Genome-wide association analysis reveals insights into the genetic architecture of right ventricular structure and function

Using Genomics to Develop Personalized Cardiovascular Treatments

Advances in genomic technologies have significantly enhanced our understanding of both monogenic and polygenic etiologies of cardiovascular disease. In this review, we explore how the utilization of genomic information is bringing personalized medicine approaches to the forefront of cardiovascular disease management. We discuss how genomic data can resolve diagnostic uncertainty, support cascade screening, and inform treatment strategies. The role that genome-wide association studies have had in identifying thousands of risk variants for polygenic cardiovascular diseases, and how these insights, harnessed through the development of polygenic risk scores, could advance personalized risk prediction beyond traditional clinical algorithms. We detail how pharmacogenomics approaches leverage genotype information to guide drug selection and mitigate adverse events. Finally, we present the paradigm-shifting approach of gene therapy, which holds the promise of being a curative intervention for cardiovascular conditions.

Evaluating the effects of coffee consumption on the structure and function of the heart from multiple perspectives

Objective

To assess the causal relationship between coffee consumption and cardiac structure and function in elderly European populations using multiple genetic methodologies.

Methods

Leveraging genome-wide association study (GWAS) data from elderly European populations, we conducted linkage disequilibrium score regression (LDSC), two-step Mendelian randomization (MR), and colocalization analyses to investigate genetic associations, causal relationships, and mediating effects among these factors. Robustness of findings was verified through comprehensive sensitivity analyses.

Results

LDSC regression analysis revealed positive genetic correlations between coffee consumption and cardiac parameters, excluding left ventricular (LV) ejection fraction and right ventricular (RV) ejection fraction. MR results demonstrated favorable associations between increased coffee consumption and cardiac parameters. After applying the Bonferroni adjustment to IVW analysis, as coffee consumption increased by each 1-cup/day, LV end-diastolic volume increased (β = 0.128; 95% CI: 0.043-0.212; P = 0.002), an increase in LV end-systolic volume (β = 0.143; 95% CI: 0.053-0.232; P = 0.001), an increase in RV end-diastolic volume (β = 0.200; 95% CI: 0.095-0.305; P < 0.001), and an increase in RV stroke volume (β = 0.209; 95% CI: 0.104-0.313; P < 0.001). Mediation analyses indicated that each 1-cup/day increase in coffee consumption significantly correlated with reduced diastolic blood pressure (DBP) and elevated body mass index (BMI). Notably, higher DBP exhibited inverse associations with ventricular systolic/diastolic functional parameters, whereas increased BMI demonstrated positive associations with these parameters, collectively mitigating age-related ventricular volume loss. No U-shaped associations were detected in linear MR frameworks. Colocalization analyses confirmed shared causal genetic variants between coffee intake and cardiac remodeling phenotypes.

Conclusions

Genetically predicted coffee consumption may counteract age-associated ventricular volume loss in elderly Europeans through dual mediation pathways involving DBP reduction and BMI elevation. These structural adaptations suggest potential cardioprotective mechanisms against senile cardiac atrophy. Future studies should prioritize the integration of coffee consumption into cardiovascular risk assessment frameworks and develop personalized recommendations based on individual health profiles.

Common-variant and rare-variant genetic architecture of heart failure across the allele-frequency spectrum

Heart failure is a complex trait, influenced by environmental and genetic factors, affecting over 30 million individuals worldwide. Here we report common-variant and rare-variant association studies of all-cause heart failure and examine how different classes of genetic variation impact its heritability. We identify 176 common-variant risk loci at genome-wide significance in 2,358,556 individuals and cluster these signals into five broad modules based on pleiotropic associations with anthropomorphic traits/obesity, blood pressure/renal function, atherosclerosis/lipids, immune activity and arrhythmias. In parallel, we uncover exome-wide significant associations for heart failure and rare predicted loss-of-function variants in TTN, MYBPC3, FLNC and BAG3 using exome sequencing of 376,334 individuals. We find that total burden heritability of rare coding variants is highly concentrated in a small set of Mendelian cardiomyopathy genes, while common-variant heritability is diffusely spread throughout the genome. Finally, we show that common-variant background modifies heart failure risk among carriers of rare pathogenic truncating variants in TTN. Together, these findings discern genetic links between dysregulated metabolism and heart failure and highlight a polygenic component to heart failure not captured by current clinical genetic testing.

