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A Preclinical Rat Model of Heart Failure With Preserved Ejection Fraction With Multiple Comorbidities.

ABSTRACT: Heart failure with preserved ejection fraction (HFpEF) is a common complex clinical syndrome for which there are currently few evidence-based therapies. As patients with HFpEF very often present with comorbidities comprising the metabolic syndrome, we hypothesized, that metabolic syndrome could lead over time to the development of diastolic dysfunction and HFpEF. Obesity-prone rats were exposed to high-fat diet and compared to obesity-resistant rats fed with standard chow. Phenotyping of metabolic syndrome, associated with echocardiographic and cardiac hemodynamic measurements, was performed after 4 and 12 months. Blood and myocardial tissue sampling were performed for pathobiological evaluation. High-fat diet in obesity-prone rats elicited metabolic syndrome, characterized by increased body and abdominal fat weights, glucose intolerance and hyperlipidemia, as well as increased left ventricular (LV) systolic pressure (after 12 months). This was associated with LV diastolic dysfunction (assessed by increased LV end-diastolic pressure) and pulmonary hypertension (assessed by increased right ventricular systolic pressure). Echocardiography revealed significant concentric LV hypertrophy, while LV ejection fraction was preserved. LV remodeling was associated with cardiomyocyte hypertrophy, as well as myocardial and perivascular fibrosis. Circulating levels of soluble ST2 (the interleukin-1 receptor-like) markedly increased in rats with HFpEF, while plasma NT-proBNP levels decreased. RNA-sequencing analysis identified clusters of genes implicated in fatty acid metabolism and calcium-dependent contraction as upregulated pathways in the myocardium of rats with HFpEF. High-fat diet during 12 months in obesity-prone rats led to the development of a relevant preclinical model of HFpEF with multiple comorbidities, suitable for investigating novel therapeutic interventions.

SUBMITTER: Hubesch G

PROVIDER: S-EPMC8793630 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundComorbidities 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.MethodsWe 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.ResultsWe 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.ConclusionsWe 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.

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A Preclinical Rat Model of Heart Failure With Preserved Ejection Fraction With Multiple Comorbidities.

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