Efficacy of the sphingosine-1-phosphate receptor agonist fingolimod in animal models of stroke: an updated meta-analysis

Abstract

Objective: Neuroinflammation is a central part of cerebral ischemia/reperfusion injury. The novel immune suppressant, fingolimod, is a promising candidate to ameliorate stroke-induced damage. Fingolimod is efficacious in experimental ischemic models, but a rigorous meta-analysis is lacking that considers how different experiment variables affect outcomes.

Methods: We conducted a systematic literature review of fingolimod in stroke models, with the aim of rigorously evaluating fingolimod’s effects on reducing infarct volume improving neurological outcomes. Seventeen variables were evaluated as covariates for the source of heterogeneity, and effect sizes were combined by using normalized mean difference meta-analysis to evaluate efficacy. Study quality was evaluated by the CAMARADES ten-item checklist, and publication bias was evaluated by funnel plots and Egger’s tests.

Results: About 123 unduplicated articles were identified in the literature research. Of these papers, 118 articles were excluded after reading titles and abstracts. Another 17 articles were selected in this study. Study quality was moderate (median = 6; interquartile range = 4), and publication bias was statistically insignificant. fingolimod reduced infarct volume by 30.4% (95% CI 22.4%−38.3%; n = 24; I² = 90.0%; p < 0.0001) and consistently enhanced neurobehavioral outcome by 34.2% (95% CI 23.1%−45.2%; n = 14; I² = 76.5%; p < 0.0001). No single factors accounted for heterogeneity.

Conclusions: Our rigorous statistical evaluation confirmed the neuroprotective properties of fingolimod. New data can be used in designing future clinical trials.

Keywords:

• Fingolimod
• ischemic stroke
• meta-analysis
• neuroprotection
• systematic review
• animal studies

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Xiaofeng Ma designed research; Chun Dang and Yaoheng Lu analyzed data; Chunyang Wang and Q. Li performed research; Chun Dang wrote the paper; Yaoheng Lu contributed analytic tools.

Acknowledgments

We thank Dr. Zi-Long Zhao and Dr. Bin Han for valuable discussions.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (881401361 and 81870954 to X.M.).