Victor Roth Cardoso

In patients with atrial fibrillation (AF), recurrent AF and sinus rhythm during follow-up are determined by interactions between cardiovascular disease processes and rhythm-control therapy. Predictors of attaining sinus rhythm at follow-up are not well known. To quantify the interaction between cardiovascular disease processes and rhythm outcomes, 14 biomarkers reflecting AF-related cardiovascular disease processes in 1586 patients in the EAST-AFNET 4 biomolecule study (71 years old, 46% women) were quantified at baseline. Mixed logistic regression models including clinical features were constructed for each biomarker. Biomarkers were interrogated for interaction with early rhythm control. Outcome was sinus rhythm at 12 months. Results were validated at 24 months and in external datasets. Higher baseline concentrations of three biomarkers were independently associated with a lower chance of sinus rhythm at 12 months: angiopoietin 2 (ANGPT2) (odds ratio [OR] 0.76 [95% confidence interval 0.65-0.89], p=0.001), bone morphogenetic protein 10 (BMP10) (OR 0.83 [0.71-0.97], p=0.017) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) (OR 0.73 [0.60–0.88], p=0.001). Analysis of rhythm at 24 months confirmed the results. Early rhythm control interacted with the predictive potential of NT-proBNP (p_interaction=0.033). The predictive effect of NT-proBNP was reduced in patients randomized to early rhythm control (usual care: OR 0.64 [0.51-0.80], p<0.001; early rhythm control: OR 0.90 [0.69-1.18], p=0.453). External validation confirmed that low concentrations of ANGPT2, BMP10 and NT-proBNP predict sinus rhythm during follow-up. Low concentrations of ANGPT2, BMP10 and NT-proBNP identify patients with AF who are likely to attain sinus rhythm during follow-up. The predictive ability of NT-proBNP is attenuated in patients receiving rhythm control.

Reduced left atrial PITX2 is associated with atrial cardiomyopathy and atrial fibrillation. PITX2 is restricted to left atrial cardiomyocytes in the adult heart. The links between PITX2 deficiency, atrial cardiomyopathy and atrial fibrillation are not fully understood. To identify mechanisms linking PITX2 deficiency to atrial fibrillation, we generated and characterized PITX2-deficient human atrial cardiomyocytes derived from human induced pluripotent stem cells (hiPSC) and their controls. PITX2-deficient hiPSC-derived atrial cardiomyocytes showed shorter and disorganised sarcomeres and increased mononucleation. Electron microscopy found an increased number of smaller mitochondria compared to the control. Mitochondrial protein expression was altered in PITX2-deficient hiPSC-derived atrial cardiomyocytes. Single-nuclear RNA-sequencing found differences in cellular respiration pathways and differentially expressed mitochondrial and ion channel genes in PITX2-deficient hiPSC-derived atrial cardiomyocytes. PITX2 repression in hiPSC-derived atrial cardiomyocytes replicated dysregulation of cellular respiration. Mitochondrial respiration was shifted to increased glycolysis in PITX2-deficient hiPSC-derived atrial cardiomyocytes. PITX2-deficient human hiPSC-derived atrial cardiomyocytes showed higher spontaneous beating rates. Action potential duration was more variable with an overall prolongation of early repolarization, consistent with metabolic defects. Gene expression analyses confirmed changes in mitochondrial genes in left atria from 42 patients with atrial fibrillation compared to 43 patients in sinus rhythm. Dysregulation of left atrial mitochondrial (COX7C) and metabolic (FOXO1) genes was associated with PITX2 expression in human left atria. In summary, PITX2 deficiency causes mitochondrial dysfunction and a metabolic shift to glycolysis in human atrial cardiomyocytes. PITX2-dependent metabolic changes can contribute to the structural and functional defects found in PITX2-deficient atria.

