Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we used machine learning approaches to quantify cardiovascular ageing from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing was found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identified prescribed medications that were potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.
Background Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous structural phenotypes but there is no systematic framework for classifying morphology or assessing associated risks. In this study we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression for automated patient stratification. Methods An observational, single-centre study enrolled 436 HCM patients (median age 60 years; 28.8% women) with clinical, genetic and imaging data. An independent cohort of 60 HCM patients from Singapore (median age 59 years; 11% women) and a normative reference population from UK Biobank (n = 16,691, mean age 55 years; 52.5% women) with equivalent data were also recruited. We used machine learning to analyse the three dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree. Results The prevalence of pathogenic or likely pathogenic variants for HCM (P/LP) was 24.6%, while 66% were genotype negative. Carriers of P/LP variants had lower left ventricular mass, but greater basal septal hypertrophy, with reduced lifespan (mean follow-up 9.9 years) compared to genotype negative individuals (hazard ratio: 2.66; 95% confidence interval [CI]: 1.42-4.96; P < 0.002). Four main phenotypic branches were identified using unsupervised learning of three dimensional shape: 1) non-sarcomeric hypertrophy with co-existing hypertension; 2) diffuse and basal asymmetric hypertrophy associated with outflow tract obstruction; 3) isolated basal hypertrophy; 4) milder non-obstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for P/LP variants: 2.18 [95% CI: 1.93-2.28, P = 0.0001]). Phenotypic variation and associated risks could be visualised as a continuous distribution across the taxonomic tree. The model was generalisable to an independent cohort (trustworthiness M1: 0.86-0.88). Conclusions We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk and outcomes. This approach will be of value for developing personalized clinical profiles to guide diagnosis, surveillance and intervention in patients with HCM, and improve understanding of the drivers of heterogeneity.
BackgroundStructural changes caused by spinal curvature may impact the organs within the thoracic cage, including the heart. Cardiac abnormalities in patients with idiopathic scoliosis are often studied post-corrective surgery or secondary to diseases. To investigate cardiac structure, function and outcomes in participants with scoliosis, phenotype and imaging data of the UK Biobank (UKB) adult population cohort were analysed.MethodsHospital episode statistics of 502 324 adults were analysed to identify participants with scoliosis. Summary 2D cardiac phenotypes from 39 559 cardiac MRI (CMR) scans were analysed alongside a 3D surface-to-surface (S2S) analysis.ResultsA total of 4095 (0.8%, 1 in 120) UKB participants were identified to have all-cause scoliosis. These participants had an increased lifetime risk of major adverse cardiovascular events (MACEs) (HR=1.45, p<0.001), driven by heart failure (HR=1.58, p<0.001) and atrial fibrillation (HR=1.54, p<0.001). Increased radial and decreased longitudinal peak diastolic strain rates were identified in participants with scoliosis (+0.29, Padj<0.05; −0.25, Padj<0.05; respectively). Cardiac compression of the top and bottom of the heart and decompression of the sides was observed through S2S analysis. Additionally, associations between scoliosis and older age, female sex, heart failure, valve disease, hypercholesterolemia, hypertension and decreased enrolment for CMR were identified.ConclusionThe spinal curvature observed in participants with scoliosis alters the movement of the heart. The association with increased MACE may have clinical implications for whether to undertake surgical correction. This work identifies, in an adult population, evidence for altered cardiac function and an increased lifetime risk of MACE in participants with scoliosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.