Evidence map›Paper›PMID 33628196›Full record

ArticleFrontiers in endocrinology2020

Association of Genetic Polymorphisms in MicroRNAs With Type 2 Diabetes Mellitus in a Chinese Population.

Zaihan Zhu, Yanfen Zhang, Ruocen Bai, Ru Yang, Zhongyan Shan, Chunyan Ma, Jun Yang, Dandan Sun

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.1field-weighted citation impact, top 26% 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

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Genetic Variants ofGenes · 2022
    Article
  7. Article
  8. Micro-RNA Implications in Type-1 Diabetes Mellitus: A Review of Literature.International journal of molecular sciences · 2021
    Review
  9. 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

8 authors at 2 institutions in 1 country.

Zaihan ZhuDepartment of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
Yanfen ZhangDepartment of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
Ruocen BaiDepartment of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
Ru YangDepartment of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
Zhongyan ShanDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
Chunyan MaDepartment of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
Jun YangDepartment of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
Dandan SunDepartment of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
China Medical University · CNFirst Hospital of China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: MicroRNAs (miRNA) involved in the insulin signaling pathways deeply affect the pathogenesis of T2DM. The aim of this study was to assess the association between single nucleotide polymorphisms (SNP) of the related miRNAs (let-7f rs10877887, let-7a-1 rs13293512, miR-133a-1 rs8089787, miR-133a-2 rs13040413, and miR-27a rs895819) and susceptibility to type 2 diabetes mellitus (T2DM), and its possible mechanisms. Methods: Five SNPs in miRNAs (let-7f rs10877887, let-7a-1 rs13293512, miR-133a-1 rs8089787, miR-133a-2 rs13040413, and miR-27a rs895819) involved in the insulin signaling pathways were selected and genotyped in a case-control study that enrolled 371 T2DM patients and 381 non-diabetic controls. The individual SNP association analyses, interaction analyses of SNP-SNP, SNP-environmental factors were performed. The effect the risk-associated polymorphism on regulating its mature miRNA expression was also evaluated. Results: In overall analyses, miR-133a-2 rs13040413 and let-7a-1 rs13293512 were related to the susceptibility to T2DM. In stratified analyses, miR-133a-2 rs13040413, let-7a-1 rs13293512 and miR-27a rs895819 showed associations with T2DM in the age ≥ 60 years subgroup. Moreover, let-7a-1 rs13293512 and miR-27a rs895819 showed associations with T2DM in male subgroup. In SNP-environmental factors interaction analyses, there were interaction effects of miR-133a-2 rs13040413 with dyslipidemia, let-7a-1 rs13293512 with smoking, and let-7a-1 rs13293512 with dyslipidemia on T2DM. In SNP-SNP interaction analyses, there were also interaction effects of miR-133a-1 rs8089787 with let-7a-1 rs13293512, and miR-133a-1 rs8089787 with let-7f rs10877887 on T2DM. Furthermore, for miR-133a-2 rs13040413, the variant T allele showed a trend toward decreased miR-133a expression in comparison with the wild C allele. For let-7a-1 rs13293512, the variant C allele expressed a lower let-7a compared to the wild T allele. Conclusion: MiRNAs polymorphisms involved in the insulin signaling pathways and the interaction effects of SNP-SNP, SNP-environmental factors were related to T2DM susceptibility in a Chinese population.

Indexed as

Genetic Predisposition to DiseasePolymorphism, Single NucleotideAdultAgedAllelesCase-Control StudiesChinaDiabetes Mellitus, Type 2DyslipidemiasFemaleGenotypeHumansInsulinInsulin SecretionMaleMicroRNAsInsulinMicroRNAsChinese populationinsulin signaling pathwaymicroRNApolymorphismtype 2 diabetes mellitus

Identifiers

PMID33628196
PMCPMC7897684
OpenAlexW3126263326

What OpenQuestion holds

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Registered trials

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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.