A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis

Insights into the Inherited Basis of Valvular Heart Disease

PURPOSE OF REVIEW: Increases in the availability of genetic data and advances in the tools and methods for their analyses have enabled well-powered genetic association studies that have significantly enhanced our understanding of the genetic factors underlying both rare and common valve diseases. Valvular heart diseases, such as congenital valve malformations and degenerative valve lesions, increase the risk of heart failure, arrhythmias, and sudden death. In this review, we provide an updated overview of our current understanding of the genetic mechanisms underlying valvular heart diseases. With a focus on discoveries from the past 5 years, we describe recent insights into genetic risk and underlying biological pathways. RECENT FINDINGS: Recently acquired knowledge around valvular heart disease genetics has provided important insights into novel mechanisms related to disease pathogenesis. Newly identified risk loci associated valvular heart disease mainly regulate the composition of the extracellular matrix, accelerate the endothelial-to-mesenchymal transition, contribute to cilia formation processes, and play roles in lipid metabolism. Large-scale genomic analyses have identified numerous risk loci, genes, and biological pathways associated with degenerative valve disease and congenital valve malformations. Shared risk genes suggest common mechanistic pathways for various valve pathologies. More recent studies have combined cardiac magnetic resonance imaging and machine learning to offer a novel approach for exploring genotype-phenotype relationships regarding valve disease. Progress in the field holds promise for targeted prevention, particularly through the application of polygenic risk scores, and innovative therapies based on the biological mechanisms for predominant forms of valvular heart diseases.

Multimodal Analytical Tools to Enhance Mechanistic Understanding of Aortic Valve Calcification

This review focuses on technologies at the core of calcific aortic valve disease (CAVD) and drug target research advancement, including transcriptomics, proteomics, and molecular imaging. We examine how bulk RNA sequencing and single-cell RNA sequencing have engendered organismal genomes and transcriptomes, promoting the analysis of tissue gene expression profiles and cell subpopulations, respectively. We bring into focus how the field is also largely influenced by increasingly accessible proteome profiling techniques. In unison, global transcriptional and protein expression analyses allow for increased understanding of cellular behavior and pathogenic pathways under pathologic stimuli including stress, inflammation, low-density lipoprotein accumulation, increased calcium and phosphate levels, and vascular injury. We also look at how direct investigation of protein signatures paves the way for identification of targetable pathways for pharmacologic intervention. Here, we note that imaging techniques, once a clinical diagnostic tool for late-stage CAVD, have since been refined to address a clinical need to identify microcalcifications using positron emission tomography/computed tomography and even detect in vivo cellular events indicative of early stage CAVD and map the expression of identified proteins in animal models. Together, these techniques generate a holistic approach to CAVD investigation, with the potential to identify additional novel regulatory pathways.

Integrative genomic analyses identify candidate causal genes for calcific aortic valve stenosis involving tissue-specific regulation

There is currently no medical therapy to prevent calcific aortic valve stenosis (CAVS). Multi-omics approaches could lead to the identification of novel molecular targets. Here, we perform a genome-wide association study (GWAS) meta-analysis including 14,819 cases among 941,863 participants of European ancestry. We report 32 genomic loci, among which 20 are novel. RNA sequencing of 500 human aortic valves highlights an enrichment in expression regulation at these loci and prioritizes candidate causal genes. Homozygous genotype for a risk variant near TWIST1, a gene involved in endothelial-mesenchymal transition, has a profound impact on aortic valve transcriptomics. We identify five genes outside of GWAS loci by combining a transcriptome-wide association study, colocalization, and Mendelian randomization analyses. Using cross-phenotype and phenome-wide approaches, we highlight the role of circulating lipoproteins, blood pressure and inflammation in the disease process. Our findings pave the way for the development of novel therapies for CAVS.

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.

Matrisome and Immune Pathways Contribute to Extreme Vascular Outcomes in Williams-Beuren Syndrome

BACKGROUND

Supravalvar aortic stenosis (SVAS) is a characteristic feature of Williams-Beuren syndrome (WBS). Its severity varies: ~20% of people with Williams-Beuren syndrome have SVAS requiring surgical intervention, whereas ~35% have no appreciable SVAS. The remaining individuals have SVAS of intermediate severity. Little is known about genetic modifiers that contribute to this variability.

METHODS AND RESULTS

We performed genome sequencing on 473 individuals with Williams-Beuren syndrome and developed strategies for modifier discovery in this rare disease population. Approaches include extreme phenotyping and nonsynonymous variant prioritization, followed by gene set enrichment and pathway-level association tests. We next used GTEx v8 and proteomic data sets to verify expression of candidate modifiers in relevant tissues. Finally, we evaluated overlap between the genes/pathways identified here and those ascertained through larger aortic disease/trait genome-wide association studies. We show that SVAS severity in Williams-Beuren syndrome is associated with increased frequency of common and rarer variants in matrisome and immune pathways. Two implicated matrisome genes (ACAN and LTBP4) were uniquely expressed in the aorta. Many genes in the identified pathways were previously reported in genome-wide association studies for aneurysm, bicuspid aortic valve, or aortic size.

CONCLUSIONS

Smaller sample sizes in rare disease studies necessitate new approaches to detect modifiers. Our strategies identified variation in matrisome and immune pathways that are associated with SVAS severity. These findings suggest that, like other aortopathies, SVAS may be influenced by the balance of synthesis and degradation of matrisome proteins. Leveraging multiomic data and results from larger aorta-focused genome-wide association studies may accelerate modifier discovery for rare aortopathies like SVAS.

An ensemble of bioinformatics and machine learning approaches to identify shared breast cancer biomarkers among diverse populations

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.

In vivo proximity proteomics uncovers palmdelphin (PALMD) as a Z-line-associated mitigator of isoproterenol-induced cardiac injury

Z-lines are core ultrastructural organizers of cardiomyocytes that modulate many facets of cardiac pathogenesis. Yet a comprehensive proteomic atlas of Z-line-associated components remain incomplete. Here, we established an adeno-associated virus (AAV)-delivered, cardiomyocyte-specific, proximity-labeling approach to characterize the Z-line proteome in vivo. We found palmdelphin (PALMD) as a novel Z-line-associated protein in both adult murine cardiomyocytes and human pluripotent stem cell-derived cardiomyocytes. Germline and cardiomyocyte-specific palmd knockout mice were grossly normal at baseline but exhibited compromised cardiac hypertrophy and aggravated cardiac injury upon long-term isoproterenol treatment. By contrast, cardiomyocyte-specific PALMD overexpression was sufficient to mitigate isoproterenol-induced cardiac injury. PALMD ablation perturbed transverse tubules (T-tubules) and their association with sarcoplasmic reticulum, which formed the Z-line-associated junctional membrane complex (JMC) essential for calcium handling and cardiac function. These phenotypes were associated with disrupted localization of T-tubule markers caveolin-3 (CAV3) and junctophilin-2 (JPH2) and the reduction of nexilin (NEXN) protein, a crucial Z-line-associated protein that is essential for both Z-line and JMC structures and functions. PALMD was found to interact with NEXN and enhance its protein stability while the Nexn mRNA level was not affected. Together, this study discovered PALMD as a potential target for myocardial protection and highlighted in vivo proximity proteomics as a powerful approach to nominate novel players regulating cardiac pathogenesis.

