Evidence map›Paper›PMID 36274151›Full record

ArticleClinical epigenetics2022

Temporal associations between leukocytes DNA methylation and blood lipids: a longitudinal study.

Zhiyu Wu, Lu Chen, Xuanming Hong, Jiahui Si, Weihua Cao, Canqing Yu, Tao Huang, Dianjianyi Sun, Chunxiao Liao, Yuanjie Pang and 10 more

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

10 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Methylation status ofEpigenomics · 2024
    Article
  10. 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

20 authors at 8 institutions in 2 countries.

Zhiyu Wu *Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Lu Chen *Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Xuanming HongDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Jiahui SiNational Institute of Health Data Science at Peking University, Peking University, Beijing, 100191, China.
Weihua CaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Tao HuangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Dianjianyi SunDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Chunxiao LiaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Yuanjie PangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Zengchang PangQingdao Center for Disease Control and Prevention, Qingdao, 266033, China.
Liming CongZhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China.
Hua WangJiangsu Center for Disease Control and Prevention, Nanjing, 210008, China.
Xianping WuSichuan Center for Disease Control and Prevention, Chengdu, 610041, China.
Yu LiuHeilongjiang Center for Disease Control and Prevention, Harbin, 150090, China.
Yu GuoFuwai hospital Chinese Academy of Medical Sciences, Beijing, 100037, China.
Zhengming ChenClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Wenjing GaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China. pkuepigwj@126.com.
Liming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China. lmlee@vip.163.com.
Peking University · CNZhejiang Center for Disease Control and Prevention · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNHeilongjiang Center for Tuberculosis Control and Prevention · CNJiangsu Provincial Center for Disease Control and Prevention · CNQingdao Municipal Center for Disease Control and Prevention · CNSichuan Center for Disease Control and Prevention · CNUniversity of Oxford · GB

Funding

Medical Research Council MC_PC_14135Medical Research Council MC_U137686851Medical Research Council MC_UU_00017/1Medical Research Council MC_UU_12026/2Wellcome TrustWellcome Trust 088158/Z/09/ZWellcome Trust 104085/Z/14/ZWellcome Trust 202922/Z/16/ZWellcome Trust 212946/Z/18/Z
6 · The paper itself

Abstract

backgroundThe associations between blood lipids and DNA methylation have been investigated in epigenome-wide association studies mainly among European ancestry populations. Several studies have explored the direction of the association using cross-sectional data, while evidence of longitudinal data is still lacking.

resultsWe tested the associations between peripheral blood leukocytes DNA methylation and four lipid measures from Illumina 450 K or EPIC arrays in 1084 participants from the Chinese National Twin Registry and replicated the result in 988 participants from the China Kadoorie Biobank. A total of 23 associations of 19 CpG sites were identified, with 4 CpG sites located in or adjacent to 3 genes (TMEM49, SNX5/SNORD17 and CCDC7) being novel. Among the validated associations, we conducted a cross-lagged analysis to explore the temporal sequence and found temporal associations of methylation levels of 2 CpG sites with triglyceride and 2 CpG sites with high-density lipoprotein-cholesterol (HDL-C) in all twins. In addition, methylation levels of cg11024682 located in SREBF1 at baseline were temporally associated with triglyceride at follow-up in only monozygotic twins. We then performed a mediation analysis with the longitudinal data and the result showed that the association between body mass index and HDL-C was partially mediated by the methylation level of cg06500161 (ABCG1), with a mediation proportion of 10.1%.

conclusionsOur study indicated that the DNA methylation levels of ABCG1, AKAP1 and SREBF1 may be involved in lipid metabolism and provided evidence for elucidating the regulatory mechanism of lipid homeostasis.

Indexed as

DNA MethylationEpigenesis, GeneticCholesterolCpG IslandsCross-Sectional StudiesGenome-Wide Association StudyHumansLeukocytesLipidsLipoproteins, HDLLongitudinal StudiesTriglyceridesCholesterolLipidsLipoproteins, HDLTriglyceridesBlood lipidsCross-lagged analysisDNA methylationEWASMediation analysis

Identifiers

PMID36274151
PMCPMC9588246
OpenAlexW4307102506

What Socratic holds

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LicenceCC BY
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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 Socratic graph.