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Chinese Herbal Medicine for Weight Management: A Systematic Review and Meta-Analyses of Randomised Controlled Trials
Author(s) -
Ann Rann Wong,
Angela Wei Hong Yang,
Kangxiao Li,
Harsharn Gill,
Mingdi Li,
George Binh Le
Publication year - 2021
Publication title -
journal of obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 53
eISSN - 2090-0716
pISSN - 2090-0708
DOI - 10.1155/2021/3250723
Subject(s) - medicine , blinding , meta analysis , placebo , adverse effect , weight management , confidence interval , body mass index , medline , relative risk , randomized controlled trial , systematic review , weight loss , alternative medicine , traditional medicine , physical therapy , obesity , pathology , political science , law
Objective This review investigated the effects and safety of Chinese herbal medicine (CHM) formulas on weight management.Methods Eighteen databases in English, Chinese, Korean, and Japanese were searched from their inceptions to September 2019. The treatment groups included CHM formulations, and the control included placebo, Western medication (WM), and lifestyle intervention (LI), with or without cointerventions (WM and/or LI). Quality of studies was assessed using Cochrane Collaboration's risk of bias assessment tool. Body weight and body mass index (BMI) were analysed in RevMan v5.4.1 and expressed as mean differences with 95% confidence intervals (CI), while adverse events were expressed as risk ratio with 95% CI.Results Thirty-nine RCTs were eligible for qualitative analysis, 34 of which were included in the meta-analyses. The majority of studies had a high or unclear risk of selection, performance, and detection bias. Twenty-five CHM studies involving cointerventions revealed that CHM had significant adjunct effects on body weight and BMI at the end of treatment compared to control. No serious adverse events were reported in the CHM groups.Conclusion CHM indicates a promising adjunct to facilitate WM or lifestyle change for weight management. However, methodological barriers such as lack of allocation concealment and double-blinding may have led to challenges in data synthesis. More rigorously designed RCTs involving cointerventions are warranted.

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