The Genetic Determinants of Aortic Distention

Toward a rational therapeutic for elastin related disease: Key considerations for elastin based regenerative medicine strategies

Elastin is a connective tissue protein, produced from the ELN gene, that provides elasticity and recoil to tissues that stretch, such as the large arteries of the body, lung parenchyma, skin, ligaments and elastic cartilages. It is produced as a soluble monomer, tropoelastin, that when cross-linked in the extracellular space generates a polymer that is extraordinarily stable, with a predicted half-life of >70 years. Although data suggest ongoing elastin transcription, it is rare to see new elastin deposited outside of its tight developmental window. Consequently, elastin-related disease comes about primarily in one of three scenarios: (1) inadequate elastin deposition, (2) production of poor-quality elastic fibers, or (3) increased destruction of previously deposited elastin. By understanding the pathways controlling elastin production and maintenance, we can design new therapeutics to thwart those abnormal processes. In this review, we will summarize the diseases arising from genetic and environmental alteration of elastin (Williams syndrome, supravalvar aortic stenosis, autosomal dominant cutis laxa, and ELN-related vascular and connective tissue dysfunction) and then describe the mechanisms controlling elastin production and maintenance that might be manipulated to generate novel therapeutics aimed at these conditions. We will end by summarizing existing therapeutic strategies targeting these disease mechanisms before outlining future approaches that may better solve the challenges associated with elastin based regenerative medicine.

Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies

AIMS

Understanding determinants of thoracic aortic morphology is crucial for precise diagnostics and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resonance angiography (NC-MRA) data from the epidemiological cross-sectional German National Cohort (NAKO) and to investigate possible determinants of mid-ascending aortic diameter (mid-AAoD).

METHODS AND RESULTS

Deep learning (DL) automatically segmented the thoracic aorta and ascending aortic length, volume, and diameter was extracted from 25 073 NC-MRAs. Statistical analyses investigated relationships between mid-AAoD and demographic factors, hypertension, diabetes, alcohol, and tobacco consumption. Males exhibited significantly larger mid-AAoD than females (M: 35.5 ± 4.8 mm, F: 33.3 ± 4.5 mm). Age and body surface area (BSA) were positively correlated with mid-AAoD (age: male: r²=0.20, P < 0.001, female: r²=0.16, P < 0.001; BSA: male: r²=0.08, P < 0.001, female: r²=0.05, P < 0.001). Hypertensive and diabetic subjects showed higher mid-AAoD (ΔHypertension=2.9±0.5 mm; ΔDiabetes=1.5±0.6 mm). Hypertension was linked to higher mid-AAoD regardless of age and BSA, while diabetes and mid-AAoD were uncorrelated across age-stratified subgroups. Daily alcohol consumption (male: 37.4 ± 5.1 mm, female: 35.0 ± 4.8 mm) and smoking history exceeding 16.5 pack-years (male: 36.6 ± 5.0 mm, female: 33.9 ± 4.3 mm) exhibited the highest mid-AAoD. Causal analysis (Peter-Clark algorithm) suggested that age, BSA, hypertension, and alcohol consumption are possibly causally related to mid-AAoD, while diabetes and smoking are likely spuriously correlated.

CONCLUSION

This study demonstrates the potential of DL and causal analysis for understanding ascending aortic morphology. By disentangling observed correlations using causal analysis, this approach identifies possible causal determinants, such as age, BSA, hypertension, and alcohol consumption. These findings can inform targeted diagnostics and preventive strategies, supporting clinical decision-making for cardiovascular health.

Present and future of aortic risk assessment in patients with heritable thoracic aortic diseases

Heritable thoracic aortic diseases (HTAD) are a group of diverse genetic conditions characterized by an increased risk of aortic complications. The standard surveillance of these patients involves monitoring aortic diameters until a defined threshold is reached, at which point preventive aortic surgery is recommended. However, assessing aortic risk in these patients is far more complex and, in many aspects, remains incompletely understood. Several factors contribute to this complexity, including the diversity and low prevalence of the conditions within HTAD and the limited understanding of the factors influencing the progression of aortic dilation and the advent of acute aortic events. This article reviews current knowledge on clinical, genetic, and imaging factors related to aortic risk in HTAD and explores their potential future roles in improving risk assessment. By advancing our understanding of these factors, we aim to enhance the precision of risk stratification and develop more effective, personalized management strategies for HTAD patients, with the final goal of improving clinical outcomes and quality of life in individuals affected by these genetic disorders.

