Details

Autor(en) / Beteiligte

• Hillary, Robert F.
• Ng, Hong Kiat
• McCartney, Daniel L.
• Elliott, Hannah R.
• Walker, Rosie M.
• Campbell, Archie
• Huang, Felicia
• Direk, Kenan
• Welsh, Paul
• Sattar, Naveed
• Corley, Janie
• Hayward, Caroline
• McIntosh, Andrew M.
• Sudlow, Cathie
• Evans, Kathryn L.
• Cox, Simon R.
• Chambers, John C.
• Loh, Marie
• Relton, Caroline L.
• Marioni, Riccardo E.
• Yousefi, Paul D.
• Suderman, Matthew

Titel

Blood-based epigenome-wide analyses of chronic low-grade inflammation across diverse population cohorts

Ist Teil von

• Cell genomics, 2024-05, Vol.4 (5), p.100544-100544, Article 100544

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• Chronic inflammation is a hallmark of age-related disease states. The effectiveness of inflammatory proteins including C-reactive protein (CRP) in assessing long-term inflammation is hindered by their phasic nature. DNA methylation (DNAm) signatures of CRP may act as more reliable markers of chronic inflammation. We show that inter-individual differences in DNAm capture 50% of the variance in circulating CRP (N = 17,936, Generation Scotland). We develop a series of DNAm predictors of CRP using state-of-the-art algorithms. An elastic-net-regression-based predictor outperformed competing methods and explained 18% of phenotypic variance in the Lothian Birth Cohort of 1936 (LBC1936) cohort, doubling that of existing DNAm predictors. DNAm predictors performed comparably in four additional test cohorts (Avon Longitudinal Study of Parents and Children, Health for Life in Singapore, Southall and Brent Revisited, and LBC1921), including for individuals of diverse genetic ancestry and different age groups. The best-performing predictor surpassed assay-measured CRP and a genetic score in its associations with 26 health outcomes. Our findings forge new avenues for assessing chronic low-grade inflammation in diverse populations. [Display omitted] •Existing DNAm predictors explain 10% of variance in circulating CRP levels•An elastic-net-based predictor outperforms existing models and explains 20% of variance•Newly described predictor is consistent across life course and ancestries•This predictor outperforms CRP and existing methods in health outcome associations Hillary et al. use DNA methylation data from six population cohorts and state-of-the-art algorithms to develop a predictor of C-reactive protein (CRP) levels. Their predictor performs consistently across diverse populations, outperforms blood CRP and existing algorithms in associations with cardiometabolic health outcomes, and thereby refines opportunities to probe chronic inflammation.