Description

In the US, the estimated prevalence of Heart Failure (HF) is 6.2 million and increasing. The mortality approaches 50% within 5 years of diagnosis and HF is the main cause of hospitalization among patients over 65 years of age. Information on molecular states may enhance understanding of causes and pathophysiological processes, improve risk prediction, identify new therapeutic targets, and improve subclassification for targeted therapy. Time-dependent phenomapping has been used to identify clinical and genomic differences in a longitudinal fashion, which provides a mechanistic link underlying pathophysiology of the disease and associated outcomes. Studies including high-throughput molecular approaches (multi-omics) along with time-dependent phenomapping would likely be even more powerful. The overarching hypothesis of the study is that the integration of Multi-Omics studies with clinical variables can be implemented to identify patients at risk of adverse outcomes. To test this hypothesis we propose: (1) Longitudinal profiling based on clinical data; (2) Longitudinal Multi-Omics Profiling; and (3) randomized of a telemonitoring intervention (Sensor Profiling) and the effectiveness in the optimization of guideline-directed medical therapy. To achieve these aims we propose to: (1) recruit a cohort of 1000 participants; (2) perform cardiovascular clinical characterization from electronic medical records; (3) perform multi-omics studies and (4) randomization of a telemonitoring intervention in the optimization of guideline-directed medical therapy. Proving these hypotheses would identify different meaningful clinical groups of patients within a cohort of patients with similar clinical conditions, health care providers would have a better understanding of the heterogeneity of the disease and the need for different preventive and therapeutic approaches in the context of precision medicine.