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

Heart failure (HF) with preserved ejection fraction (HFpEF) represents an unmet public health concern with rising prevalence [1, 2]. Accumulating evidence indicates HFpEF is clinically and biologically distinct from HF with reduced ejection fraction (HFrEF), as reflected by missing therapy response in HFpEF patients to drugs effective in HFrEF [3]. HF patients suffer from a wide range of comorbidities, which are considered important for HF development and progression [4]. In the pathogenesis of HFpEF, comorbidities have been suggested as causal factors [3, 5] and could possibly be linked to genetic etiology. Treatment of comorbidity has also been shown to have beneficial effects of cardiac physiology [6], emphasizing the potential to address HF subtypes through their comorbidities.

Systems medicine attempts to model disease in a holistic manner. One facet of this, network medicine, is used to analyze complex systems such as patients, organs, or cells via network representation [7, 8]. Comorbidity networks represent diseases as nodes, connected via edges based on co-occurrence in patients. These networks can be used to define disease modules or explore topological changes between patient cohorts [9‐12]. Previous work has shown that disease comorbidity is also often linked to shared disease genes that locate close together in gene-based networks like protein–protein interaction networks [12, 13]. This observation is often the basis of network-based gene prediction, where novel disease genes are predicted based on network proximity to known disease genes.

Cardiovascular diseases are particularly suited for system medicine approaches due to the typical multiorgan involvement [14] and multifactorial etiology [15]. To date, such approaches to study HFpEF have been limited, though the comorbidity-driven pathophysiology of HFpEF makes it a promising subject. In addition, despite the technological advances in multi-omics, knowledge of molecular characteristics of HFpEF remains limited, possibly due to difficulties of biopsy acquisition in HFpEF patients [16] and heterogeneity of HFpEF patients [17].

In this study, we applied a network medicine approach to describe comorbidity patterns in HFpEF and investigate a shared genetic background associated with these patterns. We first demonstrated that comorbidity profiles vary between HFpEF and HFrEF patients and derived distinct comorbidity profiles for each cohort. Then, we built a comorbidity network that contained disease clusters relevant for HF patients. The construction of a multilayer heterogeneous network by integration of prior knowledge resources allowed us to translate the comorbidity profiles into a gene signature for HFpEF. We corroborated this signature in the cardiac transcriptome of a murine HFpEF model. This network medicine approach allowed us to identify distinct comorbidity profiles and novel genetic patterns in HFpEF.

In this study, we provided a retrospective systems level analysis of comorbidities in a large cohort of HF patients. We derived clinically relevant insights by comparing comorbidity profiles between HFpEF and HFrEF patients and biological insights by defining genes associated with HFpEF and HFrEF comorbidity profiles.

Patient clustering has been previously shown to yield novel subgroups of HFpEF defined by multivariate similarity [109‐111]. In contrast, the clustering of features (i.e., comorbidities) can inform about patterns of co-occurring disease groups. Our study demonstrated that this approach can be useful to interpret comorbidity profiles: the aggregation of co-occurrence patterns of diseases can help to organize illness into different levels of clinical concepts like organs (DC7—gastrointestinal tract), illness severity (DC5—intensive care), or disease categories (DC8—cancer). This aggregation via network clustering may also reduce multiple testing burdens and provide insights into the relevance of low prevalence diseases where comparisons for a single disease may be problematic.

In the patient classifier, HFpEF was characterized by a larger number of comorbidities with lesser emphasis on cardiac disorders. This supports the hypothesis of HFpEF as a comorbidity-driven systemic syndrome [112, 113]. We found that hypertensive heart disease was the most discriminant feature for HFpEF, which has been viewed as a major etiology for diastolic HF involving cardiac hypertrophy and myocardial stiffness [114, 115]. In contrast, ischemic etiologies including myocardial infarction characterized HFrEF consistent with other studies [116].

We identified more novel disease associations with HFpEF such as neoplastic diseases including breast cancer. HF related hospitalizations in breast cancer survivors have been recently associated more with HFpEF than with HFrEF [117], though the reasons for this remain incompletely elucidated [118]. The association to other cancerous diseases remains largely unexplored and should be addressed in future studies. Another interesting aspect of the HFpEF comorbidity profile was the high similarity to DC6, which contained rheumatic, osteologic, and mental diseases. Systemic inflammatory diseases could be a driving factor for HFpEF and rheumatic disease could constitute a pathophysiologic linkage [112, 119‐121]. Bone mineralization also has been reported to be lowered in HFpEF patients [122] and is a symptomatic link to postmenopausal endocrinology [123]. While mental health has been studied in the context of HF extensively, differences between HFpEF and HFrEF are largely unexplored. The joint clustering of these disease complexes and their similarity to female patients provides a potential link between female sex and HFpEF. Future work should further explore these relationships.

HFpEF and HFrEF clearly displayed distinguishable comorbidity profiles. By contrast, HFmrEF, introduced as a unique form of HF in 2016 [124], appeared to be a combination of attributes from HFrEF and HFpEF. Thus, from the comorbidity perspective, it may be a transitional state instead of a unique syndrome as suggested before [125].

