High heritability of ascending aortic diameter and trans-ancestry prediction of thoracic aortic disease

The 9p21.3 coronary artery disease risk locus drives vascular smooth muscle cells to an osteochondrogenic state

Genome-wide association studies have identified common genetic variants at ~400 human genomic loci linked to coronary artery disease (CAD) susceptibility. Among these genomic regions, the most impactful is the 9p21.3 CAD risk locus, which spans a 60 kb gene desert and encompasses ~80 SNPs in high linkage disequilibrium. Despite nearly two decades since its discovery, the functional mechanism of this genomic region remains incompletely resolved. To investigate the transcriptional gene programs mediated by 9p21.3 risk locus, we applied a model of induced pluripotent stem cells (iPSCs) from risk and non-risk donors at 9p21.3, as well as isogenic lines with a full haplotype deletion. Upon differentiation to vascular smooth muscle cells (VSMC), single-cell transcriptomic profiling demonstrated iPSC-VSMC phenotypes resembling those from native human coronary arteries, confirming the robustness of this model. Remarkably, our analyses revealed that VSMCs harboring the 9p21.3 risk haplotype preferentially adopt an osteochondrogenic state. Importantly, we identified LIMCH1 and CRABP1 as signature genes critical for defining this transcriptional program. Our study provides new insights into the mechanism at the 9p21.3 risk locus and defines its role in driving a disease-prone transcriptional state in VSMCs.

Contributions of Germline and Somatic Mosaic Genetics to Thoracic Aortic Aneurysms in Nonsyndromic Individuals

BACKGROUND

Thoracic aortic aneurysm (TAA) is associated with significant morbidity and mortality. Although individuals with family histories of TAA often undergo clinical molecular genetic testing, adults with nonsyndromic TAA are not typically evaluated for genetic causes. We sought to understand the genetic contribution of both germline and somatic mosaic variants in a cohort of adult individuals with nonsyndromic TAA at a single center.

METHODS AND RESULTS

One hundred eighty-one consecutive patients <60 years who presented with nonsyndromic TAA at the Massachusetts General Hospital underwent deep (>500×) targeted sequencing across 114 candidate genes associated with TAA and its related functional pathways. Samples from 354 age- and sex-matched individuals without TAA were also sequenced, with a 2:1 matching. We found significant enrichments for germline (odds ratio [OR], 2.44, P=4.6×10-6 [95% CI, 1.67-3.58]) and also somatic mosaic variants (OR, 4.71, P=0.026 [95% CI, 1.20-18.43]) between individuals with and without TAA. Likely genetic causes were present in 24% with nonsyndromic TAA, of which 21% arose from germline variants and 3% from somatic mosaic alleles. The 3 most frequently mutated genes in our cohort were FLNA (encoding Filamin A), NOTCH3 (encoding Notch receptor 3), and FBN1 (encoding Fibrillin-1). There was increased frequency of both missense and loss of function variants in TAA individuals.

CONCLUSIONS

Likely contributory dominant acting genetic variants were found in almost one quarter of nonsyndromic adults with TAA. Our findings suggest a more extensive genetic architecture to TAA than expected and that genetic testing may improve the care and clinical management of adults with nonsyndromic TAA.

Diagnosis and treatment of cardiovascular disease in patients with heritable connective tissue disorders or heritable thoracic aortic diseases

Patients with heritable connective tissue disorders (HCTDs), represented by Marfan syndrome, can develop fatal aortic and/or arterial complications before age 50. Therefore, accurate diagnosis, appropriate medical treatment, and early prophylactic surgical treatment of aortic and arterial lesions are essential to improve prognosis. Patients with HCTDs generally present with specific physical features due to connective tissue abnormalities, while some patients with heritable thoracic aortic diseases (HTADs) have few distinctive physical characteristics. The development of genetic testing has made it possible to provide accurate diagnoses for patients with HCTDs/HTADs. This review provides an overview of the diagnosis and treatment of HCTDs/HTADs, including current evidence on cardiovascular interventions for this population.

