Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development

Prevalence, Cardiac Phenotype, and Outcomes of Transthyretin Variants in the UK Biobank Population

Importance

The population prevalence of cardiac transthyretin amyloidosis (ATTR) caused by pathogenic variation in the TTR gene (vATTR) is unknown.

Objective

To estimate the population prevalence of disease-causing TTR variants and evaluate associated phenotypes and outcomes.

Design, Setting, and Participants

This population-based cohort study analyzed UK Biobank (UKB) participants with whole-exome sequencing, electrocardiogram, and cardiovascular magnetic resonance data. Participants were enrolled from 2006 to 2010, with a median follow-up of 12 (IQR, 11-13) years (cutoff date for the analysis, March 12, 2024). Sixty-two candidate TTR variants were extracted based on rarity (minor allele frequency ≤0.0001) and/or previously described associations with amyloidosis if more frequent.

Exposure

Carrier status for TTR variants.

Main Outcomes and Measures

Associations of TTR carrier status with vATTR prevalence and cardiovascular imaging and electrocardiogram traits were explored using descriptive statistics. Associations between TTR carrier status and atrial fibrillation, conduction disease, heart failure, and all-cause mortality were evaluated using adjusted Cox proportional hazards models. Genotypic and diagnostic concordance was examined using International Statistical Classification of Diseases, Tenth Revision codes from the hospital record.

Results

The overall cohort included 469 789 UKB participants (mean [SD] age, 56.5 [8.1] years; 54.2% female and 45.8% male). A likely pathogenic/pathogenic (LP/P) TTR variant was detected in 473 (0.1%) participants, with Val142Ile being the most prevalent (367 [77.6%]); 91 individuals (0.02%) were carriers of a variant of unknown significance . The overall prevalence of LP/P variants was 0.02% (105 of 444 243) in participants with European ancestry and 4.3% (321 of 7533) in participants with African ancestry. The LP/P variants were associated with higher left ventricular mass indexed to body surface area (β = 4.66; 95% CI, 1.87-7.44), and Val142Ile was associated with a longer PR interval (β = 18.34; 95% CI, 5.41-31.27). The LP/P carrier status was associated with a higher risk of heart failure (hazard ratio [HR], 2.68; 95% CI, 1.75-4.12) and conduction disease (HR, 1.88; 95% CI, 1.25-2.83). Higher all-cause mortality risk was observed for non-Val142Ile LP/P variants (HR, 1.98; 95% CI, 1.06-3.67). Thirteen participants (2.8%) with LP/P variants had diagnostic codes compatible with cardiac or neurologic amyloidosis. Variants of unknown significance were not associated with outcomes.

Conclusions and Relevance

This study found that approximately 1 in 1000 UKB participants were LP/P TTR variant carriers, exceeding previously reported prevalence. The findings emphasize the need for clinical vigilance in identifying individuals at risk of developing vATTR and associated poor outcomes.

Causal relationship between inflammatory markers and left ventricle geometry and function: A 2-sample Mendelian randomization study

Studies have shown that some inflammatory markers can predict the risk of cardiovascular disease (CVD) and affect the structure and function of the heart. However, a causal relationship between inflammatory markers and the cardiac structure and function has not yet been established. Thus, we conducted a 2-sample Mendelian randomization (MR) study to explore the potential causal relationship between inflammatory markers and prognostically-related left ventricular (LV) parameters. Instrumental variables (IVs) for C-reactive protein (CRP), interleukin-6 (IL-6), and myeloperoxidase (MPO) levels were selected from the databases of large genome-wide association studies (GWAS). Summary statistics for LV parameters, including LV mass, ejection fraction, end-diastolic and systolic volumes, and the ratio of LV mass to end-diastolic volume, were obtained from cardiovascular magnetic resonance studies of the UK Biobank (n = 16923). The inverse-variance weighted (IVW) method was the primary analytical method used, and was complemented with the MR-Egger, weighted median, simple mode, weighted mode, and MR pleiotropy residual sum and outlier (MR-PRESSO) methods. Sensitivity analysis was performed to evaluate the robustness of the results. CRP was significantly associated with the LV mass in the IVW method (β = -0.13 g [95% confidence interval [CI], 0.78 g-1.00 g], P = .046). A higher standard deviation of genetically-predicted CRP levels was associated with a 0.13 ± 0.06 g lower LV mass. No causal relationships of IL-6 and MPO with LV parameters were found. No evidence of heterogeneity and pleiotropy was detected. Sensitivity analyses confirmed the robustness of the results. Two-sample MR analysis revealed a causal association between increased CRP level and decreased LV mass, whereas IL-6 and MPO levels did not influence the LV parameters. However, further research is required to validate our findings.

Using Omics to Identify Novel Therapeutic Targets in Heart Failure

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.

Epistasis regulates genetic control of cardiac hypertrophy

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141 , IGF1R , TTN , and TNKS. Several loci where variants were deemed insignificant in univariate genome-wide association analyses are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we found strong gene co-expression correlations between these statistical epistasis contributors in healthy hearts and a significant connectivity decrease in failing hearts. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R . Our results expand the scope of genetic regulation of cardiac structure to epistasis.

A history of cardiovascular magnetic resonance imaging in clinical practice and population science

Cardiovascular magnetic resonance (CMR) imaging has become an invaluable clinical and research tool. Starting from the discovery of nuclear magnetic resonance, this article provides a brief overview of the key developments that have led to CMR as it is today, and how it became the modality of choice for large-scale population studies.

Biomarkers of heart failure: advances in omics studies

Heart failure is a complex syndrome characterized by progressive circulatory dysfunction, manifesting clinically as pulmonary and systemic venous congestion, alongside inadequate tissue perfusion. The early identification of HF, particularly at the mild and moderate stages (stages B and C), presents a clinical challenge due to the overlap of signs, symptoms, and natriuretic peptide levels with other cardiorespiratory pathologies. Nonetheless, early detection coupled with timely pharmacological intervention is imperative for enhancing patient outcomes. Advances in high-throughput omics technologies have enabled researchers to analyze patient-derived biofluids and tissues, discovering biomarkers that are sensitive and specific for HF diagnosis. Due to the diversity of HF etiology, it is insufficient to study the diagnostic data of early HF using a single omics technology. This study reviewed the latest progress in genomics, transcriptomics, proteomics, and metabolomics for the identification of HF biomarkers, offering novel insights into the early clinical diagnosis of HF. However, the validity of biomarkers depends on the disease status, intervention time, genetic diversity and comorbidities of the subjects. Moreover, biomarkers lack generalizability in different clinical settings. Hence, it is imperative to conduct multi-center, large-scale and standardized clinical trials to enhance the diagnostic accuracy and utility of HF biomarkers.

Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology

Recent genome-wide association studies have successfully identified associations between genetic variants and simple cardiac morphological parameters derived from cardiac magnetic resonance images. However, the emergence of large databases, including genetic data linked to cardiac magnetic resonance facilitates the investigation of more nuanced patterns of cardiac shape variability than those studied so far. Here we propose a framework for gene discovery coined unsupervised phenotype ensembles. The unsupervised phenotype ensemble builds a redundant yet highly expressive representation by pooling a set of phenotypes learnt in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analysed via genome-wide association studies, retaining only highly confident and stable associations across the ensemble. We applied our approach to the UK Biobank database to extract geometric features of the left ventricle from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes that influence left ventricle shape, identifying 49 loci with study-wide significance and 25 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.

