Yan LN, Zhang X, Xu F, Fan YY, Ge B, Guo H, Li ZL. Four-microRNA signature for detection of type 2 diabetes. World J Clin Cases 2020; 8(10): 1923-1931 [PMID: 32518782 DOI: 10.12998/wjcc.v8.i10.1923]
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Yan LN, Zhang X, Xu F, Fan YY, Ge B, Guo H, Li ZL. Four-microRNA signature for detection of type 2 diabetes. World J Clin Cases 2020; 8(10): 1923-1931 [PMID: 32518782 DOI: 10.12998/wjcc.v8.i10.1923]
Li-Na Yan, Fang Xu, Yuan-Yuan Fan, Biao Ge, Hui Guo, Zi-Ling Li, Department of Endocrinology, Inner Mongolia Baogang Hospital, Baotou 014010, Inner Mongolia Autonomous Region, China
Xin Zhang, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Interventional Therapy Department, Peking University Cancer Hospital and Institute, Beijing 100142, China
Author contributions: Yan LN and Zhang X designed the study; Yan LN, Xu F, Fan YY, Ge B, and Guo H performed the research; Yan LN, Zhang X, and Li ZL analyzed the data; Yan LN wrote the paper and Li ZL revised the manuscript for final submission; Yan LN and Zhang X contributed equally to this study.
Supported bythe National Key R& D Program of China, No. 2016YFC0106604.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of the Third Hospital Affiliated to Inner Mongolia Medical University (Inner Mongolia Baogang Hospital).
Informed consent statement: All study participants or their legal guardian provided written informed consent prior to study enrollment.
Conflict-of-interest statement: We declare that we have no financial or personal relationships with other individuals or organizations that can inappropriately influence our work and that there is no professional or other personal interest of any nature in any product, service and/or company that could be construed as influencing the position presented in or the review of the manuscript.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement – checklist of items, and the manuscript was prepared and revised according to the STROBE Statement – checklist of items.
Received: February 24, 2020 Peer-review started: February 24, 2020 First decision: March 24, 2020 Revised: April 2, 2020 Accepted: April 15, 2020 Article in press: April 15, 2020 Published online: May 26, 2020 Processing time: 91 Days and 5.2 Hours
Abstract
BACKGROUND
Sensitive, novel, and accurate biomarkers for the detection of physiological changes in type 2 diabetes (T2DM) at an early stage are urgently needed.
AIM
To build a multi-parameter diagnostic model for the early detection of T2DM.
METHODS
MiR-148b, miR-223, miR-130a, and miR-19a levels were detected by real-time polymerase chain reaction in serum of healthy controls, individuals with impaired glucose regulation, and T2DM patients. The diagnostic value of miR-148b, miR-223, miR-130a, and miR-19a, alone or in combination, was analyzed.
RESULTS
The area under the curve (AUC) of miR-223, which had the best diagnostic value for discriminating the impaired glucose regulation and T2DM groups, was 0.84, and the sensitivity and specificity were 73.37% and 81.37%, respectively. The AUC of the four-miRNA signature was 0.90, and the sensitivity and specificity were 78.82% and 88.23%, respectively. In the validation set, the AUC was 0.88, and the sensitivity and specificity were 78.36% and 87.63%, respectively.
CONCLUSION
In summary, we have built a multi-parameter diagnostic model consisting of miR-148b, miR-223, miR-130a, and miR-19a for the detection of T2DM. It may be a potential tool for the early detection of T2DM.
Core tip: The expression of microRNAs in serum is stable and can be reproducibly detected, and they have the potential to be biomarkers for type 2 diabetes. We built a multi-parameter diagnostic model containing miR-148b, miR-223, miR-130a, and miR-19a. In the validation set, the area under curve of this model for the detection of type 2 diabetes was 0.90, and the sensitivity and specificity were 78.36% and 87.63%, respectively. This diagnostic model may be a novel tool for the detection of type 2 diabetes.
