Relationship between daytime napping and cardiovascular disease: A two-sample mendelian randomization study.

OBJECTIVE:Daytime napping has been reported to have a potential association with an increased risk of cardiovascular diseases (CVDs) in several cohort studies, but the causal effects are unclear. In this study, we aimed to investigate the relationship between daytime napping and CVDs, as well as to validate causality in this relationship by Mendelian randomization (MR).METHODS:A two-sample MR method was used to evaluate the causal effect of daytime napping on CVDs. The exposure of daytime napping was extracted from publicly available genome-wide association studies (GWASs) in the UK Biobank, and the outcomes of 14 CVDs were obtained from the FinnGen consortium. A total of 49 single-nucleotide polymorphisms (SNPs) were used as the instrumental variables. The effect estimates were calculated by using the inverse-variance weighted method.RESULTS:The MR analyses showed that genetically predicted daytime napping was associated with an increased risk of five CVDs, including heart failure (odds ratio (OR): 1.71, 95% CI: 1.19-2.44, p = 0.003), hypertension (OR: 1.51, 95% CI: 1.05-2.16, p = 0.026), atrial fibrillation (OR: 1.71, 95% CI: 1.02-2.88, p = 0.042), cardiac arrythmias (OR: 1.47, 95% CI: 1.47, 95% CI: 1.01-2.13, p = 0.042) and coronary atherosclerosis (OR: 1.77, 95% CI: 1.17-2.68, p = 0.006). No significant influence was observed for other CVDs.CONCLUSION:This two-sample MR analysis suggested that daytime napping was causally associated with an increased risk of heart failure, hypertension, atrial fibrillation, cardiac arrythmias and coronary atherosclerosis.

Multifaceted Spatial and Functional Zonation of Cardiac Cells in Adult Human Heart.

Novel plasma biomarkers predicting biventricular involvement in arrhythmogenic right ventricular cardiomyopathy.

BACKGROUND:Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease characterized by fibrofatty replacement of the myocardium and ventricular arrhythmias. Biventricular involvement in ARVC may lead to heart failure. This study aimed to investigate the role of plasma biomarkers soluble (s)ST2, Galectin-3 (Gal-3) and GDF-15 in predicting biventricular involvement and adverse outcomes in ARVC.METHODS AND RESULTS:ARVC patients from 2 independent cohorts, were studied. The Bejing (Chinese) cohort (n = 108) was the discovery cohort, whereas the Zurich (Swiss) cohort (n = 47) served as validation. All patients had a definite ARVC diagnosis at time of blood withdrawal. Biomarkers were independently correlated with NT-proBNP and left ventricular (LV)-function. ARVC patients with LV involvement had higher levels of sST2 and GDF-15 as compared to controls and patients with isolated right ventricle (RV) involvement. sST2 and GDF-15 were significantly correlated with late gadolinium enhancement in CMR and with adverse heart failure outcomes. Gal-3 was elevated in ARVC patients with and without LV involvement. The combined use of the three biomarkers (sST2, GDF-15 and NT-proBNP) showed the best performance in predicting LV involvement in both cohorts. Plasma drawn from the coronary arteries and coronary sinus indicated a transmyocardial elevation of sST2, but no transmyocardial gradient of GDF-15. After heart transplantation, both sST2 and GDF-15 returned to near-normal levels.CONCLUSION:Our study showed that sST2 and GDF-15 may predict biventricular involvement in ARVC. The combined use of sST2, GDF-15 and NT-proBNP showed the best prediction of biventricular involvement in ARVC.

Plasma Metabolites-Based Prediction in Cardiac Surgery-Associated Acute Kidney Injury.

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 (R2, 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.

Novel Potential Biomarker of Adult Cardiac Surgery-Associated Acute Kidney Injury.

BACKGROUND:Acute kidney injury (AKI) occurs in about 30% of patients with cardiac surgery, but the pathogenesis of cardiac surgery-associated acute kidney injury (CSA-AKI) remains unclear and there are no predictive biomarkers or diagnostic criteria specific for CSA-AKI beyond the general clinical variables for AKI like serum creatinine (SCr).METHODS AND RESULTS:We measured the plasma levels of 48 cytokines within 24 h after cardiac surgery in a total of 306 adult patients including 204 with and 102 without AKI, and then evaluated the diagnostic efficacy of these cytokines for the development of CSA-AKI via ANOVA and Pearson correlation analysis. Among these 48 cytokines, 20 of them were significantly different in the AKI patients compared with the non-AKI patients. In particularly, 13 cytokines displayed tremendous changes with the P < 1E-5. Moreover, 10 of the 48 cytokines in the plasma were significantly different among the patients with different stages of AKI. Specifically, 6 cytokines exhibited immense differences with the P < 1E-5. Additionally, 7 of the 48 cytokines have the correlation coefficient of r > 0.5 with the postoperative changes of SCr after cardiac surgery.CONCLUSION:Taken all the results together, IFN-γ and SCGF-β were the most relevant two cytokines that were not only remarkably changed in adult CSA-AKI patients during the first 24 h after cardiac surgery, but also significantly correlated with the postoperative changes of SCr after cardiac surgery. Therefore, IFN-γ and SCGF-β might be novel predictive plasma biomarker, as well as potential therapeutic targets specific for adult CSA-AKI.

Elevated plasma β-hydroxybutyrate predicts adverse outcomes and disease progression in patients with arrhythmogenic cardiomyopathy.

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.

A novel genotype-based clinicopathology classification of arrhythmogenic cardiomyopathy provides novel insights into disease progression.

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.