Atrial fibrillation (AF) and concomitant cardiometabolic disease processes interact and combine to lead to adverse events, such as stroke, heart failure, myocardial infarction, and cardiovascular death. Circulating biomolecules provide quantifiable proxies for cardiometabolic disease processes. The aim of this study was to test whether biomolecule combinations can define phenotypes in patients with AF. This pre-specified analysis of the EAST-AFNET 4 biomolecule study assigned patients to clusters using polytomous variable latent-class analysis based on baseline concentrations of 13 precisely quantified biomolecules potentially reflecting ageing, cardiac fibrosis, metabolic dysfunction, oxidative stress, cardiac load, endothelial dysfunction, and inflammation. In each cluster, rates of cardiovascular death, stroke, or hospitalization for heart failure or acute coronary syndrome, the primary outcome of EAST-AFNET 4, were calculated and compared between clusters over median 5.1 years follow-up. Findings were independently validated in a prospective cohort of 748 patients with AF (BBC-AF; median follow-up 2.9 years). Unsupervised biomolecule analysis assigned 1586 patients (71 years old, 46% women) into four clusters. The highest risk cluster was dominated by elevated bone morphogenetic protein 10, insulin-like growth factor–binding protein 7, N-terminal pro-B-type natriuretic peptide, angiopoietin 2, and growth differentiation factor 15. Patients in the lowest risk cluster showed low concentrations of these biomolecules. Two intermediate-risk clusters differed by high or low concentrations of C-reactive protein, interleukin-6, and D-dimer. Patients in the highest risk cluster had a five-fold higher cardiovascular event rate than patients in the low-risk cluster. Early rhythm control was effective across clusters (P_interaction = 0.63). Sensitivity analyses and external validation in BBC-AF replicated clusters and risk gradients. Biomolecule concentrations identify cardiometabolic subphenotypes in patients with AF at high and low cardiovascular risk. The quantification of 13 biomolecules pre-selected for their ability to provide quantitative proxies for cardiovascular disease processes assigns patients with recently diagnosed atrial fibrillation and comorbidities to four clusters. The main biomolecules contributing to the clustering process are N-terminal pro-B-type natriuretic peptide, insulin-like growth factor–binding protein 7, bone morphogenetic protein 10, angiopoietin 2, growth differentiation factor 15, interleukin-6, and C-reactive protein. These biomolecules explain 66–85% of the assignment to a cluster. The four clusters differ in their risk of cardiovascular events (cardiovascular death, stroke, or unplanned hospitalisation for heart failure or acute coronary syndrome) over a 5-year time horizon. Early rhythm control therapy is effective in all four clusters (P_interaction = 0.63). The clusters and their associated risk are replicated in an independent cohort.

Androgenic anabolic steroids (AAS) are commonly abused by young men. Male sex and increased AAS levels are associated with earlier and more severe manifestation of common cardiac conditions, such as atrial fibrillation, and rare ones, such as arrhythmogenic right ventricular cardiomyopathy (ARVC). Clinical observations suggest a potential atrial involvement in ARVC. Arrhythmogenic right ventricular cardiomyopathy is caused by desmosomal gene defects, including reduced plakoglobin expression. Here, we analysed clinical records from 146 ARVC patients to identify that ARVC is more common in males than females. Patients with ARVC also had an increased incidence of atrial arrhythmias and P wave changes. To study desmosomal vulnerability and the effects of AAS on the atria, young adult male mice, heterozygously deficient for plakoglobin (Plako^+/−), and wild type (WT) littermates were chronically exposed to 5α‐dihydrotestosterone (DHT) or placebo. The DHT increased atrial expression of pro‐hypertrophic, fibrotic and inflammatory transcripts. In mice with reduced plakoglobin, DHT exaggerated P wave abnormalities, atrial conduction slowing, sodium current depletion, action potential amplitude reduction and the fall in action potential depolarization rate. Super‐resolution microscopy revealed a decrease in Na_V1.5 membrane clustering in Plako^+/− atrial cardiomyocytes after DHT exposure. In summary, AAS combined with plakoglobin deficiency cause pathological atrial electrical remodelling in young male hearts. Male sex is likely to increase the risk of atrial arrhythmia, particularly in those with desmosomal gene variants. This risk is likely to be exaggerated further by AAS use. image Key points: Androgenic male sex hormones, such as testosterone, might increase the risk of atrial fibrillation in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC), which is often caused by desmosomal gene defects (e.g. reduced plakoglobin expression). In this study, we observed a significantly higher proportion of males who had ARVC compared with females, and atrial arrhythmias and P wave changes represented a common observation in advanced ARVC stages. In mice with reduced plakoglobin expression, chronic administration of 5α‐dihydrotestosterone led to P wave abnormalities, atrial conduction slowing, sodium current depletion and a decrease in membrane‐localized Na_V1.5 clusters. 5α‐Dihydrotestosterone, therefore, represents a stimulus aggravating the pro‐arrhythmic phenotype in carriers of desmosomal mutations and can affect atrial electrical function.

