RESUMO
BACKGROUND: Atrial fibrillation (AF) is the most common type of arrhythmia worldwide and is associated with serious complications. This study investigated the metabolic biomarkers associated with AF and the differences in metabolomics and associated metabolic biomarkers between paroxysmal AF (AFPA) and persistent AF. METHODS AND RESULTS: Plasma samples were prospectively collected from patients with AF and patients in sinus rhythm with negative coronary angiography. The patients were divided into 3 groups: AFPA, persistent AF, and sinus rhythm (N=54). Metabolomics (n=36) using ultra-high-performance liquid chromatography mass spectrometry was used to detect differential metabolites that were validated in a new cohort (n=18). The validated metabolites from the validation phase were further analyzed by receiver operating characteristic. Among the 36 differential metabolites detected by omics assay, 4 were successfully validated with area under the curve >0.8 (P<0.05). Bioinformatics analysis confirmed the enrichment pathways of unsaturated fatty acid biosynthesis, glyoxylate and dicarboxylate metabolism, and carbon metabolism. Arachidonic acid was a potential biomarker of AFPA, glycolic acid and L-serine were biomarkers of AFPA and persistent AF, and palmitelaidic acid was a biomarker of AFPA. CONCLUSIONS: In this metabolomics study, we detected 36 differential metabolites in AF, and 4 were validated with high sensitivity and specificity. These differential metabolites are potential biomarkers for diagnosis and monitoring of disease course. This study therefore provides new insights into the precision diagnosis and management of AF.
Assuntos
Fibrilação Atrial , Humanos , Fibrilação Atrial/complicações , Biomarcadores , Metabolômica/métodosRESUMO
BACKGROUND: Limited data were available on the current trends in optimal medical therapy (OMT) after PCI and its influence on clinical outcomes in China. We aimed to evaluate the utilization and impact of OMT on the main adverse cardiovascular and cerebrovascular events (MACCEs) in post-PCI patients and analyzed the factors predictive of OMT after discharge. METHODS: We collected data from 3812 individuals from 2016.10 to 2017.09 at TEDA International Cardiovascular Hospital. They were classified into an OMT group and a non-OMT group according to their OMT status, which was defined as the combination of dual antiplatelet therapy, statins, ß-blockers, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers after PCI. Multivariable Cox regression models were developed to assess the association between OMT and MACCEs, defined as all-cause mortality, nonfatal myocardial infarction, stroke, and target vessel revascularization. A logistic regression model was established to analyze the factors predictive of OMT. RESULTS: Our results revealed that the proportion of patients receiving OMT and its component drugs decreased over time. A total of 36.0% of patients were still adherent to OMT at the end of follow-up. Binary logistic regression analysis revealed that baseline OMT (P < 0.001, OR = 52.868) was the strongest predictor of OMT after PCI. The Cox hazard model suggested that smoking after PCI was associated with the 1-year risk of MACCE (P = 0.001, HR = 2.060, 95% CI 1.346-3.151), while OMT (P = 0.001, HR = 0.486, 95% CI 0.312-0.756) was an independent protective factor against postoperative MACCEs. CONCLUSIONS: There was still a gap between OMT utilization after PCI and the recommendations in the evidence-based guidelines. Sociodemographic and clinical factors influence the application of OMT. The management of OMT and smoking cessation after PCI should be emphasized.
