Systematic review of preclinical evidence reveals the anti-inflammatory
potential of Icariin based on meta-analysis and machine learning
Abstract
Purpose: The aim of this study was to construct a reference system for
preclinical evidence and drug superiority characterization of the
anti-inflammatory effects of icariin glycosides and their derivatives
through meta-analysis combined with machine learning, and to excavate
the biological mechanisms behind them. Methods: The data were obtained
from databases of PubMed, Embase, Cochrane Library, and Web of Science.
STATA software was used for meta-analysis of indicators, and subgroup
analysis was conducted based on animal species, gender, type of disease,
drug dosage, and course to obtain more particulars. Furthermore, model
construction were performed using R software to explore influential
features on drug efficacy. In addition, the pharmacological mechanisms
of icariin and its derivatives in anti-inflammation were summarized
based on a comprehensive understanding of relevant literature. Results:
After searching and screeningThe results showed that icariin and its
derivatives significantly inhibit inflammation indicators such as TNF-α
and IL-1β. Besides, machine learning with TNF-α as the output variable
showed that icariin and its derivatives had stronger anti-inflammatory
effects when the type of disease was respiratory, urological,
neurological, and digestive, and when the dose and duration of Icariin
were greater than 27.52 mg/kg/day and 31.22 days, respectively.
Conclusion: Icariin and its derivatives demonstrate strong
anti-inflammatory effects, particularly for respiratory, urinary,
neurological, and digestive disorders. When given at doses of 27.52
mg/kg/day or more, with treatment lasting 31.22 days or beyond, these
compounds hold significant potential as drugs for inflammation
inhibition across multiple dis