Ohyama, Y., Redheuil, A., Kachenoura, N., Ambale Venkatesh, B. & Lima, J. A. C. Imaging insights on the aorta in aging. Circ. Cardiovasc. Imaging 11, e005617 (2018).
Teixido-Tura, G. et al. Aortic biomechanics by magnetic resonance: early markers of aortic disease in Marfan syndrome regardless of aortic dilatation? Int J. Cardiol. 171, 56–61 (2014).
de Wit, A., Vis, K. & Jeremy, R. W. Aortic stiffness in heritable aortopathies: relationship to aneurysm growth rate. Heart Lung Circ. 22, 3–11 (2013).
Nollen, G. J., Groenink, M., Tijssen, J. G., Van Der Wall, E. E. & Mulder, B. J. Aortic stiffness and diameter predict progressive aortic dilatation in patients with Marfan syndrome. Eur. Heart J. 25, 1146–1152 (2004).
Redheuil, A. et al. Proximal aortic distensibility is an independent predictor of all-cause mortality and incident CV events: the MESA study. J. Am. Coll. Cardiol. 64, 2619–2629 (2014).
Mattace-Raso, F. U. et al. Arterial stiffness and risk of coronary heart disease and stroke: the Rotterdam Study. Circulation 113, 657–663 (2006).
Ben-Shlomo, Y. et al. Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects. J. Am. Coll. Cardiol. 63, 636–646 (2014).
Cuspidi, C. et al. Aortic root diameter and risk of cardiovascular events in a general population: data from the PAMELA study. J. Hypertens. 32, 1879–1887 (2014).
Kamimura, D. et al. Increased proximal aortic diameter is associated with risk of cardiovascular events and all-cause mortality in blacks the Jackson Heart Study. J. Am. Heart Assoc. 6, https://doi.org/10.1161/JAHA.116.005005 (2017).
Lam, C. S. et al. Aortic root remodeling and risk of heart failure in the Framingham Heart study. JACC Heart Fail. 1, 79–83 (2013).
de Roos, A., van der Grond, J., Mitchell, G. & Westenberg, J. Magnetic resonance imaging of cardiovascular function and the brain: is dementia a cardiovascular-driven disease. Circulation 135, 2178–2195 (2017).
van Sloten, T. T. et al. Association between arterial stiffness, cerebral small vessel disease and cognitive impairment: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 53, 121–130 (2015).
Qiu, C., Winblad, B., Viitanen, M. & Fratiglioni, L. Pulse pressure and risk of Alzheimer disease in persons aged 75 years and older: a community-based, longitudinal study. Stroke 34, 594–599 (2003).
Raisi-Estabragh, Z. et al. Associations of cognitive performance with cardiovascular magnetic resonance phenotypes in the UK Biobank. Eur. Heart J. Cardiovasc. Imaging, https://doi.org/10.1093/ehjci/jeab075 (2021).
Moroni, F. et al. Cardiovascular disease and brain health: Focus on white matter hyperintensities. Int J. Cardiol. Heart Vasc. 19, 63–69 (2018).
Debette, S., Schilling, S., Duperron, M. G., Larsson, S. C. & Markus, H. S. Clinical significance of magnetic resonance imaging markers of vascular brain injury: a systematic review and meta-analysis. JAMA Neurol. 76, 81–94 (2019).
Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 12, 822–838 (2013).
Debette, S. & Markus, H. S. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341, c3666 (2010).
King, K. S. et al. White matter hyperintensities: use of aortic arch pulse wave velocity to predict volume independent of other cardiovascular risk factors. Radiology 267, 709–717 (2013).
Mitchell, G. F. et al. Arterial stiffness, pressure and flow pulsatility and brain structure and function: the age, gene/environment susceptibility-Reykjavik study. Brain 134, 3398–3407 (2011).
Henskens, L. H. et al. Increased aortic pulse wave velocity is associated with silent cerebral small-vessel disease in hypertensive patients. Hypertension 52, 1120–1126 (2008).