Genetics and etiology of congenital heart disease

Congenital heart disease (CHD) is the most common severe birth anomaly, affecting almost 1% of infants. Most CHD is genetic, but only 40% of patients have an identifiable genetic risk factor for CHD. Chromosomal variation contributes significantly to CHD but is not readily amenable to biological follow-up due to the number of affected genes and lack of evolutionary synteny. The first CHD genes were implicated in extended families with syndromic CHD based on the segregation of risk alleles in affected family members. These have been complemented by more CHD gene discoveries in large-scale cohort studies. However, fewer than half of the 440 estimated human CHD risk genes have been identified, and the molecular mechanisms underlying CHD genetics remains incompletely understood. Therefore, model organisms and cell-based models are essential tools for improving our understanding of cardiac development and CHD genetic risk. Recent advances in genome editing, cell-specific genetic manipulation of model organisms, and differentiation of human induced pluripotent stem cells have recently enabled the characterization of developmental stages. In this chapter, we will summarize the latest studies in CHD genetics and the strengths of various study methodologies. We identify opportunities for future work that will continue to further CHD knowledge and ultimately enable better diagnosis, prognosis, treatment, and prevention of CHD.

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.

Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease

Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.

Multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci

Genome-wide association studies (GWASs) have identified hundreds of risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWASs, and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotypes to identify quantitative trait loci for expression (eQTLs) and splicing (sQTLs) in coronary arteries from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary artery; 19% exhibited cell-type-specific expression. Colocalization revealed subgroups of eGenes unique to CAD and blood pressure GWAS. Fine-mapping highlighted additional eGenes, including TBX20 and IL5. We also identified sQTLs for 1,690 genes, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing to accurately identify disease-relevant isoform expression. Our work provides a patient-derived coronary artery eQTL resource and exemplifies the need for diverse study populations and multifaceted approaches to characterize gene regulation in disease processes.

Effects of diabetes mellitus and glycemic traits on cardiovascular morpho-functional phenotypes

BACKGROUND

The effects of diabetes on the cardiac and aortic structure and function remain unclear. Detecting and intervening these variations early is crucial for the prevention and management of complications. Cardiovascular magnetic resonance imaging-derived traits are established endophenotypes and serve as precise, early-detection, noninvasive clinical risk biomarkers. We conducted a Mendelian randomization (MR) study to examine the association between two types of diabetes, four glycemic traits, and preclinical endophenotypes of cardiac and aortic structure and function.

METHODS

Independent genetic variants significantly associated with type 1 diabetes, type 2 diabetes, fasting insulin (FIns), fasting glucose (FGlu), 2 h-glucose post-challenge (2hGlu), and glycated hemoglobin (HbA1c) were selected as instrumental variables. The 96 cardiovascular magnetic resonance imaging traits came from six independent genome-wide association studies. These traits serve as preclinical endophenotypes and offer an early indication of the structure and function of the four cardiac chambers and two aortic sections. The primary analysis was performed using MR with the inverse-variance weighted method. Confirmation was achieved through Steiger filtering and testing to determine the causal direction. Sensitivity analyses were conducted using the weighted median, MR-Egger, and MR-PRESSO methods. Additionally, multivariable MR was used to adjust for potential effects associated with body mass index.

RESULTS

Genetic susceptibility to type 1 diabetes was associated with increased ascending aortic distensibility. Conversely, type 2 diabetes showed a correlation with a reduced diameter and areas of the ascending aorta, as well as decreased distensibility of the descending aorta. Genetically predicted higher levels of FGlu and HbA1c were correlated with a decrease in diameter and areas of the ascending aorta. Furthermore, higher 2hGlu levels predominantly showed association with a reduced diameter of both the ascending and descending aorta. Higher FIns levels corresponded to increased regional myocardial-wall thicknesses at end-diastole, global myocardial-wall thickness at end-diastole, and regional peak circumferential strain of the left ventricle.

CONCLUSIONS

This study provides evidence that diabetes and glycemic traits have a causal relationship with cardiac and aortic structural and functional remodeling, highlighting the importance of intensive glucose-lowering for primary prevention of cardiovascular diseases.

