ArticleNature communications2023
Environmental and genetic predictors of human cardiovascular ageing.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 51 citations in OpenAlex.
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- Association between particulate matter air pollution and subclinical myocardial fibrosis in structurally normal hearts: a CMR-based study.European heart journal. Cardiovascular Imaging · 2026Article
- Psychosis as a multisystem disorder of aberrant aging.npj aging · 2026Review
- Discovery and characterization of gene-by-environment and epistatic genetic effects in a vertebrate model.Cell genomics · 2026Article
- Machine learning cross-platform proteomic imputation enables protein quality scoring and replication of epidemiological associations.bioRxiv : the preprint server for biology · 2026Article
- Cardiovascular ageing: hallmarks, signaling pathways, diseases and therapeutic targets.Signal transduction and targeted therapy · 2026Review
- Imaging-derived biological age across multiple organs links to mortality and aging-related health outcomes.npj aging · 2026Article
- Electrocardiogram derived heart age models agreement, accuracy and predictive ability in the Tromsø study.npj aging · 2026Article
- A streamlined CMR-derived machine-learning model for estimating cardiovascular biological age: development and validation in the UK-biobank and multi-ethnic study of atherosclerosis.European heart journal. Cardiovascular Imaging · 2026Article
- Automated generation of personalized trajectories of aging phenotypes with DyViA-GAN.Frontiers in aging · 2026Article
- Sex-specific body fat distribution predicts cardiovascular ageing.European heart journal · 2025Article
- Sickle Cell Diastolic Cardiomyopathy and Mortality Risk: A Novel Echocardiographic Framework for Prognostic Stratification.American journal of hematology · 2025Article
- New Genetic Loci Implicated in Cardiac Morphology and Function Using Three-Dimensional Population Phenotyping.Circulation. Genomic and precision medicine · 2025Article
- Early Vascular Aging Determined by 3-Dimensional Aortic Geometry: Genetic Determinants and Clinical Consequences.Circulation · 2025Article
- Utilizing multimodal artificial intelligence to advance cardiovascular diseases.Precision clinical medicine · 2025Review
- How to measure and model cardiovascular aging.Cardiovascular research · 2025Review
- Article
- Brain Aging in Patients With Cardiovascular Disease From the UK Biobank.Human brain mapping · 2025Article
- Anti-senescence therapies: a new concept to address cardiovascular disease.Cardiovascular research · 2025Review
- Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.Nature cardiovascular research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 4 institutions in 2 countries.
Funding
Abstract
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 use machine learning approaches to quantify cardiovascular age 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 is 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 identify prescribed medications that are 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.