ArticleAmerican journal of preventive cardiology2027
Use of an AI metabolomics score to predict cardiovascular risk among patients with Psoriasis in the MGB and UK Biobanks.
Article in American journal of preventive cardiology, 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Abstract
Background: Systemic inflammation in psoriasis accelerates atherosclerosis and confers excess cardiovascular risk that is not fully captured by traditional clinical predictors. Metabolomic profiling may identify disease-specific metabolic signatures associated with cardiovascular events and enhance risk prediction beyond conventional factors. Objectives: To determine whether plasma metabolomic profiling improves cardiovascular risk prediction in patients with psoriasis and to develop and externally validate an artificial intelligence-driven metabolomic risk score (MetaPsoAI) for incident cardiovascular events. Methods: We analyzed 1086 individuals with psoriasis and no prior coronary artery disease from the Mass General Brigham (MGB) Biobank who underwent nuclear magnetic resonance-based quantification of 168 circulating metabolites. Associations between metabolites and incident all-cause death or myocardial infarction were assessed. An extreme gradient boosting algorithm was used to develop MetaPsoAI for prediction of myocardial infarction or stroke. External validation was performed in the UK Biobank ( Results: Multiple metabolites, particularly HDL subfractions and amino acid metabolites, were associated with cardiovascular outcomes. MetaPsoAI stratified risk across quartiles, with individuals in the highest quartile demonstrating a 3.6-fold higher risk of myocardial infarction or stroke in the MGB cohort. In the UK Biobank, the highest quartile was associated with a 1.9-fold increased risk among psoriasis patients, with stronger effect sizes compared with non-psoriasis controls. Conclusions: Our findings highlight the potential of AI-driven metabolomics to refine cardiovascular risk prediction in immune-mediated diseases and enable precision prevention strategies for patients with psoriasis.
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