A novel framework to build saliva‐based DNA methylation biomarkers: Quantifying systemic chronic inflammation as a case study
Accessible and non‐invasive biomarkers that measure human ageing processes and the risk of developing age‐related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva‐based DNA methylation (DNAm) biomarkers that are reproducible and biologically inter...
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| Published in | Aging cell Vol. 24; no. 4; pp. e14444 - n/a |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
John Wiley & Sons, Inc
01.04.2025
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Accessible and non‐invasive biomarkers that measure human ageing processes and the risk of developing age‐related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva‐based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva‐based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all‐cause mortality, disease outcomes, lifestyle factors, and immunosenescence; in many cases outperforming the widely used SCI biomarker C‐reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero‐protective interventions.
In this study, we describe a novel multi‐omics framework to train saliva‐based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate knowledge transfer from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva‐based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. |
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| AbstractList | Accessible and non‐invasive biomarkers that measure human ageing processes and the risk of developing age‐related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva‐based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva‐based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all‐cause mortality, disease outcomes, lifestyle factors, and immunosenescence; in many cases outperforming the widely used SCI biomarker C‐reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero‐protective interventions.
In this study, we describe a novel multi‐omics framework to train saliva‐based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate knowledge transfer from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva‐based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors, and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions. Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors, and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions.Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors, and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions. |
| Audience | Academic |
| Author | Martin‐Herranz, Daniel E. Woods, David Robinson, Ian McCartney, Daniel L. Gavrilov, Miloš Descamps, Pierric Stubbs, Thomas M. Sullivan, Jack McIntosh, Andrew M. Call, Toby P. Jackson, Thomas Paiusi, Chuck Behrens, Christian E. Lord, Janet M. Taylor, Emily Wishart, Karl Marioni, Riccardo E. Óvári, Veronika McGee, Kirsty C. Schmunk, Lisa J. Campbell, Archie Thompson, Rob Javaid, Hira Hastings, Waylon J. Tomkinson, Natacha Zlamalova, Eliska Magalia, Ana Girr, Leona Mc Philpott, Ollie Russell, Dave Jovicevic, Vanja Watson, Hector Kojadinović, Dragoljub Stone, Emma |
| AuthorAffiliation | Hurdle.Bio/Chronomics Ltd., London, UK |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39888134$$D View this record in MEDLINE/PubMed |
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| Copyright | 2025 Chronomics Ltd. Bayer and The Author(s). published by Anatomical Society and John Wiley & Sons Ltd. 2025 Chronomics Ltd. Bayer and The Author(s). Aging Cell published by Anatomical Society and John Wiley & Sons Ltd. COPYRIGHT 2025 John Wiley & Sons, Inc. 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | ageing DNA methylation systemic chronic inflammation (SCI) biomarker machine learning inflammageing epigenetic clock |
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| Snippet | Accessible and non‐invasive biomarkers that measure human ageing processes and the risk of developing age‐related disease are paramount in preventative... Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative... |
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| SubjectTerms | Adult Aged ageing Aging Aging - genetics Biological markers biomarker Biomarkers Biomarkers - metabolism Blood Blood proteins C-reactive protein Case studies Chronic Disease Datasets Development and progression DNA DNA methylation DNA Methylation - genetics Electronic medical records epigenetic clock Epigenetic inheritance Epigenetics Female Genetic research Humans Immunosenescence inflammageing Inflammation Inflammation - genetics Inflammation - metabolism machine learning Male Medical records Medicine, Preventive Methylation Middle Aged Molecular modelling Mortality Preventive health services Saliva Saliva - metabolism systemic chronic inflammation (SCI) |
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| Title | A novel framework to build saliva‐based DNA methylation biomarkers: Quantifying systemic chronic inflammation as a case study |
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