Editorial: Vascular aging through understanding of inherited basis of aortic disease

Age estimation based on only one of the aging imaging or blood markers often fails to reflect the complexity of aging and can give suboptimal assessments of biological age. An individual's genetic predisposition establishes a lifelong baseline risk trajectory that can be altered by clinical, environmental, lifestyle and socioeconomic factors, guiding pharmacotherapy and lifestyle modifications. Conventional cardiovascular risk assessment tools and calculators use chronologic (biologic) age as the main driver of the cardiovascular disease burden. In clinical practice this assessment is further utilized for shared decisionmaking regarding initiation or postponing pharmacological and non-pharmacological interventions. However, such uniformed approach to cardiovascular prevention using traditional risk factors results in underdiagnosis and undertreatment in the large population of the younger individuals and overtreatment in the elderly population.Aorta-specific DNA methylation profiling, post-translational histone modifications, and noncoding RNAs can be used as potential tools to gain new insights into the pathogenesis of aortopathies and risk stratification. 1,2 Further, aorta-specific modulation of chromatin with an efficient and specific delivery system as well as reprograming of a pathologic epigenetic landscape can serve as a potential therapeutic target, when approaching arterial aging. It has been shown that the same area accumulated at a younger age, compared with older age, resulted in a greater risk increase, emphasizing the importance of optimal risk factor control starting early in life. Coronary artery calcium, carotid plaque, and arterial stiffness have been proposed to be used as estimates of an arterial age in adults. However, predictive value of these risk modifiers localized to a single vascular bed lacks specificity when taken in isolation, calling for more precise measures of vascular aging, especially in younger individuals.The epidemiology of the aortic disease in the population is poorly studied. Different segments of the aorta arise from distinct embryonic sources accounting for heterogeneity in segmental gene expression and cell subtype features. There are different models of aortic disease as a result of the loss of contractile function of smooth muscle cells and a phenotypic switch towards similar cells of different origins such as mesenchymal cells or myofibroblasts.Emerging research underscores gene-gene and gene-environment interactions impacting the trajectory of vascular aging. 3 The epigenetic regulations, or 'changes in non-DNA sequences,' 4,5 are reversible and dynamically control gene expression. The abnormal methylation status of candidate genes for coronary heart disease, heart failure, hypertension among others can be used as a marker to assess cardiovascular disease progression. 6 In recent years, increasing evidence has accumulated for histone acetylation and miRNA in vascular calcification progress. Targeting epigenetic key enzymes, especially the DNA methyltransferases, histone methyltransferases, histone acetylases, histone deacetylases and their regulated target genes, could represent an attractive new approach to aortic disease and arterial aging in general.This Research Topic presents a collection of articles in the Cardio-Genomics space with an emphasis on the genetic basis and architecture of syndromic and non-syndromic aortic Advances in high-throughput sequencing, chromatin profiling, and transcriptomics continue to uncover insights into vascular homeostasis across ancestral populations (Figure 1). Efforts should be made to expand representation across ethnicities, provide functional validation of molecular findings in longitudinal cohorts. Precision cardio-genomics holds promise not only to improve risk prediction but also to pave the way for novel interventions that target arterial aging at its molecular roots.Bridging systems biology with clinical insights requires a multifaceted approach that involves data integration, computational modeling, and a cross-disciplinary dialogue to set the stage for a new era of a personalised cardiovascular paradigm. Ultimately, the approach in which biological, rather than chronological age is being used aims to transform the medicine by providing a mechanistic, network-based understanding of disease, allowing physicians to select optimal therapeutic regimens for individual patients.