Evidence map›Paper›PMID 40369506›Full record

SynthesisBMC medical genomics2025

Discovering diabetes complications-related microRNAs: meta-analyses and pathway modeling approach.

Ruiyang Yin, Yanjiao Zhang, Xinyi Fang, Yuxin Zhang, Runyu Miao, Yiqi Yao, Huifang Guan, Jiaxing Tian

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMC medical genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ruiyang YinInstitute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
Yanjiao ZhangInstitute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
Xinyi FangInstitute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
Yuxin ZhangInstitute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
Runyu MiaoInstitute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
Yiqi YaoInstitute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
Huifang GuanGraduate College, Changchun University of Chinese Medicine, Changchun, 130117, China.
Jiaxing TianDepartment of Endocrinology, The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, 130022, China. tina_yai@126.com.

Funding

CACMS Outstanding Young Scientific and Technological Talents Program ZZ13-YQ-026Capital Health Development Research Project CD2020-4-4155China Academy of Chinese Medical Sciences Science and Technology Innovation Project Innovation Team CI2021B008nnovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine ZYYCXTD-D-202001Open Project of National Facility for Translational Medicine TMSK-2021-407Scientific and technological innovation project of China Academy of Chinese Medical Sciences CI2021A01601
6 · The paper itself

Abstract

purposeMicroRNAs(miRNA) play an important role in the pathogenesis of diabetic complications by regulating gene expression. The objective of this paper is to investigate micoRNA expression in diabetic nephropathy (DN), diabetic retinopathy (DR), diabetic neuropathy (DNP), and diabetic cardiopathy (DC).

methodsWe conducted this systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and retrieved eligible microRNA-related studies of diabetic complications from PubMed, Embase, and Web of science databases. We enriched pathways corresponding to differentially expressed miRNAs using the miRPath tool on the DIANA website, and predicted their target genes with DIANA microT-CDS and TargetScan.

resultsAlthough many of the selected studies were of high scientific quality, the results were heterogeneous. Among the 71 selected articles, 79 miRNAs were differentially expressed in various complications of diabetes, of which miRNA126, miRNA192 and 17 others were reported in at least two or more studies. A total of 156 target genes were predicted and 103 pathways were obtained by KEGG enrichment analysis.

conclusionThis comprehensive systematic evaluation provides experimental evidence statistics for miRNAs as circulating biomarkers and highlights promising biomarkers. These results provide preliminary data to further investigate the role of miRNAs in the diagnosis and therapeutic targets of human diabetic complications and support future broader longitudinal studies to better substantiate the role of dysregulated miRNAs as potential biomarkers and therapeutic targets of diabetic complications.

Indexed as

Diabetes ComplicationsMicroRNAsBiomarkersGene Regulatory NetworksHumansBiomarkersMicroRNAsDiabetes complicationsMeta-analysesMicroRNAPathway modelling

Identifiers

PMID40369506
PMCPMC12079836

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.