A Unified Model of Exercise-Induced Myocardial Oxygen Supply-Demand Balance

Thesis Abstract

Chronic and acute mismatches between myocardial oxygen supply and demand during exercise underpin risk of ischemia and subclinical cardiomyopathy, yet current models inadequately integrate vascular, metabolic, and autonomic determinants across varying intensities and populations. This study aims to develop and validate a unified computational and physiological model that synthesizes myocardial oxygen supply-demand balance across rest-to-exercise transitions, with explicit incorporation of coronary hemodynamics, perfusion reserve, myocardial substrate flexibility, and autonomic modulation. Specific objectives are (1) to formulate a theoretical framework integrating Fick principle, coronary autoregulation, and autonomic responsivity within a single dynamical system; (2) to empirically estimate model parameters across different fitness levels and age groups; (3) to evaluate model performance in predicting myocardial oxygen dynamics under graded exercise tests and isometric exertion; and (4) to simulate pathological scenarios (e.g., microvascular dysfunction, endothelial impairment) and assess potential compensatory mechanisms. A mixed-methods approach will be employed. The quantitative component uses a cross-sectional sample of 320 participants stratified by age (18–35, 36–55, 56–75) and fitness level (recreationally active, trained athletes). Participants will undergo symptom-limited graded treadmill testing with continuous monitoring of oxygen consumption (VO2), cardiac output (via impedance cardiography), heart rate variability, and non-invasive estimates of myocardial oxygen extraction using near-infrared spectroscopy-derived regional oxygen saturation. Coronary perfusion pressure proxies will be derived from blood pressure and estimated LV end-diastolic pressure surrogates, while myocardial perfusion reserve will be inferred through exercise-induced changes in a validated reflectance spectroscopy index. Data collection instruments include a metabolic cart (dual gas analysis), impedance cardiography module, continuous ECG, non-invasive hemodynamic monitors, and a wearable accelerometer for activity profiling. Additionally, a nested qualitative component (n=40, purposive sampling across strata) will conduct semi-structured interviews to elucidate perceived exertional limitations and autonomic symptoms, analyzed via thematic analysis. Analytical procedures will integrate system identification and parameter estimation within a Bayesian hierarchical framework. The core model will be a dynamical system combining Fick-based oxygen delivery (DO2) and consumption (VO2), with arterial-venous oxygen content differences and coronary flow as key state variables. Regression analyses will quantify relationships among VO2, heart rate, stroke volume, and perfusion indicators to calibrate transfer functions. Model validation will compare predicted myocardial oxygen balance against independent biomarkers of myocardial stress (high-sensitivity troponin, BNP) and imaging-derived estimates of myocardial perfusion reserve where available. Sensitivity analyses will examine the influence of autonomic tone (as indexed by HRV metrics) and substrate flexibility (estimated from respiratory exchange ratio) on balance dynamics. Theoretical grounding will draw on the Fick principle, Hill’s muscle energetics, and the Bezold–Jarisch reflex framework to interpret autonomic modulation within the unified model. Expected findings include (a) a robust, parsimonious set of parameters enabling accurate prediction of the timing and magnitude of supply-demand balance across exercise intensities; (b) quantification of how fitness level modulates the buffering capacity of coronary flow and myocardial oxygen extraction; (c) identification of precise thresholds where imbalance predicts early ischemic risk in older or less fit individuals; and (d) demonstration of how simulated endothelial dysfunction shifts the balance under quasi-ischemic conditions, with potential compensatory mechanisms highlighted by autonomic adjustments. The study will contribute to knowledge by delivering an integrative, testable model linking vascular, metabolic, and autonomic determinants of myocardial oxygen balance, with applicability to risk stratification, exercise prescription, and translational research on microvascular pathology. Practical implications include refined guidelines for safe exercise testing in at-risk populations and a framework to simulate pharmacological or rehabilitative interventions aimed at preserving myocardial oxygen balance. The conclusion will emphasize model utility in predicting individual responses to exercise and informing personalized conditioning programs, with recommendations for longitudinal validation in clinical cohorts and exploration of integration with imaging-based perfusion metrics.