Sex-stratified genome-wide association and transcriptome-wide Mendelian randomization studies reveal drug targets of heart failure

The prevalence of heart failure (HF) subtypes, which are classified by left ventricular ejection fraction (LVEF), demonstrate significant sex differences. Here, we perform sex-stratified genome-wide association studies (GWASs) on LVEF and transcriptome-wide Mendelian randomization (MR) on LVEF, all-cause HF, HF with reduced ejection fraction (HFrEF), and HF with preserved ejection fraction (HFpEF). The sex-stratified GWASs of LVEF identified three sex-specific loci that were exclusively detected in the sex-stratified GWASs. Three drug target genes show sex-differential effects on HF/HFrEF via influencing LVEF, with NPR2 as the target gene for the HF drug Cenderitide under phase 2 clinical trial. Our study highlights the importance of considering sex-differential genetic effects in sex-balanced diseases such as HF and emphasizes the value of sex-stratified GWASs and MR in identifying putative genetic variants, causal genes, and candidate drug targets for HF, which is not identifiable using a sex-combined strategy.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Leukocyte Telomere Length and Cardiac Structure and Function: A Mendelian Randomization Study

BACKGROUND

Existing research demonstrates the association of shorter leukocyte telomere length with increased risk of age-related health outcomes including cardiovascular diseases. However, the direct causality of these relationships has not been definitively established. Cardiovascular aging at an organ level may be captured using image-derived phenotypes of cardiac anatomy and function.

METHODS AND RESULTS

In the current study, we use 2-sample Mendelian randomization to assess the causal link between leukocyte telomere length and 54 cardiac magnetic resonance imaging measures representing structure and function across the 4 cardiac chambers. Genetically predicted shorter leukocyte telomere length was causally linked to smaller ventricular cavity sizes including left ventricular end-systolic volume, left ventricular end-diastolic volume, lower left ventricular mass, and pulmonary artery. The association with left ventricular mass (β =0.217, Pfalse discovery rate=0.016) remained significant after multiple testing adjustment, whereas other associations were attenuated.

CONCLUSIONS

Our findings support a causal role for shorter leukocyte telomere length and faster cardiac aging, with the most prominent relationship with left ventricular mass.

The Inclusion of Underrepresented Populations in Cardiovascular Genetics and Epidemiology

Novel genetic risk markers have helped us to advance the field of cardiovascular epidemiology and refine our current understanding and risk stratification paradigms. The discovery and analysis of variants can help us to tailor prognostication and management. However, populations underrepresented in cardiovascular epidemiology and cardiogenetics research may experience inequities in care if prediction tools are not applicable to them clinically. Therefore, the purpose of this article is to outline the barriers that underrepresented populations can face in participating in genetics research, to describe the current efforts to diversify cardiogenetics research, and to outline strategies that researchers in cardiovascular epidemiology can implement to include underrepresented populations. Mistrust, a lack of diverse research teams, the improper use of sensitive biodata, and the constraints of genetic analyses are all barriers for including diverse populations in genetics studies. The current work is beginning to address the paucity of ethnically diverse genetics research and has already begun to shed light on the potential benefits of including underrepresented and diverse populations. Reducing barriers for individuals, utilizing community-driven research processes, adopting novel recruitment strategies, and pushing for organizational support for diverse genetics research are key steps that clinicians and researchers can take to develop equitable risk stratification tools and improve patient care.

Genetic architecture of cardiac dynamic flow volumes

Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function.

Association of anaemia on heart failure and left ventricular function: A bidirectional Mendelian randomization study

AIMS

Observational studies have suggested that anaemia is associated with an increased risk of heart failure (HF). But the potential causal association is not clear. We aimed to investigate the association between anaemia and HF risk.

METHODS AND RESULTS

A Mendelian randomization (MR) analysis was performed to confirm the causal association of anaemia with the risk of HF and left ventricular structure and function. Furthermore, a reverse-direction MR analyses was conducted to assess the causal effect of HF on anaemia. The MR analysis indicated that genetically predicted anaemia is associated with the increased risk of HF (meta: odd ratio (OR) = 1.12; 95% confidence interval (CI) [1.04, 1.20]; P = 0.002), and left ventricular mass index (β = 1.051; 95% CI [0.384, 1.718]; P = 0.002), left ventricular mass (β = 2.063; 95% CI [0.578, 3.547]; P = 0.006), left atrial minimum volume (β = 0.076; 95% CI [0.008, 0.143]; P = 0.028), and left atrial maximum volume (β = 0.090; 95% CI [0.023, 0.157]; P = 0.009). In the reverse-direction MR analyses, we found that genetic susceptibility to HF was significantly associated with the increased risk of anaemia (meta: OR = 1.40; 95% CI [1.24, 1.59]; P = 1.79 × 10-7 ).

CONCLUSIONS

This MR study supports the genetic evidence that there is bidirectional causality between anaemia and the risk of HF as well as anaemia may cause left ventricular hypertrophy and enlargement of the left atrium. Considering the adverse causal effects between the two diseases, more attention should be paid to the prevention and treatment of anaemia in patients with HF.

Interaction between systemic iron parameters and left ventricular structure and function in the preserved ejection fraction population: a two-sample bidirectional Mendelian randomization study

BACKGROUND

Left ventricular (LV) remodeling and diastolic function in people with heart failure (HF) are correlated with iron status; however, the causality is uncertain. This Mendelian randomization (MR) study investigated the bidirectional causal relationship between systemic iron parameters and LV structure and function in a preserved ejection fraction population.

METHODS

Transferrin saturation (TSAT), total iron binding capacity (TIBC), and serum iron and ferritin levels were extracted as instrumental variables for iron parameters from meta-analyses of public genome-wide association studies. Individuals without myocardial infarction history, HF, or LV ejection fraction (LVEF) < 50% (n = 16,923) in the UK Biobank Cardiovascular Magnetic Resonance Imaging Study constituted the outcome dataset. The dataset included LV end-diastolic volume, LV end-systolic volume, LV mass (LVM), and LVM-to-end-diastolic volume ratio (LVMVR). We used a two-sample bidirectional MR study with inverse variance weighting (IVW) as the primary analysis method and estimation methods using different algorithms to improve the robustness of the results.

RESULTS

In the IVW analysis, one standard deviation (SD) increased in TSAT significantly correlated with decreased LVMVR (β = -0.1365; 95% confidence interval [CI]: -0.2092 to -0.0638; P = 0.0002) after Bonferroni adjustment. Conversely, no significant relationships were observed between other iron and LV parameters. After Bonferroni correction, reverse MR analysis showed that one SD increase in LVEF significantly correlated with decreased TSAT (β = -0.0699; 95% CI: -0.1087 to -0.0311; P = 0.0004). No heterogeneity or pleiotropic effects evidence was observed in the analysis.

CONCLUSIONS

We demonstrated a causal relationship between TSAT and LV remodeling and function in a preserved ejection fraction population.

The Role of Next-Generation Sequencing in the Management of Patients with Suspected Non-Ischemic Cardiomyopathy after Syncope or Termination of Sudden Arrhythmic Death

Cardiac arrhythmias and sudden death are frequent in patients with non-ischemic cardiomyopathy and can precede heart failure or additional symptoms where malignant cardiac arrhythmias are mostly the consequence of advanced cardiomyopathy and heart failure. Finding these subgroups and making an early diagnosis could be lifesaving. In our retrospective study, we are presenting arrhythmic types of frequent cardiomyopathies where an arrhythmogenic substrate is less well defined, as in ischemic or structural heart disease. In the period of 2 years, next-generation sequencing (NGS) tests along with standard clinical tests were performed in 208 patients (67 women and 141 men; mean age, 51.2 ± 19.4 years) without ischemic or an overt structural heart disease after syncope or aborted sudden cardiac death. Genetic variants were detected in 34.4% of the study population, with a significant proportion of pathogenic variants (P) (14.4%) and variants of unknown significance (VUS) (20%). Regardless of genotype, all patients were stratified according to clinical guidelines for aggressive treatment of sudden cardiac death with an implantable cardioverter defibrillator (ICD). The P variant identified by NGS serves for an accurate diagnosis and, thus, better prevention and specific treatment of patients and their relatives. Results in our study suggest that targeted sequencing of genes associated with cardiovascular disease is an important addendum for final diagnosis, allowing the identification of a molecular genetic cause in a vast proportion of patients for a definitive diagnosis and a more specific way of treatment. VUS in this target population poses a high risk and should be considered possibly pathogenic in reanalysis.