Relationship between daytime napping and cardiovascular disease: A two-sample mendelian randomization study.

OBJECTIVE:Daytime napping has been reported to have a potential association with an increased risk of cardiovascular diseases (CVDs) in several cohort studies, but the causal effects are unclear. In this study, we aimed to investigate the relationship between daytime napping and CVDs, as well as to validate causality in this relationship by Mendelian randomization (MR).METHODS:A two-sample MR method was used to evaluate the causal effect of daytime napping on CVDs. The exposure of daytime napping was extracted from publicly available genome-wide association studies (GWASs) in the UK Biobank, and the outcomes of 14 CVDs were obtained from the FinnGen consortium. A total of 49 single-nucleotide polymorphisms (SNPs) were used as the instrumental variables. The effect estimates were calculated by using the inverse-variance weighted method.RESULTS:The MR analyses showed that genetically predicted daytime napping was associated with an increased risk of five CVDs, including heart failure (odds ratio (OR): 1.71, 95% CI: 1.19-2.44, p = 0.003), hypertension (OR: 1.51, 95% CI: 1.05-2.16, p = 0.026), atrial fibrillation (OR: 1.71, 95% CI: 1.02-2.88, p = 0.042), cardiac arrythmias (OR: 1.47, 95% CI: 1.47, 95% CI: 1.01-2.13, p = 0.042) and coronary atherosclerosis (OR: 1.77, 95% CI: 1.17-2.68, p = 0.006). No significant influence was observed for other CVDs.CONCLUSION:This two-sample MR analysis suggested that daytime napping was causally associated with an increased risk of heart failure, hypertension, atrial fibrillation, cardiac arrythmias and coronary atherosclerosis.

Multifaceted Spatial and Functional Zonation of Cardiac Cells in Adult Human Heart.

Novel plasma biomarkers predicting biventricular involvement in arrhythmogenic right ventricular cardiomyopathy.

BACKGROUND:Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease characterized by fibrofatty replacement of the myocardium and ventricular arrhythmias. Biventricular involvement in ARVC may lead to heart failure. This study aimed to investigate the role of plasma biomarkers soluble (s)ST2, Galectin-3 (Gal-3) and GDF-15 in predicting biventricular involvement and adverse outcomes in ARVC.METHODS AND RESULTS:ARVC patients from 2 independent cohorts, were studied. The Bejing (Chinese) cohort (n = 108) was the discovery cohort, whereas the Zurich (Swiss) cohort (n = 47) served as validation. All patients had a definite ARVC diagnosis at time of blood withdrawal. Biomarkers were independently correlated with NT-proBNP and left ventricular (LV)-function. ARVC patients with LV involvement had higher levels of sST2 and GDF-15 as compared to controls and patients with isolated right ventricle (RV) involvement. sST2 and GDF-15 were significantly correlated with late gadolinium enhancement in CMR and with adverse heart failure outcomes. Gal-3 was elevated in ARVC patients with and without LV involvement. The combined use of the three biomarkers (sST2, GDF-15 and NT-proBNP) showed the best performance in predicting LV involvement in both cohorts. Plasma drawn from the coronary arteries and coronary sinus indicated a transmyocardial elevation of sST2, but no transmyocardial gradient of GDF-15. After heart transplantation, both sST2 and GDF-15 returned to near-normal levels.CONCLUSION:Our study showed that sST2 and GDF-15 may predict biventricular involvement in ARVC. The combined use of sST2, GDF-15 and NT-proBNP showed the best prediction of biventricular involvement in ARVC.

Plasma Metabolites-Based Prediction in Cardiac Surgery-Associated Acute Kidney Injury.

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 (R2, 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.

Novel Potential Biomarker of Adult Cardiac Surgery-Associated Acute Kidney Injury.

BACKGROUND:Acute kidney injury (AKI) occurs in about 30% of patients with cardiac surgery, but the pathogenesis of cardiac surgery-associated acute kidney injury (CSA-AKI) remains unclear and there are no predictive biomarkers or diagnostic criteria specific for CSA-AKI beyond the general clinical variables for AKI like serum creatinine (SCr).METHODS AND RESULTS:We measured the plasma levels of 48 cytokines within 24 h after cardiac surgery in a total of 306 adult patients including 204 with and 102 without AKI, and then evaluated the diagnostic efficacy of these cytokines for the development of CSA-AKI via ANOVA and Pearson correlation analysis. Among these 48 cytokines, 20 of them were significantly different in the AKI patients compared with the non-AKI patients. In particularly, 13 cytokines displayed tremendous changes with the P < 1E-5. Moreover, 10 of the 48 cytokines in the plasma were significantly different among the patients with different stages of AKI. Specifically, 6 cytokines exhibited immense differences with the P < 1E-5. Additionally, 7 of the 48 cytokines have the correlation coefficient of r > 0.5 with the postoperative changes of SCr after cardiac surgery.CONCLUSION:Taken all the results together, IFN-γ and SCGF-β were the most relevant two cytokines that were not only remarkably changed in adult CSA-AKI patients during the first 24 h after cardiac surgery, but also significantly correlated with the postoperative changes of SCr after cardiac surgery. Therefore, IFN-γ and SCGF-β might be novel predictive plasma biomarker, as well as potential therapeutic targets specific for adult CSA-AKI.

Elevated plasma β-hydroxybutyrate predicts adverse outcomes and disease progression in patients with arrhythmogenic cardiomyopathy.

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.

A novel genotype-based clinicopathology classification of arrhythmogenic cardiomyopathy provides novel insights into disease progression.

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.