Blood Age Test (Deep Longevity)
AI-based biological age estimation from blood biomarkers reflecting overall aging pace and system health.
Table of contents
Basic data
The Blood Age Test (Deep Longevity) is an AI-driven assessment that estimates your biological age based on standard blood biomarkers. Unlike conventional lab tests that measure individual parameters, this model analyzes the collective patterns and deviations within your blood panel to predict how fast your body is aging relative to your chronological age.
Using a proprietary deep-learning algorithm trained on thousands of blood samples, the test interprets subtle biological signals of cellular wear and systemic health decline. It provides a single, interpretable score — your “Blood Age” — and compares it to your real age, showing whether you are aging faster or slower than average.
Category: Epigenetics
Level: Advanced
Usefulness: Low
Level
Advanced
Usefulness
Low
Biological age tracking
Offers a single, interpretable "Blood Age" score reflecting biological aging pace, useful for longitudinal monitoring.
Complement to epigenetic and fitness clocks
Works best when compared with other biological age measures (e.g., methylation clocks, VO₂max), adding depth to your longevity profile.
How it works
Data collection
A standard venous blood draw is analyzed for up to 45 biochemical and hematological parameters.
AI-based modeling
An algorithm processes the biomarker pattern to infer biological age and organ system health based on statistical deviations from population norms.
Measures
Blood Age
The predicted biological age derived from AI analysis of blood biomarker patterns.
Organ health scores
Sub-scores indicating estimated health of key systems such as liver, kidney, lipid, and glucose metabolism.
Reliability
Repeatability
Blood Age results are generally stable if lifestyle and physiology remain constant; significant fluctuations may reflect short-term biological stress or lab variability.
Sensitivity to conditions
Recent illness, dehydration, or fasting deviations can alter biomarker levels and affect the predicted age.
Limitations
Indirect measurement
The model infers biological aging from biomarker correlations, not direct cellular or molecular mechanisms.
Dependent on dataset quality
Predictive accuracy depends on the representativeness of training data; individual variability may limit precision.
Frequency
Suggested cadence
Repeat every 6–12 months to track biological aging trends and assess the long-term effects of lifestyle interventions.
Cost
Typical costs
Approximately €150–300, depending on the provider and region. Requires a full blood panel for analysis.
Availability
Where available
Offered through Deep Longevity’s partner clinics and labs, including select diagnostic networks in Europe and Asia.
Preparation
How to prepare
Follow standard blood test preparation: overnight fasting (8–12 hours), avoid alcohol and strenuous exercise 24 hours prior to sample collection.
Interpretation
Negative age difference
Indicates a slower biological aging rate — your physiology appears younger than your chronological age.
Positive age difference
Suggests accelerated aging or physiological stress — should prompt review of key biomarkers and lifestyle factors.
Alternatives
DNA methylation clocks (e.g., DunedinPACE, TrueAge)
Provide a more direct molecular measure of biological aging via DNA methylation patterns.
Fitness-based biological age (e.g., VO₂max or HRV age)
Estimate biological resilience and cardiovascular fitness as complementary perspectives on aging.
FAQ
Is the Blood Age test clinically validated?
It is based on peer-reviewed AI models developed by Deep Longevity and validated on population datasets, but it is not an FDA-approved diagnostic tool.
Can I use this test to monitor lifestyle interventions?
Yes — repeating the test every few months can show whether your lifestyle changes are associated with slower or faster biological aging.
Does it replace epigenetic clocks?
No. It complements them by using biochemical, not DNA-level, data to estimate biological aging dynamics.