Highlights

In vivo proximity proteomics uncover novel Z-line components that are undetected in in vitro proximity proteomics in cardiomyocytes.PALMD is a novel Z-line-associated protein that is dispensable for baseline cardiomyocyte function in vivo.PALMD mitigates cardiac dysfunction and myocardial injury after repeated isoproterenol insults.PALMD stabilizes NEXN, an essential Z-line-associated regulator of the junctional membrane complex and cardiac systolic function.

Palmdelphin Deficiency Evokes NF-κB Signaling in Valvular Endothelial Cells and Aggravates Aortic Valvular Remodeling

Palmd-deficient mice of advanced age manifest increased aortic valve peak velocity, thickened aortic valve leaflets, and excessive extracellular matrix deposition, which are key features of calcific aortic valve disease. PALMD is predominantly expressed in endothelial cells of aortic valves, and PALMD-silenced valvular endothelial cells are prone to oscillatory shear stress-induced endothelial-to-mesenchymal transition. Mechanistically, PALMD is associated with TNFAIP3 interaction protein 1, a binding protein of TNFAIP3 and IKBKG in NF-κB signaling. Loss of PALMD impairs TNFAIP3-dependent deubiquitinating activity and promotes the ubiquitination of IKBKG and subsequent NF-κB activation. Adeno-associated virus-mediated PALMD overexpression ameliorates aortic valvular remodeling in mice with calcific aortic valve disease, indicating protection.

Novel Association of the NOTCH Pathway Regulator MIB1 Gene With the Development of Bicuspid Aortic Valve

Importance

Nonsyndromic bicuspid aortic valve (nsBAV) is the most common congenital heart valve malformation. BAV has a heritable component, yet only a few causative genes have been identified; understanding BAV genetics is a key point in developing personalized medicine.

Objective

To identify a new gene for nsBAV.

Design, Setting, and Participants

This was a comprehensive, multicenter, genetic association study based on candidate gene prioritization in a familial cohort followed by rare and common association studies in replication cohorts. Further validation was done using in vivo mice models. Study data were analyzed from October 2019 to October 2022. Three cohorts of patients with BAV were included in the study: (1) the discovery cohort was a large cohort of inherited cases from 29 pedigrees of French and Israeli origin; (2) the replication cohort 1 for rare variants included unrelated sporadic cases from various European ancestries; and (3) replication cohort 2 was a second validation cohort for common variants in unrelated sporadic cases from Europe and the US.

Main Outcomes and Measures

To identify a candidate gene for nsBAV through analysis of familial cases exome sequencing and gene prioritization tools. Replication cohort 1 was searched for rare and predicted deleterious variants and genetic association. Replication cohort 2 was used to investigate the association of common variants with BAV.

Results

A total of 938 patients with BAV were included in this study: 69 (7.4%) in the discovery cohort, 417 (44.5%) in replication cohort 1, and 452 (48.2%) in replication cohort 2. A novel human nsBAV gene, MINDBOMB1 homologue MIB1, was identified. MINDBOMB1 homologue (MIB1) is an E3-ubiquitin ligase essential for NOTCH-signal activation during heart development. In approximately 2% of nsBAV index cases from the discovery and replication 1 cohorts, rare MIB1 variants were detected, predicted to be damaging, and were significantly enriched compared with population-based controls (2% cases vs 0.9% controls; P = .03). In replication cohort 2, MIB1 risk haplotypes significantly associated with nsBAV were identified (permutation test, 1000 repeats; P = .02). Two genetically modified mice models carrying Mib1 variants identified in our cohort showed BAV on a NOTCH1-sensitized genetic background.

Conclusions and Relevance

This genetic association study identified the MIB1 gene as associated with nsBAV. This underscores the crucial role of the NOTCH pathway in the pathophysiology of BAV and its potential as a target for future diagnostic and therapeutic intervention.

Dyslipidemia, inflammation, calcification, and adiposity in aortic stenosis: a genome-wide study

AIMS

Although highly heritable, the genetic etiology of calcific aortic stenosis (AS) remains incompletely understood. The aim of this study was to discover novel genetic contributors to AS and to integrate functional, expression, and cross-phenotype data to identify mechanisms of AS.

METHODS AND RESULTS

A genome-wide meta-analysis of 11.6 million variants in 10 cohorts involving 653 867 European ancestry participants (13 765 cases) was performed. Seventeen loci were associated with AS at P ≤ 5 × 10-8, of which 15 replicated in an independent cohort of 90 828 participants (7111 cases), including CELSR2-SORT1, NLRP6, and SMC2. A genetic risk score comprised of the index variants was associated with AS [odds ratio (OR) per standard deviation, 1.31; 95% confidence interval (CI), 1.26-1.35; P = 2.7 × 10-51] and aortic valve calcium (OR per standard deviation, 1.22; 95% CI, 1.08-1.37; P = 1.4 × 10-3), after adjustment for known risk factors. A phenome-wide association study indicated multiple associations with coronary artery disease, apolipoprotein B, and triglycerides. Mendelian randomization supported a causal role for apolipoprotein B-containing lipoprotein particles in AS (OR per g/L of apolipoprotein B, 3.85; 95% CI, 2.90-5.12; P = 2.1 × 10-20) and replicated previous findings of causality for lipoprotein(a) (OR per natural logarithm, 1.20; 95% CI, 1.17-1.23; P = 4.8 × 10-73) and body mass index (OR per kg/m2, 1.07; 95% CI, 1.05-1.9; P = 1.9 × 10-12). Colocalization analyses using the GTEx database identified a role for differential expression of the genes LPA, SORT1, ACTR2, NOTCH4, IL6R, and FADS.

CONCLUSION

Dyslipidemia, inflammation, calcification, and adiposity play important roles in the etiology of AS, implicating novel treatments and prevention strategies.

Genetics of aortic valve disease

PURPOSE OF REVIEW

Aortic valve disease is a leading global cause of morbidity and mortality, posing an increasing burden on society. Advances in next-generation technologies and disease models over the last decade have further delineated the genetic and molecular factors that might be exploited in development of therapeutics for affected patients. This review describes several advances in the molecular and genetic understanding of AVD, focusing on bicuspid aortic valve (BAV) and calcific aortic valve disease (CAVD).

RECENT FINDINGS

Genomic studies have identified a myriad of genes implicated in the development of BAV, including NOTCH1 , SMAD6 and ADAMTS19 , along with members of the GATA and ROBO gene families. Similarly, several genes associated with the initiation and progression of CAVD, including NOTCH1 , LPA , PALMD , IL6 and FADS1/2 , serve as the launching point for emerging clinical trials.

SUMMARY

These new insights into the genetic contributors of AVD have offered new avenues for translational disease investigation, bridging molecular discoveries to emergent pharmacotherapeutic options. Future studies aimed at uncovering new genetic associations and further defining implicated molecular pathways are fuelling the new wave of drug discovery.

Multiancestry Genome-Wide Association Study of Aortic Stenosis Identifies Multiple Novel Loci in the Million Veteran Program

BACKGROUND

Calcific aortic stenosis (CAS) is the most common valvular heart disease in older adults and has no effective preventive therapies. Genome-wide association studies (GWAS) can identify genes influencing disease and may help prioritize therapeutic targets for CAS.

METHODS

We performed a GWAS and gene association study of 14 451 patients with CAS and 398 544 controls in the Million Veteran Program. Replication was performed in the Million Veteran Program, Penn Medicine Biobank, Mass General Brigham Biobank, BioVU, and BioMe, totaling 12 889 cases and 348 094 controls. Causal genes were prioritized from genome-wide significant variants using polygenic priority score gene localization, expression quantitative trait locus colocalization, and nearest gene methods. CAS genetic architecture was compared with that of atherosclerotic cardiovascular disease. Causal inference for cardiometabolic biomarkers in CAS was performed using Mendelian randomization and genome-wide significant loci were characterized further through phenome-wide association study.

