Aging measured. Not estimated.

Breath Spectrometry

Exhaled breath contains hundreds of volatile organic compounds — molecular byproducts of cellular metabolism, oxidative stress, and systemic inflammation. Conventional FTIR spectrometry can detect these compounds, but at sensitivities too low to resolve the subtle concentration gradients that distinguish biological age trajectories.

The EXHLS breath-to-droplet condensation method concentrates exhaled aerosol into a liquid matrix before spectrometric analysis. This single step increases effective sensitivity by two orders of magnitude — enabling detection of biomarkers previously measurable only through invasive blood sampling or mass spectrometry in a research laboratory.

The result is a non-invasive, repeatable breath panel that captures over 200 aging-relevant VOCs in a single five-minute session — including markers of mitochondrial function, lipid peroxidation, and systemic inflammatory load.

Multimodal AI Model

The EXHLS aging model is a transformer-based architecture trained on longitudinal cohort data spanning six biological modalities. Unlike single-modality models that predict biological age from one data type, the EXHLS model learns the cross-modal correlations that emerge over time — the relationship between breath VOC patterns and cognitive decline, between microbiome diversity and inflammatory load.

The model outputs a biological age estimate with confidence intervals, a rate-of-aging velocity, and a set of modality-specific intervention signals — identifying which biological systems are aging fastest and which interventions have the highest predicted impact for that individual.

Clinical Validation

The EXHLS platform has been validated against established biological age clocks — including the Horvath epigenetic clock, GrimAge, and PhenoAge — across multi-site clinical cohorts. Breath-derived biological age estimates show strong concordance with DNA methylation-based clocks while requiring no blood draw or laboratory processing.

Longitudinal validation studies demonstrate that EXHLS biological age velocity predicts all-cause mortality risk, cognitive decline trajectory, and cardiometabolic event probability at five-year horizons — with accuracy comparable to invasive multi-panel blood testing.

Aging Trajectory

The most clinically significant output of the EXHLS platform is not a single biological age estimate — it is the rate of change over time. An individual whose biological age is 45 but aging at 0.6 years per calendar year is on a fundamentally different trajectory than one aging at 1.4 years per year.

By measuring this velocity continuously — and attributing changes to specific biological systems — EXHLS enables precision intervention: identifying which lifestyle, pharmacological, or therapeutic changes are actually bending the aging curve for a specific individual, not just for a population average.