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

• Published in: Aging cell Vol. 24; no. 4; pp. e14444 - n/a
• Main Authors: Schmunk, Lisa J., Call, Toby P., McCartney, Daniel L., Javaid, Hira, Hastings, Waylon J., Jovicevic, Vanja, Kojadinović, Dragoljub, Tomkinson, Natacha, Zlamalova, Eliska, McGee, Kirsty C., Sullivan, Jack, Campbell, Archie, McIntosh, Andrew M., Óvári, Veronika, Wishart, Karl, Behrens, Christian E., Stone, Emma, Gavrilov, Miloš, Thompson, Rob, Jackson, Thomas, Lord, Janet M., Stubbs, Thomas M., Marioni, Riccardo E., Martin‐Herranz, Daniel E., Descamps, Pierric, Taylor, Emily, Paiusi, Chuck, Girr, Leona Mc, Philpott, Ollie, Robinson, Ian, Watson, Hector, Magalia, Ana, Russell, Dave, Woods, David
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.04.2025; John Wiley and Sons Inc
• Subjects: 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); ageing; DNA methylation; systemic chronic inflammation (SCI); biomarker; machine learning; inflammageing; epigenetic clock
• ISSN: 1474-9718; 1474-9726; 1474-9726
• DOI: 10.1111/acel.14444

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.