDH, and procalcitonin were identified as independent risk factors for in-hospital deaths in ABAD patients by adjusting compound variables (all P < 0.05). In addition, these independent factors were qualified as predictors to build a prediction model (AUC > 0.5, P < 0.05). The prediction model was shown a favorable discriminative ability (C index = 0.745) and demonstrated good consistency. CONCLUSIONS: The novel prediction model combined with WBC, hemoglobin, LDH, procalcitonin, and LVEF, was a practicable and valuable tool to predict in-hospital deaths in ABAD patients.

Identification of FERMT1 and SGCD as key marker in acute aortic dissection from the perspective of predictive, preventive, and personalized medicine.

Acute aortic dissection (AAD) is a severe aortic injury disease, which is often life-threatening at the onset. However, its early prevention remains a challenge. Therefore, in the context of predictive, preventive, and personalized medicine (PPPM), it is particularly important to identify novel and powerful biomarkers. This study aimed to identify the key markers that may contribute to the predictive early risk of AAD and analyze their role in immune infiltration. Three datasets, including a total of 23 AAD and 20 healthy control aortic samples, were retrieved from the Gene Expression Omnibus (GEO) database, and a total of 519 differentially expressed genes (DEGs) were screened in the training set. Using the least absolute shrinkage and selection operator (LASSO) regression model and the random forest (RF) algorithm, FERMT1 (AUC = 0.886) and SGCD (AUC = 0.876) were identified as key markers of AAD. A novel AAD risk prediction model was constructed using an artificial neural network (ANN), and in the validation set, the AUC = 0.920. Immune infiltration analysis indicated differential gene expression in regulatory T cells, monocytes, γδ T cells, quiescent NK cells, and mast cells in the patients with AAD and the healthy controls. Correlation and ssGSEA analysis showed that two key markers' expression in patients with AAD was correlated with many inflammatory mediators and pathways. In addition, the drug-gene interaction network identified motesanib and pyrazoloacridine as potential therapeutic agents for two key markers, which may provide personalized medical services for AAD patients. These findings highlight FERMT1 and SGCD as key biological targets for AAD and reveal the inflammation-related potential molecular mechanism of AAD, which is helpful for early risk prediction and targeted prevention of AAD. In conclusion, our study provides a new perspective for developing a PPPM method for managing AAD patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00302-4.

Association of genetic polymorphisms of PCSK9 with type 2 diabetes in Uygur Chinese population.

BACKGROUND: PCSK9 gene expression is associated with biological processes such as lipid metabolism, glucose metabolism, and inflammation. In the present study, our primary objective was to assess the association between the single-nucleotide polymorphisms in the PCSK9 gene and type 2 diabetes in Uygur subjects, in Xinjiang, China. METHODS: We designed a case-control study including 662 patients diagnosed with T2DM and 1220 control subjects. Four single-nucleotide polymorphisms (rs11583680, rs2483205, rs2495477 and rs562556) of PCSK9 gene were genotyped using the improved multiplex ligation detection reaction technique. RESULTS: For rs2483205, the distribution of genotypes, dominant model (CC vs CT + TT), overdominant model (CC + TT vs CT) showed significant differences between T2DM patients and the controls (P = 0.011 and P = 0.041 respectively). For rs2495477, the distribution of genotypes, the dominant model (AA vs GA + GG) showed significant differences between T2DM patients and the controls (P = 0.024). Logistic regression analysis suggested after adjustment of other confounders, the differences remained significant between the two groups [for rs2483205 CC vs CT + TT: odds ratio (OR) = 1.321, 95% confidence interval (CI) 1.078-1.617, P = 0.007; CC + TT vs CT: OR = 1.255, 95% CI 1.021-1.542, P = 0.03; for rs2495477 AA vs GA + GG: OR = 1.297, 95% CI 1.060-1.588, P = 0.012]. CONCLUSION: The present study indicated that CT + TT genotype and CT genotype of rs2483205, as well as GA + GG genotype of rs2495477 in PCSK9 gene were associated with an increased risk of type 2 diabetes in the Uygur population in Xinjiang.