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

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

The AORTA Gene score for detection and risk stratification of ascending aortic dilation

BACKGROUND AND AIMS

This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone.

METHODS

Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants.

RESULTS

AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%-41.8%) vs. 29.3% (27.0%-31.5%) in UK Biobank, 36.5% (34.4%-38.5%) vs. 32.5% (30.4%-34.5%) in MGB, 41.8% (37.7%-45.9%) vs. 33.0% (28.9%-37.2%) in FHS, and 34.9% (28.8%-41.0%) vs. 28.9% (22.9%-35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049).

CONCLUSIONS

A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%-41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors.

Improved polygenic risk prediction in migraine-first patients

BACKGROUND

Recent meta-analyses estimated 14.6% and 11.2% SNP-based heritability of migraine, compared to twin-heritability estimates of 30-60%. This study aimed to investigate heritability estimates in "migraine-first" individuals, patients for whom G43 (migraine with or without aura) was their first medical diagnosis in their lifetime.

FINDINGS

Using data from the UK Biobank (N = 199,929), genome-wide association studies (GWAS) were conducted on 6,139 migraine-first patients and 193,790 healthy controls. SNP-based heritability was estimated using SumHer, yielding 19.37% (± 0.019) for all SNPs and 21.31% (± 0.019) for HapMap3 variants, substantially surpassing previous estimates. Key risk loci included PRDM16, FHL5, ASTN2, STAT6/LRP1, and SLC24A3, and pathway analyses highlighted retinol metabolism and steroid hormone biosynthesis as important pathways in these patients.

CONCLUSIONS

The findings underscore that excluding comorbidities at onset time can enhance heritability estimates and genetic signal detection, significantly reducing the extent of "missing heritability" in migraine.

Editor-in-Chief's Top Picks From 2023

Each week, I record audio summaries for every paper in JACC, as well as an issue summary. This process has become a true labor of love due to the time they require, but I am motivated by the sheer number of listeners (16M+), and it has allowed me to familiarize myself with every paper that we publish. Thus, I have selected the top 100 papers (Original Investigations, Review Articles, Society Documents, and the Global Burden of Diseases) from distinct specialties each year. In addition to my personal choices, I have included papers that have been the most accessed or downloaded on our websites, as well as those selected by the JACC Editorial Board members. In order to present the full breadth of this important research in a consumable fashion, we will present these abstracts in this issue of JACC, as well as their Central Illustrations∗ and podcasts. The highlights comprise the following sections: Aorta; Basic and Translational Science; Cardiac Failure, Myocarditis, and Pericarditis; Cardiomyopathies and Genetics; Congenital Heart Disease; Coronary, Peripheral, and Structural Interventions; Coronavirus; Health Promotion and Preventive Cardiology; Imaging; Metabolic and Lipid Disorders; Neurovascular Disease and Dementia; Rhythm Disorders and Thromboembolism; and Valvular Heart Disease.1-104 ∗ To view the full manuscript, including the full-sized Central Illustration, please refer to the original publication in JACC.

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.

Sex differences in ascending aortic size reporting and growth on chest computed tomography and magnetic resonance imaging

PURPOSE

Diameter-based guidelines for prophylactic repair of ascending aortic aneurysms have led to routine aortic evaluation in chest imaging. Despite sex differences in aneurysm outcomes, there is little understanding of sex-specific aortic growth rates. Our objective was to evaluate sex-specific temporal changes in radiologist-reported aortic size as well as sex differences in aortic reporting.

METHOD

In this cohort study, we queried radiology reports of chest computed tomography or magnetic resonance imaging at an academic medical center from 1994 to 2022, excluding type A dissection. Aortic diameter was extracted using a custom text-processing algorithm. Growth rates were estimated using mixed-effects modeling with fixed terms for sex, age, and imaging modality, and patient-level random intercepts. Sex, age, and modality were evaluated as predictors of aortic reporting by logistic regression.

RESULTS

This study included 89,863 scans among 46,622 patients (median [interquartile range] age, 64 [52-73]; 22,437 women [48%]). Aortic diameter was recorded in 14% (12,722/89,863 reports). Temporal trends were analyzed in 7194 scans among 1998 patients (age, 68 [60-75]; 677 women [34%]) with ≥2 scans. Aortic growth rate was significantly higher in women (0.22 mm/year [95% confidence interval 0.17-0.28] vs. 0.09 mm/year [0.06-0.13], respectively). Aortic reporting was significantly less common in women (odds ratio, 0.54; 95% CI, 0.52-0.56; p < 0.001).

