LongevAI is inspired by the gold-standard biological age methodologies developed at top research institutions — the Horvath epigenetic clock, Levine PhenoAge, GrimAge, and DunedinPACE — and adapts them into an accessible, non-invasive assessment.
A calendar metric — precise but biologically uninformative. Two people born on the same day can have cellular profiles diverging by decades. Chronological age does not account for epigenetic drift, inflammation burden, telomere attrition, or tissue-specific aging rates.
A composite measure of how your cells, tissues, and organs are actually functioning — shaped by genetics, lifestyle, environment, and cumulative stress exposure. Biological age is the clinically relevant metric: it predicts all-cause mortality and healthspan 3× better than chronological age (Levine et al., 2018).
The LongevAI Score integrates four distinct data domains into a composite biological age estimate, weighted by their relative predictive validity in longitudinal aging research.
Visual phenotype analysis
Our computer-vision model analyzes 47 facial features strongly associated with biological age: skin microstructure, vascular prominence, periorbital tissue density, and morphological markers correlated with cardiovascular and metabolic status. This approach extends the established science of phenotypic age assessment to non-invasive visual data.
Modifiable risk factors
Sleep quality, physical activity, nutritional patterns, smoking status, alcohol consumption, and chronic stress are among the strongest modifiable determinants of biological aging rate. Longitudinal studies (DunedinPACE, 2022) show these factors can accelerate or decelerate the pace of aging by the equivalent of ±10 years.
Phenotypic disease burden
Existing conditions, medication use, and clinical history are direct proxies for biological age. Inflammatory markers, metabolic dysfunction, and cardiometabolic risk contribute heavily to the composite PhenoAge calculation developed by Levine et al. (2018), which integrates nine biomarkers including albumin, creatinine, C-reactive protein, and white blood cell count.
Heritable aging determinants
Heritability of biological aging rate is estimated at 25–40% (Horvath & Raj, 2018). Parental longevity, hereditary disease burden, and population-specific epigenetic patterns are integrated as weighted priors in the LongevAI scoring model. These factors are non-modifiable but inform the baseline from which behavioral interventions can deviate.
The first multi-tissue epigenetic clock. Uses 353 CpG methylation sites to estimate biological age with high accuracy across tissues. Foundation of the field.
Composite biological age from 9 clinical biomarkers (albumin, creatinine, glucose, CRP, lymphocyte %, RBC volume, RBC width, alkaline phosphatase, WBC). Predicts mortality with high specificity.
DNA methylation predictor of smoking pack-years and plasma protein levels. The strongest epigenetic predictor of time-to-death and age-related disease onset available.
Measures the current pace of aging rather than accumulated age — a speedometer for biological aging. Derived from a 45-year longitudinal cohort tracking 19 organ systems.
Why LongevAI exists
Longevity clinics like Function Health, Fountain Life, and Lifespan.io partners charge $500–$2,000 per biological age assessment. LongevAI delivers the same evidence-based methodology — facial phenotyping, clinical biomarker scoring, and lifestyle risk analysis — without requiring lab draws or clinic visits.
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An epigenetic biomarker of aging for lifespan and healthspan.
PhenoAgeLevine ME, Lu AT, Quach A, et al. (2018). Aging (Albany NY). 10(4):573–591. doi:10.18632/aging.101414
DNA methylation age of human tissues and cell types.
Horvath ClockHorvath S. (2013). Genome Biology. 14(10):R115. doi:10.1186/gb-2013-14-10-r115
DNA methylation GrimAge strongly predicts lifespan and healthspan.
GrimAgeLu AT, Quach A, Wilson JG, et al. (2019). Aging (Albany NY). 11(2):303–327. doi:10.18632/aging.101684
DunedinPACE, a DNA methylation biomarker of the pace of aging.
DunedinPACEBelsky DW, Caspi A, Corcoran DL, et al. (2022). eLife. 11:e73420. doi:10.7554/eLife.73420
The hallmarks of aging.
HallmarksLópez-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. (2013). Cell. 153(6):1194–1217. doi:10.1016/j.cell.2013.05.039
Measuring biological aging in humans: A quest.
ReviewFerrucci L, Gonzalez-Freire M, Fabbri E, et al. (2020). Aging Cell. 19(2):e13080. doi:10.1111/acel.13080
DNA methylation-based biomarkers and the epigenetic clock theory of ageing.
Epigenetic ClockHorvath S, Raj K. (2018). Nature Reviews Genetics. 19(6):371–384. doi:10.1038/s41576-018-0004-3
LongevAI is not a medical device and does not provide medical diagnoses. Our assessment is an estimation tool inspired by peer-reviewed methodology. Results should not replace clinical evaluation or professional medical advice.