A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study

Lisa J Schmunk; Toby P Call; Daniel L McCartney; Hira Javaid; Waylon J Hastings; Vanja Jovicevic; Dragoljub Kojadinovic; Natacha Tompkinson; Eliska  Zlamalova; Kirsty C McGee; Jack Sullivan; Archie Campbell; Andrew M McIntosh; Veronika Ovari; Karl Wishart; Christian E Behrens; Emma Stone; Miloš  Gavrilov; Rob  Thompson; Thomas  Jackson; Janet M Lord; Thomas M Stubbs; Riccardo E Marioni; Daniel E.  Martin-Herranz

doi:10.1111/acel.14444

A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study

Lisa J Schmunk^*, Toby P Call, Daniel L McCartney, Hira Javaid, Waylon J Hastings, Vanja Jovicevic, Dragoljub Kojadinovic, Natacha Tompkinson, Eliska Zlamalova, Kirsty C McGee, Jack Sullivan, Archie Campbell, Andrew M McIntosh, Veronika Ovari, Karl Wishart, Christian E Behrens, Emma Stone, Miloš Gavrilov, Rob Thompson, Thomas JacksonJanet M Lord, Thomas M Stubbs, Riccardo E Marioni, Daniel E. Martin-Herranz^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Journal	Aging Cell
Early online date	30 Jan 2025
DOIs	https://doi.org/10.1111/acel.14444
Publication status	E-pub ahead of print - 30 Jan 2025

Keywords / Materials (for Non-textual outputs)

DNA methylation
ageing
biomarker
epigenetic clock
inflammageing
machine learning
systemic chronic inflammation (SCI)

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Aging Cell - 2025 - Schmunk - A novel framework to build saliva‐based DNA methylation biomarkers Quantifying systemicFinal published version, 3.43 MBLicence: Creative Commons: Attribution (CC-BY)

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Cite this

Schmunk, L. J., Call, T. P., McCartney, D. L., Javaid, H., Hastings, W. J., Jovicevic, V., Kojadinovic, D., Tompkinson, N., Zlamalova, E., McGee, K. C., Sullivan, J., Campbell, A., McIntosh, A. M., Ovari, V., Wishart, K., Behrens, C. E., Stone, E., Gavrilov, M., Thompson, R., ... Martin-Herranz, D. E. (2025). A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study. Aging Cell. Advance online publication. https://doi.org/10.1111/acel.14444

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title = "A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study",

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. ",

keywords = "DNA methylation, ageing, biomarker, epigenetic clock, inflammageing, machine learning, systemic chronic inflammation (SCI)",

author = "Schmunk, {Lisa J} and Call, {Toby P} and McCartney, {Daniel L} and Hira Javaid and Hastings, {Waylon J} and Vanja Jovicevic and Dragoljub Kojadinovic and Natacha Tompkinson and Eliska Zlamalova and McGee, {Kirsty C} and Jack Sullivan and Archie Campbell and McIntosh, {Andrew M} and Veronika Ovari and Karl Wishart and Behrens, {Christian E} and Emma Stone and Milo{\v s} Gavrilov and Rob Thompson and Thomas Jackson and Lord, {Janet M} and Stubbs, {Thomas M} and Marioni, {Riccardo E} and Martin-Herranz, {Daniel E.}",

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month = jan,

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Schmunk, LJ, Call, TP, McCartney, DL, Javaid, H, Hastings, WJ, Jovicevic, V, Kojadinovic, D, Tompkinson, N, Zlamalova, E, McGee, KC, Sullivan, J, Campbell, A , McIntosh, AM, Ovari, V, Wishart, K, Behrens, CE, Stone, E, Gavrilov, M, Thompson, R, Jackson, T, Lord, JM, Stubbs, TM, Marioni, RE & Martin-Herranz, DE 2025, 'A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study', Aging Cell. https://doi.org/10.1111/acel.14444

TY - JOUR

T1 - A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study

AU - Schmunk, Lisa J

AU - Call, Toby P

AU - McCartney, Daniel L

AU - Javaid, Hira

AU - Hastings, Waylon J

AU - Jovicevic, Vanja

AU - Kojadinovic, Dragoljub

AU - Tompkinson, Natacha

AU - Zlamalova, Eliska

AU - McGee, Kirsty C

AU - Sullivan, Jack

AU - Campbell, Archie

AU - McIntosh, Andrew M

AU - Ovari, Veronika

AU - Wishart, Karl

AU - Behrens, Christian E

AU - Stone, Emma

AU - Gavrilov, Miloš

AU - Thompson, Rob

AU - Jackson, Thomas

AU - Lord, Janet M

AU - Stubbs, Thomas M

AU - Marioni, Riccardo E

AU - Martin-Herranz, Daniel E.

PY - 2025/1/30

Y1 - 2025/1/30

N2 - 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.

AB - 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.

KW - DNA methylation

KW - ageing

KW - biomarker

KW - epigenetic clock

KW - inflammageing

KW - machine learning

KW - systemic chronic inflammation (SCI)

U2 - 10.1111/acel.14444

DO - 10.1111/acel.14444

M3 - Article

SN - 1474-9718

JO - Aging Cell

JF - Aging Cell

ER -

A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study

Abstract

Keywords / Materials (for Non-textual outputs)

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Exploiting genomic approaches to identify the environmental basis of depression

Generation Scotland: NextGenScot

Using Multiple Data Sources to Stratify Depression and Identify More Effective Drug Treatments for the Disorder

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