Cardiac Molecular Analysis Reveals Aging-Associated Metabolic Alterations Promoting Glycosaminoglycans Accumulation Via Hexosamine Biosynthetic Pathway

Age is a prominent risk factor for cardiometabolic disease, and often leads to heart structural and functional changes. However, precise molecular mechanisms underlying cardiac remodeling and dysfunction resulting from physiological aging per se remain elusive. Understanding these mechanisms requires biological models with optimal translation to humans. Previous research demonstrated that baboons undergo age-related reduction in ejection fraction and increased heart sphericity, mirroring changes observed in humans. The goal of this study was to identify early cardiac molecular alterations that precede functional adaptations, shedding light on the regulation of age-associated changes. We performed unbiased transcriptomics of left ventricle (LV) samples from female baboons aged 7.5-22.1 years (human equivalent ~30-88 years). Weighted-gene correlation network and pathway enrichment analyses were performed to identify potential age-associated mechanisms in LV, with histological validation. Myocardial modules of transcripts negatively associated with age were primarily enriched for cardiac metabolism, including oxidative phosphorylation, tricarboxylic acid cycle, glycolysis, and fatty-acid β-oxidation. Transcripts positively correlated with age suggest upregulation of glucose uptake, pentose phosphate pathway, and hexosamine biosynthetic pathway (HBP), indicating a metabolic shift towards glucose-dependent anabolic pathways. Upregulation of HBP commonly results in increased glycosaminoglycan precursor synthesis. Transcripts involved in glycosaminoglycan synthesis, modification, and intermediate metabolism were also upregulated in older animals, while glycosaminoglycan degradation transcripts were downregulated with age. These alterations would promote glycosaminoglycan accumulation, which was verified histologically. Upregulation of extracellular matrix (ECM)-induced signaling pathways temporally coincided with glycosaminoglycan accumulation. We found a subsequent upregulation of cardiac hypertrophy-related pathways and an increase in cardiomyocyte width. Overall, our findings revealed a transcriptional shift in metabolism from catabolic to anabolic pathways that leads to ECM glycosaminoglycan accumulation through HBP prior to upregulation of transcripts of cardiac hypertrophy-related pathways. This study illuminates cellular mechanisms that precede development of cardiac hypertrophy, providing novel potential targets to remediate age-related cardiac diseases.

Double Heterozygous Pathogenic Variants in the LOX and PKD1 Genes in a 5-Year-Old Patient with Thoracic Aortic Aneurysm and Polycystic Kidney Disease

Familial thoracic aortic aneurysms and dissections may occur as an isolated hereditary trait or as part of connective tissue disorders with Mendelian inheritance, but severe cardiovascular disease in pediatric patients is extremely rare. There is growing knowledge on pathogenic variants causing the disease; however, much of the phenotypic variability and gene-gene interactions remain to be discovered. We present a case report of a 5.5-year-old girl with an aortic aneurysm and concomitant polycystic kidney disease. Whole exome sequencing was performed, followed by family screening by amplicon deep sequencing and diagnostic imaging studies. In the proband, two pathogenic variants were identified: p.Tyr257Ter in the LOX gene inherited from her mother, and p.Thr2977Ile in the PKD1 gene inherited from her father. All adult carriers of either of these variants showed symptoms of aortic disease. We conclude that the coexistence of two independent genetic variants in the proband may be the reason for an early onset of disease.

Using a polygenic score to account for genomic risk factors in a model to detect individuals with dilated ascending thoracic aortas

Background

Ascending thoracic aortic dilation is a complex trait that involves modifiable and non-modifiable risk factors and can lead to thoracic aortic aneurysm and dissection. Clinical risk factors have been shown to predict ascending thoracic aortic diameter. Polygenic scores (PGS) are increasingly used to assess clinical risk for multifactorial diseases. The degree to which a PGS can improve aortic diameter prediction is not known. In this study we tested the extent to which the addition of a PGS to clinical prediction algorithms improves the prediction of aortic diameter.

