Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Background Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF. Methods A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF. Results After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005). Conclusions The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF

Plasma proteomic approach in patients with heart failure:insights into pathogenesis of disease progression and potential novel treatment targets

Aims To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis. Methods and results The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two‐dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI‐MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up‐regulated and 46 down‐regulated with more than two‐fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein–protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised. Conclusions Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure

Left Ventricular Ejection Fraction Cut Point of 50% for Heart Failure With Preserved Ejection Fraction.

To the Editor I read the article by Vanderpool et al1 published in JAMA Cardiology with great interest. The authors conducted a very valuable and interesting study on a large cohort of 10 023 participants, of whom 2587 (25.8%) had pulmonary hypertension associated with heart failure with preserved ejection fraction (HFpEF). The results in this study suggest that pulmonary hypertension associated with HFpEF is very common in the invasive hemodynamic assessment. In addition, transpulmonary gradient, pulmonary vascular resistance, and diastolic pulmonary gradient were associated with mortality and cardiac hospitalizations. However, the authors used a left ventricular ejection fraction (LVEF) cut point of 45% for HFpEF.1 Could the authors use an LVEF cut point of 50% for HFpEF based on the heart failure guidelines? According to the 2013 American College of Cardiology Foundation/American Heart Association guidelines2 and the 2016 European Society of Cardiology guidelines,3 the diagnosis of HFpEF is defined as an LVEF of 50% or greater. Patients with an LVEF in the range of 41% to 49% are classified into a borderline or intermediate group in the 2013 American College of Cardiology Foundation/American Heart Association guidelines2 and are considered to have heart failure with mid-range ejection fraction in the 2016 European Society of Cardiology guidelines.3 Using an LVEF cut point of 50% for HFpEF would keep this article up to date in the future and have a greater effect for this study

The plasma proteome and outcome in patients with heart failure

Heart failure is a complex clinical syndrome that occurs at the end stage of heart disease with high costs and poor outcomes. Despite advances in therapy, improving clinical outcomes remains a challenge for physicians with 50% of patients dying within 5 years. The main aim of this study was to discover novel biomarkers in plasma that could predict treatment response in patients with heart failure using plasma proteomics. The use of two dimensional liquid chromatography coupled to electrospray ionisation tandem mass spectrometry in high definition ion mobility combined with a multiple affinity removal system column and immunoluminometric assay discovered CD180 antigen, Heat shock 70 kDa protein 4L, Leukemia inhibitory factor receptor and Neurotrimin as novel biomarkers which are able to predict treatment response in patients with heart failure. Moreover, Thyroid receptor interacting protein 11, Patatin like phospholipase domain containing protein 2 and Mannan binding lectin serine protease 2 were identified as novel biomarkers for prediction of death in patients with heart failure. Furthermore, two multiple biomarker models were developed from the findings obtained of using matrix assisted laser desorption ionisation mass spectrometry combined with C18 solid phase extraction which are able to predict treatment response in patients with heart failure. The model with seven peptide peaks showed an excellent area under the receiver operating characteristic curve (AUC) of 0.907. In particular, the model with seventeen peptide peaks achieved the maximum AUC of 1.000 (100% sensitivity and 100% specificity). The discovery of novel biomarkers in this study not only adds information to understand the pathophysiological mechanisms of heart failure better, but also may provide a more accurate prediction of treatment response to guide medical therapy. This may enable the practice of stratified medicine in the future. Moreover, novel therapeutic targets could be identified for design of new drugs to improve outcomes

Proenkephalin in Heart Failure

The opioid system is activated in heart failure, which may be cardioprotective but may also be counter-regulatory. Recently, systemic proenkephalin activation has been investigated in various conditions predicting mortality and kidney injury. In acute heart failure, proenkephalin independently predicts mortality and heart failure rehospitalization in addition to traditional risk markers. It also predicts worsening renal function, increasingly recognized as an important risk predictor for poor outcome in heart failure. This article explores the role of enkephalins and delta-opioid receptors in the heart, then reviews studies measuring proenkephalin levels in the circulation and their associations with prognosis

Not so robust: robusta coffee production is highly sensitive to temperature

Coffea canephora (robusta coffee) is the most heat‐tolerant and ‘robust’ coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated mean R2 = .51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar coffee industry and the livelihoods of millions of farmers

Proenkephalin, Renal Dysfunction, and Prognosis in Patients With Acute Heart Failure: A GREAT Network Study.

BACKGROUND: Proenkephalin A (PENK) and its receptors are widely distributed. Enkephalins are cardiodepressive and difficult to measure directly. PENK is a stable surrogate analyte of labile enkephalins that is correlated inversely with renal function. Cardiorenal syndrome is common in acute heart failure (HF) and portends poor prognosis. OBJECTIVES: This study assessed the prognostic value of PENK in acute HF, by identifying levels that may be useful in clinical decisions, and evaluated its utility for predicting cardiorenal syndrome. METHODS: This multicenter study measured PENK in 1,908 patients with acute HF (1,186 male; mean age 75.66 ± 11.74 years). The primary endpoint was 1-year all-cause mortality; secondary endpoints were in-hospital mortality, all-cause mortality or HF rehospitalization within 1 year, and in-hospital worsening renal function, defined as a rise in plasma creatinine ≥26.5 μmol/l or 50% higher than the admission value within 5 days of presentation. RESULTS: During 1-year follow-up, 518 patients died. Measures of renal function were the major determinants of PENK levels. PENK independently predicted worsening renal function (odds ratio: 1.58; 95% confidence interval [CI]: 1.24 to 2.00; p 211.3 pmol/l detected low-risk and high-risk patients, respectively. CONCLUSIONS: PENK levels reflect cardiorenal status in acute HF and are prognostic for worsening renal function and in-hospital mortality as well as mortality during follow-up