{
  "abstract": "Background Deep learning can estimate age from 12-lead electrocardiograms (ECGs), but whether this reflects true ageing biology is uncertain. We developed an artificial intelligence (AI)-derived ECG clock and tested whether ECG clock acceleration (bias-corrected ECG clock age minus chronological age) captures electrophysiological, structural, molecular and genetic ageing signals.Methods A one-dimensional residual neural network (1D-ResNet) ECG clock was trained on 1.2 million Beth Israel Deaconess Medical Center (BIDMC) ECGs. Prognostic associations were tested using age- and sex-adjusted Cox models in BIDMC (n=62K), Airwave Health Monitoring Study (AHMS; n=45K), and Massachusetts General Hospital (MGH; n=1.2M). Contemporaneous echocardiographic, metabolomic, lipoprotein and proteomic correlates were evaluated in the multi-omic subset. Genetic validation used Mendelian randomisation (MR) across 10 outcomes: atrial fibrillation (AF), dilated cardiomyopathy (DCM), heart failure (HF), diastolic blood pressure, low-density lipoprotein cholesterol, type 2 diabetes, ischaemic heart disease, ischaemic stroke, venous thromboembolism (VTE), and thoracic aortic aneurysm (TAA), using single-nucleotide polymorphism (SNP)-level Wald ratio estimates with false discovery rate (FDR) correction.Results The ECG clock performance was robust across cohorts (mean absolute error (MAE) in age estimation 7.48 years in BIDMC, 6.44 in AHMS, and 8.16 in MGH). Higher ECG clock age acceleration showed graded risk increases, strongest for electrophysiological outcomes: atrial flutter (hazard ratio (HR) 1.62; 1.26; 1.26), AF (1.37; 1.35; 1.26), sick sinus syndrome (1.55; NA; 1.29), bundle branch block (1.23; 1.79; 1.23), complete heart block (1.65; 2.07; 1.29), ventricular tachycardia (VT; 1.34; NA; 1.21), ventricular fibrillation (VF; 1.41; NA; 1.20), and sudden cardiac death (1.34; 1.09; 1.21). Structural associations were also consistent: DCM (1.59; 1.84; 1.22), HF (1.27; 1.37; 1.24), aortic aneurysm (1.27; 1.35; 1.21), and hypertrophic cardiomyopathy (HCM; 1.33; 1.67; 1.19). Multi-omic profiling identified convergent dysregulation of lipid metabolism, arginine-urea pathways, mitochondrial beta-oxidation and glutathione-redox biology, with a high-density lipoprotein-depleted, apolipoprotein B-enriched lipoprotein profile. MR showed the strongest and most coherent genetic support for AF, including multiple FDR-significant loci; HF/cardiomyopathy signals were more modest, and vascular/cardiometabolic associations were weaker and likely indirect, consistent with predominantly electrophysiological clinical risk.Conclusion The ECG clock captures a scalable cardiovascular and electrophysiological ageing phenotype with linked structural, molecular and genetic correlates. Classifying individuals as accelerated versus decelerated ECG ageing improves interpretability and supports use in risk stratification, trial enrichment and therapeutic target discovery.",
  "authors": [
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Joseph Barker"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Sina Fathieh"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Mohsen Mazidi"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Yi-an Chen"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Rui Pinto"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Libor Pastika"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Aidan Birdi"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Gul Rukh Khattak"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Boroumand Zeidaabadi"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Hesham Aggour"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Jiayu Huo"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Ahmed El-Medany"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Clara Radovanovic"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Jasjit Syan"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Alex Jenkins"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Johanna Tonko"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Jean-Baptiste Guichard"
    },
    {
      "affiliations": [
        "Havard Medical School, Boston, United Kingdom"
      ],
      "name": "Daniel B Kramer"
    },
    {
      "affiliations": [
        "Havard Medical School, Boston, United Kingdom"
      ],
      "name": "Jonathan W Waks"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Nicholas Peters"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Arun Sau"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Ioanna Tzoulaki"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Konstantinos Patlatzoglou"
    },
    {
      "affiliations": [
        "Imperial College, London, United Kingdom"
      ],
      "name": "Fu Siong Ng"
    }
  ],
  "title": "375 Decoding cardiovascular biological ageing using an AI-derived ECG age clock with multi-omic integration",
  "uid": "4a2cdc10-b4a9-516c-924b-dc4d0467545d"
}