ReviewFrontiers in pharmacology2026
AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms.
Review in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases (CVD) remain the leading cause of disease burden and mortality worldwide. Despite significant progress in drug treatment, this situation indicates that persistent residual risks still exist even after all feasible risk control measures have been implemented. Nutrition is increasingly recognized as an important modulator of cardiovascular biology; however, its integration into pharmacological frameworks for biomarker discovery and drug target identification has remained limited, largely due to insufficient mechanistic resolution and analytical complexity. Recent progress in high-throughput multi-omics technologies has revealed that nutrients and nutrient-derived metabolites directly regulate key pathways involved in lipid metabolism, inflammation, and mitochondrial function, many of which overlap with established or emerging cardiovascular drug targets. In parallel, artificial intelligence (AI) has emerged as a powerful discovery engine capable of integrating high-dimensional nutritional, molecular, and clinical data to prioritize biomarkers and uncover therapeutically actionable targets. In this mini-review, unlike previous studies that focused on dietary patterns and behavioral recommendations, we have summarized the current evidence regarding the drugable pathways for nutritional regulation in cardiovascular diseases, and have particularly highlighted the strategies based on artificial intelligence - including machine learning, network pharmacology, and multi-omics integration - for identifying biomarkers and elucidating therapeutic mechanisms. We further discuss the translational implications of AI-enabled nutritional pharmacology for precision cardiovascular therapeutics. By reframing nutrition as a source of modifiable molecular signals rather than a lifestyle exposure, this review provides a mechanistic framework for harnessing AI to advance biomarker discovery and drug target identification in cardiovascular disease.
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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 Socratic graph.