Cocciolone, A. J. et al. Elastin, arterial mechanics, and cardiovascular disease. Am. J. Physiol. Heart Circ. Physiol. 315, H189–H205 (2018).
Duca, L. et al. Matrix ageing and vascular impacts: focus on elastin fragmentation. Cardiovasc. Res. 110, 298–308 (2016).
Ferrucci, L. & Fabbri, E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat. Rev. Cardiol. 15, 505–522 (2018).
Bai, W. et al. Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations. In: (eds Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C. & Fichtinger, G.). Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11073. Springer, Cham. https://doi.org/10.1007/978-3-030-00937-3_67 (2018).
Littlejohns, T. J. et al. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11, 2624 (2020).
Bai, W. et al. A population-based phenome-wide association study of cardiac and aortic structure and function. Nat. Med. 26, 1654–1662 (2020).
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).
Consortium, G. GTEx v8 2021: https://gtexportal.org/home/.
Pirruccello, J. Deep learning enables genetic analysis of the human thoracic aorta. BioRXiV https://doi.org/10.1101/2020.05.12.091934 (2020).
Tcheandjieu, C. High heritability of ascending aortic diameter and multi-ethnic prediction of thoracic aortic disease. MedRXiV https://doi.org/10.1101/2020.05.29.20102335 (2021).
Benjamins, J. W. et al. Genomic insights in ascending aortic size and distensibility. EBioMedicine 75, 103783 (2022).
Volzke, H. et al. Cohort profile: the study of health in Pomerania. Int J. Epidemiol. 40, 294–307 (2011).
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Chen, H. et al. WWP2 regulates pathological cardiac fibrosis by modulating SMAD2 signaling. Nat. Commun. 10, 3616 (2019).
Boucher, P. et al. LRP1 functions as an atheroprotective integrator of TGFbeta and PDFG signals in the vascular wall: implications for Marfan syndrome. PLoS One 2, e448 (2007).
Elbitar, S. et al. Pathogenic variants in THSD4, encoding the ADAMTS-like 6 protein, predispose to inherited thoracic aortic aneurysm. Genet. Med. 23, 111–122 (2021).
Joannes, A. et al. FGF9 and FGF18 in idiopathic pulmonary fibrosis promote survival and migration and inhibit myofibroblast differentiation of human lung fibroblasts in vitro. Am. J. Physiol. Lung Cell Mol. Physiol. 310, L615–629 (2016).
Wu, X. et al. Homocysteine causes vascular endothelial dysfunction by disrupting endoplasmic reticulum redox homeostasis. Redox Biol. 20, 46–59 (2019).
Lang, W. et al. Identification of shared genes between ischemic stroke and Parkinson’s Disease using genome-wide association studies. Front. Neurol. 10, 297 (2019).
Zhang, W. et al. A single-cell transcriptomic landscape of primate arterial aging. Nat. Commun. 11, 2202 (2020).
Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).
Sargurupremraj, M. et al. Cerebral small vessel disease genomics and its implications across the lifespan. Nat. Commun. 11, 6285 (2020).
Tzourio, C., Cohen, A., Lamisse, N., Biousse, V. & Bousser, M. G. Aortic root dilatation in patients with spontaneous cervical artery dissection. Circulation 95, 2351–2353 (1997).
Kozel, B. A. et al. Williams syndrome. Nat. Rev. Dis. Prim. 7, 42 (2021).
Van Maldergem, L. & Loeys, B. in GeneReviews(R) (eds R. A. Pagon et al.) (1993).
Loeys, B. et al. Homozygosity for a missense mutation in fibulin-5 (FBLN5) results in a severe form of cutis laxa. Hum. Mol. Genet. 11, 2113–2118 (2002).
Li, N. et al. Mutations in the histone modifier PRDM6 are associated with isolated nonsyndromic patent ductus arteriosus. Am. J. Hum. Genet. 99, 1000 (2016).
Hiraki, Y. et al. Aortic aneurysm and craniosynostosis in a family with Cantu syndrome. Am. J. Med Genet. A 164A, 231–236 (2014).
