DNA Methylation Clocks and Their Predictive Capacity for Aging
Phenotypes and Healthspan

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Abstract

The number of age predictors based on DNA methylation (DNAm) profile is rising due to their potential in predicting healthspan and application in age-related illnesses, such as neurodegenerative diseases. The cumulative assessment of DNAm levels at age-related CpGs (DNAm clock) may reflect biological aging. Such DNAm clocks have been developed using various training models and could mirror different aspects of disease/aging mechanisms. Hence, evaluating several DNAm clocks together may be the most effective strategy in capturing the complexity of the aging process. However, various confounders may influence the outcome of these age predictors, including genetic and environmental factors, as well as technical differences in the selected DNAm arrays. These factors should be taken into consideration when interpreting DNAm clock predictions. In the current review, we discuss 15 reported DNAm clocks with consideration for their utility in investigating neurodegenerative diseases and suggest research directions towards developing a more optimal measure for biological aging.

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DAVID: Database for Annotation, Visualization, and Integrated Discovery.

Glynn Dennis, Brad T. Sherman, Douglas A Hosack … (2003)

Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.

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An epigenetic biomarker of aging for lifespan and healthspan

Morgan E Levine, Ake T. Lu, Austin Quach … (2018)

Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.

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Biological Age Predictors

Juulia Jylhävä, Nancy Pedersen, Sara Hägg (2017)

The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.

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Author and article information

Journal

Journal ID (nlm-ta): Neurosci Insights

Journal ID (iso-abbrev): Neurosci Insights

Journal ID (publisher-id): EXN

Journal ID (hwp): spexn

Title: Neuroscience Insights

Publisher: SAGE Publications (Sage UK: London, England )

ISSN (Electronic): 2633-1055

Publication date (Electronic): 21 July 2020

Publication date Collection: 2020

Volume: 15

Electronic Location Identifier: 2633105520942221

Affiliations

[1 ]Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada

[2 ]Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands

[3 ]Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada

Author notes

[*]Ekaterina Rogaeva, Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, 60 Leonard Avenue, 4th Floor -4KD481, Toronto, ON M5T 0S8, Canada. Email: ekaterina.rogaeva@ 123456utoronto.ca

Author information

Ekaterina Rogaeva https://orcid.org/0000-0002-2852-0329

Article

Publisher ID: 10.1177_2633105520942221 Publisher-manuscript ID: EXN-20-0011.R1

DOI: 10.1177/2633105520942221

PMC ID: 7376380

PubMed ID: 32743556

SO-VID: 82def7de-ae9c-4b34-851f-10213897f195

License:

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

History

Date received : 31 March 2020

Date accepted : 23 June 2020

Funding

Funded by: The McLaughlin Accelerator Grant in Genomic Medicine 2020, ;

Funded by: National Institutes of Health, FundRef https://doi.org/10.13039/100000002;

Award ID: RF1AG054080

Funded by: consortium canadien en neurodégénérescence associée au vieillissement, FundRef https://doi.org/10.13039/100015569;

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cover-date January-December 2020

typesetter ts1

Keywords: dna methylation,chronological age,biological age,age-related disease,neurodegenerative disorders

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Keywords: dna methylation, chronological age, biological age, age-related disease, neurodegenerative disorders

DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan

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Aging Pathobiology and Therapeutics

Most cited references 74

DAVID: Database for Annotation, Visualization, and Integrated Discovery.

An epigenetic biomarker of aging for lifespan and healthspan

Biological Age Predictors

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