Obstructive Sleep Apnea, Chronic Obstructive Pulmonary Disease, and Heart Failure with Preserved Ejection Fraction: A Cardiopulmonary Perspective

Abstract

In a recent issue of the Journal, Ayas and colleagues put their finger on the pulse of cardiovascular (CV) interactions in obstructive sleep apnea (OSA) (1). The authors eloquently highlighted the pathophysiologic bases of the adverse effect of chronic intermittent hypoxia marked by proinflammatory and adverse neurohormonal effects and raised the important question of whether adjunctive pharmacotherapy could alter CV outcomes in OSA (1). In this context, it is important to remind the scientific community that the effect of such cardiopulmonary interactions extends beyond OSA. This is particularly relevant for chronic obstructive pulmonary disease (COPD) and heart failure with preserved ejection fraction (HFpEF), two diseases that often coexist and adversely impact outcomes (2). Chronic inflammation plays a central role in COPD, and in addition to pulmonary manifestations, is characterized by systemic inflammation with potential adverse CV impact (3). Indeed, inflammation and heart failure are strongly interconnected, and in HFpEF, the proinflammatory state is driven by comorbidities (4). Although blockage of the renin–angiotensin system exerts multisystem antiinflammatory effects, renin–angiotensin system inhibition and multiple other drug classes have not shown improvement in prespecified endpoints in HFpEF trials, thus highlighting the need for better patient selection (5). In this regard, COPD and OSA appear to be prime avenues for a precision medicine approach to HFpEF. However, identifying patients most suited for tailored adjuvant pharmacotherapy for the prevention and management of HFpEF is made challenging by confounding factors, as discussed by Ayas and colleagues (1). Encouragingly, precision medicine in HFpEF is gaining traction, with a focus on diabetes, and the cardiopulmonary community can learn from this. Most recently, machine learning has been incorporated to predict the risk for heart failure in type 2 diabetes, using readily available clinical variables (6). An integrated cardiopulmonary approach leveraging machine learning may be the next best step to investigate the role of adjuvant pharmacotherapy in COPD, OSA, and HFpEF. As a scientific community, we cannot and should not delay a multidisciplinary approach to solving important multisystem problems. n