Evidence map›Paper›PMID 31636290›Full record

ArticleScientific reports2019

DNA methylation-based biological age, genome-wide average DNA methylation, and conventional breast cancer risk factors.

Minyuan Chen, Ee Ming Wong, Tuong L Nguyen, Gillian S Dite, Jennifer Stone, Pierre-Antoine Dugué, Graham G Giles, Melissa C Southey, Roger L Milne, John L Hopper and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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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

11 authors.

Minyuan ChenCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
Ee Ming WongGenetic Epidemiology Laboratory, Department of Clinical Pathology, University of Melbourne, Melbourne, Australia.
Tuong L NguyenCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.ORCID http://orcid.org/0000-0002-6597-8363
Gillian S DiteCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
Jennifer StoneCentre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Australia.ORCID http://orcid.org/0000-0001-5077-0124
Pierre-Antoine DuguéCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
Graham G GilesCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
Melissa C SoutheyGenetic Epidemiology Laboratory, Department of Clinical Pathology, University of Melbourne, Melbourne, Australia.
Roger L MilneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.ORCID http://orcid.org/0000-0001-5764-7268
John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
Shuai LiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia. shuai.li@unimelb.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA methylation-based biological age (DNAm age), as well as genome-wide average DNA methylation, have been reported to predict breast cancer risk. We aimed to investigate the associations between these DNA methylation-based risk factors and 18 conventional breast cancer risk factors for disease-free women. A sample of 479 individuals from the Australian Mammographic Density Twins and Sisters was used for discovery, a sample of 3354 individuals from the Melbourne Collaborative Cohort Study was used for replication, and meta-analyses pooling results from the two studies were conducted. DNAm age based on three epigenetic clocks (Hannum, Horvath and Levine) and genome-wide average DNA methylation were calculated using the HumanMethylation 450 K BeadChip assay data. The DNAm age measures were positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche (all nominal P < 0.05). Genome-wide average DNA methylation was negatively associated with smoking and number of live births, and positively associated with age at first live birth (all nominal P < 0.05). The association of DNAm age with BMI was also evident in within-twin-pair analyses that control for familial factors. This study suggests that some lifestyle and hormonal risk factors are associated with these DNA methylation-based breast cancer risk factors, and the observed associations are unlikely to be due to familial confounding but are likely causal. DNA methylation-based risk factors could interplay with conventional risk factors in modifying breast cancer risk.

Indexed as

Genome, HumanAdultAgedAgingAustraliaBreast NeoplasmsDNA MethylationFemaleHumansMammographyMeta-Analysis as TopicMiddle AgedRisk FactorsSiblingsTwins

Identifiers

PMID31636290
PMCPMC6803691

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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.