Evidence map›Paper›PMID 35445885›Full record

ArticleGeroScience2022

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume.

Lidija Milicic, Michael Vacher, Tenielle Porter, Vincent Doré, Samantha C Burnham, Pierrick Bourgeat, Rosita Shishegar, James Doecke, Nicola J Armstrong, Rick Tankard and 7 more

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

Article in GeroScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.7field-weighted citation impact, top 9% 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

20 citing papers in PubMed, 32 citations in OpenAlex.

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  14. A Deep Learning Approach to Predict Chronological Age.Healthcare (Basel, Switzerland) · 2023
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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

17 authors at 9 institutions in 3 countries.

Lidija MilicicCentre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia.
Michael VacherCentre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia.
Tenielle PorterCentre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia.
Vincent DoréAustralian E-Health Research Centre, CSIRO, Parkville, Victoria, 3052, Australia.
Samantha C BurnhamCentre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia.
Pierrick BourgeatAustralian E-Health Research Centre, CSIRO, Herston, Queensland, 4029, Australia.
Rosita ShishegarAustralian E-Health Research Centre, CSIRO, Parkville, Victoria, 3052, Australia.
James DoeckeCentre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia.
Nicola J ArmstrongDepartment of Mathematics and Statistics, Curtin University, Bentley, Western Australia, Australia.
Rick TankardSchool of Mathematics and Statistics, Murdoch University, Perth, Western Australia, Australia.
Paul MaruffFlorey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3052, Australia.
Colin L MastersFlorey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3052, Australia.
Christopher C RoweDepartment of Molecular Imaging and Therapy and Centre for PET, Austin Health, Heidelberg, Victoria, Australia.
Victor L VillemagneCentre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia.
Simon M LawsCentre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia. s.laws@ecu.edu.au.
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Australian Imaging Biomarkers and Lifestyle (AIBL) Study
The University of Melbourne · AUEdith Cowan University · AUCommonwealth Scientific and Industrial Research Organisation · AUAustralian e-Health Research Centre · AUMurdoch University · AUCSIRO Health and Biosecurity · AUCurtin University · AUAustin Health · AUMonash University · AU

Funding

Research Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Miguel Arce · 2020 to 2026
$30.1M
Multicultural Community Dementia ScreeningR01AG071514 · NIA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI GALVIN, JAMES E · 2021 to 2025
$13.8M
NIA NIH HHS P30 AG066462NIA NIH HHS R01 AG071514
6 · The paper itself

Abstract

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer's Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer's disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes.

Indexed as

Alzheimer DiseaseAgingAmyloid beta-PeptidesAustraliaCross-Sectional StudiesEpigenesis, GeneticHippocampusHumansAmyloid beta-PeptidesAgeingAlzheimer’s diseaseCognitionDNA methylationEpigeneticsHippocampal volume

Identifiers

PMID35445885
PMCPMC9213584
OpenAlexW4224298274

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.