Evidence-Based Assessment of Congenital Heart Disease Genes to Enable Returning Results in a Genomic Study

BACKGROUND

Congenital heart disease (CHD) is the most common major congenital anomaly and causes significant morbidity and mortality. Epidemiologic evidence supports a role of genetics in the development of CHD. Genetic diagnoses can inform prognosis and clinical management. However, genetic testing is not standardized among individuals with CHD. We sought to develop a list of validated CHD genes using established methods and to evaluate the process of returning genetic results to research participants in a large genomic study.

METHODS

Two-hundred ninety-five candidate CHD genes were evaluated using a ClinGen framework. Sequence and copy number variants involving genes in the CHD gene list were analyzed in Pediatric Cardiac Genomics Consortium participants. Pathogenic/likely pathogenic results were confirmed on a new sample in a clinical laboratory improvement amendments-certified laboratory and disclosed to eligible participants. Adult probands and parents of probands who received results were asked to complete a post-disclosure survey.

RESULTS

A total of 99 genes had a strong or definitive clinical validity classification. Diagnostic yields for copy number variants and exome sequencing were 1.8% and 3.8%, respectively. Thirty-one probands completed clinical laboratory improvement amendments-confirmation and received results. Participants who completed postdisclosure surveys reported high personal utility and no decision regret after receiving genetic results.

CONCLUSIONS

The application of ClinGen criteria to CHD candidate genes yielded a list that can be used to interpret clinical genetic testing for CHD. Applying this gene list to one of the largest research cohorts of CHD participants provides a lower bound for the yield of genetic testing in CHD.

Dominantly acting KIF5B variants with pleiotropic cellular consequences cause variable clinical phenotypes

Kinesins are motor proteins involved in microtubule (MT)-mediated intracellular transport. They contribute to key cellular processes, including intracellular trafficking, organelle dynamics and cell division. Pathogenic variants in kinesin-encoding genes underlie several human diseases characterized by an extremely variable clinical phenotype, ranging from isolated neurodevelopmental/neurodegenerative disorders to syndromic phenotypes belonging to a family of conditions collectively termed as 'ciliopathies.' Among kinesins, kinesin-1 is the most abundant MT motor for transport of cargoes towards the plus end of MTs. Three kinesin-1 heavy chain isoforms exist in mammals. Different from KIF5A and KIF5C, which are specifically expressed in neurons and established to cause neurological diseases when mutated, KIF5B is an ubiquitous protein. Three de novo missense KIF5B variants were recently described in four subjects with a syndromic skeletal disorder characterized by kyphomelic dysplasia, hypotonia and DD/ID. Here, we report three dominantly acting KIF5B variants (p.Asn255del, p.Leu498Pro and p.Leu537Pro) resulting in a clinically wide phenotypic spectrum, ranging from dilated cardiomyopathy with adult-onset ophthalmoplegia and progressive skeletal myopathy to a neurodevelopmental condition characterized by severe hypotonia with or without seizures. In vitro and in vivo analyses provide evidence that the identified disease-associated KIF5B variants disrupt lysosomal, autophagosome and mitochondrial organization, and impact cilium biogenesis. All variants, and one of the previously reported missense changes, were shown to affect multiple developmental processes in zebrafish. These findings document pleiotropic consequences of aberrant KIF5B function on development and cell homeostasis, and expand the phenotypic spectrum resulting from altered kinesin-mediated processes.

Predicting the development of adverse cardiac events in patients with hypertrophic cardiomyopathy using machine learning

BACKGROUND

Only a subset of patients with hypertrophic cardiomyopathy (HCM) develop adverse cardiac events - e.g., end-stage heart failure, cardiovascular death. Current risk stratification methods are imperfect, limiting identification of high-risk patients with HCM. Our aim was to improve the prediction of adverse cardiac events in patients with HCM using machine learning methods.

