Jaume Barallat

Head-to-head comparison of 2 myocardial fibrosis biomarkers for long-term heart failure risk stratification: ST2 versus galectin-3.

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.

Biological Signatures of Asymptomatic Extra- and Intracranial Atherosclerosis The Barcelona-AsIA (Asymptomatic Intracranial Atherosclerosis) Study

Background and Purpose— Intracranial atherosclerotic disease (ICAD) remains a challenge for stroke primary and secondary prevention. Molecular pathways involved in the development of ICAD from its asymptomatic stages are largely unknown. In our population-based study, we aimed to compare the risk factor and biomarker profiles associated with intracranial and extracranial asymptomatic cerebral atherosclerosis. Methods— The Asymptomatic Intracranial Atherosclerosis (AsIA) study cohort includes a random sample population of 933 white subjects >50 years with a moderate to high vascular risk (based on REGICOR score) and without a history of stroke (64% males; mean age, 66 years). Carotid and intracranial atherosclerosis were screened by cervical and transcranial color-coded Duplex ultrasound, being moderate to severe stenoses confirmed by MR angiography. We registered clinical and anthropometric data and created a biobank with blood samples at baseline. A panel of biomarkers involved in atherothrombogenesis was determined: C-reactive protein, asymmetric-dimethylarginine, resistin, and plasminogen activator inhibitor-1. Insulin resistance was quantified by Homeostasis Model Assessment index. Results— After multinomial regression analyses, male sex, hypertension, smoking, and alcoholic habits were independent risk factors of isolated extracranial atherosclerotic disease. Diabetes and metabolic syndrome conferred a higher risk for ICAD than for extracranial atherosclerotic disease. Moreover, metabolic syndrome and insulin resistance were independent risk factors of moderate to severe ICAD but were not risk factors of moderate to severe extracranial atherosclerotic disease. Regarding biomarkers, asymmetric-dimethylarginine was independently associated with isolated ICAD and resistin with combined ICAD–extracranial atherosclerotic disease. Conclusions— Our findings show distinct clinical and biological profiles in subclinical ICAD and extracranial atherosclerotic disease. Insulin resistance emerged as an important molecular pathway involved in the development of ICAD from its asymptomatic stage.

Prognostic Value and Kinetics of Soluble Neprilysin in Acute Heart Failure: A Pilot Study.

OBJECTIVES This study sought to examine the prognostic value of the soluble form of neprilysin (sNEP) in acute heart failure (AHF) and sNEP kinetics during hospital admission. BACKGROUND sNEP was recently identified in chronic heart failure (HF) and was associated with cardiovascular outcomes. METHODS A total of 350 patients (53% women, mean 72.6 ± 10.7 years of age) were included in the study. Primary endpoints were composites of cardiovascular death or HF hospitalizations at short-term (2 months) and long-term (mean: 1.8 ± 1.2 years) follow-up. sNEP was measured using an ad hoc-modified enzyme-linked immunosorbent assay, and its prognostic value was assessed using Cox regression analyses. In a subgroup of patients, sNEP was measured both at admission and at discharge (n = 92). RESULTS Median admission sNEP concentrations were 0.67 ng/ml (Q1 to Q3: 0.37 to 1.29), and sNEP was significantly associated, in age-adjusted Cox regression analyses, with the composite endpoint at short-term (hazard ratio [HR]: 1.29; 95% confidence interval [CI]: 1.04 to 1.61; p = 0.02) and long-term (HR: 1.23; 95% CI: 1.01 to 1.05; p = 0.003) follow-up. In multivariate Cox analyses that included clinical variables and N-terminal prohormone of brain natriuretic peptide (NT-proBNP) concentration, sNEP concentration at admission showed a clear trend toward significance for the composite endpoint at 2 months (HR: 1.22; 95% CI: 0.97 to 1.53; p = 0.09) and remained significant at the end of follow-up (HR: 1.21; 95% CI: 1.04 to 1.40; p = 0.01). At discharge, sNEP levels decreased from 0.70 to 0.52 ng/ml (p = 0.06). CONCLUSIONS Admission sNEP concentration was associated with short- and long-term outcomes in AHF, and dynamic sNEP concentrations were observed during hospital admission. These preliminary data may be hypothesis-generating for the use of NEP inhibitors in AHF.

Multimarker Strategy for Heart Failure Prognostication. Value of Neurohormonal Biomarkers: Neprilysin vs NT-proBNP

