RNA interference targeting ANGPTL3 for triglyceride and cholesterol lowering: phase 1 basket trial cohorts.

Elevated triglycerides and non-high-density lipoprotein cholesterol (HDL-C) are risk factors for atherosclerotic cardiovascular disease (ASCVD). ARO-ANG3 is an RNA interference therapy that targets angiopoietin-like protein 3 (ANGPTL3), a regulator of lipoprotein metabolism. This first-in-human, phase 1, randomized, placebo-controlled, open-label trial investigated single and repeat ARO-ANG3 doses in four cohorts of fifty-two healthy participants and one cohort of nine participants with hepatic steatosis, part of a basket trial. Safety (primary objective) and pharmacokinetics (in healthy participants) and pharmacodynamics (secondary objectives) of ARO-ANG3 were evaluated. ARO-ANG3 was generally well tolerated, with similar frequencies of treatment-emergent adverse events in active and placebo groups. Systemic absorption of ARO-ANG3 in healthy participants was rapid and sustained, with a mean Tmax of 6.0-10.5 h and clearance from plasma within 24-48 h after dosing with a mean t½ of 3.9-6.6 h. In healthy participants, ARO-ANG3 treatment reduced ANGPTL3 (mean -45% to -78%) 85 days after dose. Reductions in triglyceride (median -34% to -54%) and non-HDL-C (mean -18% to -29%) (exploratory endpoints) concentrations occurred with the three highest doses. These early-phase data support ANGPTL3 as a potential therapeutic target for ASCVD treatment. ClinicalTrials.gov identifier: NCT03747224.

Prognostic modelling of clinical outcomes after first-time acute coronary syndrome in New Zealand.

OBJECTIVE: The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS) was established to investigate the drivers of secondary events after first-time acute coronary syndrome (ACS), including addressing inequitable outcomes by ethnicity. Herein, the first clinical outcomes and prognostic modelling approach are reported. METHODS: First, in 28 176 New Zealanders with first-time ACS from a national registry, a clinical summary score for predicting 1-year death/cardiovascular readmission was created using Cox regression of 20 clinical variables. This score was then calculated in the 2015 participant MENZACS study to represent clinical risk. In MENZACS, Cox regression was used to assess N-terminal pro-B-type natriuretic peptide (NT-proBNP) as a prognostic marker for death/cardiovascular readmission in four models, adjusting for (1) age and sex; (2) age, sex, ethnicity; (3) clinical summary score; (4) clinical summary score and ethnicity. RESULTS: Of the 2015 MENZACS participants (mean age 61 years, 79% male, 73% European, 14% Maori, 5% Pacific people), 2003 were alive at discharge. Of the 2003, 416 (20.8%) experienced all-cause death/cardiovascular readmission over a median of 3.5 years. In a simple model, age, male sex, Maori ethnicity and NT-proBNP levels were significant predictors of outcome. After adjustment for the clinical summary score, which includes age and sex, NT-proBNP and ethnicity were no longer statistically significant: log2(NT-proBNP) hazard ratio (HR) 1.03, 95% confidence interval (95% CI) 0.98 to 1.08, p=0.305; Maori ethnicity HR 1.26, 95% CI 0.97 to 1.62, p=0.084. CONCLUSIONS: In 2015 patients with first-time ACS, recurrent events were common (20.8%). Increasing NT-proBNP levels and Maori ethnicity were predictors of death/cardiovascular readmission, but not after adjustment for the 20 clinical risk factors represented by the clinical summary score. TRIAL REGISTRATION NUMBER: ACTRN12615000676516.

Metabolomics and a Breath Sensor Identify Acetone as a Biomarker for Heart Failure.

BACKGROUND: Multi-omics delivers more biological insight than targeted investigations. We applied multi-omics to patients with heart failure with reduced ejection fraction (HFrEF). METHODS: 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography mass spectrometry (LC-MS/GC-MS) and solid-phase microextraction (SPME) volatilomics in plasma and urine. HFrEF was defined using left ventricular global longitudinal strain, ejection fraction and NTproBNP. A consumer breath acetone (BrACE) sensor validated results in n = 73. RESULTS: 28 metabolites were identified by GCMS, 35 by LCMS and 4 volatiles by SPME in plasma and urine. Alanine, aspartate and glutamate, citric acid cycle, arginine biosynthesis, glyoxylate and dicarboxylate metabolism were altered in HFrEF. Plasma acetone correlated with NT-proBNP (r = 0.59, 95% CI 0.4 to 0.7), 2-oxovaleric and cis-aconitic acid, involved with ketone metabolism and mitochondrial energetics. BrACE > 1.5 ppm discriminated HF from other cardiac pathology (AUC 0.8, 95% CI 0.61 to 0.92, p < 0.0001). CONCLUSION: Breath acetone discriminated HFrEF from other cardiac pathology using a consumer sensor, but was not cardiac specific.

A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data.

AIM: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). MATERIALS & METHODS: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. RESULTS: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73-0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67-0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79-0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77-0.78; p < 0.0001. CONCLUSION: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.

Multiomics, virtual reality and artificial intelligence in heart failure.

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Lay abstract Multiomics is the integration of multiple sources of health information, for example, genomic, metabolite, etc. This delivers more insight than targeted single investigations and provides an ability to perceive subtle individual differences between people. In this study we applied multiomics to patients with heart failure (HF) using DNA sequencing, metabolomics and machine learning applied to ECG echocardiography. We demonstrated significant differences between subsets of patients with HF using these methods. We also showed that machine learning has significant diagnostic potential in identifying HF patients more efficiently than manual or conventional techniques.

Afterdepolarizations and abnormal calcium handling in atrial myocytes with modulated SERCA uptake: a sensitivity analysis of calcium handling channels.

Delayed afterdepolarizations (DADs) and spontaneous depolarizations (SDs) are typically triggered by spontaneous diastolic Ca2+ release from the sarcoplasmic reticulum (SR) which is caused by an elevated SR Ca2+-ATPase (SERCA) uptake and dysfunctional ryanodine receptors. However, recent studies on the T-box transcription factor gene (TBX5) demonstrated that abnormal depolarizations could occur despite a reduced SERCA uptake. Similar findings have also been reported in experimental or clinical studies of diabetes and heart failure. To investigate the sensitivity of SERCA in the genesis of DADs/SDs as well as its dependence on other Ca2+ handling channels, we performed systematic analyses using the Maleckar et al. model. Results showed that the modulation of SERCA alone cannot trigger abnormal depolarizations, but can instead affect the interdependency of other Ca2+ handling channels in triggering DADs/SDs. Furthermore, we discovered the existence of a threshold value for the intracellular concentration of Ca2+ ([Ca2+]i) for abnormal depolarizations, which is modulated by the maximum SERCA uptake and the concentration of Ca2+ in the uptake and release compartments in the SR ([Ca2+]up and [Ca2+]rel). For the first time, our modelling study reconciles different mechanisms of abnormal depolarizations in the setting of 'lone' AF, reduced TBX5, diabetes and heart failure, and may lead to more targeted treatment for these patients. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

In silico investigation of the mechanisms underlying atrial fibrillation due to impaired Pitx2.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is a major cause of stroke and morbidity. Recent genome-wide association studies have shown that paired-like homeodomain transcription factor 2 (Pitx2) to be strongly associated with AF. However, the mechanisms underlying Pitx2 modulated arrhythmogenesis and variable effectiveness of antiarrhythmic drugs (AADs) in patients in the presence or absence of impaired Pitx2 expression remain unclear. We have developed multi-scale computer models, ranging from a single cell to tissue level, to mimic control and Pitx2-knockout atria by incorporating recent experimental data on Pitx2-induced electrical and structural remodeling in humans, as well as the effects of AADs. The key findings of this study are twofold. We have demonstrated that shortened action potential duration, slow conduction and triggered activity occur due to electrical and structural remodelling under Pitx2 deficiency conditions. Notably, the elevated function of calcium transport ATPase increases sarcoplasmic reticulum Ca2+ concentration, thereby enhancing susceptibility to triggered activity. Furthermore, heterogeneity is further elevated due to Pitx2 deficiency: 1) Electrical heterogeneity between left and right atria increases; and 2) Increased fibrosis and decreased cell-cell coupling due to structural remodelling slow electrical propagation and provide obstacles to attract re-entry, facilitating the initiation of re-entrant circuits. Secondly, our study suggests that flecainide has antiarrhythmic effects on AF due to impaired Pitx2 by preventing spontaneous calcium release and increasing wavelength. Furthermore, our study suggests that Na+ channel effects alone are insufficient to explain the efficacy of flecainide. Our study may provide the mechanisms underlying Pitx2-induced AF and possible explanation behind the AAD effects of flecainide in patients with Pitx2 deficiency.

Polygenic Risk Scores in Coronary Artery Disease and Atrial Fibrillation.