Cardiac Fibrosis in the Multi-Omics Era: Implications for Heart Failure

Cardiac fibrosis, a hallmark of heart failure and various cardiomyopathies, represents a complex pathological process that has long challenged therapeutic intervention. High-throughput omics technologies have begun revolutionizing our understanding of the molecular mechanisms driving cardiac fibrosis and are providing unprecedented insights into its heterogeneity and progression. This review provides a comprehensive analysis of how techniques-encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics-are providing insight into our understanding of cardiac fibrosis. Genomic studies have identified novel genetic variants and regulatory networks associated with fibrosis susceptibility and progression, and single-cell transcriptomics has unveiled distinct cardiac fibroblast subpopulations with unique molecular signatures. Epigenomic profiling has revealed dynamic chromatin modifications controlling fibroblast activation states, and proteomic analyses have identified novel biomarkers and potential therapeutic targets. Metabolomic studies have uncovered important alterations in cardiac energetics and substrate utilization during fibrotic remodeling. The integration of these multi-omic data sets has led to the identification of previously unrecognized pathogenic mechanisms and potential therapeutic targets, including cell-type-specific interventions and metabolic modulators. We discuss how these advances are driving the development of precision medicine approaches for cardiac fibrosis while highlighting current challenges and future directions in translating multi-omic insights into effective therapeutic strategies. This review provides a systems-level perspective on cardiac fibrosis that may inform the development of more effective, personalized therapeutic approaches for heart failure and related cardiovascular diseases.

Precision Medicine for Pulmonary Vascular Disease: The Future Is Now (2023 Grover Conference Series)

Pulmonary vascular disease is not a single condition; rather it can accompany a variety of pathologies that impact the pulmonary vasculature. Applying precision medicine strategies to better phenotype, diagnose, monitor, and treat pulmonary vascular disease is increasingly possible with the growing accessibility of powerful clinical and research tools. Nevertheless, challenges exist in implementing these tools to optimal effect. The 2023 Grover Conference Series reviewed the research landscape to summarize the current state of the art and provide a better understanding of the application of precision medicine to managing pulmonary vascular disease. In particular, the following aspects were discussed: (1) Clinical phenotypes, (2) genetics, (3) epigenetics, (4) biomarker discovery, (5) application of precision biology to clinical trials, (6) the right ventricle (RV), and (7) integrating precision medicine to clinical care. The present review summarizes the content of these discussions and the prospects for the future.

Novel Insights into Causal Effects of Serum Lipids and Apolipoproteins on Cardiovascular Morpho-Functional Phenotypes

Previous observational studies have explored the association between serum lipids, apolipoproteins, and adverse ventricular/aortic structure and function. However, whether a causal link exists is uncertain. This study employed a two-sample Mendelian randomization (MR), colocalization, reverse, and multivariable MR (MVMR) approach to examine the causal associations among five serum lipids, two apolipoproteins, and 32 cardiac magnetic resonance (CMR) traits. Utilizing single-nucleotide polymorphisms (SNPs) linked to serum lipids and apolipoproteins as instrumental variables. CMR traits from seven independent genome-wide association studies served as preclinical endophenotypes, offering insights into aortic and cardiac structure/function. The primary analysis utilized a random-effects inverse variance method (IVW), followed by sensitivity and validation analyses. In the primary IVW MR analyses, genetically predicted low-density lipoprotein cholesterol (LDL-C) levels were positively correlated with increased descending aorta strain (DAo strain) (β = 0.098; P = 2.69E-07) and ascending aorta strain (AAo strain) (β = 0.079; P = 5.19E-05). Genetically predicted high-density lipoprotein cholesterol (HDL-C) levels were positively correlated with left ventricular radial peak diastolic strain rate (LV-PDSRll) (β = 0.176; P = 2.89E-05) and the left ventricular longitudinal peak diastolic strain rate (LV-PDSRrr) (β = 0.059; P = 2.44E-06), and negatively correlated with left ventricular regional wall thickness (LVRWT). While apolipoprotein B (ApoB) levels were positively correlated with AAo strain (β = 0.076; P = 1.16E-05), DAo strain (β = 0.065; P = 2.77E-05). A shared causal variant was identified to demonstrate the associations of ApoB with AAo strain and DAo strain using colocalization analysis. Sensitivity analyses confirmed the robustness of these associations. Targeting lipid and apolipoprotein levels through interventions may provide novel strategies for the primary prevention of CVDs.