Methods

The patient cohort comprised 6,790 Penn Medicine Biobank (PMBB) participants with available echocardiography and clinical data linked to genome-wide genotype data. Linear regression models were used to integrate PGS weights derived from a large genome wide association study of thoracic aortic diameter in the UK biobank and were compared to the performance of the standard and a reweighted variation of the recently published AORTA Score.

Results

Cohort participants were 56% male, had a median age of 61 years (IQR 52-70) with a mean ascending aortic diameter of 3.4 cm (SD 0.5). Compared to the AORTA Score which explained 28.4% (95% CI 28.1% to 29.2%) of the variance in aortic diameter, AORTA Score + PGS explained 28.8%, (95% CI 28.1% to 29.6%), the reweighted AORTA score explained 30.4% (95% CI 29.6% to 31.2%), and the reweighted AORTA Score + PGS explained 31.0% (95% CI 30.2% to 31.8%). The addition of a PGS to either the AORTA Score or the reweighted AORTA Score improved model sensitivity for the identifying individuals with a thoracic aortic diameter ≥ 4 cm. The respective areas under the receiver operator characteristic curve for the AORTA Score + PGS (0.771, 95% CI 0.756 to 0.787) and reweighted AORTA Score + PGS (0.785, 95% CI 0.770 to 0.800) were greater than the standard AORTA Score (0.767, 95% CI 0.751 to 0.783) and reweighted AORTA Score (0.780 95% CI 0.765 to 0.795).

Conclusions

We demonstrated that inclusion of a PGS to the AORTA Score results in a small but clinically meaningful performance enhancement. Further investigation is necessary to determine if combining genetic and clinical risk prediction improves outcomes for thoracic aortic disease.

68Ga-labeled WVP peptide as a novel PET probe for molecular biological diagnosis of unstable thoracic aortic aneurysm and early dissection: an animal study

Objective

Type IV collagen (Col-IV) is a prospective biomarker for diagnosing and treating of unstable thoracic aortic aneurysm and dissection (TAAD). This study aims to evaluate the feasibility of ⁶⁸Ga-labeled WVP peptide (⁶⁸Ga-DOTA-WVP) as a novel Col-IV-targeted probe for TAAD biological diagnosis using PET/CT.

Methods

WVP peptide was modified with bifunctional chelator DOTA for ⁶⁸Ga radiolabeling. Immunohistochemical staining was used to evaluate the expression and location of Col-IV and elastin in aortas treated with 3-aminopropionitrile fumarate (BAPN) at different time points (0, 2, and 4 weeks). The imaging performance of ⁶⁸Ga-DOTA-WVP was investigated using Micro-PET/CT in a BAPN-induced TAAD mouse model. The relationship between ⁶⁸Ga-DOTA-WVP uptake in aortic lesions and the serum levels of TAAD-related biomarkers including D-dimer, C-reactive protein (CRP), and serum soluble suppression of tumorigenicity-2 (sST2) was also analyzed.

Results

⁶⁸Ga-DOTA-WVP was readily prepared with high radiochemical purity and stability in vitro. ⁶⁸Ga-DOTA-WVP Micro-PET/CT could detect Col-IV exposure of unstable aneurysms and early dissection in BAPN-induced TAAD mice, but little ⁶⁸Ga-DOTA-WVP uptake was shown in the control group at each imaging time point. The differences of Col-IV expression and distribution of ⁶⁸Ga-DOTA-WVP both in TAAD and control groups further verified the imaging efficiency of ⁶⁸Ga-DOTA-WVP PET/CT. Additionally, a higher sST2 level was found in the imaging positive (n = 14) than the negative (n = 8) group (9.60 ± 1.14 vs. 8.44 ± 0.52, P = 0.014).

Conclusion

⁶⁸Ga-DOTA-WVP could trace the exposure and abnormal deposition of Col-IV in enlarged and early injured aortas, showing a potential for biological diagnosis, whole-body screening, and progression monitoring of TAAD.

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.

Genetic Determinants of the Interventricular Septum Are Linked to Ventricular Septal Defects and Hypertrophic Cardiomyopathy

BACKGROUND

A large proportion of genetic risk remains unexplained for structural heart disease involving the interventricular septum (IVS) including hypertrophic cardiomyopathy and ventricular septal defects. This study sought to develop a reproducible proxy of IVS structure from standard medical imaging, discover novel genetic determinants of IVS structure, and relate these loci to diseases of the IVS, hypertrophic cardiomyopathy, and ventricular septal defect.

