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Galectin-1 modulates acute and chronic inflammation, and is associated with glucose homeostasis and chronic renal disease. Whether the serum galectin-1 level can predict short-term and long-term renal outcomes after contrast exposure in patients undergoing coronary angiography (CAG) remains uncertain. This study aimed to evaluate the relationship between the serum galectin-1 level and the incidence of contrast-induced nephropathy (CIN), and to investigate the predictive role of the circulating galectin-1 level for renal function decline in patients undergoing CAG. In total, 798 patients who had undergone CAG were enrolled. Baseline creatinine and serum galectin-1 levels were determined before CAG. CIN was defined as an increase in the serum creatinine level of 0.5 mg/dl or a 25% increase from baseline within 48 h after the procedure, and renal function decline was defined as > 30% reduction of the estimated glomerular filtration rate from baseline. All patients were followed for at least 1 year or until the occurrence of death after CAG. Overall, CIN occurred in 41 (5.1%) patients. During a median follow-up period of 1.4 ± 1.1 years, 80 (10.0%) cases showed subsequent renal function decline. After adjustment for demographic characteristics, kidney function, traditional risk factors, and medications, higher galectin-1 levels were found to be associated independently with a greater risk of renal function decline [tertile 2: hazard ratio (HR) 5.56, 95% confidence interval (CI) 1.79-17.22; tertile 3: HR 5.56, 95% CI 1.97-16.32], but not with CIN, regardless of the presence of diabetes. In conclusion, higher baseline serum galectin-1 levels were associated with a greater risk of renal function decline in patients undergoing CAG, but were not associated independently with CIN.

Background: Ruling out obstructive coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) is time‐consuming and challenging. This study developed a deep learning (DL) model to assist in detecting obstructive CAD on CCTA to streamline workflows. Methods: In total, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted CCTA images. A modified Inception V3 model was adopted. To validate the artificial intelligence (AI) model, two cardiologists labelled and adjudicated the classification of coronary stenosis on CCTA. The model was trained to differentiate the coronary artery into binary stenosis classifications <50% and ≥50% stenosis. Using the quantitative coronary angiography (QCA) consensus results as a reference standard, the performance of the AI model and CCTA radiology readers was compared by calculating Cohen's kappa coefficients at patient and vessel levels. The net reclassification index was used to evaluate the net benefit of the DL model. Results: The diagnostic accuracy of the AI model was 92.3% and 88.4% at the patient and vessel levels, respectively. Compared with CCTA radiology readers, the AI model had a better agreement for binary stenosis classification at both patient and vessel levels (Cohen kappa coefficient:.79 vs..39 and.77 vs..40, p <.0001). The AI model also exhibited significantly improved model discrimination and reclassification (Net reclassification index =.350; Z = 4.194; p <.001). Conclusions: The developed AI model identified obstructive CAD, and the model results correlated well with QCA results. Incorporating the model into the reporting system of CCTA may improve workflows. [ABSTRACT FROM AUTHOR]

Insulin resistance (IR) is associated with cardiovascular disease in non-diabetic patients. The triglyceride-glucose (TyG) index, incorporating serum glucose and insulin concentrations, is a surrogate insulin resistance marker. We investigated its association with obstructive coronary artery disease (CAD) and sex differences therein. Patients with stable angina pectoris requiring invasive coronary angiography between January 2010 and December 2018 were enrolled. They were divided into two groups according to TyG index. Two interventional cardiologists diagnosed obstructive CAD by angiography review. Demographic characteristics and clinical outcomes were compared between groups. Relative to lower index, patients with higher (≥ 8.60) TyG index had higher BMIs and more prevalent hypertension, diabetes, and elevated lipid profiles [total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), fasting plasma glucose (FPG)]. Higher TyG index increased women's obstructive CAD risk after multivariate adjustment (adjusted odds ratio (aOR) 2.15, 95% confidence interval (95% CI) 1.08–4.26, p = 0.02) in non-diabetic populations compared with men. No sex difference was found for diabetic patients. Higher TyG index significantly increased the obstructive CAD risk, overall and for non-diabetic women. Larger-scale studies are needed to confirm our findings. [ABSTRACT FROM AUTHOR]

Background: Trimethylamine N-oxide (TMAO) is a microbiota-derived metabolite, which is linked to vascular inflammation and atherosclerosis in cardiovascular (CV) diseases. But its effect in infectious diseases remains unclear. We conducted a single-center prospective study to investigate association of TMAO with in-hospital mortality in septic patients admitted to an intensive care unit (ICU). Methods: Totally 95 septic, mechanically ventilated patients were enrolled. Blood samples were obtained within 24 h after ICU admission, and plasma TMAO concentrations were determined. Septic patients were grouped into tertiles according to TMAO concentration. The primary outcome was in-hospital death, which further classified as CV and non-CV death. Besides, we also compared the TMAO concentrations of septic patients with 129 non-septic patients who were admitted for elective coronary angiography (CAG). Results: Septic patients had significantly lower plasma TMAO levels than did subjects admitted for CAG (1.0 vs. 3.0 μmol/L, p < 0.001). Septic patients in the lowest TMAO tertile (< 0.4 μmol/L) had poorer nutrition status and were given longer antibiotic courses before ICU admission. Circulating TMAO levels correlated positively with daily energy intake, the albumin and prealbumin concentration. Compared with those in the highest TMAO tertile, septic patients in the lowest TMAO tertile were at greater risk of non-CV death (hazard ratio 2.51, 95% confidence interval 1.21–5.24, p = 0.014). However, TMAO concentration was no longer an independent predictor for non-CV death after adjustment for disease severity and nutritional status. Conclusion: Plasma TMAO concentration was inversely associated with non-CV death among extremely ill septic patients, which could be characterized as TMAO paradox. For septic patients, the impact of malnutrition reflected by circulating TMAO levels was greater than its pro-inflammatory nature. [ABSTRACT FROM AUTHOR]