Genome-wide association and Mendelian randomization analysis provide insights into the shared genetic architecture between high-dimensional electrocardiographic features and ischemic heart disease

Observational studies have revealed that ischemic heart disease (IHD) has a unique manifestation on electrocardiographic (ECG). However, the genetic relationships between IHD and ECG remain unclear. We took 12-lead ECG as phenotypes to conduct genome-wide association studies (GWAS) for 41,960 samples from UK-Biobank (UKB). By leveraging large-scale GWAS summary of ECG and IHD (downloaded from FinnGen database), we performed LD score regression (LDSC), Mendelian randomization (MR), and polygenic risk score (PRS) regression to explore genetic relationships between IHD and ECG. Finally, we constructed an XGBoost model to predict IHD by integrating PRS and ECG. The GWAS identified 114 independent SNPs significantly (P value < 5 × 10-8/800, where 800 denotes the number of ECG features) associated with ECG. LDSC analysis indicated significant (P value < 0.05) genetic correlations between 39 ECG features and IHD. MR analysis performed by five approaches showed a putative causal effect of IHD on four S wave related ECG features at lead III. Integrating PRS for these ECG features with age and gender, the XGBoost model achieved Area Under Curve (AUC) 0.72 in predicting IHD. Here, we provide genetic evidence supporting S wave related ECG features at lead III to monitor the IHD risk, and open up a unique approach to integrate ECG with genetic factors for pre-warning IHD.

Prognostic Significance of Different Ventricular Ectopic Burdens During Submaximal Exercise in Asymptomatic UK Biobank Subjects

BACKGROUND

The consequences of exercise-induced premature ventricular contractions (PVCs) in asymptomatic individuals remain unclear. This study aimed to assess the association between PVC burdens during submaximal exercise and major adverse cardiovascular events (MI/HF/LTVA: myocardial infarction [MI], heart failure [HF], and life-threatening ventricular arrhythmia [LTVA]), and all-cause mortality. Additional end points were MI, LTVA, HF, and cardiovascular mortality.

METHODS

A neural network was developed to count PVCs from ECGs recorded during exercise (6 minutes) and recovery (1 minute) in 48 315 asymptomatic participants from UK Biobank. Associations were estimated using multivariable Cox proportional hazard models. Explorative studies were conducted in subgroups with cardiovascular magnetic resonance imaging data (n=6290) and NT-proBNP (N-terminal Pro-B-type natriuretic peptide) levels (n=4607) to examine whether PVC burden was associated with subclinical cardiomyopathy.

RESULTS

Mean age was 56.8±8.2 years; 51.1% of the participants were female; and median follow-up was 12.6 years. Low PVC counts during exercise and recovery were both associated with MI/HF/LTVA risk, independently of clinical factors: adjusted hazard ratio (HR), 1.2 (1-5 exercise PVCs, P<0.001) and HR, 1.3 (1-5 recovery PVCs, P<0.001). Risks were higher with increasing PVC count: HR, 1.8 (>20 exercise PVCs, P<0.001) and HR, 1.6 (>5 recovery PVCs, P<0.001). A similar trend was observed for all-cause mortality, although associations were only significant for high PVC burdens: HRs, 1.6 (>20 exercise PVCs, P<0.001) and 1.5 (>5 recovery PVCs, P<0.001). Complex PVC rhythms were associated with higher risk compared with PVC count alone. PVCs were also associated with incident HF, LTVA, and cardiovascular mortality, but not MI. In the explorative studies, high PVC burden was associated with larger left ventricular volumes, lower ejection fraction, and higher levels of NT-proBNP compared with participants without PVCs.

CONCLUSIONS

In this cohort of middle-aged and older adults, PVC count during submaximal exercise and recovery were both associated with MI/HF/LTVA, all-cause mortality, HF, LTVAs, and cardiovascular mortality, independent of clinical and exercise test factors, indicating an incremental increase in risk as PVC count rises. Complex PVC rhythms were associated with higher risk compared with PVC count alone. Underlying mechanisms may include the presence of subclinical cardiomyopathy.

Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy

Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype (P < 5 × 10-8), which were revealed to actively participate in heart development and contraction pathways. Significant causal relationships were observed between the LVRWT traits and hypertrophic cardiomyopathy (HCM) using genetic correlation and Mendelian randomization analyses (P < 0.01). The polygenic risk score of inferoseptal LVRWT at end systole exhibited a notable association with incident HCM, facilitating the identification of high-risk individuals. The findings yield insights into the genetic determinants of LVRWT phenotypes and shed light on the biological basis for HCM etiology.

Genome-Wide Association Study of Pericardial Fat Area in 28 161 UK Biobank Participants

BACKGROUND Pericardial adipose tissue (PAT) is the visceral adipose tissue compartment surrounding the heart. Experimental and observational research has suggested that greater PAT deposition might mediate cardiovascular disease, independent of general or subcutaneous adiposity. We characterize the genetic architecture of adiposity-adjusted PAT and identify causal associations between PAT and adverse cardiac magnetic resonance imaging measures of cardiac structure and function in 28 161 UK Biobank participants. METHODS AND RESULTS The PAT phenotype was extracted from cardiac magnetic resonance images using an automated image analysis tool previously developed and validated in this cohort. A genome-wide association study was performed with PAT area set as the phenotype, adjusting for age, sex, and other measures of obesity. Functional mapping and Bayesian colocalization were used to understand the biologic role of identified variants. Mendelian randomization analysis was used to examine potential causal links between genetically determined PAT and cardiac magnetic resonance-derived measures of left ventricular structure and function. We discovered 12 genome-wide significant variants, with 2 independent sentinel variants (rs6428792, P=4.20×10-9 and rs11992444, P=1.30×10-12) at 2 distinct genomic loci, that were mapped to 3 potentially causal genes: T-box transcription factor 15 (TBX15), tryptophanyl tRNA synthetase 2, mitochondrial (WARS2) and early B-cell factor-2 (EBF2) through functional annotation. Bayesian colocalization additionally suggested a role of RP4-712E4.1. Genetically predicted differences in adiposity-adjusted PAT were causally associated with adverse left ventricular remodeling. CONCLUSIONS This study provides insights into the genetic architecture determining differential PAT deposition, identifies causal links with left structural and functional parameters, and provides novel data about the pathophysiological importance of adiposity distribution.

Unraveling the relationships between alpha- and beta-adrenergic modulation and the risk of heart failure

Background

The effects of α and ß adrenergic receptor modulation on the risk of developing heart failure (HF) remains uncertain due to a lack of randomized controlled trials. This study aimed to estimate the effects of α and ß adrenergic receptors modulation on the risk of HF and to provide proof of principle for genetic target validation studies in HF.