RESULTS

We identified 23 genome-wide significant lead variants in our GWAS representing 17 unique genomic regions. Of the 23 lead variants, 14 were significant in replication, representing 11 unique genomic regions. Five replicated genomic regions were previously known risk loci for CAS (PALMD, TEX41, IL6, LPA, FADS) and 6 were novel (CEP85L, FTO, SLMAP, CELSR2, MECOM, CDAN1). Two novel lead variants were associated in non-White individuals (P<0.05): rs12740374 (CELSR2) in Black and Hispanic individuals and rs1522387 (SLMAP) in Black individuals. Of the 14 replicated lead variants, only 2 (rs10455872 [LPA], rs12740374 [CELSR2]) were also significant in atherosclerotic cardiovascular disease GWAS. In Mendelian randomization, lipoprotein(a) and low-density lipoprotein cholesterol were both associated with CAS, but the association between low-density lipoprotein cholesterol and CAS was attenuated when adjusting for lipoprotein(a). Phenome-wide association study highlighted varying degrees of pleiotropy, including between CAS and obesity at the FTO locus. However, the FTO locus remained associated with CAS after adjusting for body mass index and maintained a significant independent effect on CAS in mediation analysis.

CONCLUSIONS

We performed a multiancestry GWAS in CAS and identified 6 novel genomic regions in the disease. Secondary analyses highlighted the roles of lipid metabolism, inflammation, cellular senescence, and adiposity in the pathobiology of CAS and clarified the shared and differential genetic architectures of CAS with atherosclerotic cardiovascular diseases.

Using multi-tissue transcriptome-wide association study to identify candidate susceptibility genes for respiratory infectious diseases

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms.

Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella.

Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p &lt; 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p &lt; 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p &lt; 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p &lt; 0.05.

Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases.

Network-Guided Multiomic Mapping of Aortic Valve Calcification

Despite devastating clinical sequelae of calcific aortic valve disease that range from left ventricular remodeling to arrhythmias, heart failure, and early death, the molecular insights into disease initiation and progression are limited and pharmacotherapies remain unavailable. The pathobiology of calcific aortic valve disease is complex and comprehensive studies are challenging valvular calcification is heterogeneous and occurs preferentially on the aortic surface, along a fibrocalcific spectrum. Here, we review efforts to study (epi-)genomic, transcriptomic, proteomic, and metabolomic aspects of aortic valve calcification in combination with network medicine-/systems biology-based strategies to integrate multilayered omics datasets and prioritize druggable targets for experimental validation studies. Ultimately, such holistic approach efforts may open therapeutic avenues that go beyond invasive and costly valve replacement therapy.

Calcific aortic valve disease: mechanisms, prevention and treatment

Calcific aortic valve disease (CAVD) is the most common disorder affecting heart valves and is characterized by thickening, fibrosis and mineralization of the aortic valve leaflets. Analyses of surgically explanted aortic valve leaflets have shown that dystrophic mineralization and osteogenic transition of valve interstitial cells co-occur with neovascularization, microhaemorrhage and abnormal production of extracellular matrix. Age and congenital bicuspid aortic valve morphology are important and unalterable risk factors for CAVD, whereas additional risk is conferred by elevated blood pressure and plasma lipoprotein(a) levels and the presence of obesity and diabetes mellitus, which are modifiable factors. Genetic and molecular studies have identified that the NOTCH, WNT-β-catenin and myocardin signalling pathways are involved in the control and commitment of valvular cells to a fibrocalcific lineage. Complex interactions between valve endothelial and interstitial cells and immune cells promote the remodelling of aortic valve leaflets and the development of CAVD. Although no medical therapy is effective for reducing or preventing the progression of CAVD, studies have started to identify actionable targets.

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.

Transcriptome-wide association study identifies novel genes associated with bone mineral density and lean body mass in children

OBJECTIVE

To identify novel candidate genes whose expression is associated with bone mineral density (BMD) and body lean mass (LM) in children.

METHODS

A tissue-specific transcriptome-wide association study (TWAS) was conducted utilizing a large-scale genome-wide association study (GWAS) dataset associated with BMD and LM and involving 10,414 participants. The measurement of BMD and LM phenotypes was made based on total-body dual-energy X-ray absorptiometry (TB-DXA) scans. TWAS was conducted by using FUSION software. Reference panels for muscle skeleton (MS), peripheral blood (NBL) and whole blood (YBL) were used for TWAS analysis. Functional enrichment and protein-protein interaction (PPI) analyses of the genes identified by TWAS were performed by using the online tool Metascape ( http://metascape.org ).

RESULTS

For BMD, we identified 174 genes with P < 0.05, such as IKZF1 (P = 1.46 × 10^-9) and CHKB (P = 8.31 × 10^-7). For LM, we identified 208 genes with P < 0.05, such as COPS5 (P = 3.03 × 10^-12) and MRPS33 (P = 5.45 × 10^-10). Gene ontology (GO) enrichment analysis of the BMD-associated genes revealed 200 GO terms, such as protein catabolic process (Log P = -5.09) and steroid hormone-mediated signaling pathway (Log P = -3.13). GO enrichment analysis of the LM-associated genes detected 287 GO terms, such as the apoptotic signaling pathway (Log P = -8.08) and lipid storage (Log P = -3.55).

CONCLUSION

This study identified several candidate genes for BMD and LM in children, providing novel clues to the genetic mechanisms underlying the development of childhood BMD and LM.

Abnormally elevated EZH2-mediated H3K27me3 enhances osteogenesis in aortic valve interstitial cells by inhibiting SOCS3 expression

BACKGROUND AND AIMS

The osteogenic transition of aortic valve interstitial cells (AVICs) plays a critical role for the progression of calcific aortic valve disease (CAVD). Enhancer of zeste homolog 2 (EZH2) is an important methyltransferase for histone H3 Lys27 (H3K27) that has been found to be involved in osteogenesis. Here, we investigated the effect and mechanism of EZH2 in CAVD progression.

METHODS

High throughout mRNA sequencing, qRT-PCR and immunoblot were performed to screen differentially expressed genes in non-CAVD and CAVD aortic valves. To investigate the role of EZH2 and SOCS3 in osteogenesis, AVICs were treated with siRNA, adenovirus and specific inhibitors, then osteogenic markers and mineralized deposits were examined. In vivo, the morphology and function of aortic valves were investigated by HE stain and echocardiography in ApoE^-/- mice fed a long-term western diet (WD).

RESULTS

We discovered that EZH2 was upregulated and SOCS3 was downregulated in calcified aortic valves. In AVICs, inhibition or silencing of EZH2 attenuated the osteogenic responses. On the other hand, demethylases inhibitor (GSK-J4) enhanced osteogenic transition of AVICs. Moreover, SOCS3 knockdown enhanced the expression of osteogenic markers, while SOCS3 overexpression suppressed osteogenesis and calcification. The chromatin immunoprecipitation and restored experiments indicated that EZH2 directly targeted SOCS3 to promote osteogenic responses of AVICs. In vivo, treatment with EZH2 inhibitor through intraperitoneal injection attenuated aortic valve thickening, calcification and dysfunction induced by WD.

