Epigenomic research · DNA methylation · Clinical science
Medical Affairs
Science
The genome is the sequence you inherit. The epigenome helps decide how that sequence is used. This page explains the science that connects the two to biological aging.
Core concepts
Each concept builds on the previous one. Together they explain what an epigenetic assessment measures and why.
Molecular marks that regulate gene activity without changing the DNA sequence.
Epigenetic mechanisms include DNA methylation, histone modification and non coding RNA. They help determine which genes are active in which cells, and they can change over a lifetime.
Methyl groups at CpG sites form patterns that shift in characteristic ways with age.
Chronological age counts years. Biological aging describes change within cells and tissues. Because methylation patterns track aging, researchers use them to estimate biological age relative to a reference population.
Statistical models that convert methylation data into an age estimate.
Clocks are trained on selected CpG sites in defined populations, then tested in independent cohorts. The gap between estimate and chronological age is described as age acceleration or deceleration. Newer models aim to separate distinct aging processes more precisely.
Glucose regulation, insulin signalling and adiposity are closely studied in aging research.
Metabolic dysfunction has been associated with epigenetic change in research settings. Epigenetic data may add context to metabolic evaluation; it does not replace it.
Chronic low grade inflammation is a recurring theme in aging biology.
Links between inflammatory states and methylation patterns come largely from observational studies. They are presented here as associations and hypotheses, not as established individual effects.
Factors associated with epigenetic patterns across populations.
Age, smoking, alcohol, physical activity, nutrition, metabolic health, inflammation, stress, environmental exposures, disease states and genetics are all associated with epigenetic patterns. None should be read as the direct cause of an individual's biological age.
Figures
Illustrative figures of the kinds of data used in epigenomic research. Conceptual visualisations, not clinical or patient data.
Beta values from 0 (unmethylated) to 1 (fully methylated) across CpG sites and samples.
Read coverage, CpG island annotation and per site methylation calls across a genomic interval.
Genomic, epigenomic and transcriptomic layers analysed together in research settings.
Evidence framework
Throughout this website, statements are placed at the level the evidence actually supports.
A biologically plausible pathway not yet demonstrated in clinical populations.
A statistical relationship seen in studies; not proof of cause in an individual.
Findings from studies in relevant clinical populations with defined endpoints.
A measure with documented analytical and clinical validation for a defined use.
Questions the science addresses
Each is treated in the Science Library with the evidence, its limits and its clinical relevance.
Methylation patterns are dynamic, but individual change is not guaranteed or predictable.
The difference between an epigenetic age estimate and chronological age.
Population associations exist; individual prediction is not established.
No. Biological age measures are not diagnostic tests.