Methods

Genetic variants within the cis regions encoding the adrenergic receptors α1A, α2B, ß1, and ß2 associated with blood pressure in a 757,601-participant genome-wide association study (GWAS) were selected as instruments to perform a drug target Mendelian randomization study. Effects of these variants on HF risk were derived from the HERMES GWAS (542,362 controls; 40,805 HF cases).

Results

Lower α1A or ß1 activity was associated with reduced HF risk: odds ratio (OR) 0.83 (95% CI 0.74-0.93, P = 0.001) and 0.95 (95% CI 0.93-0.97, P = 8 × 10-6). Conversely, lower α2B activity was associated with increased HF risk: OR 1.09 (95% CI 1.05-1.12, P = 3 × 10-7). No evidence of an effect of lower ß2 activity on HF risk was found: OR 0.99 (95% CI 0.92-1.07, P = 0.95). Complementary analyses showed that these effects were consistent with those on left ventricular dimensions and acted independently of any potential effect on coronary artery disease.

Conclusions

This study provides genetic evidence that α1A or ß1 receptor inhibition will likely decrease HF risk, while lower α2B activity may increase this risk. Genetic variant analysis can assist with drug development for HF prevention.

Assessment of Prevalence, Clinical Characteristics, and Risk Factors Associated With "Low Flow State" Using Cardiac Magnetic Resonance

Objective

To assess prevalence, clinical characteristics, and risk factors associated with low flow state (LFS) in a multiethnic population with normal left ventricular ejection fraction (LVEF).

Patients and Methods

The study included 4398 asymptomatic participants undergoing cardiac magnetic resonance from July 17, 2000, to August 29, 2002. Left ventricular (LV) mass, volume, and myocardial contraction fraction were assessed. Low flow state was defined as stroke volume index (SVi of <35 mL/m2). Clinical characteristics, cardiac risk factors, and cardiac magnetic resonance findings were compared between LFS and normal flow state (NFS) groups (NFS: SVi of ≥35 mL/m2).

Results

There were significant differences in the prevalence of LFS in different ethnic groups. Individuals with LFS were older (66±9.6 vs 61±10 years; P<.0001). The prevalence of LFS was 19% in the group aged older than 70 years. The logistic multivariable regression analysis found that age was independently associated with LFS. The LFS group had significantly higher prevalence of diabetes (30% vs 24%; P=.001), LV mass-volume ratio (1.13±0.22 vs 0.91±0.15; P<.0001), inflammatory markers, a lower LV mass index (59±10 vs 65±11 kg/m2; P<.001), lower myocardial contraction fraction (58.1±10.6% vs 75.7±13%; P<.001), and a lower left atrial size index (32.2±4.6 vs 36.7±5.9 mm/m2; P<.0001) than NFS.

Conclusion

Low flow state may be considered an under-recognized clinical entity associated with increasing age, multiple risk factors, increased inflammatory markers, a lower LV mass index, and suboptimal myocardial performance despite the presence of normal LVEF and absence of valvular disease.

Cardiomyopathy in Asian Cohorts: Genetic and Epigenetic Insights

Previous studies on cardiomyopathies have been particularly valuable for clarifying pathological mechanisms in heart failure, an etiologically heterogeneous disease. In this review, we specifically focus on cardiomyopathies in Asia, where heart failure is particularly pertinent. There has been an increase in prevalence of cardiomyopathies in Asia, in sharp contrast with the decline observed in Western countries. Indeed, important disparities in cardiomyopathy incidence, clinical characteristics, and prognosis have been reported in Asian versus White cohorts. These have been accompanied by emerging descriptions of a distinct rare and common genetic basis for disease among Asian cardiomyopathy patients marked by an increased burden of variants with uncertain significance, reclassification of variants deemed pathogenic based on evidence from predominantly White cohorts, and the discovery of Asian-specific cardiomyopathy-associated loci with underappreciated pathogenicity under conventional classification criteria. Findings from epigenetic studies of heart failure, particularly DNA methylation studies, have complemented genetic findings in accounting for the phenotypic variability in cardiomyopathy. Though extremely limited, findings from Asian ancestry-focused DNA methylation studies of cardiomyopathy have shown potential to contribute to general understanding of cardiomyopathy pathophysiology by proposing disease and cause-relevant pathophysiological mechanisms. We discuss the value of multiomics study designs incorporating genetic, methylation, and transcriptomic information for future DNA methylation studies in Asian cardiomyopathy cohorts to yield Asian ancestry-specific insights that will improve risk stratification in the Asian population.

Association of Longer Leukocyte Telomere Length With Cardiac Size, Function, and Heart Failure

Importance

Longer leukocyte telomere length (LTL) is associated with a lower risk of adverse cardiovascular outcomes. The extent to which variation in LTL is associated with intermediary cardiovascular phenotypes is unclear.

Objective

To evaluate the associations between LTL and a diverse set of cardiovascular imaging phenotypes.

Design, Setting, and Participants

This is a population-based cross-sectional study of UK Biobank participants recruited from 2006 to 2010. LTL was measured using a quantitative polymerase chain reaction method. Cardiovascular measurements were derived from cardiovascular magnetic resonance using machine learning. The median (IQR) duration of follow-up was 12.0 (11.3-12.7) years. The associations of LTL with imaging measurements and incident heart failure (HF) were evaluated by multivariable regression models. Genetic associations between LTL and significantly associated traits were investigated by mendelian randomization. Data were analyzed from January to May 2023.

Exposure

LTL.

Main Outcomes and Measures

Cardiovascular imaging traits and HF.

Results

Of 40 459 included participants, 19 529 (48.3%) were men, and the mean (SD) age was 55.1 (7.6) years. Longer LTL was independently associated with a pattern of positive cardiac remodeling (higher left ventricular mass, larger global ventricular size and volume, and higher ventricular and atrial stroke volumes) and a lower risk of incident HF (LTL fourth quartile vs first quartile: hazard ratio, 0.86; 95% CI, 0.81-0.91; P = 1.8 × 10-6). Mendelian randomization analysis suggested a potential causal association between LTL and left ventricular mass, global ventricular volume, and left ventricular stroke volume.

Conclusions and Relevance

In this cross-sectional study, longer LTL was associated with a larger heart with better cardiac function in middle age, which could potentially explain the observed lower risk of incident HF.

Myocardial Fibrosis and Cardiomyopathy Risk: A Genetic Link in the MESA

BACKGROUND

Common genetic variants are associated with risk for hypertrophic cardiomyopathy and dilated cardiomyopathy and with left ventricular (LV) traits. Whether these variants are associated with myocardial fibrosis, an important pathophysiological mediator of cardiomyopathy, is unknown.

METHODS

Multi-Ethnic Study of Atherosclerosis participants with T1-mapping cardiac magnetic resonance imaging in-whom extracellular volume was assessed, and genotyping information was available were included (N=1255). Log extracellular volume (%) was regressed on 50 candidate single nucleotide polymorphisms (previously identified to be associated with hypertrophic cardiomyopathy, dilated cardiomyopathy, and LV traits) adjusting for age, sex, diabetes, blood pressure, and principal components of ancestry. Ancestry-specific results were pooled by fixed-effect meta-analyses. Gene knockdown experiments were performed in human cardiac fibroblasts.