CONCLUSIONS

Collectively, we found that EZH2-mediated H3K27me3 enhanced osteogenesis and microcalcification of AVICs via inhibiting SOCS3 expression, which provides potential targets for future therapeutic interventions of CAVD.

Multi-omics of in vitro aortic valve calcification

Heart valve calcification is an active cellular and molecular process that partly remains unknown. Osteogenic differentiation of valve interstitial cells (VIC) is a central mechanism in calcific aortic valve disease (CAVD). Studying mechanisms in CAVD progression is clearly needed. In this study, we compared molecular mechanisms of osteogenic differentiation of human VIC isolated from healthy donors or patients with CAVD by RNA-seq transcriptomics in early timepoint (48 h) and by shotgun proteomics at later timepoint (10th day). Bioinformatic analysis revealed genes and pathways involved in the regulation of VIC osteogenic differentiation. We found a high amount of stage-specific differentially expressed genes and good accordance between transcriptomic and proteomic data. Functional annotation of differentially expressed proteins revealed that osteogenic differentiation of VIC involved many signaling cascades such as: PI3K-Akt, MAPK, Ras, TNF signaling pathways. Wnt, FoxO, and HIF-1 signaling pathways were modulated only at the early timepoint and thus probably involved in the commitment of VIC to osteogenic differentiation. We also observed a significant shift of some metabolic pathways in the early stage of VIC osteogenic differentiation. Lentiviral overexpression of one of the most upregulated genes (ZBTB16, PLZF) increased calcification of VIC after osteogenic stimulation. Analysis with qPCR and shotgun proteomics suggested a proosteogenic role of ZBTB16 in the early stages of osteogenic differentiation.

Cellular Senescence, Aging and Non-Aging Processes in Calcified Aortic Valve Stenosis: From Bench-Side to Bedside

Aortic valve stenosis (AS) is the most common valvular heart disease. The incidence of AS increases with age, however, a significant proportion of elderly people have no significant AS, indicating that both aging and nonaging pathways are involved in the pathomechanism of AS. Age-related and stress-induced cellular senescence accompanied by further active processes represent the key elements of AS pathomechanism. The early stage of aortic valve degeneration involves dysfunction and disruption of the valvular endothelium due to cellular senescence and mechanical stress on blood flow. These cells are replaced by circulating progenitor cells, but in an age-dependent decelerating manner. When endothelial denudation is no longer replaced by progenitor cells, the path opens for focal lipid deposition, initiating subsequent oxidation, inflammation and micromineralisation. Later stages of AS feature a complex active process with extracellular matrix remodeling, fibrosis and calcification. Echocardiography is the gold standard method for diagnosing aortic valve disease, although computed tomography and cardiac magnetic resonance are useful additional imaging methods. To date, no medical treatment has been proven to halt the progression of AS. Elucidation of differences and similarities between vascular and valvular calcification pathomechanisms may help to find effective medical therapy and reduce the increasing health burden of the disease.

IL6 gene polymorphism association with calcific aortic valve stenosis and influence on serum levels of interleukin-6

Aortic valve stenosis is the most frequent valve disease in developed countries and its prevalence will increase with population aging. There is still no pharmaceutical treatment nor biomarker to determine the susceptibility to develop aortic stenosis. Therefore, we analyzed the association of polymorphisms in risk loci with calcific aortic stenosis. Patients with aortic valve disease were genotyped for PALMD rs6702619, LPA rs10455872, and IL6 rs1800795 polymorphisms and circulating levels of interleukin-6 (IL-6) were measured. Calcium content of leaflets obtained in valve replacement surgeries was determined by micro-computed tomography. In the genotyping of 578 individuals, we found significant association between PALMD and IL6 polymorphisms and aortic stenosis in patients with tricuspid aortic valve, independently of other potentially confounding variables such as age and dyslipidemia. There was no association of these polymorphisms with valve calcium content, but this value correlated with the mean aortic pressure gradient (r = 0.44; P < 0.001). The CC genotype of IL6 polymorphism was associated with higher levels of serum IL-6 compared to other genotypes (23.5 vs. 10.5 pg/ml, respectively; P = 0.029). Therefore, patients carrying the CC genotype of IL6 rs1800795 polymorphism present higher levels of circulating IL-6 and this could contribute to the severity of the aortic valve stenosis. Our results agree with the identification of IL6 as a locus risk for stenosis and also with the intervention of this cytokine in aortic valve calcification. A more exhaustive follow-up of those patients carrying risk genotypes is therefore recommended.

Integrated proteomic and metabolomic profile analyses of cardiac valves revealed molecular mechanisms and targets in calcific aortic valve disease

Background

This study aimed to define changes in the metabolic and protein profiles of patients with calcific aortic valve disease (CAVD).

Methods and results

We analyzed cardiac valve samples of patients with and without (control) CAVD (n = 24 per group) using untargeted metabolomics and tandem mass tag-based quantitative proteomics. Significantly different metabolites and proteins between the CAVD and control groups were screened; then, functional enrichment was analyzed. We analyzed co-expressed differential metabolites and proteins, and constructed a metabolite-protein-pathway network. The expression of key proteins was validated using western blotting. Differential analysis identified 229 metabolites in CAVD among which, 2-aminophenol, hydroxykynurenine, erythritol, carnosine, and choline were the top five. Proteomic analysis identified 549 differentially expressed proteins in CAVD, most of which were localized in the nuclear, cytoplasmic, extracellular, and plasma membranes. Levels of selenium binding protein 1 (SELENBP1) positively correlated with multiple metabolites. Adenosine triphosphate-binding cassette transporters, starch and sucrose metabolism, hypoxia-inducible factor 1 (HIF-1) signaling, and purine metabolism were key pathways in the network. Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), calcium²⁺/calmodulin-dependent protein kinase II delta (CAMK2D), and ATP binding cassette subfamily a member 8 (ABCA8) were identified as hub proteins in the metabolite-protein-pathway network as they interacted with ADP, glucose 6-phosphate, choline, and other proteins. Western blotting confirmed that ENPP1 was upregulated, whereas ABCA8 and CAMK2D were downregulated in CAVD samples.

Conclusion

The metabolic and protein profiles of cardiac valves from patients with CAVD significantly changed. The present findings provide a holistic view of the molecular mechanisms underlying CAVD that may lead to the development of novel diagnostic biomarkers and therapeutic targets to treat CAVD.

A transcriptome-wide association study provides new insights into the etiology of osteoarthritis

Background

Osteoarthritis (OA) is a common clinical disease caused by a variety of factors, including genetic variants. Although genome-wide association studies (GWAS) have been performed to elucidate the genetic basis of OA, some loci of risk located in noncoding regions of the genome have been neglected. Therefore, we integrated multiple data types to detect the genetic component of gene expression in OA patients through transcriptome-wide association studies (TWAS) and summary-data-based Mendelian randomization (SMR) analysis.

Methods

TWAS was performed by integrating the larger GWAS summary-data for OA (n=30,727 cases, n=297,191 controls) and 2 expression weight sets (muscle-skeletal tissue and whole blood). Colocalization analysis, conditional analysis, and fine-mapping analysis were also conducted. A broad description of the identified associations was obtained. In addition, a causal relationship between certain risk genes and OA was identified with SMR.

