Heart Failure and Stress Response

Abstract

With the current special issue on HEart FAilure and STress Response (HEFAST), the Biomedical Data Journal (BMDJ) aims to introduce a holistic approach to the publication, sharing, linking, review and evaluation of heart failure (HF) studies, based on the open access to scientific information. In cardiology research this is critical in order to promote interoperability between different centres through integration of published biomedical datasets. In particular, this can serve as a source for simulation and integration of computational technologies of diseases and biological processes. It is increasingly recognized that the integration of a variety of biological and medical research data to produce or refine disease models using advanced statistical, computational and mathematical approaches could facilitate the understanding of biological systems’ complexity. To reflect on these developments, publication of datasets on experimental in vivo and ex vivo models of HF and on patients with chronic ischemic and non-ischemic HF can be applied to study development of diseases via systems medicine. Heart failure starts as single-organ disease and advances as a systemic disease during its evolution in which the dysfunction of other organs has a relevant clinical and prognostic impact. 1 In a pathophysiology view, HF can be seen in a unique scenario of altered systemic homeostasis, in which heart and peripheral organ dysfunction, derangement of the neuroendocrine and immune systems represent chronic stress stimuli, with continuous activation of stress mechanisms. This response, defined by McEwen as allostatic load, which can evolve into allostatic overload as an extreme form of allostasis, is the price the body pays for being continuously forced to adapt to adverse physical and pathophysiological conditions. 2,3 Considering the intriguing model of HF as chronic disease, experimental (animal datasets) and clinical observations (human datasets), could help to address the current knowledge gaps in disease pathophysiology in order to support innovation in the development of novel, evidence-based treatments. The previous issue of the Biomedical Data Journal already presented datasets, related to the subject of heart failure. Bunevicius et al published data collected with the aim to foresee functional and cognitive outcomes of patients with ischemic stroke, 4 while Stropute with colleagues described data used to identify Type D (distressed) personality in Lithuanian patients with coronary artery disease. 5