| Title: | Predicting Cardiovascular Events with Time-Lagged Inflammatory Dynamics: Stochastic Delay Modeling |
| Journal: | Computational and Structural Biotechnology Journal |
| Published: | 1 Mar 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41971942/ |
| DOI: | https://doi.org/10.34133/csbj.0005 |
Publication 18246
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Abstract
Background: Inflammatory responses are often delayed, nonlinear, and subject to stochastic variability, features typically absent from conventional cardiovascular events models. This study investigates whether delayed inflammatory dynamics, simulated via stochastic delay differential equations (SDDEs), can predict cardiovascular events and enrich digital twin architectures. Methods: A mathematical model was developed to simulate interactions between 3 biomarkers, neutrophils (rapid), C-reactive protein (CRP; intermediate), and albumin (slow negative regulator), under delayed and stochastic dynamics. Using an SDDE framework, 100 virtual individuals were simulated. Cardiovascular event was defined as exceeding a threshold in a latent clinical output Y(t), representing a composite cardio-inflammatory stress state. In parallel, proxy delay ratios (CRP/neutrophils and CRP/albumin) were derived in the UK Biobank cohort (n = 502,478) as structural analogs of the simulated delays to assess epidemiological consistency with the model's predictions over 11.9 years of follow-up. Results: Simulated individuals with delayed symptom peaks (Time_to_Peak_Y) showed a strong association with cardiovascular events (20% prevalence): Delayed responders were disproportionately associated with simulated events. Stochastic noise introduced interindividual variability, and temporal delay emerged as a strong model-internal discriminator of trajectories within the SDDE framework. In UK Biobank, delay-based ratios were significantly associated with incident heart failure (P < 0.001), even after multivariable adjustment. Kaplan-Meier curves showed early risk separation across delay tertiles. Conclusion: Delay-aware inflammatory dynamics offer a powerful lens to simulate disease trajectories in digital twins. SDDE-based models capture both timing and regulatory imbalance, bridging mechanistic simulation and real-world epidemiological consistency. This framework strengthens the potential of digital twins to deliver personalized risk modeling.</p>
1 Author
- Alexandre Vallée
1 Application
| Application ID | Title |
|---|---|
| 55917 | Data mining for cardiovascular and cerebrovascular diseases diagnosis discrimination in the UK Biobank |
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