Results

New significant genome-wide associations were found, including on chromosome 1q36.12 (rs1555024, P=4.24E-07) near the ASAP3 and TCEA3 genes, on chromosome 17q24.2 (rs2521348, P=1.01E-06) near the ABCA9 gene, on chromosome 20q11.22 (rs224331, P=8.17E-09) near the UQCC1 and MYH7B genes, and on chromosome 21q21.3 (rs2832155, P=5.39E-08) near the RWDD2B gene. In addition, SMR results exhibited that upregulated UQCC1 and downregulated ASAP3 were associated with OA development and both had a significant causal relationship with OA.

Conclusions

We revealed some novel OA-associated genes and risk loci by integrating multiple data types and analysis methods, thus providing new clues for the study of genetic mechanisms of OA.

Spatially Distinct Genetic Determinants of Aortic Dimensions Influence Risks of Aneurysm and Stenosis

BACKGROUND

The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane.

OBJECTIVES

We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta.

METHODS

Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence.

RESULTS

A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10-21). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n = 426,502; mean HR: 1.08 per SD; 95% CI: 1.03-1.12; P = 5 × 10-6).

CONCLUSIONS

We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets.

Transcriptome-wide association study identifies PSMB9 as a susceptibility gene for coal workers' pneumoconiosis

Coal workers' pneumoconiosis (CWP) is a type of typical occupational lung disease caused by prolonged inhalation of coal mine dust. The individuals' different genetic background may underlie their different susceptibility to develop pneumoconiosis, even under the same exposure level. This study aimed to identify susceptibility genes associated with CWP. Based on our previous genome-wide association study (GWAS, 202 CWP cases vs. 198 controls) and gene expression data obtained by analyzing human lungs and whole blood from the Genotype-Tissue Expression (GTEx) Portal, a transcriptome-wide association study (TWAS) was applied to identify CWP risk-related genes. Luciferase report gene assay, qRT-PCR, Western blot, immunofluorescence assay, and TUNEL assay were conducted to explore the potential role of the candidate gene in CWP. Proteasome 20S subunit beta 9 (PSMB9) was identified as a strong risk-related gene of CWP in both lungs and whole blood (Lungs: P_TWAS  = 4.22 × 10^-4 ; Whole blood: P_TWAS  = 2.11 × 10^-4 ). Single nucleotide polymorphisms (SNPs) rs2071480 and rs1351383, which locate in the promoter region and the first intron of the PSMB9 gene, were in high linkage disequilibrium (LD, r²  = 0.98) with the best GWAS SNP rs4713600 (G>T, OR = 0.55, 95% CI: 0.42-0.74, P = 6.86 × 10^-5 ). Both rs2071480 and rs1351383 significantly enhanced the transcriptional activity of PSMB9. Functional experiments revealed that silica exposure remarkably reduced the PSMB9 expression and caused cell apoptosis, while overexpression of PSMB9 markedly abolished silica-induced cell apoptosis. We here identified PSMB9 as a novel susceptibility gene for CWP and provided important insights into the further exploration of the CWP pathogenesis.

Genetic analysis of right heart structure and function in 40,000 people

Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10-13) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function.

Sex-Specific Cell Types and Molecular Pathways Indicate Fibro-Calcific Aortic Valve Stenosis

Background

Aortic stenosis (AS) is the most common valve disorder characterized by fibro-calcific remodeling of leaflets. Recent evidence indicated that there is a sex-related difference in AS development and progression. Fibrotic remodeling is peculiar in women’s aortic valves, while men’s leaflets are more calcified. Our study aimed to assess aortic valve fibrosis (AVF) in a severe AS cohort using non-invasive diagnostic tools and determine whether sex-specific pathological pathways and cell types are associated with severe AS.

Materials and Methods

We have included 28 men and 28 women matched for age with severe AS who underwent echocardiography and cardiac contrast-enhanced computed tomography (CT) before intervention. The calcium and fibrosis volumes were assessed and quantified using the ImageJ thresholding method, indexed calcium and fibrosis volume were calculated by dividing the volume by the aortic annular area. For a deeper understanding of molecular mechanisms characterizing AS disorder, differentially expressed genes and functional inferences between women and men’s aortic valves were carried out on a publicly available microarray-based gene expression dataset (GSE102249). Cell types enrichment analysis in stenotic aortic valve tissues was used to reconstruct the sex-specific cellular composition of stenotic aortic valves.

Results

In agreement with the literature, our CT quantifications showed that women had significantly lower aortic valve calcium content compared to men, while fibrotic tissue composition was significantly higher in women than men. The expression profiles of human stenotic aortic valves confirm sex-dependent processes. Pro-fibrotic processes were prevalent in women, while pro-inflammatory ones, linked to the immune response system, were enhanced in men. Cell-type enrichment analysis showed that mesenchymal cells were over-represented in AS valves of women, whereas signatures for monocytes, macrophages, T and B cells were enriched men ones.

Conclusions

Our data provide the basis that the fibro-calcific process of the aortic valve is sex-specific, both at gene expression and cell type level. The quantification of aortic valve fibrosis by CT could make it possible to perform population-based studies and non-invasive assessment of novel therapies to reduce or halt sex-related calcific aortic valve stenosis (CAVS) progression, acting in an optimal window of opportunity early in the course of the disease.

Low Level of PALMD Contributes to the Metastasis of Uveal Melanoma

Uveal melanoma (UM) is a highly aggressive disease. There is an urgent need to develop the metastasis prediction markers of UM. This study aims to detect the key role of PALMD in UM metastasis. Transcriptome sequencing results of 2 sets of UM metastatic samples (GSE22138 and GSE156877) were downloaded from the Gene Expression Omnibus (GEO), and 18 overlapping differentially expressed genes were screened out, including PALMD. PALMD was significantly underexpressed in metastatic UM tissue. Low expression of PALMD was associated with poor prognosis in UM patients. The decreased expression of PALMD promoted the invasion and migration of 92-1 and Mel270 cells, while the high expression of PALMD inhibited the invasion and migration of UM cells. Furthermore, the levels of matrix metallopeptidase (MMP) 2 and MMP9 increased after transfection of siRNAs specifically targeting PALMD, whereas the levels of MMP2 and MMP9 were decreased after PALMD overexpression. However, PALMD did not affect the proliferation of UM cells. In addition, ZNF263 promoted the transcription of PALMD through the putative binding sequence using the JASPAR database, luciferase reporter gene analysis and chromatin immunoprecipitation assay. In summary, the expression of PALMD regulated by ZNF263 plays an important role in UM metastasis.

PALMD regulates aortic valve calcification via altered glycolysis and NF-κB-mediated inflammation

Recent genome-wide association and transcriptome-wide association studies have identified an association between the PALMD locus, encoding palmdelphin, a protein involved in myoblast differentiation, and calcific aortic valve disease (CAVD). Nevertheless, the function and underlying mechanisms of PALMD in CAVD remain unclear. We herein investigated whether and how PALMD affects the pathogenesis of CAVD using clinical samples from CAVD patients and a human valve interstitial cell (hVIC) in vitro calcification model. We showed that PALMD was upregulated in calcified regions of human aortic valves and calcified hVICs. Furthermore, silencing of PALMD reduced hVIC in vitro calcification, osteogenic differentiation, and apoptosis, whereas overexpression of PALMD had the opposite effect. RNA-Seq of PALMD-depleted hVICs revealed that silencing of PALMD reduced glycolysis and nuclear factor-κB (NF-κB)–mediated inflammation in hVICs and attenuated tumor necrosis factor α–induced monocyte adhesion to hVICs. Having established the role of PALMD in hVIC glycolysis, we examined whether glycolysis itself could regulate hVIC osteogenic differentiation and inflammation. Intriguingly, the inhibition of PFKFB3-mediated glycolysis significantly attenuated osteogenic differentiation and inflammation of hVICs. However, silencing of PFKFB3 inhibited PALMD-induced hVIC inflammation, but not osteogenic differentiation. Finally, we showed that the overexpression of PALMD enhanced hVIC osteogenic differentiation and inflammation, as opposed to glycolysis, through the activation of NF-κB. The present study demonstrates that the genome-wide association– and transcriptome-wide association–identified CAVD risk gene PALMD may promote CAVD development through regulation of glycolysis and NF-κB–mediated inflammation. We propose that targeting PALMD-mediated glycolysis may represent a novel therapeutic strategy for treating CAVD.

