Natriuretic peptide NP; B‐type NP (BNP), N‐terminal proBNP (NT‐proBNP), and midregional proANP (MR‐proANP) concentrations are quantitative plasma biomarkers for the presence and severity of ...haemodynamic cardiac stress and heart failure (HF). End‐diastolic wall stress, intracardiac filling pressures, and intracardiac volumes seem to be the dominant triggers. This paper details the most important indications for NPs and highlights 11 key principles underlying their clinical use shown below.
NPs should always be used in conjunction with all other clinical information.
NPs are reasonable surrogates for intracardiac volumes and filling pressures.
NPs should be measured in all patients presenting with symptoms suggestive of HF such as dyspnoea and/or fatigue, as their use facilitates the early diagnosis and risk stratification of HF.
NPs have very high diagnostic accuracy in discriminating HF from other causes of dyspnoea: the higher the NP, the higher the likelihood that dyspnoea is caused by HF.
Optimal NP cut‐off concentrations for the diagnosis of acute HF (very high filling pressures) in patients presenting to the emergency department with acute dyspnoea are higher compared with those used in the diagnosis of chronic HF in patients with dyspnoea on exertion (mild increase in filling pressures at rest).
Obese patients have lower NP concentrations, mandating the use of lower cut‐off concentrations (about 50% lower).
In stable HF patients, but also in patients with other cardiac disorders such as myocardial infarction, valvular heart disease, atrial fibrillation or pulmonary embolism, NP concentrations have high prognostic accuracy for death and HF hospitalization.
Screening with NPs for the early detection of relevant cardiac disease including left ventricular systolic dysfunction in patients with cardiovascular risk factors may help to identify patients at increased risk, therefore allowing targeted preventive measures to prevent HF.
BNP, NT‐proBNP and MR‐proANP have comparable diagnostic and prognostic accuracy.
In patients with shock, NPs cannot be used to identify cause (e.g. cardiogenic vs. septic shock), but remain prognostic.
NPs cannot identify the underlying cause of HF and, therefore, if elevated, must always be used in conjunction with cardiac imaging.
Extracellular vesicles (EVs), which include a variety of nano‐sized membrane‐encapsulated particles, are released to the extracellular microenvironment by the vast majority of cells and carry lipids, ...proteins, mRNA, and miRNA or non‐coding RNA. Increasing evidence suggests the great versatility and potential of EV‐based applications in humans. In this issue, van Balkom et al. explore and compare the reported proteomic signature of mesenchymal stromal cell (MSC)‐derived small EVs. In particular, their paper offers a valuable approach and point of view on MSC‐EV manufacturing and therapeutic potential. Briefly, van Balkom et al. aimed to identify a common protein signature that may be useful in ensuring the homogeneity of therapeutic MSC‐EVs. In addition to excessive variability in EV‐producing cell sources and culture conditions, the harvesting time for the EV‐containing conditioned medium, and EV isolation procedure, the authors found a specific protein signature from the publicly available MSC‐EVs proteome. In light of their findings and those from the plentiful studies published in this continuously growing area of research, potential focus areas and issues are outlined for the more rational design and optimization of MSC‐EV production and potency for therapeutics.
Background
Sparse evidence of the prognostic benefit of the anti‐inflammatory drug colchicine in chronic and acute coronary syndromes (CCS/ACS) exists.
Methods
We performed a systematic search of ...studies on CCS or ACS comparing colchicine vs. placebo and reporting data on cardiovascular outcomes (primary end points of each study) and/or changes in hs‐CRP.
