Metformin Therapeutic Targets for Aortic Aneurysms: A Mendelian Randomization and Colocalization Study.

Background: Identifying effective pharmacological interventions to prevent the progressive enlargement and rupture of aortic aneurysms (AAs) is critical. Previous studies have suggested links between metformin use and a decreased incidence of AAs. In this study, we employed Mendelian randomization (MR) to investigate causal effects of metformin's targets on AA risk and to explore the underlying mechanisms underlying these effects. Methods: To examine the relationship between metformin use and AA risk, we implemented both two-sample MR and multivariable MR analyses. Utilizing genetic instrumental variables, we retrieved cis-expression quantitative trait loci (cis-eQTL) data for potential targets of metformin from the Expression Quantitative Trait Loci Genetics Consortium (eQTLGen) Consortium and Genotype-Tissue Expression (GTEx) project. Colocalization analysis was employed to ascertain the probability of shared causal genetic variants between single nucleotide polymorphisms (SNPs) associated with eQTLs and AA. Results: Our findings reveal that metformin use reduces AA risk, exhibiting a protective effect with an odds ratio (OR) of 4.88 × 10 - 3 (95% confidence interval [CI]: 7.30 × 10 - 5 -0.33, p = 0.01). Furthermore, the protective effect of type 2 diabetes on AA risk appears to be driven by metformin use ( OR MVMR = 1.34 × 10 - 4 , 95% CI: 3.97 × 10 - 8 -0.45, p = 0.03). Significant Mendelian randomization (MR) results were observed for the expression of two metformin-related genes in the bloodstream: NADH:ubiquinone oxidoreductase subunit A6 (NDUFA6) and cytochrome b5 type B (CYB5B), across two independent datasets ( OR CYB5B = 1.35, 95% CI: 1.20-1.51, p = 2.41 × 10 - 7 ; OR NDUFA6 = 1.12; 95% CI: 1.07-1.17, p = 1.69 × 10 - 6 ). The MR analysis of tissue-specific expression also demonstrated a positive correlation between increased NDUFA6 expression and heightened AA risk. Lastly, NDUFA6 exhibited evidence of colocalization with AA. Conclusions: Our study suggests that metformin may play a significant role in lowering the risk of AA. This protective effect could potentially be linked to the mitigation of mitochondrial and immune dysfunction. Overall, NDUFA6 has emerged as a potential mechanism through which metformin intervention may confer AA protection.

Radiomics Signature of Epicardial Adipose Tissue for Predicting Postoperative Atrial Fibrillation after Off-Pump Coronary Artery Bypass Surgery.

Background: Postoperative new atrial fibrillation (POAF) is a commonly observed complication after off-pump coronary artery bypass surgery (OPCABG), and models based on radiomics features of epicardial adipose tissue (EAT) on non-enhanced computer tomography (CT) to predict the occurrence of POAF after OPCABG remains unclear. This study aims to establish and validate models based on radiomics signature to predict POAF after OPCABG. Methods: Clinical characteristics, radiomics signature and features of non-enhanced CT images of 96 patients who underwent OPCABG were collected. The participants were divided into a training and a validation cohort randomly, with a ratio of 7:3. Clinical characteristics and EAT CT features with statistical significance in the multivariate logistic regression analysis were utilized to build the clinical model. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify significant radiomics features to establish the radiomics model. The combined model was constructed by integrating the clinical and radiomics models. Results: The area under the curve (AUC) of the clinical model in the training and validation cohorts were 0.761 (95% CI: 0.634-0.888) and 0.797 (95% CI: 0.587-1.000), respectively. The radiomics model showed better discrimination ability than the clinical model, with AUC of 0.884 (95% CI: 0.806-0.961) and 0.891 (95% CI: 0.772-1.000) respectively for the training and the validation cohort. The combined model performed best and exhibited the best predictive ability among the three models, with AUC of 0.922 (95% CI: 0.853-0.990) in the training cohort and 0.913 (95% CI: 0.798-1.000) in the validation cohort. The calibration curve demonstrated strong concordance between the predicted and actual observations in both cohorts. Furthermore, the Hosmer-Lemeshow test yielded p value of 0.241 and 0.277 for the training and validation cohorts, respectively, indicating satisfactory calibration. Conclusions: The superior performance of the combined model suggests that integrating of clinical characteristics, radiomics signature and features on non-enhanced CT images of EAT may enhance the accuracy of predicting POAF after OPCABG.

New-onset postoperative atrial fibrillation after pulmonary endarterectomy is associated with adverse outcomes.