Genetic basis of right and left ventricular heart shape

Heart shape captures variation in cardiac structure beyond traditional phenotypes of mass and volume. Although observational studies have demonstrated associations with cardiometabolic risk factors and diseases, its genetic basis is less understood. We utilised cardiovascular magnetic resonance images from 45,683 UK Biobank participants to construct a heart shape atlas from bi-ventricular end-diastolic surface mesh models through principal component (PC) analysis. Genome-wide association studies were performed on the first 11 PCs that captured 83.6% of shape variance. We identified 43 significant loci, 14 were previously unreported for cardiac traits. Genetically predicted PCs were associated with cardiometabolic diseases. In particular two PCs (2 and 3) linked with more spherical ventricles being associated with increased risk of atrial fibrillation. Our study explores the genetic basis of multidimensional bi-ventricular heart shape using PCA, reporting new loci and biology, as well as polygenic risk scores for exploring genetic relationships of heart shape with cardiometabolic diseases.

Multi-trait association analysis reveals shared genetic loci between Alzheimer's disease and cardiovascular traits

Several cardiovascular traits and diseases co-occur with Alzheimer's disease. We mapped their shared genetic architecture using multi-trait genome-wide association studies. Subsequent fine-mapping and colocalisation highlighted 16 genetic loci associated with both Alzheimer's and cardiovascular diseases. We prioritised rs11786896, which colocalised with Alzheimer's disease, atrial fibrillation and expression of PLEC in the heart left ventricle, and rs7529220, which colocalised with Alzheimer's disease, atrial fibrillation and expression of C1Q family genes. Single-cell RNA-sequencing data, co-expression network and protein-protein interaction analyses provided evidence for different mechanisms of PLEC, which is upregulated in left ventricular endothelium and cardiomyocytes with heart failure and in brain astrocytes with Alzheimer's disease. Similar common mechanisms are implicated for C1Q in heart macrophages with heart failure and in brain microglia with Alzheimer's disease. These findings highlight inflammatory and pleomorphic risk determinants for the co-occurrence of Alzheimer's and cardiovascular diseases and suggest PLEC, C1Q and their interacting proteins as potential therapeutic targets.

Thoracic Aortic Three-Dimensional Geometry

Background

Aortic structure impacts cardiovascular health through multiple mechanisms. Aortic structural degeneration occurs with aging, increasing left ventricular afterload and promoting increased arterial pulsatility and target organ damage. Despite the impact of aortic structure on cardiovascular health, three-dimensional (3D) aortic geometry has not been comprehensively characterized in large populations.

Methods

We segmented the complete thoracic aorta using a deep learning architecture and used morphological image operations to extract multiple aortic geometric phenotypes (AGPs, including diameter, length, curvature, and tortuosity) across various subsegments of the thoracic aorta. We deployed our segmentation approach on imaging scans from 54,241 participants in the UK Biobank and 8,456 participants in the Penn Medicine Biobank.

Conclusion

Our method provides a fully automated approach towards quantifying the three-dimensional structural parameters of the aorta. This approach expands the available phenotypes in two large representative biobanks and will allow large-scale studies to elucidate the biology and clinical consequences of aortic degeneration related to aging and disease states.

Sex-specific Genetic Determinants of Right Ventricular Structure and Function

RATIONALE

While sex differences in right heart phenotypes have been observed, the molecular drivers remain unknown.

OBJECTIVES

To provide biological insights into sex differences in the structure and function of the right ventricle (RV) using common genetic variation.

METHODS

RV phenotypes were obtained from cardiac magnetic resonance imaging in 18,156 women and 16,171 men from the UK Biobank. Observational analyses and sex-stratified genome-wide association studies were performed. Candidate female-specific loci were evaluated against invasively measured cardiac performance in 479 female patients with idiopathic or heritable pulmonary arterial hypertension (PAH), recruited to the UK NIHR BioResource Rare Diseases study.

