Evidence map›Paper›PMID 37904219›Full record

ArticleBMC endocrine disorders2023

Identification of novel circulating miRNAs biomarkers for healthy obese and lean children.

Feifei Ma, Dingding Cao, Zhuo Liu, Yuanyuan Li, Shengrong Ouyang, Jianxin Wu

Open access · goldAbstract read
In one paragraph

Article in BMC endocrine disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

11 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
  4. Review
  5. Article
  6. Review
  7. Review
  8. Therapeutic Applications of Poly-miRNAs and miRNA Sponges.International journal of molecular sciences · 2025
    Review
  9. Article
  10. Article
  11. 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

6 authors at 2 institutions in 1 country.

Feifei MaDepartment of Biochemistry and Immunology, Capital Institute of Pediatrics, 2 Yabao street, Beijing, 100020, People's Republic of China.
Dingding CaoDepartment of Biochemistry and Immunology, Capital Institute of Pediatrics, 2 Yabao street, Beijing, 100020, People's Republic of China.
Zhuo LiuDepartment of Biochemistry and Immunology, Capital Institute of Pediatrics, 2 Yabao street, Beijing, 100020, People's Republic of China.
Yuanyuan LiDepartment of Biochemistry and Immunology, Capital Institute of Pediatrics, 2 Yabao street, Beijing, 100020, People's Republic of China.
Shengrong OuyangDepartment of Biochemistry and Immunology, Capital Institute of Pediatrics, 2 Yabao street, Beijing, 100020, People's Republic of China. cipbiochem@163.com.
Jianxin WuDepartment of Biochemistry and Immunology, Capital Institute of Pediatrics, 2 Yabao street, Beijing, 100020, People's Republic of China. cipbiolab@163.com.
Capital Institute of Pediatrics · CNBeijing Tongren Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of childhood obesity and overweight has risen globally, leading to increased rates of metabolic disorders. Various factors, including genetic, epigenetic, and environmental influences such as diet and physical activity, contribute to pediatric obesity. This study aimed to identify specific circulating miRNAs as potential biomarkers for assessing obesity in children.

methodsThirty children, including 15 obese and 15 extremely thin individuals, were selected for this study. MiRNA expression in circulating plasma was assessed using miRNA microarrays. The reliability of differential miRNA expression was confirmed using TaqMan qPCR. The correlation between miRNAs and obesity was analyzed through multiple linear regression, receiver operator characteristic (ROC) curve analysis, and odds ratio (OR) calculations. Bioinformatics tools were utilized to identify target genes for the selected miRNAs, and a functional network map was constructed.

resultsA total of 36 differentially expressed miRNAs were identified through gene chip analysis, and TaqMan qPCR validation confirmed the upregulation of seven miRNAs: hsa-miR-126-3p, hsa-miR-15b-5p, hsa-miR-199a-3p, hsa-miR-20a-5p, hsa-miR-223-3p, hsa-miR-23a-3p, and hsa-miR-24-3p. Among these, hsa-miR-15b-5p and hsa-miR-223-3p exhibited a statistically significant difference except for hsa-miR-23a-3p. These two miRNAs showed more predicted target genes related to obesity than others. Multiple linear regression analysis revealed an association between obesity and hsa-miR-15b-5p and hsa-miR-223-3p [10.529 (4.974-16.084), -10.225 (-17.852~ -2.657)]. Even after adjusting for age and sex, these two miRNAs remained associated with obesity [8.936 (3.572-14.301), -8.449(-15.634~ -1.303)]. The area under the ROC curve (AUC) reached values of 0.816, 0.711, and 0.929, respectively. Odds ratio analysis demonstrated a significant correlation between obesity and hsa-miR-15b-5p (OR = 143, 95% CI 5.80 to 56,313, p = 0.024) and between obesity and hsa-miR-223-3p (OR = 0.01, 95% CI 0.00 to 0.23, p = 0.037). Importantly, hsa-miR-15b-5p was found to have numerous target genes associated with the FoxO, insulin, Ras, and AMPK signaling pathways.

conclusionsDifferential miRNA expression profiles in the circulation of obese children compared to controls suggest underlying metabolic abnormalities. Hsa-miR-15b-5p and hsa-miR-223-3p may be considered as molecular markers for the screening of obese children and populations at risk of developing metabolic syndrome.

Indexed as

MicroRNAsPediatric ObesityBiomarkersChildHumansReproducibility of ResultsSignal TransductionBiomarkersMicroRNAsChildmicroRNASimple obesityTarget gene

Identifiers

PMID37904219
PMCPMC10614305
OpenAlexW4388034532

What OpenQuestion holds

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LicenceCC BY
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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.