Introduction: Increasing numbers of patients with advanced organ disease are being considered for bariatric and metabolic surgery (BMS). There is no prospective study on the safety of BMS in these patients. This study aimed to capture outcomes for patients with advanced cardiac, renal, or liver disease undergoing BMS. Materials and methods: This was a multinational, prospective cohort study on the safety of elective BMS in adults (≥18 years) with advanced disease of the heart, liver, or kidney. Results: Data on 177 patients with advanced diseases of heart, liver, or kidney were submitted by 75 centres in 33 countries. Mean age and BMI was 48.56±11.23 years and 45.55±7.35 kg/m², respectively. Laparoscopic sleeve gastrectomy was performed in 124 patients (70%). The 30-day morbidity and mortality were 15.9% (n=28) and 1.1% (n=2), respectively. Thirty-day morbidity was 16.4%, 11.7%, 20.5%, and 50.0% in patients with advanced heart (n=11/61), liver (n=8/68), kidney (n=9/44), and multi-organ disease (n=2/4), respectively. Cardiac patients with left ventricular ejection fraction less than or equal to 35% and New York Heart Association classification 3 or 4, liver patients with model for end-stage liver disease score greater than or equal to 12, and patients with advanced renal disease not on dialysis were at increased risk of complications. Comparison with a propensity score-matched cohort found advanced disease of the heart, liver, or kidney to be significantly associated with higher 30-day morbidity. Conclusion: Patients with advanced organ disease are at increased risk of 30-day morbidity following BMS. This prospective study quantifies that risk and identifies patients at the highest risk.

Background: A recent subanalysis of the EAST-AFNET 4 (Early Treatment of Atrial Fibrillation for Stroke Prevention Trial) suggests a stronger benefit of early rhythm control (ERC) in patients with atrial fibrillation and a high comorbidity burden when compared to patients with a lower comorbidity burden. Methods: We identified 109 739 patients with newly diagnosed atrial fibrillation in a large United States deidentified administrative claims database (OptumLabs) and 11 625 patients in the population-based UKB (UK Biobank). ERC was defined as atrial fibrillation ablation or antiarrhythmic drug therapy within the first year after atrial fibrillation diagnosis. Patients were classified as (1) ERC and high comorbidity burden (CHA ₂ DS ₂ -VASc score ≥4); (2) ERC and lower comorbidity burden (CHA ₂ DS ₂ -VASc score 2–3); (3) no ERC and high comorbidity burden; and (4) no ERC and lower comorbidity burden. Patients without an elevated comorbidity burden (CHA ₂ DS ₂ -VASc score 0–1) were excluded. Propensity score overlap weighting and cox proportional hazards regression were used to balance patients and compare groups for the primary composite outcome of all-cause mortality, stroke, or hospitalization with the diagnoses heart failure or myocardial infarction as well as for a primary composite safety outcome of death, stroke, and serious adverse events related to ERC. Results: In both cohorts, ERC was associated with a reduced risk for the primary composite outcome in patients with a high comorbidity burden (OptumLabs: hazard ratio, 0.83 [95% CI 0.72–0.95]; P =0.006; UKB: hazard ratio, 0.77 [95% CI, 0.63–0.94]; P =0.009). In patients with a lower comorbidity burden, the difference in outcomes was not significant (OptumLabs: hazard ratio, 0.92 [95% CI, 0.54–1.57]; P =0.767; UKB: hazard ratio, 0.94 [95% CI, 0.83–1.06]; P =0.310). The comorbidity burden interacted with ERC in the UKB (interaction- P =0.027) but not in OptumLabs (interaction- P =0.720). ERC was not associated with an increased risk for the primary safety outcome. Conclusions: ERC is safe and may be more favorable in a population-based sample of patients with high a comorbidity burden (CHA ₂ DS ₂ -VASc score ≥4).

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management.Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Androgenic anabolic steroids (AAS) are commonly abused by young men. Male sex associates with earlier manifestation of common and rare cardiac conditions including atrial fibrillation and arrhythmogenic right ventricular cardiomyopathy (ARVC). Clinical data suggest an atrial involvement in ARVC. The disease is caused by desmosomal gene defects such as reduced plakoglobin expression. Analysis of clinical records from 146 ARVC patients identified male preponderance and increased prevalence of atrial arrhythmias in patients with definite ARVC. Definite patients displayed ECG changes suggesting atrial remodelling. To study mechanisms of atrial remodelling due to desmosomal vulnerability and AAS, young adult male mice, heterozygously deficient for plakoglobin (Plako ^+/- ) and wildtype (WT) littermates, were chronically exposed to 5α-dihydrotestosterone (DHT) or placebo. DHT increased atrial expression of pro-hypertrophic, fibrotic and inflammatory transcripts. DHT caused atrial conduction slowing, decreased peak sodium current density, reduced action potential amplitude and lowered the peak depolarisation rate in Plako ^+/- but not WT atria. Super-resolution microscopy revealed a reduction in Na _v 1.5 clustering in Plako ^+/- atrial cardiomyocytes following DHT exposure. These data reveal that AAS combined with plakoglobin deficiency cause pathological atrial electrical remodelling in young male hearts. AAS abuse may increase the risk of atrial myopathy in males with desmosomal gene variants.

Background: Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics. Methods: A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation. Results: For pure, two locus interactions, PLINK’s implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e − 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e − 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e − 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e − 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.