Key Points
• Source: Herz
• Relevance: “Here we provide a framework for understanding the phenotype-based approach to HFpEF by reviewing (1) the historical context of HFpEF; (2) the current HFpEF paradigm of comorbidity-induced inflammation and endothelial dysfunction; (3) various methods of sub-phenotyping HFpEF; (4) comorbidity-based classification and treatment of HFpEF; (5) machine learning approaches to classifying HFpEF; (6) examples from HFpEF clinical trials; and (7) the future of phenomapping (machine learning and other advanced analytics) for the classification of HFpEF.”
• Various HFpEF clinical trials have examined the sub-phenotyping of HFpEF to identify specific treatments for HFpEF subtypes. Different forms of -omics data can pick out overrepresented biological pathways, which offers insight into the biological mechanisms of HFpEF subtypes.
• Sodium-dependent glucose contransporter‑2 (SGLT2) inhibitors could help most HFpEF patients but these and other therapies must be personalized.
• Independent machine learning (phenomapping) plus -omics analyses is becoming more popular in the diagnosis of HFpEF and heralds a precision-medicine approach.
• The different presentations of HFpEF in clinical trials and registries, as well as the lack of efficacy of generalized medical approaches such as renin–angiotensin–receptor signaling cascade blockers to help HFpEF patients, have highlighted the need for a differentiated therapeutic strategy. Treatment could then be based on the leading comorbidities related to the clinical phenotype, which could direct interventions
• “Investigators and clinicians should understand the potential limitations of such approaches and should augment their initial phenomapping studies of HFpEF with follow-up studies to identify underlying molecular mechanisms with the hope of conducting successful precision medicine trials in the future,” the authors wrote.