RESULTS

The SMARCB1 rs2186370 intronic variant (minor allele frequency: 0.18 in White and 0.50 in Black participants), previously identified as a risk variant for dilated cardiomyopathy and hypertrophic cardiomyopathy, was significantly associated with increased extracellular volume (P=0.0002) after adjusting for confounding clinical variables. The SMARCB1 rs2070458 locus previously associated with increased LV wall thickness and mass was similarly significantly associated with increased extracellular volume (P=0.0002). The direction of effect was similar in all 4 ancestry groups, but the effect was strongest in Black participants. The variants are strong expression quantitative loci in human LV tissue and associated with genotype-dependent decreased expression of SMARCB1 (P=7.3×10-22). SMARCB1 knockdown in human cardiac fibroblasts resulted in increased TGF (transforming growth factor)-β1-mediated α-smooth muscle actin and collagen expression.

CONCLUSIONS

Common genetic variation in SMARCB1 previously associated with risk for cardiomyopathies and increased LV wall thickness is associated with increased cardiac magnetic resonance imaging-based myocardial fibrosis and increased TGF-β1 mediated myocardial fibrosis in vitro. Whether these findings suggest a pathophysiologic link between myocardial fibrosis and cardiomyopathy risk remains to be proven.

Genetics of Pulmonary Pressure and Right Ventricle Stress Identify Diabetes as a Causal Risk Factor

Background Epidemiologic studies have identified risk factors associated with pulmonary hypertension and right heart failure, but causative drivers of pulmonary hypertension and right heart adaptation are not well known. We sought to leverage unbiased genetic approaches to determine clinical conditions that share genetic architecture with pulmonary pressure and right ventricular dysfunction. Methods and Results We leveraged Vanderbilt University's deidentified electronic health records and DNA biobank to identify 14 861 subjects of European ancestry who underwent at least 1 echocardiogram with available estimates of pulmonary pressure and right ventricular function. Analyses of the study were performed between 2020 and 2022. The final analytical sample included 14 861 participants (mean [SD] age, 63 [15] years and mean [SD] body mass index, 29 [7] kg/m2). An unbiased phenome-wide association study identified diabetes as the most statistically significant clinical International Classifications of Diseases, Ninth Revision (ICD-9) code associated with polygenic risk for increased pulmonary pressure. We validated this finding further by finding significant associations between genetic risk for diabetes and a related condition, obesity, with pulmonary pressure estimate. We then used 2-sample univariable Mendelian randomization and multivariable Mendelian randomization to show that diabetes, but not obesity, was independently associated with genetic risk for increased pulmonary pressure and decreased right ventricle load stress. Conclusions Our findings show that genetic risk for diabetes is the only significant independent causative driver of genetic risk for increased pulmonary pressure and decreased right ventricle load stress. These findings suggest that therapies targeting genetic risk for diabetes may also potentially be beneficial in treating pulmonary hypertension and right heart dysfunction.

Missense Genetic Variation of ICAM1 and Incident Heart Failure

BACKGROUND

Intercellular adhesion molecule-1 (ICAM-1) is a cell surface protein that participates in endothelial activation and is hypothesized to play a central role in heart failure (HF). We evaluated associations of ICAM1 missense genetic variants with circulating ICAM-1 levels and with incident HF.

METHODS AND RESULTS

We identified 3 missense variants within ICAM1 (rs5491, rs5498 and rs1799969) and evaluated their associations with ICAM-1 levels in the Coronary Artery Risk Development in Young Adults Study and the Multi-Ethnic Study of Atherosclerosis (MESA). We determined the association among these 3 variants and incident HF in MESA. We separately evaluated significant associations in the Atherosclerosis Risk in Communities (ARIC) study. Of the 3 missense variants, rs5491 was common in Black participants (minor allele frequency [MAF] > 20%) and rare in other race/ethnic groups (MAF < 5%). In Black participants, the presence of rs5491 was associated with higher levels of circulating ICAM-1 at 2 timepoints separated by 8 years. Among Black participants in MESA (n = 1600), the presence of rs5491 was associated with an increased risk of incident HF with preserved ejection fraction (HFpEF; HR = 2.30; [95% CI 1.25-4.21; P = 0.007]). The other ICAM1 missense variants (rs5498 and rs1799969) were associated with ICAM-1 levels, but there were no associations with HF. In ARIC, rs5491 was significantly associated with incident HF (HR = 1.24 [95% CI 1.02 - 1.51]; P = 0.03), with a similar direction of effect for HFpEF that was not statistically significant.

CONCLUSIONS

A common ICAM1 missense variant among Black individuals may be associated with increased risk of HF, which may be HFpEF-specific.

Clinical characteristics of heart failure with reduced ejection fraction patients with rare pathogenic variants in dilated cardiomyopathy-associated genes: A subgroup analysis of the PARADIGM-HF trial

AIMS

To evaluate the prevalence of pathogenic variants in genes associated with dilated cardiomyopathy (DCM) in a clinical trial population with heart failure and reduced ejection fraction (HFrEF) and describe the baseline characteristics by variant carrier status.

METHODS AND RESULTS

This was a post hoc analysis of the Phase 3 PARADIGM-HF trial. Forty-four genes, divided into three tiers, based on definitive, moderate or limited evidence of association with DCM, were assessed for rare predicted loss-of-function (pLoF) variants, which were prioritized using ClinVar annotations, measures of gene transcriptional output and evolutionary constraint, and pLoF confidence predictions. Prevalence was reported for pLoF variant carriers based on DCM-associated gene tiers. Clinical features were compared between carriers and non-carriers. Of the 1412 HFrEF participants with whole-exome sequence data, 68 (4.8%) had at least one pLoF variant in the 8 tier-1 genes (definitive/strong association with DCM), with Titin being most commonly affected. The prevalence increased to 7.5% when considering all 44 genes. Among patients with idiopathic aetiology, 10.0% (23/229) had tier-1 variants only and 12.6% (29/229) had tier-1, -2 or -3 variants. Compared to non-carriers, tier-1 carriers were younger (4 years; adjusted p-value [padj ] = 4 × 10-3 ), leaner (27.8 kg/m2 vs. 29.4 kg/m2 ; padj  = 3.2 × 10-3 ), had lower ejection fraction (27.3% vs. 29.8%; padj  = 5.8 × 10-3 ), and less likely to have ischaemic aetiology (37.3% vs. 67.4%; padj  = 4 × 10-4 ).

CONCLUSION

Deleterious pLoF variants in genes with definitive/strong association with DCM were identified in ∼5% of HFrEF patients from a PARADIGM-HF trial subset, who were younger, had lower ejection fraction and were less likely to have had an ischaemic aetiology.

Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study

BACKGROUND

Observational studies suggest that electrocardiogram (ECG) indices might be influenced by obesity and other anthropometric measures, though it is difficult to infer causal relationships based on observational data due to risk of residual confounding. We utilized mendelian randomization (MR) to explore causal relevance of multiple anthropometric measures on P-wave duration (PWD), PR interval, QRS duration, and corrected QT interval (QTc).