Results
Ten studies were selected: three on CCS (LoDoCo, LoDoCo2 and the CCS subgroup of COLCHICINE‐PCI; total patient number = 6256), three on ACS (COLCOT, COPS, ACS subgroup of COLCHICINE‐PCI; n = 5,654) and five (n = 532) on hs‐CRP changes from 1 week to 12 months, in CCS and/or ACS. In patients with CCS, colchicine reduced by 49% risk of a composite end point (hazard ratio HR 0.51, 95% confidence interval CI 0.32 to 0.81, P = .005). The favourable effect of colchicine on the risk of cardiovascular events did not change when excluding COLCHICINE‐PCI from analysis (HR 0.51, 95% CI 0.25 to 1.03, P = .061). In patients with ACS, the use of colchicine tended to decrease the occurrence of the combined end point compared with placebo (HR = 0.77, 95% CI 0.56 to 1.05, P = .100), and colchicine became significantly protective when removing COLCHICINE‐PCI from analysis (HR = 0.72, 95% CI 0.56 to 0.92, P = .009). Furthermore, colchicine tended to reduce the hs‐CRP increase (standardized mean difference=−0.31, 95% CI −0.72 to 0.1, P = .133) compared with placebo.
Conclusions
Colchicine therapy near halves the risk of cardiovascular events in CCS compared with placebo and is associated with a nonsignificant 23% risk reduction in ACS, together with a trend towards a greater reduction of hs‐CRP.
Objectives ST2 and galectin-3 (Gal-3) were compared head-to-head for long-term risk stratification in an ambulatory heart failure (HF) population on top of other risk factors including N-terminal ...pro–B-type natriuretic peptide. Background ST2 and Gal-3 are promising biomarkers of myocardial fibrosis and remodeling in HF. Methods This cohort study included 876 patients (median age: 70 years, median left ventricular ejection fraction: 34%). The 2 biomarkers were evaluated relative to conventional assessment (11 risk factors) plus N-terminal pro–B-type natriuretic peptide in terms of discrimination, calibration, and reclassification analysis. Endpoints were 5-year all-cause and cardiovascular mortality, and the combined all-cause death/HF hospitalization. Results During a median follow-up of 4.2 years (5.9 for alive patients), 392 patients died. In bivariate analysis, Gal-3 and ST2 were independent variables for all endpoints. In multivariate analysis, only ST2 remained independently associated with cardiovascular mortality (hazard ratio: 1.27, 95% confidence interval CI: 1.05 to 1.53, p = 0.014). Incorporation of ST2 into a full-adjusted model for all-cause mortality (including clinical variables and N-terminal pro–B-type natriuretic peptide) improved discrimination (C-statistic: 0.77, p = 0.004) and calibration, and reclassified significantly better (integrated discrimination improvement: 1.5, 95% CI: 0.5 to 2.5, p = 0.003; net reclassification index: 9.4, 95% CI: 4.8 to 14.1, p < 0.001). Incorporation of Gal-3 showed no significant increase in discrimination or reclassification and worse calibration metrics. On direct model comparison, ST2 was superior to Gal-3. Conclusions Head-to-head comparison of fibrosis biomarkers ST2 and Gal-3 in chronic HF revealed superiority of ST2 over Gal-3 in risk stratification. The incremental predictive contribution of Gal-3 to existing clinical risk factors was trivial.
Background
Atrial fibrillation (AF) is the most common arrhythmia and has significant morbidity. A score composed of easily measured electrocardiographic variables to identify patients at risk of AF ...would be of great value in order to stratify patients for increased monitoring and surveillance. The purpose of this study was to develop an electrocardiographic risk score for new‐onset AF.
Methods
A total of 676 patients without previous AF undergoing coronary angiography were retrospectively studied. Points were allocated based on P‐wave morphology in inferior leads, voltage in lead 1, and P‐wave duration (MVP). Patients were divided into three risk groups and followed until development of AF or last available clinical appointment.
Results
Mean age was 65 years, and 68% were male. The high‐ and intermediate‐risk groups were more likely to develop AF than the low‐risk group (odds ratio OR 2.4, 95% confidence interval CI 1.3–4.4; p = 0.006 and OR 2.1, 95% CI 1.4–3.27; p = 0.009, respectively). The high‐risk group had a significantly shorter mean time to development of AF (258 weeks; 95% CI 205–310 weeks) compared to the intermediate‐ (278 weeks; 95% CI 252–303 weeks) and low‐risk groups (322 weeks 95% CI 307–338 weeks), p = 0.005.
