Evidence map›Paper›PMID 37711129›Full record

ReviewCurrent medicinal chemistry2024

Role of LncRNA MIAT in Diabetic Complications.

Lijun Wang, Hailin Wang, Yiyang Luo, Wei Wu, Yibei Gui, Jiale Zhao, Ruisi Xiong, Xueqin Li, Ding Yuan, Chengfu Yuan

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 24% of its field
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

5 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
  3. LncRNA-miRNA‒mRNA Network in Schizophrenia.Journal of molecular neuroscience : MN · 2025
    Article
  4. Article
  5. Investigating circulating expression profile forFrontiers in molecular biosciences · 2025
    Article
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

10 authors at 1 institution in 1 country.

Lijun WangThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
Hailin WangThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
Yiyang LuoThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
Wei WuThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
Yibei GuiThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
Jiale ZhaoThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
Ruisi XiongThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
Xueqin LiThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
Ding YuanCollege of Medicine and Health Science, China Three Gorges University, Yichang, 443002, China.
Chengfu YuanThird-grade Pharmacological Laboratory on Traditional Chinese Medicine, State Administration of Traditional Chinese Medicine, China Three Gorges University, Yichang, 443002, China.
China Three Gorges University · CN

Funding

Central Funds Guiding the Local Science and Technology Development 2020ZYYD016Innovational Group Project of the Hubei Province Natural Science Foundation in China 2021CFA015National Natural Science Foundation of China 81974528, 82174035, 81773959
6 · The paper itself

Abstract

Long non-coding RNA (LncRNA) refers to a large class of RNAs with over 200 nucleotides that do not have the function of encoding proteins. In recent years, more and more literature has revealed that lncRNA is involved in manipulating genes related to human health and disease, playing outstanding biological functions, which has attracted widespread attention from researchers. The newly discovered long-stranded non-coding RNA myocardial infarction-related transcript (LncRNA MIAT) is abnormally expressed in a variety of diseases, especially in diabetic complications, and has been proven to have a wide range of effects. This review article aimed to summarize the importance of LncRNA MIAT in diabetic complications, such as diabetic cardiomyopathy, diabetic nephropathy, and diabetic retinopathy, and highlight the latest findings on the pathway and mechanism of its participation in regulating diabetic complications, which may aid in finding new intervention targets for the treatment of diabetic complications. LncRNA MIAT competitively binds microRNAs to regulate gene expression as competitive endogenous RNAs. Thus, this review article has reviewed the biological function and pathogenesis of LncRNA MIAT in diabetic complications and described its role in diabetic complications. This paper will help in finding new therapeutic targets and intervention strategies for diabetes complications.

Indexed as

Diabetes ComplicationsRNA, Long NoncodingAnimalsDiabetic CardiomyopathiesDiabetic RetinopathyHumansMicroRNAsMiat long non-coding RNAMicroRNAsRNA, Long NoncodingcomplicationsdiabetesfunctionLncRNAmechanism.MIATvascular lesions

Identifiers

PMID37711129
OpenAlexW4386757943

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.