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

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 (BAmech) is the age at which the reference population has the same 10year 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 holdout (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 BAmech, 0.842 for PhenoAge and 0.823 for chronological age. The hazard ratio per SD of the biological age gap was 1.55 (1.48 1.62) for BAemech and 1.45 (1.40 1.50) for PhenoAge. Risk ordering transferred across eras (calibration slope 1.01 1.03), but absolute 10year risk was overpredicted (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 topline 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.