Epigenetic Age – DeepM Age clock, proprietary DNA Methylation clock is the most accurate

Chronological age represents the number of years a person has lived. Epigenetic Age, however, reflects the functional and molecular condition of the body.
An individual’s Epigenetic Age may be lower or higher than their chronological age, indicating slower biological aging or accelerated aging, respectively.

Epigenetic Age is calculated using scientifically validated machine-learning models trained on large population and longitudinal datasets. 
These models analyze DNA methylation data from biological samples and convert complex molecular patterns into an age estimate that correlates with health outcomes, disease risk, and mortality. 

Epigenetic Age analysis requires a DNA-based biological sample using blood sample. 
The specific sample type depends on laboratory workflows, regulatory standards, and regional requirements.

Epigenetic Age is among the most scientifically validated biomarkers of biological aging.
DNA methylation–based aging clocks demonstrate a high level of accuracy, with a typical prediction error of approximately 2.77 years, placing them among the most precise epigenetic aging models currently available.

Epigenetic Age is designed as a decision-support and wellness analytics tool.
It is not a diagnostic test and should always be interpreted alongside clinical evaluations by qualified healthcare professionals.

Artificial intelligence and machine-learning models identify complex, non-linear relationships between epigenetic markers and aging outcomes.
These models continuously improve as new population-level and longitudinal data are incorporated, enhancing prediction accuracy and real-world relevance.

Scientific evidence suggests that epigenetic patterns are dynamic and can respond to positive lifestyle changes.
Factors such as improved nutrition, regular physical activity, stress reduction, quality sleep, and healthier environments may slow or partially reverse epigenetic age acceleration over time.

Epigenetic Age is typically measured at intervals of several months.
This timeframe allows sufficient biological change to occur following lifestyle or therapeutic interventions, making trend analysis more meaningful.

Once laboratory processing and data ingestion are complete, Epigenetic Age reports are generated automatically by the Deep Longevity platform.
Turnaround time depends on laboratory workflows and system integration, but is generally short.

All epigenetic data are processed using secure, encrypted infrastructure that complies with global data-protection standards.
Personal identifiers are removed and anonymized data is processed along with strict privacy controls, access management, and regulatory-compliant data-handling policies.

Yes. Epigenetic Age insights are available through secure APIs, enabling seamless integration with healthcare platforms, wellness applications, research systems, and enterprise longevity solutions. 

Epigenetic Age solutions are designed for: 

• Healthcare providers 

• Research institutions 

• Wellness and digital health platforms 

• Insurers and employers 

• Longevity-focused organizations 

They support data-driven strategies for healthier aging and preventive care.

Epigenetic Age is influenced by DNA methylation patterns shaped by lifestyle and environmental exposures.
Key factors include stress, physical activity, sleep quality, nutrition, smoking, alcohol use, and environmental toxins.
These influences can accelerate or slow biological aging at the cellular level.

A genetic DNA test analyzes inherited DNA sequences that remain largely unchanged throughout life.
An Epigenetic Age test, by contrast, measures chemical modifications on DNA that regulate gene activity and change over time in response to lifestyle and environment.

Epigenetic clocks provide a dynamic view of how the body is aging biologically—beyond genetic predisposition alone.

Epigenetic Age testing is based on decades of research in molecular biology, epigenetics, and population health.
DNA methylation patterns have been shown to change predictably with age and to correlate strongly with health outcomes, disease risk, and longevity.
By combining these biomarkers with AI-driven models, Epigenetic Age provides a robust, science-backed measure of biological aging.