Evidence map›Paper›PMID 33092643›Full record

ArticleClinical epigenetics2020

Genetic and environmental causes of variation in epigenetic aging across the lifespan.

Shuai Li, Tuong L Nguyen, Ee Ming Wong, Pierre-Antoine Dugué, Gillian S Dite, Nicola J Armstrong, Jeffrey M Craig, Karen A Mather, Perminder S Sachdev, Richard Saffery and 7 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical epigenetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
3.5field-weighted citation impact, top 6% 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

29 citing papers in PubMed, 67 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Association between the epigenetic lifespan predictor GrimAge and history of suicide attempt in bipolar disorder.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023
    Article
  18. Article
  19. Article
  20. 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

17 authors at 7 institutions in 4 countries.

Shuai LiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
Tuong L NguyenCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
Ee Ming WongPrecision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia.
Pierre-Antoine DuguéCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
Gillian S DiteCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
Nicola J ArmstrongMathematics and Statistics, Murdoch University, Perth, WA, Australia.
Jeffrey M CraigCentre for Molecular and Medical Research, School of Medicine, Faculty of Health, Deakin University, Waurn Ponds, VIC, Australia.
Karen A MatherCentre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Perminder S SachdevCentre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Richard SafferyMurdoch Childrens Research Institute, Parkville, VIC, Australia.
Joohon SungInstitute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea.
Qihua TanEpidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.
Anbupalam ThalamuthuCentre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Roger L MilneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
Graham G GilesCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
Melissa C SoutheyPrecision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, VIC, Australia.
John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia. j.hopper@unimelb.edu.au.ORCID 0000-0002-8567-173X
The University of Melbourne · AUUNSW Sydney · AUDeakin University · AUMurdoch Children's Research Institute · AUMurdoch University · AUSeoul National University · KRUniversity of Southern Denmark · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDNA methylation-based biological age (DNAm age) is an important biomarker for adult health. Studies in specific age ranges have found widely varying results about its genetic and environmental causes of variation. However, these studies are not able to provide a comprehensive view of the causes of variation over the lifespan.

resultsIn order to investigate the genetic and environmental causes of DNAm age variation across the lifespan, we pooled genome-wide DNA methylation data for 4217 people aged 0-92 years from 1871 families. DNAm age was calculated using the Horvath epigenetic clock. We estimated familial correlations in DNAm age for monozygotic (MZ) twin, dizygotic (DZ) twin, sibling, parent-offspring, and spouse pairs by cohabitation status. Genetic and environmental variance components models were fitted and compared. We found that twin pair correlations were - 0.12 to 0.18 around birth, not different from zero (all P > 0.29). For all pairs of relatives, their correlations increased with time spent living together (all P < 0.02) at different rates (MZ > DZ and siblings > parent-offspring; P < 0.001) and decreased with time spent living apart (P = 0.02) at similar rates. These correlation patterns were best explained by cohabitation-dependent shared environmental factors, the effects of which were 1.41 (95% confidence interval [CI] 1.16 to 1.66) times greater for MZ pairs than for DZ and sibling pairs, and the latter were 2.03 (95% CI 1.13 to 9.47) times greater than for parent-offspring pairs. Genetic factors explained 13% (95% CI - 10 to 35%) of variation (P = 0.27). Similar results were found for another two epigenetic clocks, suggesting that our observations are robust to how DNAm age is measured. In addition, results for the other clocks were consistent with there also being a role for prenatal environmental factors in determining their variation.

conclusionsVariation in DNAm age is mostly caused by environmental factors, including those shared to different extents by relatives while living together and whose effects persist into old age. The equal environment assumption of the classic twin study might not hold for epigenetic aging.

Indexed as

AdolescentAdultAgedAged, 80 and overAgingCase-Control StudiesChildChild, PreschoolDNA MethylationEpigenesis, GeneticEpigenomicsFemaleGene-Environment InteractionGenetic VariationGenome-Wide Association StudyHumansAgingBiological ageDNA methylationEpigenetic agingEpigenetic clockTwin study

Identifiers

PMID33092643
PMCPMC7583207
OpenAlexW3094578495

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.