Abstract 4344227: Flux Balance Analysis Predicts Cardio-Metabolic Benefits of Food Products in Cancer Patients

Abstract

Cardiovascular disease (CVD) and cancer are the leading causes of morbidity and mortality in the U.S. Diet is a significant risk factor for both CVD and cancer and has been shown to influence survival and treatment response. Importantly, dietary interventions exacerbate chemotherapy-related cardiotoxicities. To effectively integrate dietary interventions into treatment recommendations, it is critical to understand the complex interactions between nutrients and metabolic changes in the heart. We used the Periodic Table of Food Initiative (PTFI) dataset within the American Heart Association Precision Medicine 2024 Data Challenge. Mass spectrometry analysis of molecular compositions from 500 food products was integrated into flux balance analysis using the mammalian network of cardiac metabolism, CardioNet. We developed an algorithm to compare the metabolic efficacy of PTFI food products and their combinations as diets in computational simulations. We simulated physiological health conditions, oncometabolic stress, and anthracycline chemotherapy treatment by integrating proteomics datasets. In total, over 600,000 simulations mimicking diets were conducted. Our analysis revealed that the availability and composition of food sources directly impact cardiac metabolism, depending on a patient’s health status. Simulations of cancer patients showed an overall reduction in metabolic efficacy for most food products and their combinations. Our analysis revealed that cancer patients require distinct food compositions to ensure cardio-metabolic health. In anthracycline treatment simulations, we identified food combinations that impaired cardiac metabolism by (1) decreased energy provision, (2) significant increases in oxidative stress, reflecting increased beta-oxidation of saturated long-chain fatty acids, and (3) rapid reduction in biomass provision. Using network analysis, we identified food pairings that enable the optimization of cardiac metabolic efficacy. These networks are a starting point for further mechanistic studies and clinical validation. Our findings directly impact cancer patients by developing data-driven recommendations for improved food and meal plans during the different disease stages and treatment. Our results highlight the potential of exploring food interactions for improving cardiometabolic health. Computational workflow simulating food and diets to evaluate metabolic efficacy in the heart during cancer and cancer-related treatment.