METHODS AND FINDINGS

Uncorrelated (r2 < 0.001) genome-wide significant (p < 5 × 10-8) single nucleotide polymorphisms (SNPs) were extracted from genome-wide association studies (GWAS) on body mass index (BMI, n = 806,834), waist:hip ratio adjusted for BMI (aWHR, n = 697,734), height (n = 709,594), weight (n = 360,116), fat mass (n = 354,224), and fat-free mass (n = 354,808). Genetic association estimates for the outcomes were extracted from GWAS on PR interval and QRS duration (n = 180,574), PWD (n = 44,456), and QTc (n = 84,630). Data source GWAS studies were performed between 2018 and 2022 in predominantly European ancestry individuals. Inverse-variance weighted MR was used for primary analysis; weighted median MR and MR-Egger were used as sensitivity analyses. Higher genetically predicted BMI was associated with longer PWD (β 5.58; 95%CI [3.66,7.50]; p = < 0.001), as was higher fat mass (β 6.62; 95%CI [4.63,8.62]; p < 0.001), fat-free mass (β 9.16; 95%CI [6.85,11.47]; p < 0.001) height (β 4.23; 95%CI [3.16, 5.31]; p < 0.001), and weight (β 8.08; 95%CI [6.19,9.96]; p < 0.001). Finally, genetically predicted BMI was associated with longer QTc (β 3.53; 95%CI [2.63,4.43]; p < 0.001), driven by both fat mass (β 3.65; 95%CI [2.73,4.57]; p < 0.001) and fat-free mass (β 2.08; 95%CI [0.85,3.31]; p = 0.001). Additionally, genetically predicted height (β 0.98; 95%CI [0.46,1.50]; p < 0.001), weight (β 3.45; 95%CI [2.54,4.36]; p < 0.001), and aWHR (β 1.92; 95%CI [0.87,2.97]; p = < 0.001) were all associated with longer QTc. The key limitation is that due to insufficient power, we were not able to explore whether a single anthropometric measure is the primary driver of the associations observed.

CONCLUSIONS

The results of this study support a causal role of BMI on multiple ECG indices that have previously been associated with atrial and ventricular arrhythmic risk. Importantly, the results identify a role of both fat mass, fat-free mass, and height in this association.

Genome-wide association study on coordination and agility in 461 Chinese Han males

There is growing evidence that genetic factors can influence human athletic performance. In many sports performances, excellent coordination and agility are the keys to mastery. However, few studies have been devoted to identifying genetic influences on athletic performance. Methods: We generated a derived measure of coordination and agility from the data of hexagonal jumps and T-runs and conducted genome-wide association and meta-analysis studies focused on coordination and agility. Results: The phenotypic correlation and genetic covariance analysis indicated that hexagonal jumps and T-runs were possibly influenced by the same set of genetic factors (R = 0.27, genetic covariance = 0.59). Meta-analysis identified rs117047321 genome-wide significant association (N = 143, P < 10E-5) with coordination and agility, and this association was replicated in the replication group (N = 318, P < 0.05). The CG genotype samples of this single nucleotide polymorphism (SNP) required a longer average movement time than the CC genotype samples, and the CG genotype only exists in Asia, which may belong to the East Asia-specific variation. This SNP is located on MYO5B, which is highly expressed in tissues such as the brain, heart, and muscle, suggesting that this locus might be a genetic factor related to human energy metabolism. Conclusion: Our study indicated that genetic factors can affect the athletic performance of coordination and agility. These findings may provide valuable insights for using genetic factors to evaluate sports characteristics.

Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure

We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven (CAMK2D, PRKD1, PRKD3, MAPK3, TNFSF12, APOC3 and NAE1) proteins as potential targets for interventions to be used in primary prevention of heart failure.

Genome-wide association studies: utility and limitations for research in physiology

Physiological systems are subject to interindividual variation encoded by genetics. Genome-wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans-ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non-European populations.

Functional analysis of a common BAG3 allele associated with protection from heart failure

Multiple genetic association studies have correlated a common allelic block linked to the BAG3 gene with a decreased incidence of heart failure, but the molecular mechanism remains elusive. In this study, we used induced pluripotent stem cells to test if the only coding variant in this allele block, BAG3C151R, alters protein and cellular function in human cardiomyocytes. Quantitative protein interaction analysis identified changes in BAG3C151R protein partners specific to cardiomyocytes. Knockdown of genes encoding for BAG3-interacting factors in cardiomyocytes followed by myofibrillar analysis revealed that BAG3C151R associates more strongly with proteins involved in the maintenance of myofibrillar integrity. Finally, we demonstrate that cardiomyocytes expressing the BAG3C151R variant have improved response to proteotoxic stress in a dose-dependent manner. This study suggests that BAG3C151R could be responsible for the cardioprotective effect of the haplotype block, by increasing cardiomyocyte protection from stress. Preferential binding partners of BAG3C151R may reveal potential targets for cardioprotective therapies.

Population-Level Prevalence of Rare Variants Associated With Atrial Fibrillation and its Impact on Patient Outcomes

BACKGROUND

Whole exome sequencing may identify rare pathogenic/likely pathogenic variants (LPVs) that are linked to atrial fibrillation (AF). The impact of LPVs associated with AF on a population level on outcomes is unclear.

OBJECTIVES

This study sought to examine the association of LPVs with AF and their impact on clinical outcomes using the UK Biobank, a national repository of participants with available whole exome sequencing data.

METHODS

A total of 200,631 individuals in the UK Biobank were studied. Incident and prevalent AF, comorbidities, and outcomes were identified using self-reported assessments and hospital stay operative, and death registry records. LPVs were determined using arrhythmia and cardiomyopathy gene panels with LOFTEE and ClinVar to predict variants of functional significance.

RESULTS

Compared with control subjects, there was a modestly increased prevalence of LPVs among 9,585 patients with AF (2.0% vs 1.7%, respectively; P = 0.01). Among those with prevalent AF at <45 years of age, 4.2% were LPV carriers. LPVs in TTN and PKP2 were associated with AF with adjusted odds ratios of 2.69 (95% CI: 1.57-4.61) and 2.69 (95% CI: 1.54-4.68), respectively. There was no significant difference in combined ischemic stroke, heart failure hospitalization, and mortality among patients who have AF with and without LPVs (25.1% vs 23.8%; P = 0.49). Among participants with AF and available cardiac magnetic resonance imaging data, LPV carriers had lower left ventricular ejection fractions than non-LPV carriers (42% vs 52%; P = 0.027).

CONCLUSIONS

Patients with AF had a modestly increased prevalence of LPVs. Among reference arrhythmia and cardiomyopathy genes, the contribution of rare variants to AF risk at a population level is modest and its impact on outcomes appears to be limited, despite an association of LPVs with reduced left ventricular ejection fraction among patients with AF.

Association of rs35006907 Polymorphism with Risk of Dilated Cardiomyopathy in Han Chinese Population

Background

Several investigations have demonstrated the association of MTSS1 with left ventricular (LV) structure and function. A recently published study has even revealed that rs35006907 was associated with both MTSS1 expression and the risk of dilated cardiomyopathy (DCM).

Objective

Our study intended to investigate the relationship between rs35006907 and the risk of DCM in the Han Chinese population.

Methods

A total of 529 DCM and 600 healthy controls were recruited. We conducted genotyping for rs35006907 in all participants. Gene association studies were performed to assess the association between rs35006907 and the risk of DCM. A series of functional assays including western blot, realtime PCR and firefly luciferase reporter gene assays were conducted to illuminate the underlying mechanism.

Results

We found that rs35006907-A allele was significantly associated with reduced risk of DCM in additive (p= 0.004; OR=0.78; 95% CI=0.66-0.93) and recessive models (p= 0.0005; OR=0.56; 95%CI=0.41-0.78) when compared with the rs35006907-C allele. There were significant differences in the left ventricular end-diastolic diameter (LVEDD) and left ventricular ejection fraction (LVEF) between rs35006907-CC/AC and AA genotypes. Furthermore, the variant rs35006907-A allele presented lower reporter gene activity, reduced mRNA and protein expression levels when compared with the C allele.

Conclusions

Our findings demonstrated that rs35006907-C allele increased the risk of DCM in Han Chinese population. Besides, rs35006907-C displayed higher reporter gene activity and increased MTSS1 expression in human samples.

Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility

Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.

Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images

Cardiovascular health interacts with cognitive and mental health in complex ways, yet little is known about the phenotypic and genetic links of heart-brain systems. We quantified heart-brain connections using multiorgan magnetic resonance imaging (MRI) data from more than 40,000 subjects. Heart MRI traits displayed numerous association patterns with brain gray matter morphometry, white matter microstructure, and functional networks. We identified 80 associated genomic loci (P < 6.09 × 10-10) for heart MRI traits, which shared genetic influences with cardiovascular and brain diseases. Genetic correlations were observed between heart MRI traits and brain-related traits and disorders. Mendelian randomization suggests that heart conditions may causally contribute to brain disorders. Our results advance a multiorgan perspective on human health by revealing heart-brain connections and shared genetic influences.

Cross-modal autoencoder framework learns holistic representations of cardiovascular state

A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes

BACKGROUND

Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology.

METHODS

We measured the left ventricle (LV) sphericity index (short axis length/long axis length) using deep learning-enabled image segmentation of cardiac magnetic resonance imaging data from the UK Biobank. Subjects with abnormal LV size or systolic function were excluded. The relationship between LV sphericity and cardiomyopathy was assessed using Cox analyses, genome-wide association studies, and two-sample Mendelian randomization.

FINDINGS

In a cohort of 38,897 subjects, we show that a one standard deviation increase in sphericity index is associated with a 47% increased incidence of cardiomyopathy (hazard ratio [HR]: 1.47, 95% confidence interval [CI]: 1.10-1.98, p = 0.01) and a 20% increased incidence of atrial fibrillation (HR: 1.20, 95% CI: 1.11-1.28, p < 0.001), independent of clinical factors and traditional magnetic resonance imaging (MRI) measurements. We identify four loci associated with sphericity at genome-wide significance, and Mendelian randomization supports non-ischemic cardiomyopathy as causal for LV sphericity.

CONCLUSIONS

Variation in LV sphericity in otherwise normal hearts predicts risk for cardiomyopathy and related outcomes and is caused by non-ischemic cardiomyopathy.

FUNDING

This study was supported by grants K99-HL157421 (D.O.) and KL2TR003143 (S.L.C.) from the National Institutes of Health.

Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass

Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.

Conventional and genetic associations between resting heart rate, cardiac morphology and function as assessed by magnetic resonance imaging: Insights from the UK biobank population study

Aim

To examine the direction, strength and causality of the associations of resting heart rate (RHR) with cardiac morphology and function in 20,062 UK Biobank participants.

Methods and results

Participants underwent cardiac magnetic resonance (CMR) and we extracted CMR biventricular structural and functional metrics using automated pipelines. Multivariate linear regression adjusted for the main cardiovascular risk factors and Two-sample Mendelian Randomization analyses were performed to assess the potential relationship, grouped by heart rate and stratified by sex. Each 10 beats per minute increase in RHR was linked with smaller ventricular structure (lower biventricular end-diastolic volume and end-systolic volume), poorer left ventricular (LV) function (lower LV ejection fraction, global longitude strain and global function index) and unhealthy pattern of LV remodeling (higher values of myocardial contraction fraction), but there is no statistical difference in LV wall thickness. These trends are more pronounced among males and consistent with the causal effect direction of genetic variants interpretation. These observations reflect that RHR has an independent and broad impact on LV remodeling, however, genetically-predicted RHR is not statistically related to heart failure.

Conclusion

We demonstrate higher RHR cause smaller ventricular chamber volume, poorer systolic function and unhealthy cardiac remodeling pattern. Our findings provide effective evidence for the potential mechanism of cardiac remodeling and help to explore the potential scope or benefit of intervention.

Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine

Cardiovascular disease (CVD) is the leading cause of mortality and loss of disability adjusted life years (DALYs) globally. CVDs like Heart Failure (HF) and Atrial Fibrillation (AF) are associated with physical effects on the heart muscles. As a result of the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. Rightful application of artificial intelligence (AI) and machine learning (ML) approaches can lead to new insights into CVDs for providing better personalized treatments with predictive analysis and deep phenotyping. In this study we focused on implementing AI/ML techniques on RNA-seq driven gene-expression data to investigate genes associated with HF, AF, and other CVDs, and predict disease with high accuracy. The study involved generating RNA-seq data derived from the serum of consented CVD patients. Next, we processed the sequenced data using our RNA-seq pipeline and applied GVViZ for gene-disease data annotation and expression analysis. To achieve our research objectives, we developed a new Findable, Accessible, Intelligent, and Reproducible (FAIR) approach that includes a five-level biostatistical evaluation, primarily based on the Random Forest (RF) algorithm. During our AI/ML analysis, we have fitted, trained, and implemented our model to classify and distinguish high-risk CVD patients based on their age, gender, and race. With the successful execution of our model, we predicted the association of highly significant HF, AF, and other CVDs genes with demographic variables.

Association between systemic sclerosis and left ventricle dysfunction: Findings from observational studies

Objectives

Cardiac involvement is common in systemic sclerosis (SSc) patients. In this study, we aimed to systematically evaluate the relationship between SSc and left ventricular dysfunction (LVD), especially the left ventricular diastolic dysfunction, by ultrasound and cardiac magnetic resonance data.

Methods

We searched The Cochrane Library, PubMed and Embase databases collected studies about comparing LVD parameters in SSc patients and controls from establishment to January 2022. Furthermore, we also performed a two-sample MR using summary statistics from genome-wide association studies (GWAS) important LVD parameters, including left ventricular end-diastolic volume (LVEDV), left ventricular mass (LVM) and left ventricular ejection fraction (LVEF).

Results

Our meta-analysis included 31 eligible studies with 1448 SSc patients. According to the results, SSc patients had lower peak of early diastolic flow velocity/peak of late diastolic flow velocity ratio (E/A ratio), E, trans-mitral early filling peak velocity (E'), and left ventricular end-diastolic diameter (LVEDD) compared to controls. The E/E' ratio, A, left ventricular isovolumetric relaxation time (IVRT), deceleration Time (DT) and left atrial (LA) diameter were higher in SSc patients in comparison with controls. Moreover, we observed that the SSc patients had lower LVEF than controls. And in MR analysis, we also found that SSc was causally correlated with LVEF (OR = 0.9966, 95% CI 0.9935-0.998, P = 0.0398). However, unfortunately, there was no significant correlation between SSC and LVM (OR = 1.0048, 95% CI 0.9919-1.0179, P = 0.4661) and LVEDV (LVEDV OR = 0.9976, 95%CI 0.9888-1.0066, P = 0.6019).

Conclusion

SSc patients had diastolic/systolic dysfunction. However, MR analysis cannot confirm the genetic relationship between SSc and LVDD because of insufficient data. More research is needed to confirm the causal relationship between the two.

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Genome-Wide Analysis of Left Ventricular Maximum Wall Thickness in the UK Biobank Cohort Reveals a Shared Genetic Background With Hypertrophic Cardiomyopathy

BACKGROUND

Left ventricular maximum wall thickness (LVMWT) is an important biomarker of left ventricular hypertrophy and provides diagnostic and prognostic information in hypertrophic cardiomyopathy (HCM). Limited information is available on the genetic determinants of LVMWT.

METHODS

We performed a genome-wide association study of LVMWT measured from the cardiovascular magnetic resonance examinations of 42 176 European individuals. We evaluated the genetic relationship between LVMWT and HCM by performing pairwise analysis using the data from the Hypertrophic Cardiomyopathy Registry in which the controls were randomly selected from UK Biobank individuals not included in the cardiovascular magnetic resonance sub-study.

