Predicting Cardiovascular Events with Time-Lagged Inflammatory Dynamics: Stochastic Delay Modeling.

Area of Science:

• Mathematical biology
• Computational medicine
• Systems biology

Background:

• Conventional cardiovascular event models lack delayed, nonlinear, and stochastic inflammatory dynamics.
• Inflammation exhibits complex temporal behaviors not captured by standard models.
• This study addresses the gap by investigating delayed inflammatory dynamics.

Purpose of the Study:

• To investigate if delayed inflammatory dynamics, simulated via stochastic delay differential equations (SDDEs), can predict cardiovascular events.
• To assess the utility of these dynamics in enriching digital twin architectures for personalized risk modeling.
• To bridge mechanistic simulation with epidemiological data.

Main Methods:

• Developed an SDDE mathematical model simulating interactions of neutrophils, C-reactive protein (CRP), and albumin.
• Defined cardiovascular events based on a composite cardio-inflammatory stress state threshold in simulated individuals (n=100).
• Derived proxy delay ratios from UK Biobank data (n=502,478) and assessed epidemiological consistency over 11.9 years.

Main Results:

• Simulated delayed symptom peaks strongly associated with cardiovascular events.
• Stochastic noise introduced interindividual variability; temporal delay was a key discriminator.
• UK Biobank delay ratios significantly predicted incident heart failure (P < 0.001), showing early risk separation.

Conclusions:

• Delay-aware inflammatory dynamics provide a robust framework for simulating disease trajectories in digital twins.
• SDDE models effectively capture timing and regulatory imbalances, linking mechanistic insights to epidemiological findings.
• This approach enhances digital twins for personalized cardiovascular risk prediction.