Evidence map›Paper›PMID 34565450›Full record

ArticleClinical epigenetics2021

Combined effects of progesterone and SOCS3 DNA methylation on T2DM: a case-control study.

Lulu Wang, Zhenxing Mao, Xiaotian Liu, Dandan Wei, Pengling Liu, Luting Nie, Keliang Fan, Ning Kang, Yu Song, Qingqing Xu and 7 more

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 20% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Combined Effects ofNutrients · 2023
    Article
  7. 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

17 authors at 2 institutions in 1 country.

Lulu Wang *Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Zhenxing Mao *Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Xiaotian LiuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Dandan WeiDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Pengling LiuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Luting NieDepartment of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Keliang FanDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Ning KangDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Yu SongDepartment of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Qingqing XuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Juan WangDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Mian WangDepartment of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Wei LiaoDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Tao JingSchool of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Wenjie LiDepartment of Nutrition and Food Hygiene, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Chongjian WangDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Wenqian HuoDepartment of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China. huowenqian@zzu.edu.cn.ORCID 0000-0002-7898-093X
Zhengzhou University · CNHuazhong University of Science and Technology · CN

Funding

the excellent youth development foundation of zhengzhou university 2018ZDGGJS052the national key research and development program of china 2016YFC0900803the national key research and development program of china 2019YFC1710002the national natural science foundation of china 21806146the postdoctoral science foundation of china 2016M602264the postdoctoral science foundation of china 2020T130604the science and technique foundation of henan province 202102310046
6 · The paper itself

Abstract

backgroundThis study aims to investigate the independent and combined effects of progesterone and suppressor of cytokine signaling (SOCS)-3 DNA methylation on type 2 diabetes mellitus (T2DM) among men and postmenopausal women in rural China.

methodsA case-control study with 914 participants (329 T2DM, 585 controls) was conducted. Serum progesterone was detected with liquid chromatography-tandem mass spectrometry. DNA methylation of SOCS3 was determined by MethylTarget™. Linear regression was applied to evaluate the associations of progesterone and SOCS3 methylation with marks of glucose metabolism. Logistic regression was employed to investigate the independent and combined effects of progesterone and SOCS3 methylation with T2DM in men and postmenopausal women.

resultsAfter multiple adjustment, progesterone was positively associated with T2DM in both men (odds ratio (OR) (95% confidence interval (CI)): 2.77 (1.79, 4.29)) and postmenopausal women (OR (95% CI): 1.85 (1.26, 2.72)). Methylation level of Chr17:76,356,190 or Chr17:76,356,199 (SOCS3) was negatively associated with T2DM in both men (OR (95% CI): 0.58 (0.39, 0.86) or 0.27 (0.14, 0.51)) and postmenopausal women (OR (95% CI): 0.43 (0.29, 0.65) or 0.53 (0.28, 0.99)). Subjects with high progesterone and low Chr17:76,356,190 or Chr17:76,356,199 methylation were more susceptible to have a higher prevalence of T2DM (men: OR (95% CI): 5.20 (2.49, 10.85) or 5.62 (2.74, 11.54); postmenopausal women: OR (95% CI): 3.66 (1.85, 7.26) or 3.27 (1.66, 6.45)).

conclusionsThe independent and combined effects of progesterone and SOCS3 methylation on T2DM were found among men and postmenopausal women, suggesting that ensuring low levels of progesterone and high methylation of SOCS3 could reduce the prevalence of T2DM. Trial registration The Chinese Clinical Trial registration: The Henan Rural Cohort Study, ChiCTR-OOC-15006699. Registered 06 July 2015, http://www.chictr.org.cn/showproj.aspx?proj=11375.

Indexed as

AdultAgedCase-Control StudiesChinaDiabetes Mellitus, Type 2DNA MethylationFemaleHumansLogistic ModelsMaleMiddle AgedPrevalenceProgesteroneRisk FactorsRural PopulationSuppressor of Cytokine Signaling 3 ProteinProgesteroneSOCS3 protein, humanSuppressor of Cytokine Signaling 3 ProteinCombined effectMethylationProgesteroneSuppressor of cytokine signaling 3Type 2 diabetes mellitus

Identifiers

PMID34565450
PMCPMC8474856
OpenAlexW3202823594

What Socratic holds

Textmetadata
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 Socratic graph.