Integration of systematic review and data mining techniques to reveal core anti-obesity medicinal plants

Abstract Background: Obesity is a complex chronic disease that can lead to a variety of health problems. Despite its increasing frequency, there is still a lack of safe and effective treatment options. Traditional Chinese Medicine (TCM) herbal treatment is gaining medical attention as a potential alternative to disease treatment. Specifically, biomolecular interactions of the usage of paired herbs could yield essentially synergistic effects on the fight against obesity. Objective: This study aims to investigate the combination of core herbs and clustering patterns in obesity treatment using various data mining techniques. Methods: Eight electronic databases were searched from inception until December 2021 and 34 Randomized Control Trials (RCTs) were identified. Subsequently, 96 different herbs were extracted from the RCTs for association analysis and hierarchical clustering. The quality assessment of the trials was conducted using the Cochrane Collaboration’s Risk of Bias Tool. Results: Association analysis identified the core herb combination of Coptis chinensis , Epimedium grandiflorum , Salvia miltiorrhiza , and Poria cocos . Hierarchical clustering also identified meaningful clustering patterns amongst herbs based on similar therapeutic effects and meridian entry. Conclusion: Using an integrated approach of systematic review and data mining techniques has revealed core herbs for the treatment of obesity. However, more clinical trials/clinical studies are required to validate the clinical efficacy of the core herb combination.