Background: Insulin resistance (IR) is considered a pivotal risk factor for cardiometabolic diseases, and the triglyceride–glucose index (TyG index) has emerged as a reliable surrogate marker of IR. Although several recent studies have shown the association of the TyG index with vascular disease, no studies have further investigated the role of the TyG index in acute ST-elevation myocardial infarction (STEMI). The objective of the present study was to evaluate the potential role of the TyG index as a predictor of prognosis in STEMI patients after percutaneous coronary intervention (PCI). Methods: The study included 1092 STEMI patients who underwent PCI. The patients were divided into 4 quartiles according to TyG index levels. Clinical characteristics, fasting plasma glucose (FPG), triglycerides (TGs), other biochemical parameters, and the incidence of major adverse cardiovascular and cerebral events (MACCEs) during the follow-up period were recorded. The TyG index was calculated using the following formula: ln[fasting TGs (mg/dL) × FPG (mg/dL)/2]. Results: The incidence of MACCEs and all-cause mortality within 30 days, 6 months and 1 year after PCI were higher among STEMI patients with TyG index levels in the highest quartile. The TyG index was significantly associated with an increased risk of MACCEs in STEMI patients within 1 year after PCI, independent of confounding factors, with a value of 1.529 (95% CI 1.001–2.061; P = 0.003) for those in the highest quartile. The area under the curve (AUC) of the TyG index predicting the occurrence of MACCEs in STEMI patients after PCI was 0.685 (95% CI 0.610–0.761; P = 0.001). The results also revealed that Killip class > 1, anaemia, albumin, uric acid, number of stents and left ventricular ejection fraction (LVEF) were independent predictors of MACCEs in STEMI patients after PCI (all P < 0.05). Conclusions: This study indicated an association between higher TyG index levels and increased risk of MACCEs in STEMI patients for the first time, and the TyG index might be a valid predictor of clinical outcomes in STEMI patients undergoing PCI. Trial Registration ChiCTR1900024577.

Role of ferroptosis in the process of diabetes-induced endothelial dysfunction

The objective of this study was to construct a prediction model to assess the onset of acute heart failure (AHF) in patients with chronic obstructive pulmonary disease (COPD) without history of heart failure and to evaluate the predictive value of the nomogram. This study involved 3,730 patients with COPD and no history of heart failure. Clinical and laboratory data were collected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The patients were divided into a training set (2,611 cases) and a validation set (1,119 cases) in a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used to identify potential risk factors for AHF in patients with COPD. These factors were then subjected to multivariate logistic regression analysis to develop a prediction model for the risk of AHF. The model's differentiation, consistency, and clinical applicability were evaluated using receiver operating characteristic (ROC) analysis, a calibration curve, and decision curve analysis (DCA), respectively. LASSO regression identified 10 potential predictors. The concordance index was 0.820. The areas under the curves for the training and validation sets were 0.8195 and 0.8035, respectively. The calibration curve demonstrated strong concordance between the nomogram's predictions and the actual outcomes. DCA confirmed the clinical applicability of the nomogram. Our nomogram is a reliable and convenient tool for predicting acute heart failure in patients with chronic obstructive pulmonary disease.

Introduction: The relationship between smoking and heart disease has been frequently reported. Therefore, we aimed to explore the association between smoking initiation and atrial fibrillation. Methods: Genetic association data pertaining to smoking initiation and atrial fibrillation were...