Dietz, H. C. et al. Marfan syndrome caused by a recurrent de novo missense mutation in the fibrillin gene. Nature 352, 337–339 (1991).
Walsh, R., Rutland, C., Thomas, R. & Loughna, S. Cardiomyopathy: a systematic review of disease-causing mutations in myosin heavy chain 7 and their phenotypic manifestations. Cardiology 115, 49–60 (2010).
Kirk, E. P. et al. Mutations in cardiac T-box factor gene TBX20 are associated with diverse cardiac pathologies, including defects of septation and valvulogenesis and cardiomyopathy. Am. J. Hum. Genet. 81, 280–291 (2007).
Rooryck, C. et al. Mutations in lectin complement pathway genes COLEC11 and MASP1 cause 3MC syndrome. Nat. Genet. 43, 197–203 (2011).
Guo, D. C. et al. LOX mutations predispose to thoracic aortic aneurysms and dissections. Circ. Res. 118, 928–934 (2016).
Guo, D. C. et al. Genetic variants in LRP1 and ULK4 are associated with acute aortic dissections. Am. J. Hum. Genet. 99, 762–769 (2016).
Pyeritz, R. E. Heritable thoracic aortic disorders. Curr. Opin. Cardiol. 29, 97–102 (2014).
Tadros, R. et al. Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect. Nat. Genet. 53, 128–134 (2021).
MacCarrick, G. et al. Loeys-Dietz syndrome: a primer for diagnosis and management. Genet. Med. 16, 576–587 (2014).
Loeys, B. L. et al. A syndrome of altered cardiovascular, craniofacial, neurocognitive and skeletal development caused by mutations in TGFBR1 or TGFBR2. Nat. Genet. 37, 275–281 (2005).
Loeys, B. & De Paepe, A. New insights in the pathogenesis of aortic aneurysms. Verh. K. Acad. Geneeskd. Belg. 70, 69–84 (2008).
Gallo, E. M. et al. Angiotensin II-dependent TGF-beta signaling contributes to Loeys-Dietz syndrome vascular pathogenesis. J. Clin. Invest 124, 448–460 (2014).
Duan, C. & Xu, Q. Roles of insulin-like growth factor (IGF) binding proteins in regulating IGF actions. Gen. Comp. Endocrinol. 142, 44–52 (2005).
von der Thusen, J. H. et al. IGF-1 has plaque-stabilizing effects in atherosclerosis by altering vascular smooth muscle cell phenotype. Am. J. Pathol. 178, 924–934 (2011).
Lei, Y. et al. Metformin targets multiple signaling pathways in cancer. Chin. J. Cancer 36, 17 (2017).
Fujimura, N. et al. Metformin treatment status and abdominal aortic aneurysm disease progression. J. Vasc. Surg. 64, 46–54 e48 (2016).
Lareyre, F. & Raffort, J. Metformin to limit abdominal aortic aneurysm expansion: time for clinical trials. Eur. J. Vasc. Endovasc. Surg. 61, 1030 (2021).
Lopes-Ramos, C. M. et al. Sex differences in gene expression and regulatory networks across 29 human tissues. Cell Rep. 31, 107795 (2020).
Nethononda, R. M. et al. Gender specific patterns of age-related decline in aortic stiffness: a cardiovascular magnetic resonance study including normal ranges. J. Cardiovasc. Magn. Reson. 17, 20 (2015).
Davis, E. C. Elastic lamina growth in the developing mouse aorta. J. Histochem. Cytochem. 43, 1115–1123 (1995).
Wahart, A. et al. Role of elastin peptides and elastin receptor complex in metabolic and cardiovascular diseases. FEBS J. https://doi.org/10.1111/febs.14836 (2019).
Urban, Z. et al. Connection between elastin haploinsufficiency and increased cell proliferation in patients with supravalvular aortic stenosis and Williams-Beuren syndrome. Am. J. Hum. Genet. 71, 30–44 (2002).