MEASUREMENTS AND MAIN RESULTS

Sex was associated with differences in RV volumes and ejection fraction in models adjusting for left heart counterparts, blood pressure, lung function and sex hormone levels. Six genome-wide significant loci (13%) revealed heterogeneity of allelic effects between women and men, and significant sex-by-genotype interaction. These included two sex-specific candidate loci present in women only: a locus for RV ejection fraction in BMPR1A and a locus for RV end-systolic volume near DMRT2. Epigenetic data in RV tissue indicate that variation at the BMPR1A locus likely alters transcriptional regulation. In female patients with PAH, a variant located in the promoter of BMPR1A was significantly associated with cardiac index (effect size 0.16 l/min/m2), despite similar RV afterload.

CONCLUSIONS

BMPR1A has emerged as a biologically plausible candidate gene for female-specific genetic determination of RV function, showing associations with cardiac performance under chronically increased afterload in female patients with PAH.

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.

Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction

Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD-spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.

Causal association between serum 25-hydroxyvitamin D levels and right ventricular structure and function: A Mendelian randomization study

BACKGROUND AND AIM

Deficient concentrations of vitamin D have been linked to several cardiovascular conditions, but the causal relationship between serum 25-hydroxyvitamin D (25(OH)D) levels and right ventricular structure and function remains unclear. Mendelian randomization (MR) was employed to inspect this association.

METHODS AND RESULTS

Genetic instrumental variables associated with 25(OH)D levels were acquired from genome-wide association studies (GWAS) analyses. Summary statistics for right ventricular structure and function, including right ventricular end diastolic volume, right ventricular end systolic volume, right ventricular stroke volume, and right ventricular ejection fraction, were acquired from publicly available GWAS datasets. For the primary analysis, the inverse variance weighted (IVW) method was utilized in performing the MR analysis. Additionally, secondly analyses were conducted to estimate the robustness and consistency of the attained conclusions. The MR analysis did not reveal a considerable causal association between serum 25(OH)D levels and right ventricular end diastolic volume (β: 0.112, 95% confident interval [CI]: -0.006 to 0.230, p = 0.063), right ventricular end systolic volume (β: 0.102, 95% CI: -0.021 to 0.226, p = 0.105), right ventricular stroke volume (β: 0.095, 95% CI: -0.018 to 0.207, p = 0.099), or right ventricular ejection fraction (β: -0.005, 95% CI: -0.123 to 0.112, p = 0.928).

CONCLUSIONS

Our findings did not reveal any substantial evidence supporting a causal relationship between serum 25(OH)D levels and the structure and function of the right ventricle. These findings suggest that serum 25(OH)D levels may not directly influence right ventricular parameters assessed.

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.

The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

Utilizing multimodal AI to improve genetic analyses of cardiovascular traits

Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.

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.

HOPX-associated molecular programs control cardiomyocyte cell states underpinning cardiac structure and function

Genomic regulation of cardiomyocyte differentiation is central to heart development and function. This study uses genetic loss-of-function human-induced pluripotent stem cell-derived cardiomyocytes to evaluate the genomic regulatory basis of the non-DNA-binding homeodomain protein HOPX. We show that HOPX interacts with and controls cardiac genes and enhancer networks associated with diverse aspects of heart development. Using perturbation studies in vitro, we define how upstream cell growth and proliferation control HOPX transcription to regulate cardiac gene programs. We then use cell, organoid, and zebrafish regeneration models to demonstrate that HOPX-regulated gene programs control cardiomyocyte function in development and disease. Collectively, this study mechanistically links cell signaling pathways as upstream regulators of HOPX transcription to control gene programs underpinning cardiomyocyte identity and function.

