Abstract
Epigallocatechin-3-gallate (EGCG)a bioactive ingredient extracted in large amounts from green teais worthy of consideration for the prevention of renal injury. Neverthelessthere is a paucity of comprehensive and rigorous preclinical evidence to substantiate the therapeutic efficacy of EGCG in renal injury. To assess the therapeutic effect and potential mechanism of EGCG in rodent models of renal injury for future clinical research by meta-analysis and machine learninga systematic search of preclinical rodent studies published before April 22024was conducted using four databases. Meta-analyses were performed on a variety of indicatorsutilizing the STATA software. Additionallya machine learning model was constructed using the Python softwarewhich in turn predicted the relationship between the dosage and efficacy of EGCG in renal injury. Thirty-seven studies and 726 animals were included in the analysis. The findings suggest that EGCG can ameliorate kidney functional parameters in animals. This paper presents preliminary evidence indicating that consumption of EGCG may result in a statistically significant reduction in ScrBUNand urine protein levels while simultaneously increasing CCr. MoreoverEGCG can improve renal injurywhich is highly correlated with the systemic regulation of multiple phenotypes. The results of machine learning analyses indicated a modest correlation between EGCG dosage and efficacywith optimal dosages ranging from 94.25 to 107.76 mg/kg/d. With regard to potential mechanisms for the treatment of renal injuryEGCG exerted the renoprotective properties possibly through Nrf2/ heme oxygenase-1HIF-1α/ANGPTL4Tgf-β1/SmadMapkErkTnf-αNf-κbNlrp3/Il-1βand 67 kD laminin receptor pathways.
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The authors would like to thank the reviewers and the authors of all references. The reviewer’s advice really makes the great improvement of this article.
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This work was supported by the Xinglin Scholar Research Promotion Project of Chengdu University of TCM (Grant Nos. QJJJ2024014 and QJRC2022028)and the “Hundred Talents Program” of the Hospital of the Chengdu University of Traditional Chinese Medicine (Grant No. 22-B09)the Joint Innovation Fund of Chengdu University of Traditional Chinese Medicine (Grant No. LH202402044)and Young Elite Scientists Sponsorship Program by CACM [CACM-(2023-QNRC2-A01)].
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Jie Chen and Jinna Tian are the main contributor to this manuscript. Jie Chen performed the comprehensive and systematic sorting and analyses of the literature. Jie Chen and Yuanhao Zhang searched and downloaded references. Zexin WangMaoyuan Zhaoand Cui Guo processed the pictures and tables in the manuscript. Jia MaHebin Zhangand Jijun Zheng made critical suggestions for the optimization of the research. Yueqiang WenXiao MaJinhao Zengand Thomas Efferth (corresponding authors) conceived and conceptualized the review. All authors read and approved the final manuscript. All data were generated in-houseand no paper mill was used. All authors agree to be accountable for all aspects of work ensuring integrity and accuracy.
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ChenJ.TianJ.ZhangY. et al. Quantitative analysis of epigallocatechin-3-gallate in treating renal injury via meta-analysis and machine learning. Phytochem Rev 243315–3336 (2025). https://doi.org/10.1007/s11101-024-10058-6
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DOI: https://doi.org/10.1007/s11101-024-10058-6