Evidence map›Paper›PMID 31402871›Full record

ArticleFrontiers in physiology2019

Integrated Analysis of DNA Methylation and Biochemical/Metabolic Parameter During the Long-Term Isolation Environment.

Yuan Quan, Fengji Liang, Yuexing Zhu, Ying Chen, Zi Xu, Fang Du, Ke Lv, Hailong Chen, Lina Qu, Ruifeng Xu and 3 more

Abstract read
In one paragraph

Article in Frontiers in physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

13 authors.

Yuan QuanState Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China.
Fengji LiangState Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China.
Yuexing ZhuLab of Epigenetics and Advanced Health Technology, SPACEnter Space Science and Technology Institute, Shenzhen, China.
Ying ChenLab of Epigenetics and Advanced Health Technology, SPACEnter Space Science and Technology Institute, Shenzhen, China.
Zi XuLab of Epigenetics and Advanced Health Technology, SPACEnter Space Science and Technology Institute, Shenzhen, China.
Fang DuLab of Epigenetics and Advanced Health Technology, SPACEnter Space Science and Technology Institute, Shenzhen, China.
Ke LvState Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China.
Hailong ChenState Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China.
Lina QuState Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China.
Ruifeng XuSchool of Computer Science and Technology, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China.
Hong-Yu ZhangHubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Jianghui XiongState Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China.
Yinghui LiState Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous studies have shown that changes in the epigenome are an important cause of human biochemical or metabolic parameter changes. Biochemical/metabolic parameter disorders of the human body are usually closely related to the occurrence of disease. Therefore, constructing credible DNA methylation site-biochemical/metabolic parameter associations are key in interpreting the pathogenesis of diseases. However, there is a lack of research on systematic integration analysis of DNA methylation with biochemical/metabolic parameter and diseases. In this study, we attempted to use the four-people, multiple time point detected data from the long-term isolation experiment to conduct a correlation analysis. We used the biclustering algorithm FABIA to cluster the DNA methylation site-parameter correlation matrixes into 28 biclusters. The results of the biological function analysis for these biclusters were consistent with the biochemical/metabolic parameter change characteristics of the human body during long-term isolation, demonstrating the reliability of the biclusters identified by our method. In addition, from these biclusters, we obtained highly credible biochemical/metabolic parameter-disease associations, which is supported by several studies. Our results indicate that there is an overlap of biochemical/metabolic parameter-disease associations derived from a small sample, multiple time point data in healthy populations and the associations obtained from a large sample data in patients during disease development. These findings provide insights into understanding the role of the epigenome in biochemical/metabolic parameter change and disease development and has potential applications in biology and medicine research.

Indexed as

biochemical/metabolic parametersdiseasesDNA methylationlong-term isolation environmentmultiple time point detected data

Identifiers

PMID31402871
PMCPMC6676193

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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.