Dataset Information

Proenkephalin and prognosis in heart failure with preserved ejection fraction: a GREAT network study.

ABSTRACT:

Background

Proenkephalin (PENK), a stable endogenous opioid biomarker related to renal function, has prognostic utility in acute and chronic heart failure. We investigated the prognostic utility of PENK in heart failure with preserved ejection fraction (HFpEF), and its relationship to renal function, Body Mass Index (BMI), and imaging measures of diastolic dysfunction.

Methods

In this multicentre study, PENK was measured in 522 HFpEF patients (ejection fraction > 50%, 253 male, mean age 76.13 ± 10.73 years) and compared to 47 age and sex-matched controls. The primary endpoint was 2-years composite of all-cause mortality and/or heart failure rehospitalisation (HF). A subset (n = 163) received detailed imaging studies.

Results

PENK levels were raised in HFpEF (median [interquartile range] 88.9 [62.1-132.0]) compared to normal controls (56.3 [47.9-70.5]). PENK was correlated to urea, eGFR, Body Mass Index and E/e' (r_s 0.635, - 0.741, - 0.275, 0.476, respectively, p < 0.0005). During 2 years follow-up 144 patients died and 220 had death/HF endpoints. Multivariable Cox regression models showed PENK independently predicted 2 year death/HF [hazard ratio (for 1 SD increment of log-transformed biomarker) HR 1.45 [95% CI 1.12-1.88, p = 0.005]], even after adjustment for troponin (HR 1.59 [1.14-2.20, p = 0.006]), and Body Mass Index (HR 1.63 [1.13-2.33, p = 0.009]). PENK showed no interaction with ejection fraction status for prediction of poor outcomes. Net reclassification analyses showed PENK significantly improved classification of death/HF outcomes for multivariable models containing natriuretic peptide, troponin and Body Mass Index (p < 0.05 for all).

Conclusions

In HFpEF, PENK levels are related to BMI, and measures of diastolic dysfunction and are prognostic for all-cause mortality and heart failure rehospitalisation.

SUBMITTER: Kanagala P

PROVIDER: S-EPMC6652170 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Publications

Proenkephalin and prognosis in heart failure with preserved ejection fraction: a GREAT network study.

Kanagala Prathap P Squire Iain B IB Jones Donald J L DJL Cao Thong Huy TH Chan Daniel C S DCS McCann Gerry G Sandhu Jatinderpal K JK Quinn Paulene A PA McAdam John J Marsh Anna-Marie AM Davies Joan E JE Struck Joachim J Bergmann Andreas A Sabti Zaid Z Twerenbold Raphael R Herrmann Thomas T Kozhuharov Nikola N Mueller Christian C Ng Leong L LL

Clinical research in cardiology : official journal of the German Cardiac Society 20190214 8

<h4>Background</h4>Proenkephalin (PENK), a stable endogenous opioid biomarker related to renal function, has prognostic utility in acute and chronic heart failure. We investigated the prognostic utility of PENK in heart failure with preserved ejection fraction (HFpEF), and its relationship to renal function, Body Mass Index (BMI), and imaging measures of diastolic dysfunction.<h4>Methods</h4>In this multicentre study, PENK was measured in 522 HFpEF patients (ejection fraction > 50%, 253 male, me ...[more]

PMID: 30767059

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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.

Project description:AimsHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition, and tissue congestion manifested by oedema is not present in all patients. We compared clinical characteristics, exercise capacity, and outcomes in patients with HFpEF with and without oedema.Methods and resultsThis study was a post hoc analysis of pooled data of patients with left ventricular ejection fraction of ≥50% enrolled in the DOSE, CARRESS-HF, RELAX, ATHENA, ROSE, INDIE, and NEAT trials. Patients were dichotomized by the severity of oedema. Cox proportional hazard regression and generalized linear regression models were used to assess associations between oedema, symptoms, and clinical outcomes. The ambulatory cohort included 393 patients (228 with and 165 without oedema), and the hospitalized cohort included 338 patients (249 with ≥moderate oedema and 89 with mild or none). Among ambulatory patients, patients with oedema had a higher body mass index (35.2 kg/m2 [inter-quartile range, IQR 30.5, 41.6] vs. 31.6 kg/m2 [IQR 27.9, 36.3], P < 0.001), greater burden of co-morbidities, higher intravascular pressures estimated on physical examination (elevated jugular venous pressure: 50% vs. 24.7%, P < 0.001), poorer renal function (creatinine: 1.2 mg/dL [IQR 0.9, 1.5] vs. 1 mg/dL [IQR 0.8, 1.3], P = 0.003), and lower peak VO2 (adjusted mean difference -1.04 mL/kg/min, 95% confidence interval [-1.71, -0.37], P < 0.003). Among hospitalized patients, despite greater in-hospital fluid/weight loss in the ≥moderate oedema group, there was no difference in the improvement in dyspnoea by the visual analogue scale or well-being visual analogue scale from baseline to 3-4 days and no statistically significant difference in the rate of 60 day rehospitalization/death (adjusted hazard ratio 1.44, 95% confidence interval [0.87, 2.39], P = 0.156).ConclusionsPatients with HFpEF and oedema display higher body mass, greater burden of co-morbidities, and more severe exercise intolerance, but clinical responses to treatment appear similar. Further research is required to better understand the nature of volume distribution in different HFpEF phenotypes.

Dataset Information

Proenkephalin and prognosis in heart failure with preserved ejection fraction: a GREAT network study.

Background

Methods

Results

Conclusions

Publications

Proenkephalin and prognosis in heart failure with preserved ejection fraction: a GREAT network study.

Similar Datasets

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