CONCLUSIONS

While aortic growth rates were small overall, women had over twice the growth rate of men. Aortic dimensions were much less frequently reported in women than men. Sex-specific standardized assessment of aortic measurements may be needed to address sex differences in aneurysm outcomes.

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.

Bicuspid aortic valve and its ascending aortopathy

PURPOSE OF REVIEW

To synthesize and critically assess recent clinical and research advancements in pediatric bicuspid aortic valve (BAV) and its associated aortopathy.

RECENT FINDINGS

In pediatric patients with BAV, progressive aortic dilation (i.e. bicuspid aortopathy) is commonly present and associated with increased risk for aortic aneurysm, dissection, and surgery in adulthood. Ongoing research explores the cause, incidence, and progression of bicuspid aortopathy to promote earlier diagnosis and improve preventive management. Recent findings include: high familial incidence and need for improved familial screening; safety of recreational physical activity in most affected children; potential for medical management to slow aortic growth; feasibility of pediatric registries to evaluate longitudinal outcomes; and potential genetic and hemodynamic biomarkers for disease risk stratification.

SUMMARY

Pediatric bicuspid aortopathy is an important area for investigation and preventive management to improve long-term cardiovascular outcomes. Recent literature promotes familial screening, recreational exercise, medical prophylaxis, registry-based longitudinal evaluation, and continued scientific inquiry.

AORTA Gene: Polygenic prediction improves detection of thoracic aortic aneurysm

Background

Thoracic aortic disease is an important cause of morbidity and mortality in the US, and aortic diameter is a heritable contributor to risk. Could a polygenic prediction of ascending aortic diameter improve detection of aortic aneurysm?

Methods

Deep learning was used to measure ascending thoracic aortic diameter in 49,939 UK Biobank participants. A genome-wide association study (GWAS) was conducted in 39,524 participants and leveraged to build a 1.1 million-variant polygenic score with PRScs-auto. Aortic diameter prediction models were built with the polygenic score ("AORTA Gene") and without it. The models were tested in a held-out set of 4,962 UK Biobank participants and externally validated in 5,469 participants from Mass General Brigham Biobank (MGB), 1,298 from the Framingham Heart Study (FHS), and 610 participants from All of Us.

Results

In each test set, the AORTA Gene model explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.9% (95% CI 37.8-42.0%) vs 29.2% (95% CI 27.1-31.4%) in UK Biobank, 36.5% (95% CI 34.4-38.5%) vs 32.5% (95% CI 30.4-34.5%) in MGB, 41.8% (95% CI 37.7-45.9%) vs 33.0% (95% CI 28.9-37.2%) in FHS, and 34.9% (95% CI 28.8-41.0%) vs 28.9% (95% CI 22.9-35.0%) in All of Us. AORTA Gene had a greater AUROC for identifying diameter ≥4cm in each test set: 0.834 vs 0.765 (P=7.3E-10) in UK Biobank, 0.808 vs 0.767 in MGB (P=4.5E-12), 0.856 vs 0.818 in FHS (P=8.5E-05), and 0.827 vs 0.791 (P=7.8E-03) in All of Us.

Conclusions

Genetic information improved estimation of thoracic aortic diameter when added to clinical risk factors. Larger and more diverse cohorts will be needed to develop more powerful and equitable scores.

Thoracic Aorta: Anatomy and Pathology

The aorta is the largest elastic artery in the human body and is classically divided into two anatomical segments, the thoracic and the abdominal aorta, separated by the diaphragm. The thoracic aorta includes the aortic root, the ascending aorta, the arch, and the descending aorta. The aorta's elastic properties depend on its wall structure, composed of three distinct histologic layers: intima, media, and adventitia. The different aortic segments show different embryological and anatomical features, which account for their different physiological properties and impact the occurrence and natural history of congenital and acquired diseases that develop herein. Diseases of the thoracic aorta may present either as a chronic, often asymptomatic disorder or as acute life-threatening conditions, i.e., acute aortic syndromes, and are usually associated with states that increase wall stress and alter the structure of the aortic wall. This review aims to provide an update on the disease of the thoracic aorta, focusing on the morphological substrates and clinicopathological correlations. Information on anatomy and embryology will also be provided.