Every number in Eumaia is grounded in published research — not wellness trends. Here's exactly how we estimate biological age, which studies we rely on, and what our limitations are.
Plain-English Summary
Eumaia uses a blood-biomarker phenotypic age model — not an epigenetic DNA clock. It does not sequence your genome or measure DNA methylation. It uses 9 routine blood markers to estimate biological age based on a validated population-level mortality risk model. Results are estimates for educational and self-tracking purposes, not clinical diagnoses.
Eumaia's biological age estimate is based on the PhenoAge algorithm, developed by Dr. Morgan Levine and colleagues at Yale School of Medicine and published in Aging (2018). The algorithm was trained and validated on over 11,000 participants from the National Health and Nutrition Examination Survey (NHANES III).
PhenoAge outperforms chronological age at predicting all-cause mortality, cancer incidence, disability, and multi-morbidity — making it one of the most clinically validated biological age clocks available from standard blood tests.
Primary Citation
Levine, M.E., Lu, A.T., Quach, A., et al. (2018). "An epigenetic biomarker of aging for lifespan and healthspan." Aging, 10(4), 573–591. doi:10.18632/aging.101414
PhenoAge uses nine routine blood markers — all available from a standard metabolic panel and CBC — weighted by their contribution to biological aging:
Albumin (g/dL)
Liver function, nutritional status, longevity predictor. Declines with inflammation and poor nutrition.
Optimal range
> 4.4
Creatinine (mg/dL)
Kidney filtration efficiency. Elevated levels indicate declining renal function.
Optimal range
0.7–1.0
Fasting Glucose (mg/dL)
Metabolic health and insulin sensitivity. Chronic elevation accelerates cellular aging.
Optimal range
< 85
C-Reactive Protein (CRP) (mg/L)
Systemic inflammation — the #1 driver of accelerated biological aging across all systems.
Optimal range
< 0.5
Lymphocyte % (%)
Immune competence. Low levels predict infection risk and immune senescence.
Optimal range
25–40%
MCV (fL)
Red blood cell size. Elevated MCV indicates B12/folate deficiency or liver stress.
Optimal range
80–95
RDW (%)
Red cell variation width — a powerful, underappreciated marker of oxidative stress and inflammation.
Optimal range
< 12.5
Alkaline Phosphatase (ALP) (U/L)
Liver and bone health marker. Elevations linked to liver inflammation and accelerated aging.
Optimal range
< 80
White Blood Cell Count (K/μL)
Total immune activation. Chronically elevated WBC reflects systemic immune stress.
Optimal range
4–7
The PhenoAge calculation involves two steps:
Step 1 — Phenotypic Mortality Score
The nine biomarkers are combined using a linear equation with coefficients derived from Cox proportional hazards regression on the NHANES dataset. This produces a "mortality score" that represents 10-year mortality risk better than chronological age alone.
Step 2 — Convert to Biological Age
The mortality score is converted to an age equivalent using a Gompertz survival model — mapping the predicted mortality risk to the chronological age at which the average person would carry that same risk.
Results are clamped between 18 and 100 to avoid extrapolation artifacts. All coefficients are taken directly from the published paper and have not been modified.
NHANES participants used in training and validation
Average biological age improvement seen in lifestyle interventions (Fitzgerald et al., 2021)
Statistical significance of PhenoAge vs chronological age in predicting all-cause mortality
Body systems tracked: metabolic, immune, inflammatory, hepatic, renal, cardiovascular
Additional Supporting Research
PhenoAge is a blood-biomarker clock — it estimates biological age from routine lab values. A more advanced class of biological age tests uses epigenetic methylation patterns — directly reading which genes are being silenced or expressed as you age, at the DNA level.
Horvath Clock (2013)
Steve Horvath, UCLA
The first generation epigenetic clock. Trained on 8,000+ samples across 51 tissue types. Highly accurate but slow to respond to interventions.
GrimAge (2019)
Lu et al., Harvard
Second-generation clock trained on time-to-death rather than chronological age. Strongest predictor of all-cause mortality and healthspan of any current clock.
DunedinPACE (2022)
Belsky et al., Duke
Measures the pace of aging — how fast you are aging right now, not just your biological age. Responds to lifestyle interventions within months. Sinclair's preferred current metric.
Why Eumaia uses PhenoAge today
Epigenetic clocks require DNA methylation testing — a specialised lab process currently costing $300–500 per test (TruDiagnostic, Elysium Index, Biological Age Test by Chronomics). PhenoAge requires only a standard blood panel available at any lab for under $100. We chose accessibility without sacrificing validation quality. Epigenetic clock integration is on our roadmap — our goal is to be the platform that synthesises both blood-biomarker and methylation data into a unified biological age picture.
References: Horvath (2013) Genome Biology · Lu et al. (2019) Nature Aging · Belsky et al. (2022) eLife · Sinclair & LaPlante, Lifespan (2019)
Eumaia draws from two distinct traditions — peer-reviewed longevity science that forms the methodological core, and the practitioner-experimenters who have shaped how millions of people actually engage with longevity. We are precise about which is which.
Scientific Foundation
Published researchers whose peer-reviewed work directly informs Eumaia's biological age model, protocols, and recommendations.
Dr. Morgan Levine
PhenoAge, Biological Clocks
Yale → Altos Labs
Dr. Steve Horvath
Epigenetic Clocks, DNA Methylation
UCLA / Altos Labs
Dr. Valter Longo
Fasting Mimicking Diet, Longevity
USC Longevity Institute
Dr. Matt Kaeberlein
Aging Biology, Rapamycin, mTOR
University of Washington
Dr. Rhonda Patrick
Micronutrients, Sauna, Inflammation
FoundMyFitness
Dr. Iñigo San Millán
Zone 2, VO2 Max, Mitochondria
CU Anschutz
Dr. Matthew Walker
Sleep Science, REM, Circadian
UC Berkeley
Dr. Gabrielle Lyon
Muscle-Centric Medicine, Protein
Institute for Muscle-Centric Medicine
Dr. Jason Fung
Therapeutic Fasting, Insulin, Autophagy
IDM Program
Dr. Ben Bikman
Insulin Resistance, Metabolism
Brigham Young University
Dr. Robert Lustig
Metabolic Health, Sugar, Liver
UCSF
Dr. Tim Spector
Microbiome, Twin Research, Nutrition
King's College London
Voices Shaping Longevity
The popularisers and practitioner-experimenters who built the consumer longevity conversation — and whose protocols Eumaia's users actively follow and want to evaluate.
David Sinclair
NAD+, Sirtuins, Epigenetics
Harvard Medical School
Andrew Huberman
Neuroscience, Protocols, Performance
Stanford
Dave Asprey
Biohacking, Mitochondrial Health
Bulletproof / unlimited.life
Peter Attia
Longevity Medicine, Zone 2, VO2 Max
Early Medical
All expert names and research are referenced for educational attribution only. No endorsement implied. Eumaia is not affiliated with, or partnered with, any individual listed above.
We believe in transparency. Here's what Eumaia cannot do:
Medical Disclaimer
Eumaia is a longevity education and tracking platform. Content is for informational purposes only and does not constitute medical advice, diagnosis, or treatment. Always consult a qualified physician or licensed healthcare provider before making decisions about your health. The PhenoAge algorithm and all referenced research are used for educational purposes — Eumaia is not affiliated with Yale University, the NHANES program, or any referenced researcher.
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