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Published on: June 10, 2025
Big Data Approaches in Heart Failure Research
Jan D Lanzer1,2,3, Florian Leuschner4,5, Rafael Kramann6,7
1Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany.
Insights
Big data analyses, including omics and clinical data, offer new insights into heart failure (HF). While promising, challenges remain in translating these big data findings into improved patient care.
Area of Science:
- Cardiovascular Research
- Bioinformatics
- Genomics
Background:
- Heart failure (HF) research is increasingly leveraging large datasets.
- Understanding the molecular and clinical profiles of HF patients is crucial.
Purpose of the Study:
- To review the current applications of big data in heart failure research.
- To discuss the role of 'omics' and clinical data in HF studies.
- To explore the limitations and future potential of big data in HF.
Main Methods:
- Analysis of 'omics' data (genomics, proteomics, etc.) for molecular insights.
- Examination of clinical datasets for HF phenotyping and prognostic modeling.
- Application of machine learning and other big data methodologies.
Main Results:
- 'Omics' data reveal molecular profiles in HF patients.
- Advanced technologies like single-cell and spatial profiling enhance understanding of cellular heterogeneity and tissue architecture.
- Big data approaches are improving HF phenotyping and prognostic accuracy.
Conclusions:
- Big data, particularly 'omics' and clinical data, are transforming heart failure research.
- Machine learning holds potential for elucidating HF biology.
- Translating big data-driven insights into clinical practice remains a significant challenge.
Purpose Of Review:
The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential.
Recent Findings:
Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.
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