Conclusions
A simple risk score composed of easy‐to‐measure electrocardiographic variables can help to predict new‐onset AF. Further validation studies will be needed to assess the ability of this risk score to predict AF in other populations.
The present paper is a commentary to ‘Identification and characterization of hADSC‐derived exosome proteins from different isolation methods’ (Huang et al. 2021; 10.1111/jcmm.16775). Given the ...enthusiasm for the potential of mesenchymal stromal cell‐derived extracellular vesicles (MSC‐EVs), some considerations deserve attention as they move through successive stages of research and application into humans. We herein remark the prerequisite of generating that evidence ensuring a high consistency in safety, composition and biological activity of the intended MSC‐EV preparations, and the suitability of disparate isolation techniques to produce efficacious EV preparations and fulfil requirements for standardized clinical‐grade biomanufacturing.
Abstract
The use of sodium-glucose co-transporter 2 inhibitors to treat heart failure with preserved ejection fraction (HFpEF) is under investigation in ongoing clinical trials, but the exact ...mechanism of action is unclear. Here we aimed to use artificial intelligence (AI) to characterize the mechanism of action of empagliflozin in HFpEF at the molecular level. We retrieved information regarding HFpEF pathophysiological motifs and differentially expressed genes/proteins, together with empagliflozin target information and bioflags, from specialized publicly available databases. Artificial neural networks and deep learning AI were used to model the molecular effects of empagliflozin in HFpEF. The model predicted that empagliflozin could reverse 59% of the protein alterations found in HFpEF. The effects of empagliflozin in HFpEF appeared to be predominantly mediated by inhibition of NHE1 (Na
+
/H
+
exchanger 1), with SGLT2 playing a less prominent role. The elucidated molecular mechanism of action had an accuracy of 94%. Empagliflozin’s pharmacological action mainly affected cardiomyocyte oxidative stress modulation, and greatly influenced cardiomyocyte stiffness, myocardial extracellular matrix remodelling, heart concentric hypertrophy, and systemic inflammation. Validation of these in silico data was performed in vivo in patients with HFpEF by measuring the declining plasma concentrations of NOS2, the NLPR3 inflammasome, and TGF-β1 during 12 months of empagliflozin treatment. Using AI modelling, we identified that the main effect of empagliflozin in HFpEF treatment is exerted via NHE1 and is focused on cardiomyocyte oxidative stress modulation. These results support the potential use of empagliflozin in HFpEF.
Aims
To address the incremental usefulness of biomarkers from different disease pathways for predicting risk of death in heart failure (HF).
Methods and results
We used data from consecutive patients ...treated at a structured multidisciplinary HF unit to investigate whether a combination of biomarkers reflecting ventricular fibrosis, remodelling, and stretch ST2 and N-terminal pro brain natriuretic peptide (NTproBNP) improved the risk stratification of a HF patient beyond an assessment based on established mortality risk factors (age, sex, ischaemic aetiology, left ventricular ejection fraction, New York Heart Association functional class, diabetes, glomerular filtration rate, sodium, haemoglobin, and beta-blocker and angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker treatments). ST2 was measured with a novel high-sensitivity immunoassay. During a median follow-up time of 33.4 months, 244 of the 891 participants in the study (mean age 70.2 years at baseline) died. In the multivariable Cox proportional hazards model, both ST2 and NTproBNP significantly predicted the risk of death. The individual inclusion of ST2 and NTproBNP in the model with established mortality risk factors significantly improved the C statistic for predicting death 0.79 (0.76-0.81); P < 0.001. The net improvement in reclassification after the separate addition of ST2 to the model with established risk factors and NTproBNP was estimated at 9.90% 95% confidence interval (CI) 4.34-15.46; P < 0.001 and the integrated discrimination improvement at 1.54 (95% CI 0.29-2.78); P = 0.015).
Conclusions
Our data suggest that in a real-life cohort of HF patients, the addition of ST2 and NTproBNP substantially improves the risk stratification for death beyond that of a model that is based only on established mortality risk factors.
PDF
