Evidence map›Paper›PMID 38062913›Full record

ArticleJournal of diabetes2024

Association of methylation risk score with incident type 2 diabetes mellitus: A nested case-control study.

Weifeng Huo, Huifang Hu, Tianze Li, Lijun Yuan, Jinli Zhang, Yifei Feng, Yuying Wu, Xueru Fu, Yamin Ke, Mengmeng Wang and 12 more

Open access · goldAbstract read
In one paragraph

Article in Journal of diabetes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 39% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. 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

22 authors at 2 institutions in 1 country.

Weifeng HuoDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Huifang HuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Tianze LiDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Lijun YuanDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Jinli ZhangDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Yifei FengDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Yuying WuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Xueru FuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Yamin KeDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Mengmeng WangDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Wenkai ZhangDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Longkang WangDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Yaobing ChenDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Yajuan GaoDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Xi LiDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Jiong LiuDepartment of Preventive Medicine, School of Public Health, Shenzhen University Medical School, Shenzhen, China.
Zelin HuangDepartment of Preventive Medicine, School of Public Health, Shenzhen University Medical School, Shenzhen, China.
Fulan HuDepartment of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China.ORCID https://orcid.org/0000-0002-2386-1503
Ming ZhangDepartment of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China.ORCID https://orcid.org/0000-0002-2923-5335
Liang SunDepartment of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, China.
Dongsheng HuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-9998-8041
Yang ZhaoDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Zhengzhou University · CNShenzhen University · CN

Funding

Guangdong Basic and Applied Basic Research Foundation 2021A1515012503Guangdong Basic and Applied Basic Research Foundation 2022A1515010503Key R & D and promotion projects in Henan Province 232102311017Nanshan District Science and Technology Program Key Project of Shenzhen NS2022009National Natural Science Foundation of China 81973152National Natural Science Foundation of China 82073646National Natural Science Foundation of China 82103940National Natural Science Foundation of China 82273707National Natural Science Foundation of China 82304228Postdoctoral Research Foundation of China 2021M692903Shenzhen Science and Technology Program JCYJ20210324093612032Shenzhen Science and Technology Program JCYJ20220818095818040
6 · The paper itself

Abstract

aimsTo investigate the association of methylation risk score (MRS) and its interactions with environmental factors with type 2 diabetes mellitus (T2DM) risk.

methodsWe conducted a nested case-control study with 241 onset cases and 241 matched controls. Conditional logistic regression models were employed to identify risk CpG sites. Simple and weighted MRSs were constructed based on the methylation levels of ATP-binding cassette G1 gene, fat mass and obesity associated gene, potassium voltage-gated channel member 1 gene, and thioredoxin-interacting protein gene previously associated with T2DM to estimate the association of MRS with T2DM risk. Stratified analyses were used to investigate interactions between MRS and environmental factors.

resultsA total of 10 CpG loci were identified from the aforementioned genes to calculate MRS. After controlling for potential confounding factors, taking tertile 1 as reference, the odds ratios (ORs) and 95% confidence intervals (CIs) for T2DM of tertile 3 was 2.39 (1.36-4.20) for simple MRS and 2.59 (1.45-4.63) for weighted MRS. With per SD score increment in MRS, the OR (95% CI) was 1.66 (1.29-2.14) and 1.60 (1.24-2.08) for simple and weighted MRSs, respectively. J-curved associations were observed between both simple and weighted MRSs and T2DM risks. Additionally, multiplication interactions for smoking and hypertension with simple MRS on the risk of T2DM were found, similarly for smoking and obesity with weighted MRS on the risk of T2DM (all P

conclusionElevated simple and weighted MRSs were associated with increased risk of T2DM. Environmental risk factors may influence the association between MRS and T2DM.

Indexed as

Diabetes Mellitus, Type 2Case-Control StudiesHumansMethylationObesityRisk FactorsDNA methylationinteraction analysismethylation risk scorenested case-control studytype 2 diabetes mellitus

Identifiers

PMID38062913
PMCPMC10940902
OpenAlexW4389486423

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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.