%A Quan,Yuan %A Liang,Fengji %A Zhu,Yuexing %A Chen,Ying %A Xu,Zi %A Du,Fang %A Lv,Ke %A Chen,Hailong %A Qu,Lina %A Xu,Ruifeng %A Zhang,Hong-Yu %A Xiong,Jianghui %A Li,Yinghui %D 2019 %J Frontiers in Physiology %C %F %G English %K 多个时间点检测到的数据,长期隔离环境,DNA甲基化,化学/代谢参数,疾病 %Q %R 10.3389/fphys.2019.00917 %W %L %M %P %7 %8 2019-July-24 %9 Original Research %# %! Integrated analysis of DNA methylation and biochemical/metabolic parameter %* %< %T Integrated Analysis of DNA Methylation and Biochemical/Metabolic Parameter During the Long-Term Isolation Environment %U https://www.frontiersin.org/articles/10.3389/fphys.2019.00917 %V 10 %0 JOURNAL ARTICLE %@ 1664-042X %X 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.