3,138 men across training and validation, not a generic average
The science
How we learned to read your biology from a drop of saliva, and turn it into an age you can move.
Peer-reviewed by the founding clinical team
CLIA-certified lab, results in 4 weeks, no needles
The layer
Your DNA is the blueprint you were born with. Your epigenome is the control layer on top, chemical tags that sit at specific spots on your DNA called CpG sites and turn genes up, down or off as you live. We read those marks directly. Most tests only read your genotype and never measure the marks at all.
Your DNA · the library
Fixed for life.
The genetic code you inherited from your parents. Stable, the same in every cell, the full library you were born with. It carries your potential, but a library alone builds nothing.
Your epigenome · the reading instructions
Rewritten as you live.
Which books are open, which are bookmarked, which gather dust. Unlike your DNA it changes constantly with how you live, and unlike your DNA it is measurable.
DNA methylation
At each CpG site, a methyl tag can turn a gene down or off. Where the marks sit, and how strong they are, tells us which genes are running and which are quiet, and how a body is actually ageing.
Many methylation tests are really genotype tests. They read variants like MTHFR but never measure the marks. That is the difference between guessing someone’s weight by looking, and measuring it on calibrated scales.
What moves it
Unlike your DNA, your methylation changes across your whole life. Ten everyday forces quietly rewrite which of your genes are switched on.
Why it beats a blood test
Abloodtestreadsthemoment. ENGNEreadsthedirection.
A BLOOD TEST | THE MOMENT
BIOLOGICAL AGE (SINGLE SNAPSHOT)
Markers
Result: 34.0
Static Data Panel
A snapshot on one random day, graded against a mixed-sex range. It catches what’s acutely wrong right now, and you should still get one.
SALIVA | ENGNE IS THE DIRECTION
Trend Vector: -2.1 units/yr|Retest Frequency: Tri-annual
BIOLOGICAL AGE
Retest 1Retest 2Retest 3Retest 4Retest 5TODAYFUTURE DIRECTION
TIME
The trend underneath it, read as a man and retested over time. Not what’s wrong today, but where your body is heading, and what moves it.
What it reveals
Trouble,beforeyou feelit.
Because it reads the marks directly, one sample surfaces the things a standard panel misses until they are already a problem.
Biological ageing
How old your cells actually are, not the calendar.
Cellular health
How efficiently your body is maintaining itself.
Lifestyle impact
Which of your habits are helping, and which are hurting.
The problem
Almost every epigenetic clock, from Horvath's landmark model onward, was trained on mixed-sex or majority-female data. But male methylation follows its own trajectory. Plotted across a lifetime, the male line pulls away from the mixed-sex average right through the years that matter most. Even the direct predecessor to this clock, the Collins 2025 saliva clock, was trained on 3,408 people with no optimisation for male biology. That is the industry standard. ENGNE was built to move beyond it.
01
Training pool
Mixed-sex data goes in.
The prior saliva clock used 3,408 people with no optimisation for male biology.
Collins 2025 saliva clock
3,408 people · no male optimisation
02
Generic clock
One broad average is created.
Male and female patterns are compressed into one reference model.
The biological differences are smoothed out.
03
Model output
The male signal gets flattened.
The generic result keeps the average and loses the signals that matter in men.
PerformanceRecoveryInflammationMetabolic
That gap is the signal. A generic clock averages it away.
Ages 30 to 60, the window that matters. Where male signals for performance, recovery, inflammation and metabolic health show up most clearly.
The reference line. A generic model smooths these signals into a broad average, and clarity is lost.
Why the line diverges
Male ageing is driven at the molecular level by systems that behave differently from women. Build the clock around them, and it reads what a generic model smooths over.
5systems
Male ageing
Testosterone and androgen signalling
Cellular repair processes
Immune system activity
Metabolic regulation
Training, stress and environment
Testosterone and androgen signalling
Cellular repair processes
Immune system activity
Metabolic regulation
Training, stress and environment
The clock
AlphaAge is a saliva methylation clock on 285 CpG sites, trained and tested on 3,138 men using the Illumina EPIC 850k array. It reads male biology at R² 0.88. The same clock reads women at 0.775, and that gap is the whole point: it is tuned to a signal that is specifically male.
01The first epigenetic clock built on purely male data
0.88
R² on men.
285 CpG sites.
On women it drops to 0.775.
AlphaAge validation matrix
Male-trained methylation clock
Epigenetics lets us see ageing written into the genome. Until now, every clock treated male and female biology as interchangeable. AlphaAge was designed to change that, built for male health, with deeper insight into performance, recovery, longevity and biological age.
Predicted vs actual
Your chronological age is fixed. Your biological age is what your cells say, and it moves with how you live. AlphaAge plots one against the other, and reads across four signals at once.
Four biological signals
Chronological
42
Fixed.
Age difference
−3years
Biological
39
Moves with you.
Calendar time
Cellular state
The research
AlphaAge is a third-generation methylation clock, designed and published by Christopher Collins. Here is the study behind it.
