• Jinke Huang, Min Shen, Xiaohui Qin, Manli Wu, Simin Liang, and Yong Huang.
• The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
• Front Aging Neurosci. 2020 Jan 1; 12: 574023.

AbstractBackground: Acupuncture may be an effective complementary treatment for Alzheimer's disease (AD). The aim of this study was to summarize the evidence provided by systematic reviews (SRs)/meta-analyses (MAs) on the effect of acupuncture on AD. Methods: Eight electronic databases were searched from their inception until October 19, 2020. The methodological quality, reporting quality, and risk of bias of the included SRs were assessed by the Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR-2), the Risk of Bias in Systematic Reviews (ROBIS) tool, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Moreover, the evidence quality of the outcome measures was assessed by the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Results: Eleven SRs/MAs met all inclusion criteria. According to the results of the AMSTAR-2, all included reviews were rated critically as being of low quality. With PRISMA, the reporting checklist was relatively complete, but some reporting weaknesses remained in the topics of the protocol and registration, search strategy, risk of bias, additional analyses, and funding. Based on the ROBIS tool, only two SRs/MAs had a low risk of bias. With the GRADE system, no high-quality evidence was found, and only seven outcomes provided moderate-quality evidence. Among the downgraded factors, the risk of bias within the original trials was ranked first, followed by inconsistency, imprecision, and publication bias. Conclusions: Acupuncture is a promising complementary treatment for AD. However, due to the low quality of the SRs/MAs supporting these results, high-quality studies with rigorous study designs and larger samples are needed before widespread recommendations can be made.Copyright © 2020 Huang, Shen, Qin, Wu, Liang and Huang.

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