Increased Ca2+ influx through CaV1.2 drives aortic valve calcification

Calcific aortic valve disease (CAVD) is heritable as revealed by recent genome wide association studies. While polymorphisms linked to increased expression of CACNA1C, encoding the CaV1.2 L-type voltage-gated Ca2+ channel, and increased Ca2+ signaling are associated with CAVD, whether increased Ca2+ influx through the druggable CaV1.2 is causal for calcific aortic valve disease is unknown. With surgically removed aortic valves from patients, we confirmed the association between increased CaV1.2 expression and CAVD. We extended our studies with a transgenic mouse model that mimics increased CaV1.2 expression in within aortic valve interstitial cells (VICs). In young mice maintained on normal chow, we observed dystrophic valve lesions that mimic changes found in pre-symptomatic CAVD, and showed activation of chondrogenic and osteogenic transcriptional regulators within these valve lesions. Chronic administration of verapamil, a clinically used CaV1.2 antagonist, slowed the progression of lesion development in vivo. Exploiting VIC cultures we demonstrated that increased Ca2+ influx through CaV1.2 drives signaling programs that lead to myofibroblast activation of VICs and upregulation of genes associated with aortic valve calcification. Our data support a causal role for Ca2+ influx through CaV1.2 in CAVD and suggest that early treatment with Ca2+ channel blockers is an effective therapeutic strategy.

Heart Disease and Stroke Statistics-2022 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 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. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

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.

Machine Learning Revealed Ferroptosis Features and a Novel Ferroptosis-Based Classification for Diagnosis in Acute Myocardial Infarction

Acute myocardial infarction (AMI) is a leading cause of death and disability worldwide. Early diagnosis of AMI and interventional treatment can significantly reduce myocardial damage. However, owing to limitations in sensitivity and specificity, existing myocardial markers are not efficient for early identification of AMI. Transcriptome-wide association studies (TWASs) have shown excellent performance in identifying significant gene–trait associations and several cardiovascular diseases (CVDs). Furthermore, ferroptosis is a major driver of ischaemic injury in the heart. However, its specific regulatory mechanisms remain unclear. In this study, we screened three Gene Expression Omnibus (GEO) datasets of peripheral blood samples to assess the efficiency of ferroptosis-related genes (FRGs) for early diagnosis of AMI. To the best of our knowledge, for the first time, TWAS and mRNA expression data were integrated in this study to identify 11 FRGs specifically expressed in the peripheral blood of patients with AMI. Subsequently, using multiple machine learning algorithms, an optimal prediction model for AMI was constructed, which demonstrated satisfactory diagnostic efficiency in the training cohort (area under the curve (AUC) = 0.794) and two external validation cohorts (AUC = 0.745 and 0.711). Our study suggests that FRGs are involved in the progression of AMI, thus providing a new direction for early diagnosis, and offers potential molecular targets for optimal treatment of AMI.

Evaluating Medical Therapy for Calcific Aortic Stenosis: JACC State-of-the-Art Review

Despite numerous promising therapeutic targets, there are no proven medical treatments for calcific aortic stenosis (AS). Multiple stakeholders need to come together and several scientific, operational, and trial design challenges must be addressed to capitalize on the recent and emerging mechanistic insights into this prevalent heart valve disease. This review briefly discusses the pathobiology and most promising pharmacologic targets, screening, diagnosis and progression of AS, identification of subgroups that should be targeted in clinical trials, and the need to elicit the patient voice earlier rather than later in clinical trial design and implementation. Potential trial end points and tools for assessment and approaches to implementation and design of clinical trials are reviewed. The efficiencies and advantages offered by a clinical trial network and platform trial approach are highlighted. The objective is to provide practical guidance that will facilitate a series of trials to identify effective medical therapies for AS resulting in expansion of therapeutic options to complement mechanical solutions for late-stage disease.

Disentangling genetic feature selection and aggregation in transcriptome-wide association studies

The success of transcriptome-wide association studies (TWAS) has led to substantial research towards improving the predictive accuracy of its core component of Genetically Regulated eXpression (GReX). GReX links expression information with genotype and phenotype by playing two roles simultaneously: it acts as both the outcome of the genotype-based predictive models (for predicting expressions) and the linear combination of genotypes (as the predicted expressions) for association tests. From the perspective of machine learning (considering SNPs as features), these are actually two separable steps-feature selection and feature aggregation-which can be independently conducted. In this work, we show that the single approach of GReX limits the adaptability of TWAS methodology and practice. By conducting simulations and real data analysis, we demonstrate that disentangled protocols adapting straightforward approaches for feature selection (e.g., simple marker test) and aggregation (e.g., kernel machines) outperform the standard TWAS protocols that rely on GReX. Our development provides more powerful novel tools for conducting TWAS. More importantly, our characterization of the exact nature of TWAS suggests that, instead of questionably binding two distinct steps into the same statistical form (GReX), methodological research focusing on optimal combinations of feature selection and aggregation approaches will bring higher power to TWAS protocols.

Palmdelphin Regulates Nuclear Resilience to Mechanical Stress in the Endothelium

Supplemental Digital Content is available in the text.

PALMD (palmdelphin) belongs to the family of paralemmin proteins implicated in cytoskeletal regulation. Single nucleotide polymorphisms in the PALMD locus that result in reduced expression are strong risk factors for development of calcific aortic valve stenosis and predict severity of the disease.

webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study

The development of transcriptome-wide association studies (TWAS) has enabled researchers to better identify and interpret causal genes in many diseases. However, there are currently no resources providing a comprehensive listing of gene-disease associations discovered by TWAS from published GWAS summary statistics. TWAS analyses are also difficult to conduct due to the complexity of TWAS software pipelines. To address these issues, we introduce a new resource called webTWAS, which integrates a database of the most comprehensive disease GWAS datasets currently available with credible sets of potential causal genes identified by multiple TWAS software packages. Specifically, a total of 235 064 gene-diseases associations for a wide range of human diseases are prioritized from 1298 high-quality downloadable European GWAS summary statistics. Associations are calculated with seven different statistical models based on three popular and representative TWAS software packages. Users can explore associations at the gene or disease level, and easily search for related studies or diseases using the MeSH disease tree. Since the effects of diseases are highly tissue-specific, webTWAS applies tissue-specific enrichment analysis to identify significant tissues. A user-friendly web server is also available to run custom TWAS analyses on user-provided GWAS summary statistics data. webTWAS is freely available at http://www.webtwas.net.

From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries

Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.

kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes

The power of genotype-phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a set of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast to this, kernel methods such as sequence kernel association test (SKAT) model genotypic and phenotypic variance use various kernel functions that capture genetic similarity between subjects, allowing nonlinear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning and feature aggregation. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS- and kernel-based methods. In this work, we developed a novel method called kernel-based TWAS (kTWAS) that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in Wellcome Trust Case Control Consortium genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).

