Network Meta-Analysis of Stem Cell Therapies for Parkinson’s Disease: Exploring the Optimal Strategy Based on Animal Models

生物 干细胞 帕金森病 荟萃分析 疾病 计算生物学 神经科学 生物信息学 内科学 遗传学 医学
作者
Zehong Zhang,Yingkai Wang,Zhengmian Zhang,Zhongquan Qi
出处
期刊:Stem Cells and Development [Mary Ann Liebert, Inc.]
卷期号:34 (11-12): 227-239 被引量:2
标识
DOI:10.1089/scd.2025.0056
摘要

Stem cell therapy holds promise for Parkinson’s disease (PD). To identify optimal stem cell regimens in PD mouse models and inform translational research, we conducted a network meta-analysis (NMA). Specifically, we systematically searched for studies on stem cell therapy in PD mouse models up to September 2024 in PubMed, Embase, Scopus, Web of Science, China National Knowledge Infrastructure, WANFANG, and VIP. Based on the data collected, we conducted an NMA using GeMTC-0.14.3 software. The results of traditional meta-analysis of 148 studies demonstrated superior efficacy of most interventions versus controls at biweekly intervals (2–8 weeks post-treatment), with neural stem cells engineered with neurotrophic factors (NSC-NFs) showing the lowest weighted mean difference, indicating optimal therapeutic effect. NMA demonstrated that NF-engineered NSC therapy ranked the highest at biweekly time points (2–8 weeks post-treatment). Doses of 105 cells showed optimal efficacy at 2, 4, and 6 weeks, peaking within this range, whereas doses of 103 cells showed the best efficacy at 8 weeks. Medial forebrain bundle (MFB) administration showed superior efficacy at weeks 2 and 8, while striatum (STR) infusion showed greater therapeutic effects at weeks 4 and 6, with both approaches significantly outperforming nasal and intravenous delivery at all evaluated time points (2, 4, 6, and 8 weeks). Taken together, these results suggest that NSC-NF (dosage of 105) delivered via MFB (at 2 and 8 weeks) or STR (at 4 and 6 weeks) may represent the optimal strategy. It provides important guidance for optimizing preclinical and clinical trial designs and offers valuable insights for clinical translation.
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