HFIP: an integrated multi-omics data and knowledge platform for the precision medicine of heart failure

Multi-omic analyses and network biology in cardiovascular disease

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration

Industry 4.0 era has witnessed that more and more high-tech and precise devices are applied into medical field to provide better services. Besides EMRs, medical data include a large amount of unstructured data such as X-rays, MRI scans, CT scans and PET scans, which is still continually increasing. These massive, heterogeneous multi-modal data bring the big challenge to finding valuable data sets for healthcare researchers and other users. The traditional data warehouses are able to integrate the data and support interactive data exploration through ETL process. However, they have high cost and are not real-time. Furthermore, they lack of the ability to deal with multi-modal data in two phases—data fusion and data exploration. In the data fusion phase, it is difficult to unify the multi-modal data under one data model. In the data exploration phase, it is challenging to explore the multi-modal data at the same time, which impedes the process of extracting the diverse information underlying multi-modal data. Therefore, in order to solve these problems, we propose a highly efficient data fusion framework supporting data exploration for heterogeneous multi-modal medical data based on data lake. This framework provides a novel and efficient method to fuse the fragmented multi-modal medical data and store their metadata in the data lake. It offers a user-friendly interface supporting hybrid graph queries to explore multi-modal data. Indexes are created to accelerate the hybrid data exploration. One prototype has been implemented and tested in a hospital, which demonstrates the effectiveness of our framework.

New strategies and therapies for the prevention of heart failure in high-risk patients

Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle-aged adults (<65 years). Therefore, identification of individuals HF at-risk (Stage A) or with pre-HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP-HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk-based framework for the prevention of HF. However, traditional risk factors included in the PCP-HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID-19) are emerging as important risk-enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk-enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co-transporter-2 inhibitors that have also been termed the "statins" of HF prevention), intensive blood pressure lowering, and heart-healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk-enhancing factors and SDoH, and equitable approaches to interventions for risk-based prevention of HF.