Lipoprotein Proteomics and Aortic Valve Transcriptomics Identify Biological Pathways Linking Lipoprotein(a) Levels to Aortic Stenosis

Lipoprotein(a) (Lp(a)) is one of the most important risk factors for the development of calcific aortic valve stenosis (CAVS). However, the mechanisms through which Lp(a) causes CAVS are currently unknown. Our objectives were to characterize the Lp(a) proteome and to identify proteins that may be differentially associated with Lp(a) in patients with versus without CAVS. Our second objective was to identify genes that may be differentially regulated by exposure to high versus low Lp(a) levels in explanted aortic valves from patients with CAVS. We isolated Lp(a) from the blood of 21 patients with CAVS and 22 volunteers and performed untargeted label-free analysis of the Lp(a) proteome. We also investigated the transcriptomic signature of calcified aortic valves from patients who underwent aortic valve replacement with high versus low Lp(a) levels (n = 118). Proteins involved in the protein activation cascade, platelet degranulation, leukocyte migration, and response to wounding may be associated with Lp(a) depending on CAVS status. The transcriptomic analysis identified genes involved in cardiac aging, chondrocyte development, and inflammation as potentially influenced by Lp(a). Our multi-omic analyses identified biological pathways through which Lp(a) may cause CAVS, as well as key molecular events that could be triggered by Lp(a) in CAVS development.

Integrating Transcriptome-Wide Association Study and mRNA Expression Profiling Identifies Novel Genes Associated With Osteonecrosis of the Femoral Head

Objective

This study aims to identify novel candidate genes associated with osteonecrosis of the femoral head (ONFH).

Methods

A transcriptome-wide association study (TWAS) was performed by integrating the genome-wide association study dataset of osteonecrosis (ON) in the UK Biobank with pre-computed mRNA expression reference weights of muscle skeleton (MS) and blood. The ON-associated genes identified by TWAS were further subjected to gene ontology (GO) analysis by the DAVID tool. Finally, a trans-omics comparative analysis of TWAS and genome-wide mRNA expression profiling was conducted to identify the common genes and the GO terms shared by both DNA-level TWAS and mRNA-level expression profile for ONFH.

Results

TWAS totally identified 564 genes that were with P_TWAS value <0.05 for MS and blood, such as CBX1 (P_TWAS = 0.0001 for MS), SRPK2 (P_TWAS = 0.0002 for blood), and MYO5A (P_TWAS = 0.0005 for blood). After comparing the genes detected by TWAS with the differentially expressed genes identified by mRNA expression profiling, we detected 59 overlapped genes, such as STEAP4 [P_TWAS = 0.0270, FC (fold change)_mRNA = 7.03], RABEP1 (P_TWAS = 0.010, FC_mRNA = 2.22), and MORC3 (P_TWAS = 0.0053, FC_mRNA = 2.92). The GO analysis of TWAS-identified genes discovered 53 GO terms for ON. Further comparing the GO results of TWAS and mRNA expression profiling identified four overlapped GO terms, including cysteine-type endopeptidase activity (P_TWAS = 0.0006, P_mRNA = 0.0227), extracellular space (P_TWAS = 0.0342, P_mRNA = 0.0012), protein binding (P_TWAS = 0.0112, P_mRNA = 0.0106), and ATP binding (P_TWAS = 0.0464, P_mRNA = 0.0033).

Conclusion

Several ONFH-associated genes and GO terms were identified by integrating TWAS and mRNA expression profiling. It provides novel clues to reveal the pathogenesis of ONFH.

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.

Inflammatory and Biomechanical Drivers of Endothelial-Interstitial Interactions in Calcific Aortic Valve Disease

Calcific aortic valve disease is dramatically increasing in global burden, yet no therapy exists outside of prosthetic replacement. The increasing proportion of younger and more active patients mandates alternative therapies. Studies suggest a window of opportunity for biologically based diagnostics and therapeutics to alleviate or delay calcific aortic valve disease progression. Advancement, however, has been hampered by limited understanding of the complex mechanisms driving calcific aortic valve disease initiation and progression towards clinically relevant interventions.

Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults

As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59×10⁻¹²), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10⁻¹²). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits.

Transcriptome-wide association studies (TWAS) are a type of bioinformatics methodology for identifying complex trait-associated genes. There have been various TWAS methods, each developed under distinct biological assumptions of how genes contribute to complex traits. It is unclear, however, how powerful different TWAS methods are under a variety of biological scenarios. Here, we design an unbiased simulation strategy to evaluate the performance of multiple representative TWAS methods. We find that no one method fits all. Different TWAS methods are advantageous at dealing with different biological scenarios and answering different research questions. Thus, we propose a novel TWAS analytic framework that integrates and maximizes the performance of multiple TWAS methods, and validate its capability using a well-studied real-world dataset. In summary, our study provides quantitative evaluation of method performance to aid future TWAS experimental design and understanding of genes underlying complex human traits. The TWAS evaluation tool is made publicly available.

Artificial Intelligence Models Reveal Sex-Specific Gene Expression in Aortic Valve Calcification

•

Differences in the clinical presentation and physiology of aortic stenosis in men and women complicate the management of the condition.

•

By combining traditional inferential statistics, artificial intelligence predictive modeling, and genetic pathway analysis, one can gain further insight into sex-specific gene expression patterns, potentially driving the valvular phenotype differences between the sexes.

•

Results from this study, implementing a mixed and comprehensive methodological approach, offer a foundation for further exploration of potential drug targets.

Male and female aortic stenosis patients have distinct valvular phenotypes, increasing the complexities in the evaluation of valvular pathophysiology. In this study, we present cutting-edge artificial intelligence analyses of transcriptome-wide array data from stenotic aortic valves to highlight differences in gene expression patterns between the sexes, using both sex-differentiated transcripts and unbiased gene selections. This approach enabled the development of efficient models with high predictive ability and determining the most significant sex-dependent contributors to calcification. In addition, analyses of function-related gene groups revealed enriched fibrotic pathways among female patients. Ultimately, we demonstrate that artificial intelligence models can be used to accurately predict aortic valve calcification by carefully analyzing sex-specific gene transcripts.

Genome- and transcriptome-wide association studies provide insights into the genetic basis of natural variation of seed oil content in Brassica napus

Seed oil content (SOC) is a highly important and complex trait in oil crops. Here, we decipher the genetic basis of natural variation in SOC of Brassica napus by genome- and transcriptome-wide association studies using 505 inbred lines. We mapped reliable quantitative trait loci (QTLs) that control SOC in eight environments, evaluated the effect of each QTL on SOC, and analyzed selection in QTL regions during breeding. Six-hundred and ninety-two genes and four gene modules significantly associated with SOC were identified by analyzing population transcriptomes from seeds. A gene prioritization framework, POCKET (prioritizing the candidate genes by incorporating information on knowledge-based gene sets, effects of variants, genome-wide association studies, and transcriptome-wide association studies), was implemented to determine the causal genes in the QTL regions based on multi-omic datasets. A pair of homologous genes, BnPMT6s, in two QTLs were identified and experimentally demonstrated to negatively regulate SOC. This study provides rich genetic resources for improving SOC and valuable insights toward understanding the complex machinery that directs oil accumulation in the seeds of B. napus and other oil crops.

