ArticleNature communications2026
AI-based multiomics profiling reveals complementary omics contributions to personalized prediction of cardiovascular disease.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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.
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Who cites it
15 citing papers in PubMed.
- Residual lipid-related proteomic and metabolomic signatures associated with major adverse cardiovascular events after revascularization in peripheral artery disease.BMC cardiovascular disorders · 2026Article
- Mitokines as bioenergetic stress signals in cardiovascular disease: mitochondrial communication, endocrine adaptation, and translational implications.Journal of bioenergetics and biomembranes · 2026Review
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Heart Meets Brain: Insights into Neurocardiac Pathophysiology.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Bridging Ancestry-Stratified Bias in Pharmacogenomics AI: Toward Metabolomics-Inclusive Multi-Omics Precision Medicine.Journal of personalized medicine · 2026Review
- CardiOmicScore: a Multitask AI Model for Cardiovascular Disease Prediction.Journal of cardiovascular translational research · 2026Article
- Artificial intelligence-empowered, clinically-integrated multiomics research in thrombosis: a call to action.Research and practice in thrombosis and haemostasis · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Circulating excitation-contraction coupling proteins and incident cardiovascular outcomes: associations and incremental predictive performance in the UK Biobank.Frontiers in cardiovascular medicine · 2026Article
- Immuno-inflammatory-metabolic interactions in cardiovascular diseases: a review from basic mechanisms to clinical translation.Frontiers in immunology · 2026Review
- Emerging quantitative CCTA imaging biomarkers for cardiovascular risk stratification: a narrative review.Frontiers in cardiovascular medicine · 2026Review
- The gut microbiota-aromatic amino acid axis in cardiovascular disease: pathophysiological roles, translational biomarkers, and therapeutic targeting.Frontiers in cardiovascular medicine · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Genomics, metabolomics, and proteomics offer complementary insights into cardiovascular disease (CVD) risk. Leveraging UK Biobank data, we introduce the CardiOmicScore, a multitask deep learning framework, to learn disease-specific proteomic (ProScore) and metabolomic (MetScore) risk scores for the six most common CVDs by profiling 2920 proteins and 168 metabolites. Experiments demonstrate that ProScore and MetScore are strong sole CVD risk predictors (C-index range: 0.69-0.82 for ProScore and 0.64-0.74 for MetScore), and can significantly enhance risk prediction across CVDs up to 15 years prior to disease onset when combined with clinical data, increasing the C-index by 0.005-0.102. These findings suggest that incorporating multiomics profiling into clinical practice can improve personalized risk assessments at early stages. CardiOmicScore also identifies important CVD-related proteins and metabolites, which represent promising data-driven pathways, calling for further external validation, to develop novel biomarkers and targeted therapies, facilitating precision medicine for primary prevention of CVDs.
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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.