Prediction of Targets and Mechanisms of Top Ten Core “Food–Medicine Homologous Traditional Chinese Medicines” in Delaying Vascular Aging: An Integrative Computational Study

Background and Objectives: Many “food–medicine homologous traditional Chinese medicines (TCMs)” have been shown to delay vascular aging. In this study, we will select “food–medicine homologous TCMs” with the most potential to delay human-origin vascular aging and predict their core targets and mechanisms. Methods: Human-origin vascular-aging-related genes were screened from the NCBI and Aging Atlas databases. Candidate “food–medicine homologous TCMs” were initially filtered by constructing a protein–protein interaction network, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Key targets were validated in the Gene Expression Omnibus database and further screened by least absolute shrinkage and a selection operator. Finally, molecular docking and molecular dynamics simulations identified core targets. Results: Ten core “food–medicine homologous TCMs” with potential to delay human-derived vascular aging were identified: Crocus Sativus L., Glycyrrhiza uralensis Fisch., Chrysanthemum morifolium Ramat., Astragalus membranaceus (Fisch.) Bunge, Sophora japonica L., Hippophae rhamnoides L., Portulaca oleracea L., Lonicera japonica Thunb., Citrus aurantium L. var. amara Engl., and Morus alba L. Further analysis indicated that β-Carotene within these core “food–medicine homologous TCMs” may represent a potential active component targeting matrix metalloproteinase-1, with its action potentially linked to the interleukin-17 signaling pathway. The present study highlights the new hypothesis that immunosenescence (Th17/IL-17) is involved in vascular aging, suggesting that the top ten core “food–medicine homologous TCMs” may delay vascular aging by regulating immune cell function. Conclusions: The top ten “food–medicine homologous TCMs” provide potential candidates for functional products that delay vascular aging and provide computationally predicted mechanistic insights and a scientific basis for novel therapies.