METHODS

We applied modern machine learning methods to a prospective cohort of adults with HCM. The outcome was a composite of death due to heart failure, heart transplant, and sudden death. As the reference model, we constructed logistic regression model using known predictors. We determined 20 predictive characteristics based on random forest classification and a priori knowledge, and developed 4 machine learning models. Results Of 183 patients in the cohort, the mean age was 53 (SD = 17) years and 45% were female. During the median follow-up of 2.2 years (interquartile range, 0.6-3.8), 33 subjects (18%) developed an outcome event, the majority of which (85%) was heart transplant. The predictive accuracy of the reference model was 73% (sensitivity 76%, specificity 72%) while that of the machine learning model was 85% (e.g., sensitivity 88%, specificity 84% with elastic net regression). All 4 machine learning models significantly outperformed the reference model - e.g., area under the receiver-operating-characteristic curve 0.79 with the reference model vs. 0.93 with elastic net regression (p < 0.001).

CONCLUSIONS

Compared with conventional risk stratification, the machine learning models demonstrated a superior ability to predict adverse cardiac events. These modern machine learning methods may enhance identification of high-risk HCM subpopulations.

The Link Between CHA2DS2-VASc Score and Thromboembolic Risk in Patients Without Known Atrial Fibrillation: Are We Missing a Silent Culprit?

Stroke is a leading cause of morbidity and mortality. The majority of strokes are ischemic and a subset of these are due to atrial fibrillation (AF). Other etiologies include a variety of cardiovascular disorders. The CHA₂DS₂-VASc score is a validated stroke prediction tool for patients with non-valvular AF. However, it has also been shown to predict increased risk for stroke or thromboembolism in the absence of AF. Given how common subclinical AF (SCAF) is when looked for in patients with elevated CHA₂DS₂-VaSc scores who are not known to have AF, (especially when implanted monitors are used), the stroke/thromboembolism risk that has been associated with CHA₂DS₂-VASc scores absent known AF may be an overestimate of the true risk due to the likely presence of SCAF in some of the subjects included. This has not yet been adequately addressed in the literature. Finally, the risk of a left atrial thromboembolic event is a consequence of the altered atrial anatomy and physiology (atrial cardiomyopathy) that may result from comorbid disorders and AF itself, or, additively from both - whether or not the AF has been already recognized clinically.

Detection of Previously Unrecognized (Subclinical) Atrial Fibrillation

Atrial fibrillation (AF) has been associated with increased morbidity and mortality, even when symptoms are absent and the arrhythmia is unrecognized (e.g., subclinical AF [SCAF]). Despite substantial evidence demonstrating an association between AF and adverse outcomes, the role of mass screening for previously unrecognized SCAF, such that its individual and population risks may be reduced by prophylactic therapy, remains uncertain. Many AF screening strategies exist, from pulse palpation and single-use devices to implanted cardiac monitors; however, existing guidelines are insufficient in specifying who to screen and for how long. In general, higher age, more (and more severe) comorbidities, and longer monitoring periods are associated with greater detection of SCAF. Herein we review the significance of previously unrecognized SCAF and current status of SCAF detection methods. We then propose a clinical approach to help clinicians incorporate AF screening into their practice. In conclusion, we report that SCAF may not be rare, that inserted cardiac monitors have the highest yield of SCAF detection, that clinical concern regarding SCAF is appropriate, but that evidence for therapy mandates is still being collected.

