Evidence map›Paper›PMID 32114639›Full record

ArticleActa diabetologica2020

Genetic determinants of gestational diabetes mellitus: a case-control study in two independent populations.

Yi Shen, Yulong Jia, Yuandong Li, Xuefeng Gu, Guoqing Wan, Peng Zhang, Yafeng Zhang, Liying Jiang

Abstract read
PubMed Publisher
In one paragraph

Article in Acta diabetologica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 4 pooled it
1.3field-weighted citation impact, top 15% 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

16 citing papers in PubMed, 4 syntheses or guidelines pooled it, 20 citations in OpenAlex.

  1. Association ofBiomolecules & biomedicine · 2025
    Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. 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 3 institutions in 1 country.

Yi Shen *Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu Province, People's Republic of China.
Yulong Jia *Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu Province, People's Republic of China.
Yuandong LiSchool of Management, Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China.
Xuefeng GuShanghai Key Laboratory for Molecular Imaging, University of Medicine and Health Sciences, Shanghai, People's Republic of China.
Guoqing WanShanghai Key Laboratory for Molecular Imaging, University of Medicine and Health Sciences, Shanghai, People's Republic of China.
Peng ZhangSchool of Clinical Medicine, University of Medicine and Health Sciences, Shanghai, People's Republic of China.
Yafeng ZhangAffiliated Hospital of Nantong University, Nantong University, Nantong, Jiangsu Province, People's Republic of China. fcius2004@hotmail.com.
Liying JiangShanghai Key Laboratory for Molecular Imaging, University of Medicine and Health Sciences, Shanghai, People's Republic of China. j_meili@126.com.ORCID http://orcid.org/0000-0001-5179-2069
Shanghai University of Medicine and Health Sciences · CNNantong University · CNXuzhou Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenetic risk score (GRS) is more informative to identify the complicated associations between variants of genes and disease. Considering similar pathogenesis and shared genetic predispositions between gestational diabetes mellitus (GDM) and type 2 diabetes/obesity, we conducted this study to explore whether the GRS model integrating variants related to type 2 diabetes/obesity is also associated with GDM risk.

methodsA population-based case-control study that included 1429 subjects was conducted to investigate the association between the GRS model and GDM risk, which were analyzed employing stratified logistic regression analysis with the adjustment for age, BMI, parity and family history of diabetes.

resultsWe have screened 23 SNPs and further filtered six SNPs that were significantly associated with the risk of GDM: four risk SNPs (MTNR1B: rs10830963, rs1387153, rs2166706; MC4R: rs2229616) and two protective SNPs (MTNR1B: rs1447352 and rs4753426). The GRS model with a higher score indicated a higher genetic predisposition to develop GDM, especially in the highest quartile of GRS (all P < 0.001) and the strata of advanced maternal age (all P < 0.001) and obesity (all P = 0.005).

conclusionIn this study, six SNPs were explored and further identified to be associated with GDM risk, which suggested GRSs including these polymorphisms might participate in facilitating GDM risk. These findings offer the potential to improve our understanding of the etiology of GDM.

Indexed as

Polymorphism, Single NucleotideAdultAllelesCase-Control StudiesDiabetes, GestationalDiabetes Mellitus, Type 2FemaleGenetic Association StudiesGenetic Predisposition to DiseaseHumansObesityPregnancyRisk FactorsGenetic risk scoreGestational diabetes mellitusSingle-nucleotide polymorphism

Identifiers

PMID32114639
OpenAlexW3008600178

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.