Reinforcement learning–coupled neural ode modelling reveals ferroptotic resolution failure as a shared dynamical axis linking periodontitis and coronary atherogenesis

The molecular axis underlying the epidemiologically established association between periodontitis and coronary heart disease (CHD) remains undefined at mechanistic, systems-level resolution. Classical comparative transcriptomics captures correlational gene lists but cannot reconstruct directional disease-state dynamics or locate the point along a disease-state ordering at which inflammatory resolution fails. ReNODE-Ferro integrates three public bulk transcriptomic datasets (GSE16134, periodontitis, n = 310; GSE28829, CHD staged, n = 29; GSE100927, CHD plaque, n = 104) through a nine-module biologically curated feature space (123 genes), a fully connected latent encoder, a Neural Ordinary Differential Equation (latent dim = 12) learning continuous disease-state dynamics across six biologically defined states (S0–S5), and a model-based reinforcement learning (RL) controller identifying optimal biological intervention policies in latent space. Twenty-four genes were concordantly dysregulated (FDR < 0.05) across all three datasets. Cross-disease fold-change concordance was significant (Spearman r = 0.473, p = 3.29 × 10 − 8, periodontitis vs. CHD staged). Ferroptotic vulnerability and coronary coupling co-varied with r = 0.830 ( p = 3.89 × 10 − 114). Neural ODE tipping-point analysis identified the S2→S3 metabolic strain-to-ferroptosis-primed transition as the transition carrying the highest modelled velocity, although under a held-out validation scheme the velocity profile proved comparatively flat and the location of the maximum unstable across bootstrap refits. S2 was the least responsive state to single-step intervention (3.7%), but it contains only six specimens and was the least stable cluster on resampling. RL policy optimization identified antioxidant restoration, mitochondrial quality enhancement, and pro-resolution amplification as the universal intervention triad (mean reward improvement = 0.583). Robustness analyses added at revision support and delimit these findings. Tissue of origin explained no measurable variance in the nine-module space (PERMANOVA R² < 0.001, p = 1.00) whereas disease status explained 14.0% ( p < 1 × 10⁻⁴), and the ferroptotic vulnerability–coronary coupling association persisted after adjustment for estimated cellular composition (ρ = 0.764) and within every cohort analysed separately (ρ = 0.77–0.89). An independent diffusion-pseudotime ordering reproduced the state sequence (ρ = 0.872) and placed S2 before S3 ( p = 2.4 × 10⁻¹⁰), and leave-one-cohort-out re-derivation recovered the same structure in all three folds. Against 5,000 size-matched random gene panels the curated feature space was strongly informative (empirical p = 2.0 × 10⁻⁴), although an uncurated transcriptome-wide analysis showed the ferroptotic axis to be one of several concordant programmes rather than a uniquely dominant one. Periodontitis and coronary atherogenesis share a conserved ferroptotic-metabolic resolution failure trajectory. ReNODE-Ferro provides a mechanistically grounded, interventionally actionable map of this continuum, establishing ferroptotic vulnerability as a principal and unusually tightly coupled molecular axis of this shared programme. Because all three cohorts are cross-sectional, the reconstructed trajectory is a statistical ordering rather than a demonstrated temporal or causal sequence.