Evidence map›Paper›PMID 31443728›Full record

ArticleGenome medicine2019

Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing.

Qian Zhang, Costanza L Vallerga, Rosie M Walker, Tian Lin, Anjali K Henders, Grant W Montgomery, Ji He, Dongsheng Fan, Javed Fowdar, Martin Kennedy and 23 more

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

Article in Genome medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 290 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
290citing papers in PubMed, 3 pooled it
13.6field-weighted citation impact, top 1% 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

290 citing papers in PubMed, 3 syntheses or guidelines pooled it, 490 citations in OpenAlex.

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230 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

33 authors at 11 institutions in 4 countries.

Qian ZhangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Costanza L VallergaInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Rosie M WalkerCentre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Tian LinInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Anjali K HendersInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Grant W MontgomeryInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Ji HeDepartment of Neurology, Peking University, Third Hospital, No. 49, North Garden Road, Haidian District, Beijing, 100191, China.
Dongsheng FanDepartment of Neurology, Peking University, Third Hospital, No. 49, North Garden Road, Haidian District, Beijing, 100191, China.
Javed FowdarGriffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia.
Martin KennedyDepartment of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand.
Toni PitcherNew Zealand Brain Research Institute, Christchurch, New Zealand.
John PearsonDepartment of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand.
Glenda HallidayBrain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia.
John B KwokBrain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia.
Ian HickieBrain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia.
Simon LewisBrain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia.
Tim AndersonNew Zealand Brain Research Institute, Christchurch, New Zealand.
Peter A SilburnQueensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia.
George D MellickGriffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia.
Sarah E HarrisCentre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Paul RedmondCentre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.
Alison D MurrayAberdeen Biomedical Imaging Centre, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK.
David J PorteousCentre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Christopher S HaleyMRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Kathryn L EvansCentre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Andrew M McIntoshCentre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Jian YangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Jacob GrattenInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Riccardo E MarioniCentre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Naomi R WrayInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Ian J DearyCentre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.
Allan F McRaeInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Peter M VisscherInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia. peter.visscher@uq.edu.au.
University of Queensland · AUUniversity of Edinburgh · GBUniversity of Sydney · AUUniversity of Otago · NZGriffith University · AUMRC Institute of Genetics and Molecular Medicine · GBNew Zealand Brain Research Institute · NZPeking University · CNPeking University Third Hospital · CNRoyal Edinburgh Hospital · GBUniversity of Aberdeen · GB

Funding

Biotechnology and Biological Sciences Research Council BB/F019394/1Chief Scientist Office CZD/16/6Medical Research Council MC_PC_U127592696Medical Research Council MC_UU_00007/10Wellcome TrustWellcome Trust 104036/Z/14/Z
6 · The paper itself

Abstract

backgroundDNA methylation changes with age. Chronological age predictors built from DNA methylation are termed 'epigenetic clocks'. The deviation of predicted age from the actual age ('age acceleration residual', AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association.

methodsIn this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues.

resultsWe found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor ) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91-1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79-1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor.

conclusionsThis study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.

Indexed as

DNA MethylationEpigenesis, GeneticAgingEpigenomicsGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansOrgan SpecificityProportional Hazards ModelsReproducibility of ResultsSalivaAgeingAge predictionDNA methylationEpigenetic clockMortality

Identifiers

PMID31443728
PMCPMC6708158
OpenAlexW2969410982

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.