Mechanistic biological age clock matches PhenoAge in predicting human mortality
The systems pharmacology model splits biological age into organ axes, revealing that pharmacological drops in inflammation may not translate to expected risk reductions.
bioRxiv · Goryanin I et al. · Paper published 30 Sep 2026
In a preprint analyzing data from thousands of human adults, researchers built and tested a mechanistic biological age clock called IQN-BIOAGE-01. The quantitative systems pharmacology model links 13 routine blood, blood pressure, and body measurements to systemic senescent load across eight organ axes. The team calibrated the model on 14,049 adults from NHANES III and evaluated it on 24,650 adults from NHANES IV. The mechanistic clock matched PhenoAge with a concordance index of 0.842 for predicting mortality, outperforming chronological age. Each standard deviation in the biological age gap yielded a mortality hazard ratio of 1.55. Inflammation drove most of the gap, but trial comparisons suggested that drug-induced drops in C-reactive protein transferred only a fraction of the implied mortality benefit.
Why it matters
Organ-specific decomposition allows researchers to simulate interventions rather than relying solely on statistical composites. It also cautions that lowering blood biomarkers with drugs might not yield the mortality improvements predicted by standard clocks.
Caveats
This work is a preprint limited to observational data from a single national survey program, and it overpredicted absolute ten-year risk. Independent external validation remains locked and pending data access.
Written from the paper’s abstract, and every claim checked against it before publishing. Read the paper for the full methods and data.
The paper
A mechanistic, mortality-referenced biological age from routine blood tests: construction and temporal validation of IQN-BIOAGE-01
Goryanin I, Damms B, Goryanin I
bioRxiv · 30 Sep 2026 · Preprint, not yet peer-reviewed
- Relevance
- Core geroscience
- News value
- Important
- Evidence
- Humans
- Status
- Preprint
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