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Plasma levels of nesfatin-1 as a new biomarker in depression in Asians: evidence from meta-analysis

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Pages 228-234 | Received 30 Oct 2019, Accepted 24 Feb 2020, Published online: 11 Mar 2020
 

Abstract

Objective: This study aims to review the alteration of plasma nesfatin-1 levels in patients with depression.

Methods: Under the guidance of the latest PRISMA checklist, a systematic review and meta-analysis were conducted by searching English database (PubMed, Web of Science, EMDASE) and Chinese database for relevant studies up to August, 2019. Pooled standardised mean difference (SMD) with 95% confidence intervals (CI) was calculated with the random effects model.

Results: Nine studies that reported the association between plasma levels of nesfatin-1 and the risk of depression with 567 patients and 447 control participants were included in the meta-analysis. Compared with the healthy controls, depressive patients had a higher plasma level of nesfatin-1 [SMD (95% CI):1.58(0.75, 2.41), Z = 3.74, p for Z < 0.001; I2 = 96.8%, p for I2 < 0.001]. The subgroup analyses and meta-regression failed to find the source of the heterogeneity. No evidence of publication bias was found either in Begg’s test (p = 0.348) or the Egger’s test (p = 0.523).

Conclusion: The present meta-analysis indicated that a higher plasma level of nesfatin-1 was associated with an increased risk of depression.

Author contribution

ML was responsible for statistical analysis and wrote the manuscript. XLS was responsible for statistical design. HJ, XLS, and XXD revised the manuscript.

Disclosure statement

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

Additional information

Funding

This research was supported by the National Foundation of Natural Science of China [31771110, 31701020, 81701377], Shandong Province Natural Science Foundation (ZR2019ZD31), National Key Research and Development Program of China (2016YFC1306501), Taishan Scholars Construction Project and Innovative Research Team of High-Level Local Universities in Shanghai, the Key Research and Development Program of Shandong Province (2018GSF118042).

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