Application of graph database to identify key raw materials and active ingredients in dietary supplement networks
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Abstract
This study aims to apply a graph database to analyze structural relationships between raw materials (RM) and active ingredients (AI) associated with health claims (HC) of dietary supplements. A relational database was developed based on nine core entities and converted into a graph network. Cytoscape and its CytoNCA plugin were used to evaluate key centrality measures—degree, betweenness, closeness, and overall score—to rank the importance of RM and AI within each HC group. The results showed that certain active ingredients, such as flavonoids, polyphenols, and dietary fiber, appeared across multiple HC groups, reflecting their diverse biological functions and potential for multi-benefit formulation. Additional information, including plant part used (PU) and extraction method (EX), helped support the selection of suitable raw materials aligned with specific health outcomes. Although some limitations exist, such as incomplete sample coverage and the reliability of manufacturer claims, the findings demonstrate that graph databases are effective tools for systematic analysis. They can enhance the precision of dietary supplement development by identifying key bioactive components and their interrelations across health benefits.
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