Relationship of miRNA‑126 and miRNA‑122 expression with type 2 diabetes mellitus and related glucose metabolism parameters: A systematic review and meta‑analysis

Yaling He; Yuqian Li; Zhihan Zhai; Pengling Liu; Luting Nie; Yiquan Zheng; Jian Hou; Wenqian Huo; Zhenxing Mao; Zhenzhong Zhang; Chongjian Wang; Xiaotian Liu

doi:10.3892/etm.2022.11589

Relationship of miRNA‑126 and miRNA‑122 expression with type 2 diabetes mellitus and related glucose metabolism parameters: A systematic review and meta‑analysis

Exp Ther Med. 2022 Sep 5;24(5):652. doi: 10.3892/etm.2022.11589. eCollection 2022 Nov.

Authors

Yaling He¹, Yuqian Li², Zhihan Zhai¹, Pengling Liu¹, Luting Nie¹, Yiquan Zheng¹, Jian Hou¹, Wenqian Huo¹, Zhenxing Mao¹, Zhenzhong Zhang², Chongjian Wang¹, Xiaotian Liu^{1

2}

Affiliations

¹ Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, P.R. China.
² Henan Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, P.R. China.

Abstract

There are increasing numbers of studies investigating the potential link between microRNAs (miRNAs) and type 2 diabetes mellitus (T2DM) risk. Based on the prior evidence and the differentially expressed candidate plasma exosome miRNAs in our established discovery study, the current meta-analysis studied miR-126 and miR-122 specifically. The purpose of the present study was to systematically and quantitatively evaluate the relationship of miR-126 and miR-22 expression level with T2DM risk as well as related glucose metabolism parameters. Moreover, the present study was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline (PRISMA 2020 statement). PubMed, Embase, Web of Science, Cochrane and Chinese National Knowledge Infrastructure electronic databases were used to identify eligible original studies prior to May 3, 2022. The random-effects models were employed to explore the overall effect estimates [odds ratio (OR) and 95% confidence interval (CI), or correlation coefficient (r, 95% CI)]. The subgroup analyses were conducted to examine the potential sources of heterogeneity. The potential publication bias was assessed by the Begg's funnel plot and Egger's tests. A total of 46 articles were included in the present meta-analysis. The results revealed that higher exposure level of miR-126 was related to lower T2DM risk in 5 analytical epidemiological studies [OR=0.73, 95% CI: (0.55, 0.96)], lower fasting blood glucose (FBG) [N=22, r=-0.26, 95% CI: (-0.42, -0.10)], and lower homeostasis model assessment of insulin resistance (HOMA-IR) index [N=9, r=-0.28, 95% CI: (-0.52, -0.05)]. Besides, positive correlations were observed between miR-122 expression and FBG [N=10, r=0.34, 95% CI: (0.20, 0.48)], as well as HOMA-IR index [N=9, r=0.40, 95% CI: (0.16, 0.64)]. The relationship of miR-126 and miR-122 expression with T2DM risk and these glucose metabolism parameters may be influenced by study types, sample size, different source and mean age of participants. In conclusion, in the general healthy population, higher miR-126 expression was related to lower T2DM risk, FBG level and HOMA-IR index; higher miR-122 expression was closely correlated with higher FBG level and HOMA-IR index. These findings have notable clinical and public health implications for screening and control glucose metabolic disorders, insulin resistance and T2DM development.

Keywords: glucose metabolism parameters; meta-analysis; miRNA-122; miRNA-126; type 2 diabetes mellitus.

Grants and funding

Funding: The present study was supported by the Foundation of National Key Program of Research and Development of China (grant no. 2016YFC0900803), the China Postdoctoral Science Foundation (grant no. 2020M672297), the National Natural Science Foundation of China (grant nos. 82003543, 81573243 and 81602925), the Henan Natural Science Foundation of China (grant no. 182300410293), the Science and Technology Foundation for Innovation Talent of Henan Province (grant no. 164100510021), the Science and Technology Innovation Talents Support Plan of Henan Province Colleges and Universities (grant no. 14HASTIT035) and the High-level Personnel Special Support Project of Zhengzhou University (grant no. ZDGD13001).