Author + information
- George E. Louridas, MD, PhD∗ ( and )
- Katerina G. Lourida, MSc
- ↵∗Cardiology Clinic, AHEPA Hospital, Aristotle University of Thessaloniki, S. Kiriakidi 1, 54636, Thessaloniki, Greece
The paper by Ahmad et al. (1) used cluster analysis to describe clinical phenotypes in chronic heart failure (HF) and identified 4 “phenotypically distinct and clinically meaningful groups.” Their cluster analysis was based on 45 clinical variables and therapeutic effects from 1,619 patients of the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) study (2), which evaluated the effect of exercise training on morbidity and mortality in patients with chronic systolic HF. Ahmad et al. (1) named the patient clusters “chronic heart failure phenotypes” and emphasized “the high degree of disease heterogeneity” and the necessity for more precise phenotyping of the HF syndrome.
In our opinion, the cluster analysis of 45 variables and their grouping in 4 phenotypes is unjustified from a pathophysiological point of view. It is rather a random collection of clinical characteristics with many of those having a variable degree of life-threatening significance. In contrast to the preceding classification, a robust clinical phenotype should have characteristics that could easily identify an entity with well-defined pathophysiology. The classification of the clinical forms of HF in discrete categories (phenotypes) should recognize the basic disease process in order to apply the appropriate treatment. In general, the definition of a cluster suggests that there is an internal “togetherness” of the different clinical characteristics, but the definition does not imply that 2 particular characteristics in the same cluster obligatorily have much in common (3). In reality, the mentioned cardiac symptoms, signs, and biological elements are nodes with some associative memory in a vast network of clinical connections, but they do not form a genuine phenotype with discrete pathophysiology.
To explain the molecular, physiological, and pathological alterations of HF, we should shift attention to the integrated methodology of systems biology approach. The nature of the HF syndrome is characterized by a progressive clinical deterioration that is explained better with further integration of data from the fields of modeling, “omics,” and complex networks. A new conceptual paradigm of HF progression needs the construction of novel models (phenotypes) and clinical networks (clusters) that include the characteristic emergent properties (signs, symptoms, and biological markers) of the HF syndrome (4). The human HF syndrome is a complex entity of mechanistic nature that is interrelated with 2 adaptive functional regulatory systems, the remodeling left ventricular procedure and the homeostatic neurohumoral systems. The activation of the self-organized positive feedback stabilization mechanisms of the renin-angiotensin-aldosterone system, the adrenergic system, and the natriuretic peptide axis system are important to strengthen or suppress the cardiac remodeling procedure.
Francis et al. (5), in an editorial comment regarding the paper of Ahmad et al. (1), stress the importance of “pairing phenotypes identified with cluster analyses with an ‘omics’ approach.” This is correct, but for clinical and therapeutic reasons it seems more important for there to be a meaningful physiological relationship between the various components of “clusters.” With a systems biology approach, the pathophysiological “interconnection” of the clustering components, and their integration with “omics” and with data from genetic and molecular pathways, are important for the construction of robust clinical phenotypes. Furthermore, to explain the functional behavior of cardiac phenotypes, we should recognize the clinical significance of the regulatory and compensatory role of the remodeling myocardial mechanism and of the functional significance of the homeostatic neurohumoral systems.
- American College of Cardiology Foundation
- Ahmad T.,
- Pencina M.J.,
- Schulte P.J.,
- et al.
- Easley D.,
- Kleinberg J.
- Francis G.S.,
- Cogswell R.,
- Thenappan T.