Background: New-onset postoperative atrial fibrillation (POAF) is a common complication after pulmonary thromboendarterectomy (PEA), yet the risk factors and their impact on prognosis remain poorly understood. This study aims to investigate the risk factors associated with new-onset POAF after PEA and elucidate its underlying connection with adverse postoperative outcomes. Methods: A retrospective analysis included 129 consecutive chronic thromboembolic pulmonary hypertension (CTEPH) patients and 16 sarcoma patients undergoing PEA. Univariate and multivariate analyses were conducted to examine the potential effects of preoperative and intraoperative variables on new-onset POAF following PEA. Propensity score matching (PSM) was then employed to adjust for confounding factors. Results: Binary logistic regression revealed that age (odds ratio [OR] = 1.041, 95% confidence interval [CI] = 1.008-1.075, p = 0.014) and left atrial diameter[LAD] (OR = 1.105, 95% CI = 1.025-1.191, p = 0.009) were independent risk factors for new-onset POAF after PEA. The receiver operating characteristic (ROC) curve indicated that the predictive abilities of age and LAD for new-onset POAF were 0.652 and 0.684, respectively. Patients with new-onset POAF, compared with those without, exhibited a higher incidence of adverse outcomes (in-hospital mortality, acute heart failure, acute kidney insufficiency, reperfusion pulmonary edema). Propensity score matching (PSM) analyses confirmed the results. Conclusion: Advanced age and LAD independently contribute to the risk of new-onset POAF after PEA. Patients with new-onset POAF are more prone to adverse outcomes. Therefore, heightened vigilance and careful monitoring of POAF after PEA are warranted.

Causal Relationships between Lipid-Lowering Drug Target and Aortic Disease and Calcific Aortic Valve Stenosis: A Two-Sample Mendelian Randomization.

Background: Proprotein convertase subtilisin/kexin type 9 (PCSK9), 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), cholesteryl ester transfer protein (CETP) and apolipoprotein C3 (APOC3) are pivotal regulators of lipid metabolism, with licensed drugs targeting these genes. The use of lipid-lowering therapy via the inhibition of these genes has demonstrated a reduction in the risk of cardiovascular disease. However, concerns persist regarding their potential long-term impact on aortic diseases and calcific aortic valve disease (CAVS). This study aims to investigate causal relationships between genetic variants resembling these genes and aortic disease, as well as calcific aortic valve disease using Mendelian randomization (MR). Methods: We conducted drug-target Mendelian randomization employing summary-level statistics of low-density lipoprotein cholesterol (LDL-C) to proxy the loss-of-function of PCSK9, HMGCR, CETP and APOC3. Subsequently, we investigated the association between drug-target genetic variants and calcific aortic valve stenosis and aortic diseases, including thoracic aortic aneurysm (TAA), abdominal aortic aneurysm (AAA), and aortic dissection (AD). Results: The genetically constructed variants mimicking lower LDL-C levels were associated with a decreased risk of coronary artery disease, validating their reliability. Notably, HMGCR inhibition exhibited a robust protective effect against TAA (odds ratio (OR): 0.556, 95% CI: 0.372-0.831, p = 0.004), AAA (OR: 0.202, 95% CI: 0.107-0.315, p = 4.84 × 10-15), and AD (OR: 0.217, 95% CI: 0.098-0.480, p = 0.0002). Similarly, PCSK9, CETP and APOC3 inhibition proxies reduced the risk of AAA (OR: 0.595, 95% CI: 0.485-0.730, p = 6.75 × 10-7, OR: 0.127, 95% CI: 0.066-0.243, p = 4.42 × 10-10, and OR: 0.387, 95% CI: 0.182-0.824, p = 0.014, respectively) while showing a neutral impact on TAA and AD. Inhibition of HMGCR, PCSK9, and APOC3 showed promising potential in preventing CAVS with odds ratios of 0.554 (OR: 0.554, 95% CI: 0.433-0.707, p = 2.27 × 10-6), 0.717 (95% CI: 0.635-0.810, p = 9.28 × 10-8), and 0.540 (95% CI: 0.351-0.829, p = 0.005), respectively. However, CETP inhibition did not demonstrate any significant benefits in preventing CAVS (95% CI: 0.704-1.544, p = 0.836). The consistency of these findings across various Mendelian randomization methods, accounting for different assumptions concerning genetic pleiotropy, enhances the causal inference. Conclusions: Our MR analysis reveals that genetic variants resembling statin administration are associated with a reduced risk of AAA, TAA, AD and CAVS. HMGCR, PCSK9 and APOC3 inhibitors but not CETP inhibitors have positive benefits of reduced CAVS. Notably, PCSK9, CETP and APOC3 inhibitors exhibit a protective impact, primarily against AAA, with no discernible benefits extending to TAA or AD.

Untargeted plasma metabolome identifies biomarkers in patients with extracranial arteriovenous malformations.

Objective: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM). Method: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve. Result: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and protein translation. Through machine learning algorithms, nine metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid, and dihydrojasmonic acid. Conclusion: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.

Extraction of Medication-Effect Relations in Twitter Data with Neural Embedding and Recurrent Neural Network.

Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets.