Genetic Risk Scores for Atrial Fibrillation: Do They Improve Risk Estimation?

A comprehensive insight from molecular docking and dynamics with clinical investigation on the impact of direct oral anticoagulants on atheroprotective protein in atrial fibrillation

BACKGROUND

Direct oral anticoagulants (DOACs) have high potency against their therapeutic target and are widely used in the treatment of atrial fibrillation (AF). Most DOACs are often claimed to have adverse effects due to off-target inhibition of essential proteins. Human serum paraoxonase 1 (PON1), one of the essential proteins, known for its anti-inflammatory and antioxidant properties, could be affected by DOACs. Thus, a comparative evaluation of DOACs and their effect on PON1 protein will aid in recommending the most effective DOACs for AF treatment. This study aimed to assess the impact of DOACs on PON1 through a combination of computational and experimental analyses.

METHODS

We focus on apixaban, dabigatran, and rivaroxaban, the most recommended DOACs in AF treatment, for their impact on PON1 through molecular docking and molecular dynamics (MD) simulation to elucidate the binding affinity and drug-protein structural stability. This investigation revealed the most influential DOACs on the PON1 protein. Then experimental validation was performed in DOAC-treated AF participants (n = 42; 19 treated with dabigatran and 23 treated with rivaroxaban) compared to a healthy control group (n = 22) through gene expression analysis in peripheral blood mononuclear cells (PBMC) and serum enzyme concentration.

RESULTS

Our computational investigation showed rivaroxaban (-4.24 kcal/mol) exhibited a lower affinity against the PON1 protein compared to apixaban (-5.97 kcal/mol) and dabigatran (-9.03 kcal/mol) through molecular docking. Dabigatran holds complex interactions with PON1 at GLU53, TYR197, SER193, and ASP269 by forming hydrogen bonds. Additionally, MD simulation revealed that dabigatran disrupts PON1 stability, which may contribute functional changes. Further experimental validation revealed a significant down-regulation (p < 0.05) of PON1 gene expression in PBMC and decreased serum PON1 enzyme concentration on DOAC treatment. Rivaroxaban as about 48% has inhibitory percentage and dabigatran as about 75% of inhibitory percentage compared to healthy control.

CONCLUSION

Overall, our computational and experimental results clearly show the higher inhibitory effect of dabigatran than rivaroxaban. Hence, rivaroxaban will be a better drug candidate for improving the outcome of AF.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model

The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.

Comparisons of electrophysiological characteristics, pacing parameters and mid- to long-term effects in right ventricular septal pacing, right ventricular apical pacing and left bundle branch area pacing

Background

As a near-physiological pacing innovation, left bundle branch area pacing (LBBAP) has drawn much attention recently. This study was aimed to investigate the electrophysiological characteristics, unipolar/bipolar pacing parameters and mid- to long-term effects and safety of three different pacing methods and identify possible predictors of adverse left ventricular remodeling.

Methods

Ninety-two patients were divided into the LBBAP group, right ventricular septal pacing (RVSP) group and right ventricular apical pacing (RVAP) group. Baseline information, electrophysiological, pacing and echocardiographic parameters were collected.

Results

The three pacing methods were performed with a similar high success rate. The paced QRSd was significantly different among the LBBAP, RVSP and RVAP groups (105.93 ± 15.85 ms vs. 143.63 ± 14.71 ms vs. 155.39 ± 14.17 ms, p < 0.01). The stimulus to left ventricular activation time (Sti-LVAT) was the shortest in the LBBAP group, followed by the RVSP and RVAP groups (72.80 ± 12.07 ms vs. 86.29 ± 8.71 ms vs. 94.14 ± 10.14 ms, p < 0.001). LBBAP had a significantly lower tip impedance during the procedure and 3-month follow up as compared to RVSP and RVAP (p < 0.001). Higher bipolar captured thresholds were observed in LBBAP during the procedure (p < 0.001). Compared to the baseline values, there was a greater reduction in left ventricular end-diastolic dimension (LVEDD) in the LBBAP group (p = 0.046) and a significant enlargement in LVEDD in the RVAP group (p = 0.008). Multiple regression analysis revealed that the Sti-LVAT was a significant predictor of LVEDD at 12 months post-procedure. At the 24-h post-procedure, significant elevations were observed in the cTnI levels in LBBAP (p < 0.001) and RVSP (p < 0.05). More transient RBB injury was observed in LBBAP. But no significant difference was found in cardiac composite endpoints among three groups (p > 0.05).

Conclusions

LBBAP demonstrated a stable captured threshold, a low tip impedance and a high R-wave amplitude during the 12-month follow-up. Left ventricular remodeling was improved at 12 months post-procedure through LBBAP. The Sti-LVAT was a significant predictor of left ventricular remodeling. LBBAP demonstrated its feasibility, effectiveness, safety and some beneficial electrophysiological characteristics during this mid- to long-term follow-up, which should be confirmed by further studies.

Clinical Outcomes of Left Bundle Branch Area Pacing in Comparison with Right Ventricular Septal Pacing in Patients with High Ventricular Pacing Ratio ≥40%

Background

Methods

Results

Conclusion

A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data

Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the “Lassosum” penalty used to fit polygenic risk scores and integrated risk models using either summary statistics or raw data. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section on both Alzheimer’s disease and COPD (chronic obstructive pulmonary disease) demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm (for the datasets under consideration), allowing it to draw equal with state-of-the-art methodology such as LDpred2 when evaluated via the AUC (area under the ROC curve) metric.

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.

A pilot study to determine if left ventricular activation time is a useful parameter for left bundle branch capture: Validated by ventricular mechanical synchrony with SPECT imaging

BACKGROUND

Left bundle branch (LBB) pacing has emerged as a novel pacing modality. Left ventricular activation time (LVAT) was reported to be associated with the activation via LBB, but the value of LVAT for determining LBB pacing was unknown. We conducted a pilot study to determine if LVAT could define LBB capture by validating left ventricular (LV) mechanical synchrony.

METHODS

We analyzed LVAT in 68 bradycardia-indicated patients who received LBB pacing. LVAT was measured from the stimulus to R-wave peak in lead V5 and V6. LV mechanical synchrony assessed by SPECT MPI was compared according to the value of LVAT and the presence of LBB potential.

RESULTS

The mean LVAT was 75.4 ± 12.7 ms. LBB potential was recorded in 47 patients (69.1%). Patients with LVAT < 76 ms had better LV mechanical synchrony than those with LVAT ≥ 76 ms. Patients with LVAT < 76 ms or LBB potential had better mechanical synchrony than those with LVAT ≥ 76 ms and no potential. LVAT < 76 ms could predict the normal synchrony with a sensitivity of 88.9% and a specificity of 87.5%.

CONCLUSION

A short LVAT indicated favorable mechanical synchrony in SPECT imaging. LVAT < 76 ms might be a practical parameter for defining LBB capture.

Screening and management of atrial fibrillation in primary care

Atrial fibrillation is a common chronic disease seen in primary care offices, emergency departments, inpatient hospital services, and many subspecialty practices. Atrial fibrillation care is complicated and multifaceted, and, at various points, clinicians may see it as a consequence and cause of multi-morbidity, as a silent driver of stroke risk, as a bellwether of an acute medical illness, or as a primary rhythm disturbance that requires targeted treatment. Primary care physicians in particular must navigate these priorities, perspectives, and resources to meet the needs of individual patients. This includes judicious use of diagnostic testing, thoughtful use of novel therapeutic agents and procedures, and providing access to subspecialty expertise. This review explores the epidemiology, screening, and risk assessment of atrial fibrillation, as well as management of its symptoms (rate and various rhythm control options) and stroke risk (anticoagulation and other treatments), and offers a model for the integration of the components of atrial fibrillation care.

Comparison of electrical characteristics and pacing parameters of pacing different parts of the His-Purkinje system in bradycardia patients

PURPOSE

We aimed to evaluate the electrical characteristics and pacing parameters at different locations of His-Purkinje system pacing.

METHODS

Patients who successfully underwent His-Purkinje system pacing with bradycardia indications from April 2018 to August 2019 were retrospectively analyzed according to the lead location confirmed by visualization of the tricuspid value annulus, postoperative echocardiography, and pacing electrocardiogram. The electrical characteristics and pacing parameters were compared among these patients.

RESULTS

A total of 135 patients were retrospectively analyzed. Among them, 30 patients received atrial side HBP (aHBP group), 52 received ventricular side HBP (vHBP group), and 53 received left bundle branch pacing (LBBP group). The proportion of non-selective pacing was significantly lower in aHBP group (30.0%) than in vHBP (75.0%) and LBBP group (90.6%). LBBP had significantly shorter procedural and fluoroscopic duration than aHBP and vHBP. The capture threshold was significantly higher (1.07 ± 0.26 V/1.0 ms vs. 0.89 ± 0.22 V/1.0 ms vs. 0.77 ± 0.18 V/0.4 ms, P < 0.01, respectively), and the R-wave amplitude was significantly lower (3.71 ± 1.72 mV vs. 5.81 ± 2.37 mV vs. 10.27 ± 4.71 mV, P < 0.05 respectively) in aHBP group than those in the other two groups at implantation and during 3-month follow-up. No significant differences were observed in complications among groups during 3-month follow-up.

CONCLUSION

VHBP and LBBP had better pacing performances than aHBP and might be more ideal pacing methods for bradycardia patients.