A mechanistic, mortality-referenced biological age from routine blood tests: construction and temporal validation of IQN-BIOAGE-01

Abstract Background Blood-based biological-age clocks predict mortality well, but they are statistical composites. They do not say which physiological systems make a person biologically older, and they cannot say how an intervention would move the clock or whether moving it would change risk. Methods We built IQN-BIOAGE-01, a quantitative systems pharmacology (QSP) model of mortality-referenced biological age. A systemic senescent load follows saturating-removal stochastic dynamics and drives eight latent organ/hallmark axes, which are observed through 13 routine blood, blood-pressure and body-size measurements. Death is the first passage of the load over a threshold lowered by poor organ reserve, and biological age (BA_mech) is the age at which the reference population has the same 10-year risk. The model was calibrated on NHANES III (14,049 adults; 2,736 deaths in 20 years) and tested on a locked NHANES IV 1999–2010 hold-out (24,650 adults; 1,867 deaths). Two later versions were accepted or rejected by rules written before their validation results. We confronted the inflammation axis with three anti-inflammatory outcome trials, audited common implementations of the comparator clock, and locked a registration-ready external validation. Results On the hold-out, Harrell’s C was 0.842 for BA_mech, 0.842 for PhenoAge and 0.823 for chronological age (ΔC versus PhenoAge −0.0003, 95% CI −0.0026 to +0.0020). The hazard ratio per SD of the biological-age gap was 1.55 (1.48–1.62) for BA_mech and 1.45 (1.40–1.50) for PhenoAge. Risk ordering transferred across eras (calibration slope 1.01–1.03), but absolute 10-year risk was over-predicted (10.4% against 7.4% observed); life-table recalibration moved observed-to-expected from 0.72 to 0.81. The inflammation axis carried most of the gap. A joint re-estimate did not converge and a converged, biological-variation-anchored version narrowly failed its pre-written identifiability criterion, so v1.0 remains the locked primary. The outcome evidence available so far, chiefly top-line results from one anti-inflammatory trial, suggested that only a small fraction of a pharmacological CRP fall may carry the risk implied by the model’s cross-sectional structure (posterior median pass-through 0.22). Common software implementations of PhenoAge differed from the published formula by 0.7–2.0 years. Conclusions A mechanistic model can match a leading statistical blood clock in discrimination while adding an organ-level decomposition, explicit statements of what the data can and cannot identify, and a structure in which interventions can be simulated. Because inflammation dominates blood-based biological age while lowering CRP with drugs may carry little of the implied risk, a change in a blood clock should not be read as a change in mortality risk without outcome evidence. Validation is Tier 1 (internal and temporal, within one survey programme); the external test is locked and awaiting data access.