Papke, C. L. & Yanagisawa, H. Fibulin-4 and fibulin-5 in elastogenesis and beyond: Insights from mouse and human studies. Matrix Biol. 37, 142–149 (2014).
Nead, K. T. et al. Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals. Hum. Mol. Genet. 24, 3582–3594 (2015).
Consortium, C. A. D. et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45, 25–33 (2013).
Baxter, B. T. et al. Abdominal aortic aneurysms are associated with altered matrix proteins of the nonaneurysmal aortic segments. J. Vasc. Surg. 19, 797–802 (1994).
International Consortium for Blood Pressure Genome-Wide Association, S. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).
O’Rourke, M. F. & Safar, M. E. Relationship between aortic stiffening and microvascular disease in brain and kidney: cause and logic of therapy. Hypertension 46, 200–204 (2005).
Lacolley, P., Regnault, V., Segers, P. & Laurent, S. Vascular smooth muscle cells and arterial stiffening: relevance in development, aging, and disease. Physiol. Rev. 97, 1555–1617 (2017).
Jefferson, A. L. et al. Higher aortic stiffness is related to lower cerebral blood flow and preserved cerebrovascular reactivity in older adults. Circulation 138, 1951–1962 (2018).
Coronary Artery Disease Genetics, C. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat. Genet. 43, 339–344 (2011).
Beaudoin, M. et al. Myocardial infarction-associated SNP at 6p24 interferes with MEF2 binding and associates with PHACTR1 expression levels in human coronary arteries. Arterioscler. Thromb. Vasc. Biol. 35, 1472–1479 (2015).
Anttila, V. et al. Genome-wide meta-analysis identifies new susceptibility loci for migraine. Nat. Genet. 45, 912–917 (2013).
Kiando, S. R. et al. PHACTR1 is a genetic susceptibility locus for fibromuscular dysplasia supporting its complex genetic pattern of inheritance. PLoS Genet. 12, e1006367 (2016).
Debette, S. et al. Common variation in PHACTR1 is associated with susceptibility to cervical artery dissection. Nat. Genet. 47, 78–83 (2015).
Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).
Munafo, M. R., Tilling, K., Taylor, A. E., Evans, D. M. & Davey Smith, G. Collider scope: when selection bias can substantially influence observed associations. Int J. Epidemiol. 47, 226–235 (2018).
Petersen, S. E. et al. UK Biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson 18, 8 (2016).
Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
Visscher, P. M. et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Kalra, G. et al. Biological insights from multi-omic analysis of 31 genomic risk loci for adult hearing difficulty. PLoS Genet. 16, e1009025 (2020).
Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).
Altman, D. G. & Bland, J. M. Interaction revisited: the difference between two estimates. BMJ 326, 219 (2003).
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).
Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res 5, 2122 (2016).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Burgess, S., Foley, C. N., Allara, E., Staley, J. R. & Howson, J. M. M. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat. Commun. 11, 376 (2020).
Burgess, S., Small, D. S. & Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res. 26, 2333–2355 (2017).
Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).
Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32, 377–389 (2017).
Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).
Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J. Epidemiol. 47, 1264–1278 (2018).
Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J. Epidemiol. 44, 512–525 (2015).
Sanderson, E., Spiller, W. & Bowden, J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Stat. Med. 40, 5434–5452 (2021).
Sanderson, E., Davey Smith, G., Windmeijer, F. & Bowden, J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J. Epidemiol. 48, 713–727 (2019).
Wu, P. et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med. Inf. 7, e14325 (2019).
Shi, H., Mancuso, N., Spendlove, S. & Pasaniuc, B. Local genetic correlation gives insights into the shared genetic architecture of complex traits. Am. J. Hum. Genet. 101, 737–751 (2017).
Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).
Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).
Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016).
Lohner, V. et al. Incidental findings on 3 T neuroimaging: cross-sectional observations from the population-based Rhineland Study. Neuroradiology 64, 503–512 (2022).
Kamnitsas, K. et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017).
Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).
Coors, A. et al. Polygenic risk scores for schizophrenia are associated with oculomotor endophenotypes. Psychol. Med. 1–9, (2021).