We predicted an associated genetic profile from data-driven HFpEF comorbidity profiles. This genetic profile indicates that HFpEF comorbidities are associated with recurrent patterns of genes involved in fibrosis, inflammation, cell differentiation, metabolism, and oxidative stress. As an example, the glutathione-S-transferases, NOS1 and Xanthine dehydrogenase (XDH), were identified by our network. XDH catalyzes the rate limiting step in purine metabolism producing uric acid [126] and previous literature supports both the role of serum uric acids in HF [104] and plasma XDH activity as relevant for adverse clinical outcomes in HFpEF [127]. Nitric oxide synthase (NOS) has been proposed to contribute to endothelial dysfunction in HFpEF [105, 106], and NOS1 inhibition was recently associated with recovery of diastolic dysfunction in a murine model resembling HFpEF [128]. Glutathione-S-transferases (GSTM1, GSTT1, GSTZ1) are antioxidant enzymes and polymorphisms of these genes have been reported as potentially relevant to HF and diastolic dysfunction [107, 108]. This group of genes could constitute crucial gene candidates involved in comorbidity-based HFpEF pathophysiology.

In general, HFpEF is likely to be a disease in which multiple genes and pathways contribute to the spectrum of phenotypes. Therefore, instead of using the disease–gene prediction to identify and validate individual genes, we have corroborated the overall effect of a spectrum of identified genes in murine gene expression data. While this provided additional evidence for the relevance of comorbidity-based gene prediction, further experimental validation is necessary to explore the functional role and reproducible validity of candidate genes. In real-world populations, it is likely that the genetic heterogeneity of the HFpEF syndrome will be influenced by the specific comorbidities that are well represented in each population. In previous disease–gene prediction studies, gene prediction was performed either by selecting multiple seed genes or single seed diseases [129, 130]. We propose that our approach for gene inference based on data-driven comorbidity profiles might be suitable for systemic syndromes where multimorbidity plays an important role like HF and especially HFpEF.

In addition, several data resources were generated in this study: (i) the HFpEF gene predictions, (ii) HFhetnet, and (iii) murine HFpEF transcriptome to help facilitate future efforts to understand HFpEF-related pathophysiology and benefit the research community.

This study had important strengths and was subject to several limitations. An important strength of this study is that we analyzed clinical care data, which is a real-world representation of patients and therefore allowed us to perform a data-driven analysis of comorbidities in this patient population. However, as a result, this analysis is limited to the information captured (i) in our hospital system and (ii) at the hospital visits of a patient. Therefore, obtained results could be subject to some common biases found in medical record-derived data, such as non-random interaction with the health care system resulting in some patient populations having more data than others [131], incomplete documentation [132], selection bias [133], missing data, and lack of documentation of potential confounders [134]. In addition, possible non-observed confounders like socioeconomic status or health-related behavior could not be taken into account due to lack of documentation in the medical record. We determined subtypes using LvEF, which can be error prone [135] and might not fully provide a sufficient criterion for the HFpEF diagnosis [136]. Patients with more serious conditions will tend to visit a tertiary health care provider more often and thus could be overrepresented. In our study, at a tertiary care center with a focus on cardiomyopathy, this seemed to affect the contrast between HFrEF and HFpEF, as HFrEF patients had higher intubation prevalence and DC 5 similarity. This may also have contributed to differences between this study population and other reports of HFpEF population characteristics. However, given the known heterogeneity of HFpEF and HFrEF [109‐111], we believe these differences are plausible and a more granular approach to study HFpEF subtypes could be necessary to address inconsistent patient characteristics [137]. Another limitation of our study is the use of ICD-10 codes to capture comorbidities. Different ICD-10 codes are known to have different predictive value for disease, and therefore, it is likely that some diseases are over- or underrepresented in our data [138]. Moreover, we performed a cross-sectional analysis and therefore did not consider the timing and sequence of comorbidities when generating comorbidity profiles.

Given these limitations, future studies are necessary to address the generalizability of our findings to other HF populations and to delineate different disease trajectories by considering the time of events. Nevertheless, our study recapitulated known HF comorbidity patterns, as discussed above, that could substantiate more novel comorbidity patterns identified in this work.

Many open questions remain regarding HFpEF pathophysiology and genetics [16]. Interdisciplinary and translational approaches are needed to account for the cross-organ disease involvement that is suggested to be critical in HFpEF. The increasing abundance of routine clinical care data and novel approaches like network medicine can provide novel insights and guidance for future experimental approaches.

In our study, we found evidence for greater diversity of comorbidity profiles in patients with HFpEF compared to HFrEF. We further identified nine co-occurring disease groups which capture differences of disease prevalence regarding age, sex, and HF subtype. Here, we find that multimorbidity in HFpEF extends to disease clusters beyond typical HF comorbidities and includes rheumatoid, neoplastic, and gastrointestinal diseases. We further provided a biological interpretation of the HFpEF comorbidity profile, capturing overexpressed gene programs observed in murine HFpEF models. Oxidative stress, hypertrophy, cell differentiation, and fibrosis-related genes are recurrent patterns in genes associated to comorbidities of HFpEF and could constitute a link for the comorbid relationships of HFpEF resulting in a multiorgan disease state. Thus, our work highlights that comorbidity profiles are an important characteristic of HFpEF patients and should be incorporated into both clinical and genomic approaches to the study of HFpEF.