Enhancer-associated aortic valve stenosis risk locus 1p21.2 alters NFATC2 binding site and promotes fibrogenesis

Genome-wide association studies for calcific aortic valve stenosis (CAVS) previously reported strong signal for noncoding variants at 1p21.2. Previous study using Mendelian randomization suggested that the locus controls the expression of PALMD encoding Palmdelphin (PALMD). However, the molecular regulation at the locus and the impact of PALMD on the biology of the aortic valve is presently unknown. 3D genetic mapping and CRISPR activation identified rs6702619 as being located in a distant-acting enhancer, which controls the expression of PALMD. DNA-binding assay showed that the risk variant modified the DNA shape, which prevented the recruitment of NFATC2 and lowered the expression of PALMD. In co-expression network analysis, a module encompassing PALMD was enriched in actin-based process. Mass spectrometry and functional assessment showed that PALMD is a regulator of actin polymerization. In turn, lower level of PALMD promoted the activation of myocardin-related transcription factor and fibrosis, a key pathobiological process underpinning CAVS.

Heart Disease and Stroke Statistics-2021 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 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. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

RESULTS

Each of the 27 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, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Integrative Analysis of Transcriptome-Wide Association Study and mRNA Expression Profiles Identified Candidate Genes and Pathways Associated With Acute Myocardial Infarction

Background

Acute myocardial infarction (AMI), characterized by an event of myocardial necrosis, is a common cardiac emergency worldwide. However, the genetic mechanisms of AMI remain largely elusive.

Methods

A genome-wide association study dataset of AMI was obtained from the CARDIoGRAMplusC4D project. A transcriptome-wide association study (TWAS) was conducted using the FUSION tool with gene expression references of the left ventricle and whole blood. Significant genes detected by TWAS were subjected to Gene Ontology (GO) enrichment analysis. Then the TWAS results of AMI were integrated with mRNA expression profiling to identify common genes and biological processes. Finally, the identified common genes were validated by RT-qPCR analysis.

Results

TWAS identified 1,050 genes for the left ventricle and 1,079 genes for whole blood. Upon comparison with the mRNA expression profile, 4 common genes were detected, including HP (P_TWAS = 1.22 × 10^–3, P_GEO = 4.98 × 10^–2); CAMP (P_TWAS = 2.48 × 10^–2, P_GEO = 2.36 × 10^–5); TNFAIP6 (P_TWAS = 1.90 × 10^–2, P_GEO = 3.46 × 10^–2); and ARG1 (P_TWAS = 8.35 × 10^–3, P_GEO = 4.93 × 10^–2). Functional enrichment analysis of the genes identified by TWAS detected multiple AMI-associated biological processes, including autophagy of mitochondrion (GO: 0000422) and mitochondrion disassembly (GO: 0061726).

Conclusion

This integrative study of TWAS and mRNA expression profiling identified multiple candidate genes and biological processes for AMI. Our results may provide a fundamental clue for understanding the genetic mechanisms of AMI.

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

The transcriptome-wide association study (TWAS) has emerged as one of several promising techniques for integrating multi-scale 'omics' data into traditional genome-wide association studies (GWAS). Unlike GWAS, which associates phenotypic variance directly with genetic variants, TWAS uses a reference dataset to train a predictive model for gene expressions, which allows it to associate phenotype with variants through the mediating effect of expressions. Although effective, this core innovation of TWAS is poorly understood, since the predictive accuracy of the genotype-expression model is generally low and further bounded by expression heritability. This raises the question: to what degree does the accuracy of the expression model affect the power of TWAS? Furthermore, would replacing predictions with actual, experimentally determined expressions improve power? To answer these questions, we compared the power of GWAS, TWAS, and a hypothetical protocol utilizing real expression data. We derived non-centrality parameters (NCPs) for linear mixed models (LMMs) to enable closed-form calculations of statistical power that do not rely on specific protocol implementations. We examined two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression), and also tested the effects of various properties including expression heritability. Our analysis reveals two main outcomes: (1) Under pleiotropy, the use of predicted expressions in TWAS is superior to actual expressions. This explains why TWAS can function with weak expression models, and shows that TWAS remains relevant even when real expressions are available. (2) GWAS outperforms TWAS when expression heritability is below a threshold of 0.04 under causality, or 0.06 under pleiotropy. Analysis of existing publications suggests that TWAS has been misapplied in place of GWAS, in situations where expression heritability is low.

Association of FADS1/2 Locus Variants and Polyunsaturated Fatty Acids With Aortic Stenosis

Question

Can genetic analysis identify additional causes of aortic stenosis?

Findings

In this genome-wide association study of 44 703 participants, each copy of a FADS1/2 (fatty acid desaturase) genetic variant was associated with a 13% decrease in the odds of aortic stenosis. Results of a meta-analysis with 7 replication cohorts showed genome-wide significance, with biomarker and mendelian randomization analyses implicating elevated ω-6 fatty acid levels as having a potentially causal association with aortic valve calcium and aortic stenosis.

Meaning

These findings demonstrate that the FADS1/2 locus and fatty acid biosynthesis are associated with aortic stenosis and should be examined further for their potential as therapeutic targets.

Importance

Aortic stenosis (AS) has no approved medical treatment. Identifying etiological pathways for AS could identify pharmacological targets.

Objective

To identify novel genetic loci and pathways associated with AS.

Design, Setting, and Participants

This genome-wide association study used a case-control design to evaluate 44 703 participants (3469 cases of AS) of self-reported European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort (from January 1, 1996, to December 31, 2015). Replication was performed in 7 other cohorts totaling 256 926 participants (5926 cases of AS), with additional analyses performed in 6942 participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Follow-up biomarker analyses with aortic valve calcium (AVC) were also performed. Data were analyzed from May 1, 2017, to December 5, 2019.

Exposures

Genetic variants (615 643 variants) and polyunsaturated fatty acids (ω-6 and ω-3) measured in blood samples.

Main Outcomes and Measures

Aortic stenosis and aortic valve replacement defined by electronic health records, surgical records, or echocardiography and the presence of AVC measured by computed tomography.

Results

The mean (SD) age of the 44 703 GERA participants was 69.7 (8.4) years, and 22 019 (49.3%) were men. The rs174547 variant at the FADS1/2 locus was associated with AS (odds ratio [OR] per C allele, 0.88; 95% CI, 0.83-0.93; P = 3.0 × 10⁻⁶), with genome-wide significance after meta-analysis with 7 replication cohorts totaling 312 118 individuals (9395 cases of AS) (OR, 0.91; 95% CI, 0.88-0.94; P = 2.5 × 10⁻⁸). A consistent association with AVC was also observed (OR, 0.91; 95% CI, 0.83-0.99; P = .03). A higher ratio of arachidonic acid to linoleic acid was associated with AVC (OR per SD of the natural logarithm, 1.19; 95% CI, 1.09-1.30; P = 6.6 × 10⁻⁵). In mendelian randomization, increased FADS1 liver expression and arachidonic acid were associated with AS (OR per unit of normalized expression, 1.31 [95% CI, 1.17-1.48; P = 7.4 × 10⁻⁶]; OR per 5–percentage point increase in arachidonic acid for AVC, 1.23 [95% CI, 1.01-1.49; P = .04]; OR per 5–percentage point increase in arachidonic acid for AS, 1.08 [95% CI, 1.04-1.13; P = 4.1 × 10⁻⁴]).

Conclusions and Relevance

Variation at the FADS1/2 locus was associated with AS and AVC. Findings from biomarker measurements and mendelian randomization appear to link ω-6 fatty acid biosynthesis to AS, which may represent a therapeutic target.