METHODS

We estimated the cross-sectional area of the IVS from the 4-chamber view of cardiac magnetic resonance imaging in 32 219 individuals from the UK Biobank which was used as the basis of genome wide association studies and Mendelian randomization.

RESULTS

Measures of IVS cross-sectional area at diastole were a strong proxy for the 3-dimensional volume of the IVS (Pearson r=0.814, P=0.004), and correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. Seven loci with clear genomic consequence and relevance to cardiovascular biology were uncovered by genome wide association studies, most notably a single nucleotide polymorphism in an intron of CDKN1A (rs2376620; β, 7.7 mm2 [95% CI, 5.8-11.0]; P=6.0×10-10), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure (β, 8.4 mm2 [95% CI, 6.3-10.9]; P=4.2×10-14). Mendelian randomization suggested that inheritance of larger IVS cross-sectional area at diastole was strongly associated with hypertrophic cardiomyopathy risk (pIVW=4.6×10-10) while inheritance of smaller IVS cross-sectional area at diastole was associated with risk for ventricular septal defect (pIVW=0.007).

CONCLUSIONS

Automated estimates of cross-sectional area of the IVS supports discovery of novel loci related to cardiac development and Mendelian disease. Inheritance of genetic liability for either small or large IVS, appears to confer risk for ventricular septal defect or hypertrophic cardiomyopathy, respectively. These data suggest that a proportion of risk for structural and congenital heart disease can be localized to the common genetic determinants of size and shape of cardiovascular anatomy.

Comparison of Genes Associated with Thoracic and Abdominal Aortic Aneurysms

Aneurysms impacting the ascending thoracic aorta and the abdominal aorta affect patient populations with distinct clinical characteristics. Through a literature review, this paper compares the genetic associations of ascending thoracic aortic aneurysm (ATAA) with abdominal aortic aneurysms (AAA). Genes related to atherosclerosis, lipid metabolism, and tumor development are associated specifically with sporadic AAA, while genes controlling extracellular matrix (ECM) structure, ECM remodeling, and tumor growth factor β function are associated with both AAA and ATAA. Contractile element genes uniquely predispose to ATAA. Aside from known syndromic connective tissue disease and poly-aneurysmal syndromes (Marfan disease, Loeys-Dietz syndrome, and Ehlers-Danlos syndrome), there is only limited genetic overlap between AAA and ATAA. The rapid advances in genotyping and bioinformatics will elucidate further the various pathways associated with the development of aneurysms affecting various parts of the aorta.

Genetics and mechanisms of thoracic aortic disease

Aortic disease has many forms including aortic aneurysm and dissection, aortic coarctation or abnormalities in aortic function, such as loss of aortic distensibility. Genetic analysis in humans is one of the most important experimental approaches in uncovering disease mechanisms, but the relative infrequency of thoracic aortic disease compared with other cardiovascular conditions such as coronary artery disease has hindered large-scale identification of genetic associations. In the past decade, advances in machine learning technology coupled with large imaging datasets from biobank repositories have facilitated a rapid expansion in our capacity to measure and genotype aortic traits, resulting in the identification of dozens of genetic associations. In this Review, we describe the history of technological advances in genetic discovery and explain how newer technologies such as deep learning can rapidly define aortic traits at scale. Furthermore, we integrate novel genetic observations provided by these advances into our current biological understanding of thoracic aortic disease and describe how these new findings can contribute to strategies to prevent and treat aortic disease.

Integrative multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci

Genome-wide association studies (GWAS) have identified hundreds of genetic risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWAS and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotype information to identify quantitative trait loci (QTL) for gene expression and splicing in coronary arteries obtained from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary arteries and 19% exhibited cell-type-specific expression. Colocalization analysis with GWAS identified subgroups of eGenes unique to CAD and blood pressure. Fine-mapping highlighted additional eGenes of interest, including TBX20 and IL5 . Splicing (s)QTLs for 1,690 genes were also identified, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing events to accurately identify disease-relevant gene expression. Our work provides the first human coronary artery eQTL resource from a patient sample and exemplifies the necessity of diverse study populations and multi-omic approaches to characterize gene regulation in critical disease processes.

