A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

Abstract

Background Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles. Methods We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature. Results We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance. Conclusions We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients. Graphical Abstract

Pathophysiological Link and Treatment Implication of Heart Failure and Preserved Ejection Fraction in Patients with Chronic Kidney Disease

“…This has been the case for the GLP1 receptor antagonist semaglutide, which showed symptom and functional improvement in patients with HFpEF and obesity [130]. New studies are investigating the correlation between distinct comorbidity profiles and candidate genes, aiming to improve the diagnosis and identify treatment targets for HFpEF [4].…”

“…This is especially true for the so-called secondary HFpEF, or HFpEF mimics, namely, because of an identifiable condition (e.g., infiltrative cardiac disease, hypertrophic cardiomyopathy), that benefits from specific treatments [3]. However, it is equally important to characterize patients who develop HFpEF related to a combination of different causal, comorbidity-driven factors leading to a primary impairment in myocardial relaxation or compliance [4]. Guidelines stress the importance of identifying risk factors, specific etiologies, and comorbidities, suggesting that the treatment of the underlying phenotypes leads to improved outcomes in HFpEF [1].…”

Myocardial Inflammation in Heart Failure With Reduced and Preserved Ejection Fraction

“…While hypertension is the most prominent confounder diagnosed in 75% of all HFpEF patients, usually multiple comorbidities coexist in patients. 13 In contrast to HFrEF, these external stimuli lead to a concentric form of hypertrophy in the majority of HFpEF patients, meaning that cardiomyocytes (and consequently the ventricular wall) become thicker, which ultimately reduces ventricular chamber volume. 14 Fibrosis in HFpEF is generally more diffuse, and, in addition to the abundance of ECM proteins, the precise composition of the ECM plays a more important role.…”