AlphaAge
A male-specific epigenetic clock
285 CpG sites, saliva, trained on male biology
ORCID 0000-0003-2850-1950 Illumina EPIC 850k
01
Objective
Objective
- Build a saliva clock on male biology alone
- Test whether male focus improves prediction
- Find sex-specific methylation differences
02
Method
Method
- 1,041 men in training
- 2,097 men in test, ages 18 to 86
- 285 CpG sites, EWAS p < 1×10−8
- Linear-regression clock
03
Results
Results
- R² 0.88 on men, 0.775 on women
- ~4.46 year prediction error
- Strong male calibration
04
Conclusion
Conclusion
- Direct methylation signal of ageing
- Sex-specific differences confirmed
- Male-focused clock is more relevant
Study output
The signal, on a chart.
Predicted age tracks true age tightly across the male cohort, and the consumer analysis shows how the reading translates into the readouts you see in the app.
Consumer analysis
From data to a number you can use.
The same methylation signal is translated into your biological age, organ ages and resilience score, the readouts that drive your plan.
Biological age
39
Organ ages
Resilience score
What the sample reads
Saliva carries DNA from several cell types, so a single sample captures multiple systems at once. The lab measures methylation across the genome, then focuses on the sites that track how a man is ageing.
One saliva sample
285
CpG timestamps
Read directly from methylation marks
Multi-system read
Six biological outputs
Hormone signalling
Ageing linked to testosterone, androgen activity and resilience.
Recovery
Immune and inflammation trajectory, before it becomes visible.
Ageing
Cellular ageing at the individual level. Change, not time.
Performance
Recovery, periodisation and your peak window.
Metabolic health
How efficiently your body processes energy.
Cellular maintenance
Repair and resilience, read from the marks.
One saliva sample
285
CpG timestamps
Hormone signalling
Ageing linked to testosterone, androgen activity and resilience.
Recovery
Immune and inflammation trajectory, before it becomes visible.
Ageing
Cellular ageing at the individual level. Change, not time.
Performance
Recovery, periodisation and your peak window.
Metabolic health
How efficiently your body processes energy.
Cellular maintenance
Repair and resilience, read from the marks.
What one sample becomes
Beyond your core age, one saliva sample runs five epigenetic tests: four organ-specific age clocks, an Epi-Vitality resilience index, MethylGuard injury prediction, and full DNA genotyping.
Input 01
One saliva sample
Heart Age
Cardiovascular ageing
Brain Age
Memory and cognition
Hearing Age
Auditory ageing
Eye Age
Ocular ageing
Epi-Vitality
Resilience, measured
A composite from methylation at three CpG sites tied to immune signalling, DNA repair, stress biology and cellular resilience. In the male cohort, high scorers showed, all at p<0.001:
Superior memoryp<0.001
Stronger bone densityp<0.001
Greater muscle powerp<0.001
Better cholesterolp<0.001
MethylGuard
Injury before the injury
An inflammatory epigenetic analysis built around male athletic biology. It flags overtraining and injury risk at a cellular level, before pain, strains or fatigue appear.
Overtraining, read earlyCellular
Inflammation trajectoryTracked
Final output
Full DNA genotyping
One saliva sample
Heart Age
Cardiovascular ageing
Brain Age
Memory and cognition
Hearing Age
Auditory ageing
Eye Age
Ocular ageing
Resilience, measured
A composite from methylation at three CpG sites tied to immune signalling, DNA repair, stress biology and cellular resilience. In the male cohort, high scorers showed, all at p<0.001:
Superior memoryp<0.001
Stronger bone densityp<0.001
Greater muscle powerp<0.001
Better cholesterolp<0.001
Injury before the injury
An inflammatory epigenetic analysis built around male athletic biology. It flags overtraining and injury risk at a cellular level, before pain, strains or fatigue appear.
Overtraining, read earlyCellular
Inflammation trajectoryTracked
Final outputFull DNA genotyping
Your genotype
Reading a single variant like MTHFR explains a sliver. ENGNE uses polygenic scores, combining thousands of variants into one clearer signal.
01
Traits are polygenic
Heart health, metabolism, body composition and strength are shaped by thousands of variants.
02
Small effects add up
Each variant is a tiny weighted signal. Thousands together build a real risk profile.
03
Single SNPs mislead
One variant may raise risk while others lower it. Context and interactions matter.
Variant input
Weighted aggregation
Risk signal
04
Sharper prediction
Combining many small signals improves accuracy across key traits.
05
Risk stratification
Places you in low, average or elevated risk, for earlier action.
06
Hidden heritability
Signals weak alone but meaningful together are not missed.
Think of predicting an exam result. One SNP is a single homework score. One gene is one subject. A polygenic score is every assignment, test and project combined, the full picture. That is the difference in how much of a trait each approach can actually explain:
ApproachVariance explained
Single SNP
<0.1%
Single gene
1 to 5%
Polygenic score
10 to 40%+
For height, a polygenic score explains around 40%. For coronary artery disease it flags people at 3 to 5 times higher risk.
Why generic testing fails men
Three reasons a standard test was never going to read a man properly.
