Wednesday, January 15, 2025
1/15/2025
PROJECT SUMMARY Atrial fibrillation (AF) is a leading cause of stroke and an increasingly prevalent arrhythmia in the United States due to an aging population with predisposing comorbidities (e.g. heart failure, obesity, diabetes, high blood pressure, etc.). Although there have been great technological advances in the treatment of AF, current therapies still remain insufficient due to limited understanding of the mechanisms that drive and maintain AF. Clinical studies lack reliable functional and structural mapping approaches necessary to resolve the patient-specific arrhythmogenic transmural substrate, due to the highly complex 3D structure of the human atria. Subsequently, there remains a significant debate around the mechanisms driving AF, the cause of these drivers, and how best to locate and treat these patient-specific drivers that can occur in both left and right atrial chambers. Therefore, our proposal aims to develop a novel, paradigm-shifting translational framework that uses ex-vivo to in-vivo 3D multimodal imaging approaches to accurately define disease- and sex-specific bi-atrial arrhythmogenic substrates of AF drivers, so that we can elucidate the “fingerprint” features of AF drivers. Our preliminary data led us to hypothesize that disease- and sex-specific fibrotic cellular and extracellular remodeling is heterogeneously present in one or more LA and RA transmural layers (sub-epi, intramural, and sub-endo) and can form arrhythmogenic substrates for localized reentrant AF drivers and represent personalized targets for AF treatment. We will test this hypothesis, directly in explanted human atria and a preclinical animal AF model, by integrating transmural optical mapping, clinical multi-electrode mapping, 3D MRI and PET/CT imaging, and proteo-transcriptomic analyses to define chamber- and disease-specific signaling pathways and structural- molecular-genetic fingerprints of arrhythmogenic AF driver substrates. The validated AF driver substrate fingerprints will be used to train machine learning algorithms to define patient-specific targets in persistent AF patients for either substrate modulating ablation of reentrant tracks (SMART) or anti-fibrotic therapeutic interventions. This translational research is a critical step toward the development of new personalized, mechanism-based, and sex-specific AF treatments whereby driver substrates can be accurately defined, targeted, and successfully treated to cure the most common arrhythmia in the United States.
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