Coronary artery disease (CAD) and atrial fibrillation (AF) are two highly prevalent cardiovascular disorders that are associated with substantial morbidity and mortality. Conventional clinical risk factors for these disorders may not be identified prior to mid-adult life when pathophysiological processes are already established. A better understanding of the genetic underpinnings of disease should facilitate early detection of individuals at risk and preventative intervention. Single rare variants of large effect size that are causative for CAD, AF, or predisposing factors such as hypertension or hyperlipidaemia, may give rise to familial forms of disease. However, in most individuals, CAD and AF are complex traits in which combinations of genetic and acquired factors play a role. Common genetic variants that affect disease susceptibility have been identified by genome-wide association studies, but the predictive value of any single variant is limited. To address this issue, polygenic risk scores (PRS), comprised of suites of disease-associated common variants have been devised. In CAD and AF, incorporation of PRS into risk stratification algorithms has provided incremental prognostic information to clinical factors alone. The long-term health and economic benefits of PRS-guided clinical management remain to be determined however, and further evidence-based data are required.

A multiplex pharmacogenetics assay using the MinION nanopore sequencing device.

OBJECTIVES: The MinION nanopore sequencing device opens the opportunity to cost-effective and point-of-care DNA sequencing. As a proof of principle, we developed a multiplex assay targeting pharmacogenetic variants related to clopidogrel and warfarin, the two commonly used drugs that show response variability due to genetic polymorphisms. METHODS: Six reference and 78 clinical DNA samples were amplified by PCR to generate 15 amplicons targeting 27 key variants. These products were then barcoded to enable sample multiplexing in one sequencing run. Four variant calling tools (marginCaller, VarScan 2, nanopolish, Clairvoyante) were used to compare genotyping accuracy. RESULTS: In our cohort, 81 out of 84 samples were successfully sequenced and genotyped. Using nanopolish as the variant calling tool achieved accuracy >95% for all except two variants. A known single base deletion (CYP2C9*6) was successfully detected. CONCLUSION: While minor misgenotyping issues exist, this work demonstrates that drug-specific or broad pharmacogenetic screening assays using small PCR amplicons are possible on the MinION sequencing device.

Ionic and cellular mechanisms underlying TBX5/PITX2 insufficiency-induced atrial fibrillation: Insights from mathematical models of human atrial cells.

Transcription factors TBX5 and PITX2 involve in the regulation of gene expression of ion channels and are closely associated with atrial fibrillation (AF), the most common cardiac arrhythmia in developed countries. The exact cellular and molecular mechanisms underlying the increased susceptibility to AF in patients with TBX5/PITX2 insufficiency remain unclear. In this study, we have developed and validated a novel human left atrial cellular model (TPA) based on the ten Tusscher-Panfilov ventricular cell model to systematically investigate how electrical remodeling induced by TBX5/PITX2 insufficiency leads to AF. Using our TPA model, we have demonstrated that spontaneous diastolic depolarization observed in atrial myocytes with TBX5-deletion can be explained by altered intracellular calcium handling and suppression of inward-rectifier potassium current (IK1). Additionally, our computer simulation results shed new light on the novel cellular mechanism underlying AF by indicating that the imbalance between suppressed outward current IK1 and increased inward sodium-calcium exchanger current (INCX) resulted from SR calcium leak leads to spontaneous depolarizations. Furthermore, our simulation results suggest that these arrhythmogenic triggers can be potentially suppressed by inhibiting sarcoplasmic reticulum (SR) calcium leak and reversing remodeled IK1. More importantly, this study has clinically significant implications on the drugs used for maintaining SR calcium homeostasis, whereby drugs such as dantrolene may confer significant improvement for the treatment of AF patients with TBX5/PITX2 insufficiency.

A Machine Learning Aided Systematic Review and Meta-Analysis of the Relative Risk of Atrial Fibrillation in Patients With Diabetes Mellitus.