Study Design Overview

Relationship Between Ascending Thoracic Aortic Diameter and Blood Pressure: A Mendelian Randomization Study

BACKGROUND

Observational studies identified elevated blood pressure (BP) as a strong risk factor for thoracic aortic dilation, and BP reduction is the primary medical intervention recommended to prevent progression of aortic aneurysms. However, although BP may impact aortic dilation, aortic size may also impact BP. The causal relationship between BP and thoracic aortic size has not been reliably established.

METHODS

Genome-wide association studies summary statistics were obtained for BP and ascending thoracic aortic diameter (AscAoD). Causal effects of BP on AscAoD were estimated using 2-sample Mendelian randomization using a range of pleiotropy-robust methods.

RESULTS

Genetically predicted increased systolic BP, diastolic BP, and mean arterial pressure all significantly associate with higher AscAoD (systolic BP: β estimate, 0.0041 mm/mm Hg [95% CI, 0.0008-0.0074]; P=0.02, diastolic BP: β estimate, 0.0272 mm/mm Hg [95% CI, 0.0224-0.0320]; P<0.001, and mean arterial pressure: β estimate, 0.0168 mm/mm Hg [95% CI, 0.0130-0.0206]; P<0.001). Genetically predicted pulse pressure, meanwhile, had an inverse association with AscAoD (β estimate, -0.0155 mm/mm Hg [95% CI, -0.0213 to -0.0096]; P<0.001). Multivariable Mendelian randomization analyses showed that genetically predicted increased mean arterial pressure and reduced pulse pressure were independently associated with AscAoD. Bidirectional Mendelian randomization demonstrated that genetically predicted AscAoD was inversely associated with pulse pressure (β estimate, -2.0721 mm Hg/mm [95% CI, -3.1137 to -1.0306]; P<0.001) and systolic BP (β estimate, -1.2878 mm Hg/mm [95% CI, -2.3533 to -0.2224]; P=0.02), while directly associated with diastolic BP (0.8203 mm Hg/mm [95% CI, 0.2735-1.3672]; P=0.004).

CONCLUSIONS

BP likely contributes causally to ascending thoracic aortic dilation. Increased AscAoD likely contributes to lower systolic BP and pulse pressure, but not diastolic BP, consistent with the hemodynamic consequences of a reduced aortic diameter.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies

In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the largest collections include over a million samples. In this review, we summarize the main directions in which biobanks aid medical genetics and genomic research, from providing reference allele frequency information to allowing large-scale cross-ancestry meta-analyses. The largest biobanks greatly vary in the size of the collection, and the amount of available phenotype and genotype data. Nevertheless, all of them are extensively used in genomics, providing a rich resource for genome-wide association analysis, genetic epidemiology, and statistical research into the structure, function, and evolution of the human genome. Recently, multiple research efforts were based on trans-biobank data integration, which increases sample size and allows for the identification of robust genetic associations. We provide prominent examples of such data integration and discuss important caveats which have to be taken into account in trans-biobank research.

Fibromuscular Dysplasia and Abdominal Aortic Aneurysms Are Dimorphic Sex-Specific Diseases With Shared Complex Genetic Architecture

BACKGROUND

The risk of arterial diseases may be elevated among family members of individuals having multifocal fibromuscular dysplasia (FMD). We sought to investigate the risk of arterial diseases in families of individuals with FMD.

METHODS

Family histories for 73 probands with FMD were obtained, which included an analysis of 463 total first-degree relatives focusing on FMD and related arterial disorders. A polygenic risk score for FMD (PRSFMD) was constructed from prior genome-wide association findings of 584 FMD cases and 7139 controls and evaluated for association with an abdominal aortic aneurysm (AAA) in a cohort of 9693 AAA cases and 294 049 controls. A previously published PRSAAA was also assessed among the FMD cases and controls.