01
The data
Most tests are not male-trained
Almost every clock was trained on mixed-sex or majority-female data, so the algorithm learns the average body, not the male one. Male methylation follows its own trajectory, driven by testosterone and androgen signalling, cellular repair and immune activity. Average that in and the male signal gets diluted. What looks neutral is often just the wrong baseline.
02
The question
A biological clock is not a performance clock
A biological age score is not a readiness score. Most public methylation clocks were built to predict ageing, disease risk or mortality across broad populations, not to isolate recovery capacity, training response or injury risk. You can have a young biological age and still be overtrained and under-recovered. Different systems, treated as interchangeable by everyone else.
03
The follow-through
Data without direction changes nothing
Most platforms hand you numbers and stop. A blood panel, a biological age score, a variant list. Numbers alone do not create change. Without prioritisation, dosage, timing and re-testing, data is passive. Real optimisation is a closed loop: test, interpret, intervene, measure again. Reporting is not the same as optimising.
The field
| Clock / method | Type | Male trained | Epigenetic age | Male markers | Pace of ageing |
|---|---|---|---|---|---|
| ENGNE AlphaAge | Saliva DNAm | Yes | Yes | Yes | Yes |
| 10-CpG saliva clock | Saliva DNAm | No | Yes | Limited | No |
| Horvath pan-tissue | Multi-tissue DNAm | No | Yes | Mixed | No |
| Pace-of-ageing clocks | Blood DNAm | No | Rate only | Mixed | Yes |
| Wearable biological age | Device metrics | No | No | No | No |
| MuhdoAge | Saliva DNAm | No | Yes | Limited | No |
| GlycanAge | Blood glycomics | No | Immune | Limited | No |
The people and the papers
01
Algorithm architect
Chris Collins
Muhdo Health · ORCID 0000-0003-2850-1950
Published epigenetic-clock researcher and architect of AlphaAge. An applied health-data researcher in biological ageing and performance biomarkers who built ENGNE's saliva clock.
Published epigenetic clocks
02
Founder · Physician-scientist
Dr Saima Ajaz
King's College Hospital · Founder, LIVFIT
BSc, MBBS, MPhil, PhD. Over 20 years in precision medicine, with patents for novel biomarker panels and a focus on metabolic health and longevity science.
Precision medicine
03
Clinical authority
Prof Kypros Nicolaides
Fetal Medicine Foundation · KCL
Founder of the Fetal Medicine Foundation and a global authority in early screening and prevention, teaching across 100+ countries. His clinical standard underpins ENGNE's approach.
1,000+ publications
04
Preventive & performance medicine
Dr Kennedy Obiekwe
United Kingdom
A UK medical doctor focused on sports performance, longevity and preventive health, and how lifestyle, nutrition and metabolic health shape long-term male wellbeing.
Performance health
05
Clinical & medtech strategy
Dr Mahd Siddiqi
King's College London
A King's medical graduate with a Distinction in MSc MedTech Innovation. His work spans hepatology, digital health and NLP for health records, with roles across medtech.
MedTech innovation
06
Lifestyle medicine
Dr Nitu Bajekal
NHS · 35+ years
A doctor with 35 years' experience and 20 as an NHS consultant, and a UK pioneer of Lifestyle Medicine, bringing a clinical view of how nutrition and habits shape long-term health.
30 years NHS2025
A Cost-Effective Saliva-Based Human Epigenetic Clock Using 10 CpG Sites, Illumina EPIC 850k
Collins, C.; Brown, J.; Chung, H.C. · DNA 2025, 5, 28
2025
Creatine Monohydrate Use Is Associated with Performance-Enhancing DNA Methylation Patterns
Collins, C. & Puras, A. · SportRxiv 2025
2025
High-Intensity Resistance Training and Distinct Salivary DNA Methylation Patterns
Collins, C. & Puras, A. · SportRxiv 2025
2025
Genetic Predisposition vs. Performance-Enhancing Drug Use and Fat-Free Mass Index in Strength-Trained Men
Collins, C. & Puras, A. · SportRxiv 2025
2025
DNA Methylation Signatures Diverge Between Endurance and Resistance Training Modalities
Collins, C. · ResearchHub 2025
2026
High-Intensity Exercise and Lower Epigenetic Biological Age in Middle-Aged Men
Collins, C. · SportRxiv 2026
2024
Randomised, Double-Blind, Placebo-Controlled Trial of a Polyphenol Supplement on Healthy Ageing
Chong, J.R.; de Lucia, C.; Collins, C. et al. · Antioxidants 2024, 13, 995
2023
Responsiveness to Endurance Training and Favourable Genetic Variants
Chung, H.C. et al. · PLOS ONE 2023
2022
Born Equal: Can Genetics Make the Perfect Athlete
Collins, C. & Heasman, A. · SportRxiv 2022
2018
Can Genetics Predict Sports Injury? GDF5, AMPD1, COL5A1 and IGF2
McCabe, K.; Collins, C. · Sports 2018, 6, 21
2018
Impact of a Reduced-Calorie, Macronutrient Diet Change in Relation to Obesity Genotypes
Ellis, L. & Collins, C. · bioRxiv 2018