Prevention for contrast-induced nephropathy (CIN) is limited by the lack of a single predictor. As activin A is upregulated in heart failure and chronic kidney disease, we aimed to clarify the association between activin A levels and renal outcomes after coronary angiography (CAG). This prospective observational study included 267 patients who received CAG between 2009 and 2015. CIN was defined as elevation of serum creatinine to >0.5 mg/dL or to >25% above baseline within 48 hours after CAG. During follow-up, laboratory parameters were measured every 3–6 months. Renal decline was defined as>2-fold increase in serum creatinine or initiation of dialysis. The patients were stratified into tertiles according to serum activin A levels at baseline. High activin A tertile was significantly associated more CIN and renal function decline compared to low activin A tertile (all p < 0.001). After adjusting potential confounding factors, high serum activin A tertiles was associated to CIN (Odds ratio 4.49, 95% CI 1.07–18.86, p = 0.040) and renal function decline (Hazard ratio 4.49, 95% CI 1.27–11.41, p = 0.017) after CAG. [ABSTRACT FROM AUTHOR]

Galectin-1, a β-galactoside-binding lectin mediating inflammation and neovascularization, is reported to attenuate ventricular remodeling after myocardial infarction. But its role in stable coronary artery disease (CAD) has not been fully elucidated. This study aimed to identify the relationship between the circulating galectin-1 level and the severity of CAD in patients with suspected CAD. Pre-procedure galectin-1 and high-sensitivity C-reactive protein (hs-CRP) concentrations were measured in 834 subjects who underwent scheduled coronary angiography. Subjects were grouped into tertiles of the galectin-1 levels. SYNTAX scores were calculated to evaluate the severity of CAD. All patients were followed until January 2019 or the occurrence of major adverse cardiovascular events (MACE). Patients with higher galectin-1 concentrations were older; had greater prevalence of hypertension, diabetes, chronic kidney disease, and heart failure; and were more likely to present with higher hs-CRP levels and SYNTAX scores. During the follow-up period of 1.3 ± 1.1 years, patients in the highest tertile of galectin-1 were associated with a greater risk of MACE after adjustment for age, sex, comorbidities, co-medications, serum levels of hemoglobin, creatinine, hs-CRP, ejection fraction, SYNTAX scores, and revascularization modalities (adjusted hazard ratio 10.95, 95% confidence interval 2.29–52.47, p = 0.003). Galectin-1 showed better discriminatory performance than hs-CRP, and non-inferior performance to SYNTAX scores, in predicting the incidence of MACE. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

To evaluate the risk of coronary artery disease (CAD), the traditional approach involves assessing the patient's symptoms, traditional cardiovascular risk factors (CVRFs), and a 12-lead electrocardiogram (ECG). However, currently, there are no established criteria for interpreting an ECG to diagnose CAD. Therefore, we sought to develop an artificial intelligence (AI)-enabled ECG model to assist in identifying patients with CAD. In this study, we included patients who underwent coronary angiography (CAG) at a single center between 2017 and 2019. Preprocedural 12-lead ECG performed within 24 h was obtained. Obstructive CAD was defined as ≥ 50% diameter stenosis. Using age, gender and ECG data, we developed stacking models using both deep learning and machine learning. Then we compared the performance of our models with CVRFs and with cardiologists' ECG interpretation. Additionally, we validated our model on an external cohort from a different hospital. We included 4951 patients with a mean age of 65.5 ± 12.5 years, of whom 67.0% were men. Based on CAG, obstructive CAD was confirmed in 2637 patients (53.2%). Our best AI model demonstrated comparable performance to CVRFs in predicting CAD, with an AUC of 0.70 (95% CI: 0.66–0.75) compared to 0.71 (95% CI: 0.66–0.76). The sensitivity and specificity of the AI model were 0.75 and 0.54, respectively, while those of CVRFs were 0.67 and 0.63. Compared to cardiologists, the AI model showed better performance with an F1 score of 0.68 vs 0.41. The external validation showed generally consistent diagnostic findings, although there was a slightly lower level of agreement observed in the external cohort. Incorporating ECG and CVRFs improved the AUC to 0.72. Our study suggests that an AI-enabled ECG model can assist in identifying patients with obstructive CAD, with diagnostic performance similar to that of the traditional approach based on CVRFs. This model could serve as a useful clinical tool in an outpatient setting to identify patients who require further diagnostic tests. [Display omitted] • We built an AI-enabled ECG stacking model to assist in identifying obstructive CAD. • Our model had comparable diagnostic performance to the traditional risk factor-based approach for identifying obstructive CAD. • This model could be readily used in outpatient settings as a clinical tool to assist in patient diagnosis. [ABSTRACT FROM AUTHOR]