Sudden death could be the first symptom of patients with arrhythmogenic cardiomyopathy (AC), a disease for which clinical indicators predicting adverse progression remain lacking. Recent findings suggest that metabolic dysregulation is present in AC. We performed this study to identify metabolic indicators that predicted major adverse cardiac events (MACEs) in patients with AC and their relatives. Comparing explanted hearts from patients with AC and healthy donors, we identified deregulated metabolic pathways using quantitative proteomics. Right ventricles (RVs) from patients with AC displayed elevated ketone metabolic enzymes, OXCT1 and HMGCS2, suggesting higher ketone metabolism in AC RVs. Analysis of matched coronary artery and sinus plasma suggested potential ketone body synthesis at early-stage AC, which was validated using patient-derived induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs) in vitro. Targeted metabolomics analysis in RVs from end-stage AC revealed a “burned-out” state, with predominant medium-chain fatty acid rather than ketone body utilization. In an independent validation cohort, 65 probands with mostly non–heart failure manifestations of AC had higher plasma β-hydroxybutyrate (β-OHB) than 62 healthy volunteers (P < 0.001). Probands with AC with MACE had higher β-OHB than those without MACE (P < 0.001). Among 94 relatives of probands, higher plasma β-OHB distinguished 25 relatives having suspected AC from nonaffected relatives. This study demonstrates that elevated plasma β-OHB predicts MACE in probands and disease progression in patients with AC and their clinically asymptomatic relatives.
Aims Arrhythmogenic cardiomyopathy (AC) shows large heterogeneity in its clinical, genetic, and pathological presentation. This study aims to provide a comprehensive atlas of end-stage AC and illustrate the relationships among clinical characteristics, genotype, and pathological profiles of patients with this disease. Methods and results We collected 60 explanted AC hearts and performed standard pathology examinations. The clinical characteristics of patients, their genotype and cardiac magnetic resonance imaging findings were assessed along with pathological characteristics. Masson staining of six representative sections of each heart were performed. Digital pathology combined with image segmentation was developed to calculate distribution of myocardium, fibrosis, and adipose tissue. An unsupervised clustering based on fibrofatty distribution containing four subtypes was constructed. Patients in Cluster 1 mainly carried desmosomal mutations (except for desmoplakin) and were subjected to transplantation at early age; this group was consistent with classical ‘desmosomal cardiomyopathy’. Cluster 2 mostly had non-desmosomal mutations and showed regional fibrofatty replacement in right ventricle. Patients in Cluster 3 showed parallel progression, and included patients with desmoplakin mutations. Cluster 4 is typical left-dominant AC, although the genetic background of these patients is not yet clear. Multivariate regression analysis revealed precordial QRS voltage as an independent indicator of the residual myocardium of right ventricle, which was validated in predicting death and transplant events in the validation cohort (n = 92). Conclusion This study provides a novel classification of AC with distinct genetic backgrounds indicating different potential pathogenesis. Cluster 1 is distinct in genotype and clinicopathology and can be defined as ‘desmosomal cardiomyopathy’. Precordial QRS amplitude is an independent indicator reflecting the right ventricular remodelling, which may be able to predict transplant/death events for AC patients.
Background Cardiac surgery–associated acute kidney injury (CSA‐AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA‐AKI in hospitalized patients. This study developed and evaluated the diagnostic use of metabolomics‐based biomarkers in patients with CSA‐AKI. Methods and Results A total of 214 individuals (122 patients with acute kidney injury [AKI], 92 patients without AKI as controls) were enrolled in this study. Plasma samples were analyzed by liquid chromatography tandem mass spectrometry using untargeted and targeted metabolomic approaches. Time‐dependent effects of selected metabolites were investigated in an AKI swine model. Multiple machine learning algorithms were used to identify plasma metabolites positively associated with CSA‐AKI. Metabolomic analyses from plasma samples taken within 24 hours following cardiac surgery were useful for distinguishing patients with AKI from controls without AKI. Gluconic acid, fumaric acid, and pseudouridine were significantly upregulated in patients with AKI. A random forest model constructed with selected clinical parameters and metabolites exhibited excellent discriminative ability (area under curve, 0.939; 95% CI, 0.879–0.998). In the AKI swine model, plasma levels of the 3 discriminating metabolites increased in a time‐dependent manner ( R 2 , 0.480–0.945). Use of this AKI predictive model was then confirmed in the validation cohort (area under curve, 0.972; 95% CI, 0.947–0.996). The predictive model remained robust when tested in a subset of patients with early‐stage AKI in the validation cohort (area under curve, 0.943; 95% CI, 0.883–1.000). Conclusions High‐resolution metabolomics is sufficiently powerful for developing novel biomarkers. Plasma levels of 3 metabolites were useful for the early identification of CSA‐AKI.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.