Background: Meta-analysis is a widely used tool in which weighted information from multiple similar studies is aggregated to increase statistical power. However, the exponential growth of publications in key areas of medical science has rendered manual identification of relevant studies increasingly time-consuming. The aim of this work was to develop a machine learning technique capable of robust automatic study selection for meta-analysis. We have validated this approach with an up-to-date meta-analysis to investigate the association between diabetes mellitus (DM) and new-onset atrial fibrillation (AF). Methods: The PubMed online database was searched from 1960 to September 2017 where 4,177 publications that mentioned both DM and AF were identified. Relevant studies were selected as follows. First, publications were clustered based on common text features using an unsupervised K-means algorithm. Clusters that best matched the selected set of potentially relevant studies (a "training" set of 139 articles) were then identified by using maximum entropy classification. The 139 articles selected automatically on this basis were screened manually to identify potentially relevant studies. To determine the validity of the automated process, a parallel set of studies was also assembled by manually screening all initially searched publications. Finally, detailed manual selection was performed on the full texts of the studies in both sets using standard criteria. Quality assessment, meta-regression random-effects models, sensitivity analysis and publication bias assessment were then conducted. Results: Machine learning-assisted screening identified the same 29 studies for meta-analysis as those identified by using manual screening alone. Machine learning enabled more robust and efficient study selection, reducing the number of studies needed for manual screening from 4,177 to 556 articles. A pooled analysis using the most conservative estimates indicated that patients with DM had ~49% greater risk of developing AF compared with individuals without DM. After adjusting for three additional risk factors i.e., hypertension, obesity and heart disease, the relative risk was 23%. Using multivariate adjusted models, the risk for developing AF in patients with DM was similar for all DM subtypes. Women with DM were 24% more likely to develop AF than men with DM. The risk for new-onset AF in patients with DM has also increased over the years. Conclusions: We have developed a novel machine learning method to identify publications suitable for inclusion in meta-analysis.This approach has the capacity to provide for a more efficient and more objective study selection process for future such studies. We have used it to demonstrate that DM is a strong, independent risk factor for AF, particularly for women.

Personalized hypertension management in practice.

The revolution occurring in genomic and personalized medicine is likely to have a significant impact on the management of hypertension. However, from the perspective of translating new knowledge into clinical practice, progress has been slow. This review article summarizes recent advances in hypertension-related diagnostics while also offering new perspective on hypertension management for the future. Such new perspectives will likely require a paradigm shift toward more integrated and holistic approaches for better prevention and treatment of hypertension in both individuals and the population as a whole.

ICMA: an integrated cardiac modeling and analysis platform.

UNLABELLED: ICMA, a software framework to create 3D finite element models of the left ventricle from cardiac ultrasound or magnetic resonance imaging (MRI) data, has been made available as an open-source code. The framework is hardware vendor independent and uses speckle tracking (endocardial border detection) on ultrasound (MRI) imaging data in the form of DICOM. Standard American Heart Association segment-based strain analysis can be performed using a browser-based interface. The speckle tracking, border detection and model fitting methods are implemented in C++ using open-source tools. They are wrapped as web services and orchestrated via a JBOSS-based application server. AVAILABILITY AND IMPLEMENTATION: The source code for ICMA is freely available under MPL 1.1 or GPL 2.0 or LGPL 2.1 license at https://github.com/ABI-Software-Laboratory/ICMA and a standalone virtual machine at http://goo.gl/M4lJKH for download. CONTACT: r.jagir@auckland.ac.nz SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Open access integrated therapeutic and diagnostic platforms for personalized cardiovascular medicine.

It is undeniable that the increasing costs in healthcare are a concern. Although technological advancements have been made in healthcare systems, the return on investment made by governments and payers has been poor. The current model of care is unsustainable and is due for an upgrade. In developed nations, a law of diminishing returns has been noted in population health standards, whilst in the developing world, westernized chronic illnesses, such as diabetes and cardiovascular disease have become emerging problems. The reasons for these trends are complex, multifactorial and not easily reversed. Personalized medicine has the potential to have a significant impact on these issues, but for it to be truly successful, interdisciplinary mass collaboration is required. We propose here a vision for open-access advanced analytics for personalized cardiac diagnostics using imaging, electrocardiography and genomics.

The antiplatelet effect of six non-steroidal anti-inflammatory drugs and their pharmacodynamic interaction with aspirin in healthy volunteers.

Patients with cardiovascular disease taking aspirin and some nonsteroidal anti-inflammatory drugs (NSAIDs) appear to have increased vascular events. This study was conducted to compare the ex vivo antiplatelet effects of 6 commonly used NSAIDs and to determine whether these agents antagonize the effect of aspirin. Platelet function was assessed by Platelet Function Analyzer 100 closure time in normal subjects in a randomized, blinded, multiple-crossover study. Platelet function was measured 12 hours after the administration of each NSAID. The NSAID was then given 2 hours before aspirin 300 mg, and platelet function was reassessed 24 hours later. At 12 hours after the administration of naproxen and tiaprofenic acid, closure time was significantly prolonged, whereas the other NSAIDs did not cause significant prolongations. Compared with placebo plus aspirin, closure time was significantly reduced when ibuprofen, indomethacin, naproxen, or tiaprofenic acid was given before aspirin. In conclusion, ibuprofen, indomethacin, naproxen, and tiaprofenic acid all block the antiplatelet effect of aspirin. Sulindac and celecoxib did not demonstrate any significant antiplatelet effect or reduce the antiplatelet of aspirin and, therefore, of the NSAIDs evaluated may be the drugs of choice for patients requiring aspirin and NSAIDs.