RESULTS

Twenty-one genetic loci were discovered at P<5×10-8. Several novel candidate genes were identified including PROX1, PXN, and PTK2, with known functional roles in myocardial growth and sarcomere organization. The LVMWT genetic risk score is predictive of HCM in the Hypertrophic Cardiomyopathy Registry (odds ratio per SD: 1.18 [95% CI, 1.13-1.23]) with pairwise analyses demonstrating a moderate genetic correlation (rg=0.53) and substantial loci overlap (19/21).

CONCLUSIONS

Our findings provide novel insights into the genetic underpinning of LVMWT and highlight its shared genetic background with HCM, supporting future endeavours to elucidate the genetic etiology of HCM.

Alpha kinase 3 signaling at the M-band maintains sarcomere integrity and proteostasis in striated muscle

Muscle contraction is driven by the molecular machinery of the sarcomere. As phosphorylation is a critical regulator of muscle function, the identification of regulatory kinases is important for understanding sarcomere biology. Pathogenic variants in alpha kinase 3 (ALPK3) cause cardiomyopathy and musculoskeletal disease, but little is known about this atypical kinase. Here we show that ALPK3 is an essential component of the M-band of the sarcomere and define the ALPK3-dependent phosphoproteome. ALPK3 deficiency impaired contractility both in human cardiac organoids and in the hearts of mice harboring a pathogenic truncating Alpk3 variant. ALPK3-dependent phosphopeptides were enriched for sarcomeric components of the M-band and the ubiquitin-binding protein sequestosome-1 (SQSTM1) (also known as p62). Analysis of the ALPK3 interactome confirmed binding to M-band proteins including SQSTM1. In human pluripotent stem cell-derived cardiomyocytes modeling cardiomyopathic ALPK3 mutations, sarcomeric organization and M-band localization of SQSTM1 were abnormal suggesting that this mechanism may underly disease pathogenesis.

PRSet: Pathway-based polygenic risk score analyses and software

Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual's genome-wide risk alleles. This results in a key loss of information about an individual's genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a 'pathway polygenic' paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway -opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine.

Multi-Ethnic Study of Atherosclerosis: Relationship between Left Ventricular Shape at Cardiac MRI and 10-year Outcomes

Background Left ventricular (LV) subclinical remodeling is associated with adverse outcomes and indicates mechanisms of disease development. Standard metrics such as LV mass and volumes may not capture the full range of remodeling. Purpose To quantify the relationship between LV three-dimensional shape at MRI and incident cardiovascular events over 10 years. Materials and Methods In this retrospective study, 5098 participants from the Multi-Ethnic Study of Atherosclerosis who were free of clinical cardiovascular disease underwent cardiac MRI from 2000 to 2002. LV shape models were automatically generated using a machine learning workflow. Event-specific remodeling signatures were computed using partial least squares regression, and random survival forests were used to determine which features were most associated with incident heart failure (HF), coronary heart disease (CHD), and cardiovascular disease (CVD) events over a 10-year follow-up period. The discrimination improvement of adding LV shape to traditional cardiovascular risk factors, coronary artery calcium scores, and N-terminal pro-brain natriuretic peptide levels was assessed using the index of prediction accuracy and time-dependent area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival curves were used to illustrate the ability of remodeling signatures to predict the end points. Results Overall, 4618 participants had sufficient three-dimensional MRI information to generate patient-specific LV models (mean age, 60.6 years ± 9.9 [SD]; 2540 women). Among these participants, 147 had HF, 317 had CHD, and 455 had CVD events. The addition of LV remodeling signatures to traditional cardiovascular risk factors improved the mean AUC for 10-year survival prediction and achieved better performance than LV mass and volumes; HF (AUC, 0.83 ± 0.01 and 0.81 ± 0.01, respectively; P < .05), CHD (AUC, 0.77 ± 0.01 and 0.75 ± 0.01, respectively; P < .05), and CVD (AUC, 0.78 ± 0.0 and 0.76 ± 0.0, respectively; P < .05). Kaplan-Meier analysis demonstrated that participants with high-risk HF remodeling signatures had a 10-year survival rate of 56% compared with 95% for those with low-risk scores. Conclusion Left ventricular event-specific remodeling signatures were more predictive of heart failure, coronary heart disease, and cardiovascular disease events over 10 years than standard mass and volume measures and enable an automatic personalized medicine approach to tracking remodeling. © RSNA, 2022 Online supplemental material is available for this article.

Targeted next-generation sequencing for genetic variants of left ventricular mass status among community-based adults in Taiwan

Background: Left ventricular mass is a highly heritable disease. Previous studies have suggested common genetic variants to be associated with left ventricular mass; however, the roles of rare variants are still unknown. We performed targeted next-generation sequencing using the TruSight Cardio panel, which provides comprehensive coverage of 175 genes with known associations to 17 inherited cardiac conditions. Methods: We conducted next-generation sequencing using the Illumina TruSight Cardiomyopathy Target Genes platform using the 5% and 95% extreme values of left ventricular mass from community-based participants. After removing poor-quality next-generation sequencing subjects, including call rate <98% and Mendelian errors, 144 participants were used for the analysis. We performed downstream analysis, including quality control, alignment, coverage length, and annotation; after setting filtering criteria for depths more than 60, we found a total of 144 samples and 165 target genes for further analysis. Results: Of the 12,287 autosomal variants, most had minor allele frequencies of <1% (rare frequency), and variants had minor allele frequencies ranging from 1% to 5%. In the multi-allele variant analyses, 16 loci in 15 genes were significant using the false discovery rate of less than .1. In addition, gene-based analyses using continuous and binary outcomes showed that three genes (CASQ2, COL5A1, and FXN) remained to be associated with left ventricular mass status. One single-nucleotide polymorphism (rs7538337) was enriched for the CASQ2 gene expressed in aorta artery (p = 4.6 × 10-18), as was another single-nucleotide polymorphism (rs11103536) for the COL5A1 gene expressed in aorta artery (p = 2.0 × 10-9). Among the novel genes discovered, CASQ2, COL5A1, and FXN are within a protein-protein interaction network with known cardiovascular genes. Conclusion: We clearly demonstrated candidate genes to be associated with left ventricular mass. Further studies to characterize the target genes and variants for their functional mechanisms are warranted.

Genetics of BAG3: A Paradigm for Developing Precision Therapies for Dilated Cardiomyopathies

Nonischemic dilated cardiomyopathy is a common form of heart muscle disease in which genetic factors play a critical etiological role. In this regard, both rare disease-causing mutations and common disease-susceptible variants, in the Bcl-2-associated athanogene 3 (BAG3) gene have been reported, highlighting the critical role of BAG3 in cardiomyocytes and in the development of dilated cardiomyopathy. The phenotypic effects of the BAG3 mutations help investigators understand the structure and function of the BAG3 gene. Indeed, we report herein that all of the known pathogenic/likely pathogenic variants affect at least 1 of 3 protein functional domains, ie, the WW domain, the second IPV (Ile-Pro-Val) domain, or the BAG domain, whereas none of the missense nontruncating pathogenic/likely pathogenic variants affect the proline-rich repeat (PXXP) domain. A common variant, p.Cys151Arg, associated with reduced susceptibility to dilated cardiomyopathy demonstrated a significant difference in allele frequencies among diverse human populations, suggesting evolutionary selective pressure. As BAG3-related therapies for heart failure move from the laboratory to the clinic, the ability to provide precision medicine will depend in large part on having a thorough understanding of the potential effects of both common and uncommon genetic variants on these target proteins. The current review article provides a roadmap that investigators can utilize to determine the potential interactions between a patient's genotype, their phenotype, and their response to therapeutic interventions with both gene delivery and small molecules.