Background: Although the associations between serum lipid levels and aneurysms have been investigated in epidemiological studies, causality remains unknown. Thus, this study aimed to investigate the causal relationships of serum high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglyceride (TG) levels on five types of aneurysms, using genetic variants associated with four lipid traits as instrumental variables in a Mendelian randomization (MR) analysis. Methods: We performed two-sample Mendelian randomization (MR) analyses to evaluate the associations of HDL-C, LDL-C, TC, and TG levels with risks for five types of aneurysms and those of LDL-C- (HMGCR, NPC1L1, PCSK9, CETP, and LDLR) and TG-lowering targets (ANGPTL3 and LPL) with aneurysms. Results: The sample sizes of the included studies ranged from nearly 80,000 to 410,000. We found inverse associations between genetically predicted HDL-C levels and aortic (OR = 0.74, 95% CI = 0.65–0.85) and abdominal aortic aneurysms (0.58, 0.45–0.75). A 1-SD increase in LDL-C and TC levels was associated with increased risks for aortic (1.41, 1.26–1.58 and 1.36, 1.18–1.56, respectively) and abdominal aortic aneurysms (1.82, 1.48–2.22 and 1.55, 1.25–1.93, respectively). TG levels were significantly associated with aortic (1.36, 1.18–1.56) and lower extremity artery aneurysms (2.76, 1.48–5.14), but limited to cerebral aneurysm (1.23, 1.06–1.42). Secondary analyses revealed a relationship between genetically proxied LDL-C-lowering targets and all types of aneurysms; however, the drug targets remained heterogeneous. We found a weak association between TG-lowering therapies and aortic (ANGPTL3, 0.51, 0.29–0.89) and abdominal aortic aneurysms (LPL, 0.64, 0.44–0.94). Conclusion: According to genetic evidence, lipid dysfunction is a causal risk factor for aneurysms. Lipid-lowering drugs may be a potential effective strategy in preventing and managing aneurysms.

Background: Brugada syndrome (BrS) is an inheritable arrhythmia syndrome that can lead to sudden cardiac death in patients while the heart structure is normal. However, the genetic background of more than 65% of BrS probands remains unclear. Objectives: The purpose of this study is to report the variant spectrum in a Chinese cohort with suspected BrS and to analyze their distinct clinical and electrocardiographic features. Methods: Patients with suspected BrS from Tongji Hospital between 2008 and 2021 were analyzed retrospectively. Results: A total of 79 probands were included in this study. Patients with type 1 BrS electrocardiogram (ECG) had a prolonged QRS duration compared to patients with type 2/3 BrS ECG. Of them, 59 probands underwent genetic testing. Twenty-five patients (42.37%) showed abnormal genetic testing results, and eight of them (13.56%) carried pathogenic/likely pathogenic (P/LP) mutations. Mutation carriers presented much more prominent depolarization and repolarization abnormalities than non-carriers, including a prolonged P-wave duration, QRS duration, QTc interval, decreased QRS amplitude, and deviation of the electrocardiographic axes (T-wave axis and R-wave axis). Furthermore, our study identified four novel P/LP mutations: Q3508X in TTN, A990G in KCNH2, G1220E, and D372H (in a representative pedigree) in SCN5A. Conclusions: Our study showed the variant spectrum of a suspected Chinese BrS cohort, and we identified four novel P/LP mutations in TTN, KCNH2, and SCN5A.

Coronary Heart Disease (CHD) with renal insufficiency is a significant global health issue. This study aimed to develop and validate a predictive model for in-hospital mortality to enable early risk identification in these patients. We analyzed data from 11,830 CHD patients with renal insufficiency treated at Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (1994–2023). Among 113 clinical variables, five key features—age, high-sensitivity C-reactive protein (hs-CRP), estimated glomerular filtration rate (eGFR), creatine kinase (CK), and blood urea—were selected using Recursive Feature Elimination. Six machine learning models (Random Forest, XGBoost, Decision Tree, Neural Network, Logistic Regression, and Support Vector Machine) were developed and assessed for discrimination, calibration, and clinical utility. Temporal validation was performed using data from May 16, 2023 to October 31, 2024. SHapley Additive exPlanations (SHAP) were used for model interpretation. Of the 11,830 patients, 694 (5.9 %) died during hospitalization. Among the six models, XGBoost showed the best overall performance in the test set, achieving the highest AUC (0.926), lowest Brier score (0.034), highest accuracy (0.957), and balanced sensitivity (0.381) and F1 score (0.512). Decision curve analysis confirmed its superior clinical utility. In a temporally independent validation cohort of 5983 patients, XGBoost maintained strong predictive performance (AUC = 0.901), demonstrating excellent robustness and generalizability. The XGBoost-based model accurately predicts in-hospital mortality in CHD patients with renal insufficiency, supporting early risk stratification and clinical decision-making.