Articlenpj aging2025
Reclassification of the conventional risk assessment for aging-related diseases by electrocardiogram-enabled biological age.
Article in npj aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-Derived ECG-Age as a Predictor of Mortality and Cardiovascular Events: A Systematic Review and Meta-Analysis.Arquivos brasileiros de cardiologia · 2026Pooled it
- Article
- The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi-Omics Integration.Aging cell · 2026Review
- Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.European heart journal. Digital health · 2026Article
- The Prognostic Ability of ECG Findings in Predicting Cardiovascular Events: A Five-Year Nested Case-Cohort Study in an Iranian Population (Shiraz Heart Study).Health science reports · 2026Article
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Biomarkers of aging: from molecules and surrogates to physiology and function.Physiological reviews · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
An artificial intelligence (AI)-enabled electrocardiogram (ECG) model has been developed in a healthy adult population to predict ECG biological age (ECG-BA). This ECG-BA exhibited a robust correlation with chronological age (CA) in healthy adults and additionally significantly enhanced the prediction of aging-related diseases' onset in adults with subclinical diseases. The model showed particularly strong predictive power for cardiovascular and non-cardiovascular diseases such as stroke, coronary artery disease, peripheral arterial occlusive disease, myocardial infarction, Alzheimer's disease, osteoarthritis, and cancers. When combined with CA, ECG-BA improved diagnostic accuracy and risk classification by 21% over using CA alone, notably offering the greatest improvements in cancer prediction. The net reclassification improvement significantly reduced misclassification rates for disease onset predictions. This comprehensive study validates ECG-BA as an effective supplement to CA, advancing the precision of risk assessments for aging-related conditions and suggesting broad implications for enhancing preventive healthcare strategies, potentially leading to better patient outcomes.
Identifiers
What Socratic 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 Socratic graph.