Effect of Ventricular Pacing on Morbidity in Adults After Fontan Repair

Implantation of a permanent pacemaker is a negative prognostic marker in patients with Fontan palliation; however, data delineating outcomes in adult patients with pacemaker requirements are lacking. We hypothesize that high ventricular pacing burden is associated with adverse outcomes in adult Fontan patients. We performed a retrospective review comprising adult patients with history of Fontan repair. A high burden of ventricular pacing was defined as ≥40% pacing. Major adverse clinical events (MACE) were defined as all-cause mortality or need for advanced cardiac therapies (ventricular assist device or heart transplant). A total of 145 adult patients with Fontan were studied for a median of 3.1 years. Twenty (14%) patients had implanted pacemakers with ≥40% ventricular pacing. Twelve events occurred in those with ≥40% ventricular pacing (incidence 60.0%) versus 11 in those without (incidence 8.8%). In multivariable analysis, ≥40% ventricular-pacing (odds ratio 12.51, confidence interval [CI] 3.56 to 43.83, p <0.001) was associated with MACE independent of initial Fontan type, New York Heart Association functional class at baseline, or history of atrial tachyarrythmia. In survival analysis, patients with ≥40% ventricular pacing had nearly 8 times the risk of MACE compared with those with a lower ventricular pacing burden (hazard ratio 7.79, 95% CI 2.56 to 23.66, p <0.001), whereas patients with atrial-only or <40% ventricular pacing burden had a trend toward higher hazard of MACE compared with those without permanent pacemaker (hazard ratio 3.38, 95% CI 0.92 to 12.47, p = 0.07) that did not meet statistical significance. These findings suggest that high ventricular pacing burden contributes to poor outcomes in the adult Fontan patients and bear consideration when determining optimal treatment of tachyarrhythmias in this population.

Clinical and echocardiographic features of paradoxical low-flow and normal-flow severe aortic stenosis patients with concomitant mitral regurgitation

Mitral regurgitation (MR) coexists in a significant proportion of patients with severe aortic stenosis (AS), and portends inferior therapeutic outcomes. In severe AS, MR is thought to contribute to a low-flow state by decreasing forward stroke volume. We investigated concomitant MR on the clinical and echocardiographic features of patients with "paradoxical" low-flow (PLF) and normal-flow (NF) severe AS. Clinical and echocardiographic profiles of 886 consecutive patients with index echocardiographic diagnosis of severe AS (AVA < 1.0 cm²) were analysed retrospectively. All patients had preserved ejection fraction (LVEF  ≥ 50%, n = 645), and were divided into PLF (stroke volume index, SVI < 35 mL/m²) and NF AS. They were then further subdivided based on the presence or absence of moderate-or-severe MR (msMR). A higher prevalence of concomitant msMR was observed in patients with PLF AS (14.9%; n = 33/221) compared to those with NF AS (8.0%; n = 34/424). Concomitant msMR was associated with echocardiographic features of increased diastolic dysfunction in both PLF AS and NF AS patients, as evidenced by increased LA diameter (PLF AS 52.9 ± 12.5 to 43.9 ± 8.9 mm; NF AS 29.6 ± 10.8 to 42.4 ± 8.8 mm; p < 0.001) and increased transmitral E/A ratio (PLF AS 1.26 ± 0.56 to 0.92 ± 0.43; NF AS 1.19 ± 0.63 to 0.94 ± 0.45; p = 0.004). Amongst patients with NF AS, msMR was additionally associated with increased E:e' ratio (25.5 ± 15.1 vs 19.3 ± 10.8; p = 0.025). Concomitant MR was more common in PLF AS compared to NF. Although possibly related to the MR, patients severe AS and MR appeared to have more severe diastolic dysfunction. Further studies are warranted to evaluate prognosis and guide management.

Application of Proteomics Profiling for Biomarker Discovery in Hypertrophic Cardiomyopathy

High-throughput proteomics profiling has never been applied to discover biomarkers in patients with hypertrophic cardiomyopathy (HCM). The objective was to identify plasma protein biomarkers that can distinguish HCM from controls. We performed a case-control study of patients with HCM (n = 15) and controls (n = 22). We carried out plasma proteomics profiling of 1129 proteins using the SOMAscan assay. We used the sparse partial least squares discriminant analysis to identify 50 most discriminant proteins. We also determined the area under the curve (AUC) of the receiver operating characteristic curve using the Monte Carlo cross validation with balanced subsampling. The average AUC was 0.94 (95% confidence interval, 0.82-1.00) and the discriminative accuracy was 89%. In HCM, 13 out of the 50 proteins correlated with troponin I and 12 with New York Heart Association class. Proteomics profiling can be used to elucidate protein biomarkers that distinguish HCM from controls.