RESULTS

Of all first degree relatives of probands, 9.3% were diagnosed with FMD, aneurysms, and dissections. Aneurysmal disease occurred in 60.5% of affected relatives and 5.6% of all relatives. Among 227 female first-degree relatives of probands, 4.8% (11) had FMD, representing a relative risk (RR)FMD of 1.5 ([95% CI, 0.75-2.8]; P=0.19) compared with the estimated population prevalence of 3.3%, though not of statistical significance. Of all fathers of FMD probands, 11% had AAAs resulting in a RRAAA of 2.3 ([95% CI, 1.12-4.6]; P=0.014) compared with population estimates. The PRSFMD was found to be associated with an AAA (odds ratio, 1.03 [95% CI, 1.01-1.05]; P=2.6×10-3), and the PRSAAA was found to be associated with FMD (odds ratio, 1.53 [95% CI, 1.2-1.9]; P=9.0×10-5) as well.

CONCLUSIONS

FMD and AAAs seem to be sex-dimorphic manifestations of a heritable arterial disease with a partially shared complex genetic architecture. Excess risk of having an AAA according to a family history of FMD may justify screening in family members of individuals having FMD.

Development of a Prediction Model for Ascending Aortic Diameter Among Asymptomatic Individuals

IMPORTANCE

Ascending thoracic aortic disease is an important cause of sudden death in the US, yet most aortic aneurysms are identified incidentally.

OBJECTIVE

To develop and validate a clinical score to estimate ascending aortic diameter.

DESIGN, SETTING, AND PARTICIPANTS

Using an ongoing magnetic resonance imaging substudy of the UK Biobank cohort study, which had enrolled participants from 2006 through 2010, score derivation was performed in 30 018 participants and internal validation in an additional 6681. External validation was performed in 1367 participants from the Framingham Heart Study (FHS) offspring cohort who had undergone computed tomography from 2002 through 2005, and in 50 768 individuals who had undergone transthoracic echocardiography in the Community Care Cohort Project, a retrospective hospital-based cohort of longitudinal primary care patients in the Mass General Brigham (MGB) network between 2001-2018.

EXPOSURES

Demographic and clinical variables (11 covariates that would not independently prompt thoracic imaging).

MAIN OUTCOMES AND MEASURES

Ascending aortic diameter was modeled with hierarchical group least absolute shrinkage and selection operator (LASSO) regression. Correlation between estimated and measured diameter and performance for identifying diameter 4.0 cm or greater were assessed.

RESULTS

The 30 018-participant training cohort (52% women), were a median age of 65.1 years (IQR, 58.6-70.6 years). The mean (SD) ascending aortic diameter was 3.04 (0.31) cm for women and 3.32 (0.34) cm for men. A score to estimate ascending aortic diameter explained 28.2% of the variance in aortic diameter in the UK Biobank validation cohort (95% CI, 26.4%-30.0%), 30.8% in the FHS cohort (95% CI, 26.8%-34.9%), and 32.6% in the MGB cohort (95% CI, 31.9%-33.2%). For detecting individuals with an ascending aortic diameter of 4 cm or greater, the score had an area under the receiver operator characteristic curve of 0.770 (95% CI, 0.737-0.803) in the UK Biobank, 0.813 (95% CI, 0.772-0.854) in the FHS, and 0.766 (95% CI, 0.757-0.774) in the MGB cohorts, although the model significantly overestimated or underestimated aortic diameter in external validation. Using a fixed-score threshold of 3.537, 9.7 people in UK Biobank, 1.8 in the FHS, and 4.6 in the MGB cohorts would need imaging to confirm 1 individual with an ascending aortic diameter of 4 cm or greater. The sensitivity at that threshold was 8.9% in the UK Biobank, 11.3% in the FHS, and 18.8% in the MGB cohorts, with specificities of 98.1%, 99.2%, and 96.2%, respectively.

CONCLUSIONS AND RELEVANCE

A prediction model based on common clinically available data was derived and validated to predict ascending aortic diameter. Further research is needed to optimize the prediction model and to determine whether its use is associated with improved outcomes.

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.