Role of serum cytokines in the prediction of heart failure in patients with coronary artery disease.

AIMS: Coronary artery disease (CAD) is the most common cause of heart failure (HF). This study aimed to identify cytokine biomarkers for predicting HF in patients with CAD. METHODS AND RESULTS: Twelve patients with CAD without HF (CAD-non HF), 12 patients with CAD complicated with HF (CAD-HF), and 12 healthy controls were enrolled for Human Cytokine Antibody Array, which were used as the training dataset. Then, differentially expressed cytokines among the different groups were identified, and crucial characteristic proteins related to CAD-HF were screened using a combination of the least absolute shrinkage and selection operator, recursive feature elimination, and random forest methods. A support vector machine (SVM) diagnostic model was constructed based on crucial characteristic proteins, followed by receiver operating characteristic curve analysis. Finally, two validation datasets, GSE20681 and GSE59867, were downloaded to verify the diagnostic performance of the SVM model and expression of crucial proteins, as well as enzyme-linked immunosorbent assay was also used to verify the levels of crucial proteins in blood samples. In total, 12 differentially expressed proteins were overlapped in the three comparison groups, and then four optimal characteristic proteins were identified, including VEGFR2, FLRG, IL-23, and FGF-21. After that, the area under the receiver operating characteristic curve of the constructed SVM classification model for the training dataset was 0.944. The accuracy of the SVM classification model was validated using the GSE20681 and GSE59867 datasets, with area under the receiver operating characteristic curve values of 0.773 and 0.745, respectively. The expression trends of the four crucial proteins in the training dataset were consistent with those in the validation dataset and those determined by enzyme-linked immunosorbent assay. CONCLUSIONS: The combination of VEGFR2, FLRG, IL-23, and FGF-21 can be used as a candidate biomarker for the prediction and prevention of HF in patients with CAD.

A novel and effective ECG method to differentiate right from left ventricular outflow tract arrhythmias: Angle-corrected V2S.

Background and aims: Standard 12-lead electrocardiogram (ECG) patterns combined with the anatomical cardiac long-axis angle revealed by chest X-ray can prevent the influence of cardiac rotation, physical shape, and lead position, so it may be an ideal means to predict the origin of the outflow tract (OT) ventricular arrhythmias (OTVAs) for ablation procedures. The study explores the value of this strategy in identifying the origin of OTVA. Methods: This study was conducted using a retrospective cohort and a prospective cohort of consecutive patients at two centers. The anatomical cardiac long-axis angle was calculated by measuring the angle between the cardiac long-axis (a line joining the apex to the midpoint of the mitral annulus) and the horizontal plane on a chest X-ray. The V2S angle was calculated as the V2S amplitude times the angle. We ultimately enrolled 147 patients with symptomatic OTVAs who underwent successful radiofrequency catheter ablation (RFCA) (98 women (66.7%); mean age 46.9 ± 14.7 years; 126 right ventricular OT (RVOT) origins, 21 left ventricular OT (LVOT) origins) as a development cohort. The new algorithm was validated in 48 prospective patients (12 men (25.0%); mean age 48.0 ± 15.8 years; 36 RVOT, 12 LVOT origins). Results: Patients with RVOT VAs had greater V2S, long-axis angle, and V2S angle than patients with LVOT VA (all P < 0.001). The cut-off V2S angle obtained by receiver operating characteristic (ROC) curve analysis was 58.28 mV° for the prediction of RVOT origin (sensitivity: 85.7%; specificity: 95.2%; positive predictive value: 99.1%; negative predictive value: 52.6%). The AUC achieved using the V2S angle was 0.888 (P < 0.001), which was the highest among all indexes (V2S/V3R: 0.887 (P < 0.016); TZ index: 0.858 (P < 0.001); V1-2 SRd: 0.876 (P < 0.001); V3 transition: 0.651 (P < 0.001)). In the prospective cohort, the V2S angle had a high overall accuracy of 93.8% and decreased the procedure time (P = 0.002). Conclusion: V2S angle can be a novel measure that can be used to accurately differentiate RVOT from LVOT origins. It could help decrease ablation duration and radiation exposure.

Association between single nucleotide polymorphisms in MiR219-1 and MiR137 and susceptibility to schizophrenia in a Chinese population.

Schizophrenia is one of the most common mental disorders to severely affect human health worldwide. Single nucleotide polymorphisms (SNPs) within related genes are candidate susceptible factors for the disorder. Rs107822 within MiR219-1 and rs1625579 within MiR137 were genotyped in 589 cases and 622 controls to investigate the possible association between the loci and schizophrenia in a Chinese population. Our results showed significant association between rs107822 and the disorder in allele (C vs. T: adjusted OR = 0.773, 95%CI = 0.655-0.912), co-dominant (TC vs. TT: adjusted OR = 0.734, 95%CI = 0.571-0.943; CC vs. TT: adjusted OR = 0.655, 95%CI = 0.459-0.936), dominant (TC + CC vs. TT: adjusted OR = 0.707, 95%CI = 0.559-0.895), and recessive (CC vs. TC + TT: adjusted OR = 0.724, 95%CI = 0.524-0.999) models, respectively. Meanwhile, negative associations were also observed between rs107822 and the disorder in male and female subgroups, and genotype CC of the locus was significantly associated with a lower positive symptom score of PANSS compared to genotype TT carrier in the cases group. However, we didn't observe a significant association between rs1625579 and the disorder. These findings indicate that rs107822 within MiR219-1 might be involved in pathogenesis of schizophrenia and that genotypes TC, CC and allele C of the locus are protective factors for schizophrenia in a Chinese population.