The causality between obstructive sleep apnea and ventricular structure and function: a bidirectional Mendelian randomization study

Background: Multiple observational studies have discovered a substantial link between obstructive sleep apnea (OSA) and ventricular dysfunction. However, conventional observational studies are vulnerable to causal reversal and confounding, making it challenging to infer the causes of effects and their direction. Methods: With the help of a bidirectional, two-sample Mendelian randomization (MR) study, we assessed the potential causality between OSA and left and right ventricular (LV, RV) structure and function. We conducted our analysis utilizing summary data from genome-wide association studies of OSA (16,761 cases and 201,194 controls) in the FinnGen Study, as well as LV (36,041 participants) and RV (29,506 participants) in the UK Biobank cardiovascular magnetic resonance research. The inverse variance weighted (IVW) was selected as the main strategy, with the MR-Egger and weighted median methods serving as supplements. Other methods were employed as sensitivity analysis tools to look at heterogeneity and pleiotropy, including MR-Egger intercept, Cochran Q statistic, MR-PRESSO, and leave-one-out analysis. Results: In the primary IVW analysis, genetically predicted OSA was strongly causative on LV end-diastolic volume (β = 0.114, 95% CI = 0.034-0.194, p = 0.006) and LV stroke volume (β = 0.111, 95% CI = 0.031-0.191, p = 0.007), and genetically predicted LV ejection fraction was linked to an increased risk of OSA (OR = 1.161, 95% CI = 1.029-1.309, p = 0.015). However, there was no connection found between OSA and any RV parameters. Conclusion: Our genetic analysis raises a potential causative link between OSA and ventricular structure and function, which may improve the knowledge of OSA as a risk factor for cardiovascular disease by demonstrating a direct impact on cardiac structure and function.

Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases

High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a non-linear, low-dimensional, disentangled embedding of the data with highly heritable individual components. REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotyping. We apply REGLE to both respiratory and circulatory systems: spirograms which measure lung function and photoplethysmograms (PPG) which measure blood volume changes. Genome-wide association studies on REGLE embeddings identify more genome-wide significant loci than existing methods and replicate known loci for both spirograms and PPG, demonstrating the generality of the framework. Furthermore, these embeddings are associated with overall survival. Finally, we construct a set of PRSs that improve predictive performance of asthma, chronic obstructive pulmonary disease, hypertension, and systolic blood pressure in multiple biobanks. Thus, REGLE embeddings can quantify clinically relevant features that are not currently captured in a standardized or automated way.

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.

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.

Current State and Future of Polygenic Risk Scores in Cardiometabolic Disease: A Scoping Review

A polygenic risk score (PRS) is derived from a genome-wide association study and represents an aggregate of thousands of single-nucleotide polymorphisms that provide a baseline estimate of an individual's genetic risk for a specific disease or trait at birth. However, it remains unclear how PRSs can be used in clinical practice. We provide an overview of the PRSs related to cardiometabolic disease and discuss the evidence supporting their clinical applications and limitations. The Preferred Reporting Items For Systematic Reviews and Meta-Analysis Extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. Across the 4863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to coronary artery disease, followed by hypertension and cerebrovascular disease. Limited ancestral diversity was observed in the study sample populations. Most studies included only individuals of European ancestry. The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies reported an integrated risk model combining a derived PRS and clinical risk tools such as the Framingham Risk Score and Pooled Cohort Equations. The inclusion of a PRS into a clinical risk model tended to improve predictive accuracy consistently. This scoping review is the first of its kind and reports strong evidence for the clinical utility of PRSs in coronary artery disease, hypertension, cerebrovascular disease, and atrial fibrillation. However, most PRSs are generated in cohorts of European ancestry, which likely contributes to a lack of PRS transferability across different ancestral groups. Future prospective studies should focus on further establishing the clinical utility of PRSs and ensuring diversity is incorporated into genome-wide association study cohorts.

Druggable proteins influencing cardiac structure and function: Implications for heart failure therapies and cancer cardiotoxicity

Dysfunction of either the right or left ventricle can lead to heart failure (HF) and subsequent morbidity and mortality. We performed a genome-wide association study (GWAS) of 16 cardiac magnetic resonance (CMR) imaging measurements of biventricular function and structure. Cis-Mendelian randomization (MR) was used to identify plasma proteins associating with CMR traits as well as with any of the following cardiac outcomes: HF, non-ischemic cardiomyopathy, dilated cardiomyopathy (DCM), atrial fibrillation, or coronary heart disease. In total, 33 plasma proteins were prioritized, including repurposing candidates for DCM and/or HF: IL18R (providing indirect evidence for IL18), I17RA, GPC5, LAMC2, PA2GA, CD33, and SLAF7. In addition, 13 of the 25 druggable proteins (52%; 95% confidence interval, 0.31 to 0.72) could be mapped to compounds with known oncological indications or side effects. These findings provide leads to facilitate drug development for cardiac disease and suggest that cardiotoxicities of several cancer treatments might represent mechanism-based adverse effects.

Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models

Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case-control status from high-dimensional raw spirograms and use the model's predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design.

Microvascular and macrovascular complications of type 2 diabetes mellitus: Exome wide association analyses

Background

Type 2 diabetes mellitus (T2DM) is a chronic, metabolic disorder in which concomitant insulin resistance and β-cell impairment lead to hyperglycemia, influenced by genetic and environmental factors. T2DM is associated with long-term complications that have contributed to the burden of morbidity and mortality worldwide. The objective of this manuscript is to conduct an Exome-Wide Association Study (EWAS) on T2DM Emirati individuals to improve our understanding on diabetes-related complications to improve early diagnostic methods and treatment strategies.

Methods

This cross-sectional study recruited 310 Emirati participants that were stratified according to their medically diagnosed diabetes-related complications: diabetic retinopathy, diabetic neuropathy, diabetic nephropathy, and cardiovascular complications. The Illumina's Infinium Exome-24 array was used and 39,840 SNPs remained for analysis after quality control.

Findings

The analysis revealed the associations of various genes with each complication category: 1) diabetic retinopathy was associated to SHANK3 gene in locus 22q13.33 (SNP rs9616915; p=5.18 x10-4), ZSCAN5A gene in locus 19q13.43 (SNP rs7252603; p=7.55 x10-4), and DCP1B gene in locus 12p13.33 (SNPs rs715146, rs1044950, rs113147414, rs34730825; p=7.62 x10-4); 2) diabetic neuropathy was associated to ADH4 gene in locus 4q23 (SNP rs4148883; p=1.23 x10-4), SLC11A1 gene in locus 2q35 (SNP rs17235409; p=1.85 x10-4), and MATN4 gene in locus 20q13.12 (SNP rs2072788; p=2.68 x10-4); 3) diabetic nephropathy was associated to PPP1R3A gene in locus 7q31.1 (SNP rs1799999; p=1.91 x10-4), ZNF136 gene in locus 19p13.2 (SNP rs140861589; p=2.80 x10-4), and HSPA12B gene in locus 20p13 (SNP rs6076550; p=2.86 x10-4); and 4) cardiovascular complications was associated to PCNT gene in locus 21q22.3 (SNPs rs7279204, rs6518289, rs2839227, rs2839223; p=2.18 x10-4,3.04 x10-4,4.51 x10-4,5.22 x10-4 respectively), SEPT14 gene in locus 7p11.2 (SNP rs146350220; p=2.77 x10-4), and WDR73 gene in locus 15q25.2 (SNP rs72750868; p=4.47 x10-4).

Interpretation

We have identified susceptibility loci associated with each category of T2DM-related complications in the Emirati population. Given that only 16% of the markers from the Illumina's Infinium Exome chip passed quality control assessment, this demonstrates that multiple variants were, either, monomorphic in the Arab population or were not genotyped due to the use of a Euro-centric EWAS array that limits the possibility of including targeted ethnic-specific SNPs. Our results suggest the alarming possibility that lack of representation in reference panels could inhibit discovery of functionally important loci associated to T2DM complications. Further effort must be conducted to improve the representation of diverse populations in genotyping and sequencing studies.

Precision and genomic medicine for dilated and hypertrophic cardiomyopathy

Cardiomyopathy develops through an interaction of genetic and environmental factors. The clinical manifestations of both dilated cardiomyopathy and hypertrophic cardiomyopathy are diverse, but genetic testing defines the causative genes in about half of cases and can predict clinical prognosis. It has become clear that cardiomyopathy is caused not only by single rare variants but also by combinations of multiple common variants, and genome-wide genetic research is important for accurate disease risk assessment. Single-cell analysis research aimed at understanding the pathophysiology of cardiomyopathy is progressing rapidly, and it is expected that genomic analysis and single-cell molecular profiling will be combined to contribute to more detailed stratification of cardiomyopathy.

Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure

Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.