INTRODUCTION AND OBJECTIVES Neprilysin breaks down numerous vasoactive peptides. The soluble form of neprilysin, which was recently identified in heart failure, is associated with cardiovascular outcomes. Within a multibiomarker strategy, we directly compared soluble neprilysin and N-terminal pro-B-type natriuretic peptide as risk stratifiers in a real-life cohort of heart failure patients. METHODS Soluble neprilysin, N-terminal pro-B-type natriuretic peptide, ST2, and high-sensitivity troponin T levels were measured in 797 consecutive ambulatory heart failure patients followed up for 4.7 years. Comprehensive multivariable analyses and soluble neprilysin vs N-terminal pro-B-type natriuretic peptide head-to-head assessments of performance were performed. A primary composite endpoint included cardiovascular death or heart failure hospitalization. A secondary endpoint explored cardiovascular death alone. RESULTS Median soluble neprilysin and N-terminal pro-B-type natriuretic peptide concentrations were 0.64ng/mL and 1187 ng/L, respectively. Both biomarkers significantly correlated with age (P<.001) and ST2 (P<.001), but only N-terminal pro-B-type natriuretic peptide significantly correlated with estimated glomerular filtration rate (P<.001), body mass index (P<.001), left ventricular ejection fraction (P=.02) and high-sensitivity troponin T (P<.001). In multivariable Cox regression analyses, soluble neprilysin remained independently associated with the composite endpoint (hazard ratio=1.14; 95% confidence interval, 1.02-1.27; P=.03) and cardiovascular death (hazard ratio=1.15; 95% confidence interval, 1.01-1.31; P=.04), but N-terminal pro-B-type natriuretic peptide did not. The head-to-head soluble neprilysin vs N-terminal pro-B-type natriuretic peptide comparison showed good calibration and similar discrimination and reclassification for both neurohormonal biomarkers, but only soluble neprilysin improved overall goodness-of-fit. CONCLUSIONS When added to a multimarker strategy, soluble neprilysin remained an independent prognosticator, while N-terminal pro-B-type natriuretic peptide lost significance as a risk stratifier in ambulatory patients with heart failure. Both biomarkers performed similarly in head-to-head analyses.

Bloodstream Amyloid-beta (1-40) Peptide, Cognition, and Outcomes in Heart Failure

INTRODUCTION AND OBJECTIVES In the brain, amyloid-beta generation participates in the pathophysiology of cognitive disorders; in the bloodstream, the role of amyloid-beta is uncertain but may be linked to sterile inflammation and senescence. We explored the relationship between blood levels of amyloid-beta 1-40 peptide (Aβ40), cognition, and mortality (all-cause, cardiovascular, and heart failure [HF]-related) in ambulatory patients with HF. METHODS Bloodstream Aβ40 was measured in 939 consecutive patients with HF. Cognition was evaluated with the Pfeiffer questionnaire (adjusted for educational level) at baseline and during follow-up. Multivariate Cox regression analyses and measurements of performance (discrimination, calibration, and reclassification) were used, with competing risk for specific causes of death. RESULTS Over 5.1 ± 2.9 years, 471 patients died (all-cause): 250 from cardiovascular causes and 131 HF-related. The median Aβ40 concentration was 519.1 pg/mL [Q1-Q3: 361.8-749.9 pg/mL]. The Aβ40 concentration correlated with age, body mass index, renal dysfunction, and New York Heart Association functional class (all P < .001). There were no differences in Aβ40 in patients with and without cognitive impairment at baseline (P = .97) or during follow-up (P = .20). In multivariable analysis, including relevant clinical predictors and N-terminal pro-B-type natriuretic peptide, Aβ40 remained significantly associated with all-cause death (HR, 1.22; 95%CI, 1.10-1.35; P < .001) and cardiovascular death (HR, 1.18; 95%CI, 1.03-1.36; P = .02), but not with HF-related death (HR, 1.13; 95%CI, 0.93-1.37; P = .22). Circulating Aβ40 improved calibration and patient reclassification. CONCLUSIONS Blood levels of Aβ40 are not associated with cognitive decline in HF. Circulating Aβ40 was predictive of mortality and may indicate systemic aging.

The Dynamics of Cardiovascular Biomarkers in non-Elite Marathon Runners

The number of recreational/non-elite athletes participating in marathons is increasing, but data regarding impact of endurance exercise on cardiovascular health are conflicting. This study evaluated 79 recreational athletes of the 2016 Barcelona Marathon (72% men; mean age 39 ± 6 years; 71% ≥35 years). Blood samples were collected at baseline (24–48 h before the race), immediately after the race (1–2 h after the race), and 48-h post-race. Amino-terminal pro-B type natriuretic peptide (NT-proBNP, a marker of myocardial strain), ST2 (a marker of extracellular matrix remodeling and fibrosis, inflammation, and myocardial strain), and high-sensitivity troponin T (hs-TnT, a marker of myocyte stress/injury) were assayed. The median (interquartile range, IQR) years of training was 7 (5–11) years and median (IQR) weekly training hours was 6 (5–8) h/week, respectively. The median (IQR) race time (h:min:s) was 3:32:44 (3:18:50–3:51:46). Echocardiographic indices were within normal ranges. Immediately after the race, blood concentration of the three cardiac biomarkers increased significantly, with 1.3-, 1.6-, and 16-fold increases in NT-proBNP, ST2, and hs-TnT, respectively. We found an inverse relationship between weekly training hours and increased ST2 (p = 0.007), and a direct relationship between race time and increased hs-TnT (p < 0.001) and ST2 (p = 0.05). Our findings indicate that preparation for and participation in marathon running may affect multiple pathways affecting the cardiovascular system. More data and long-term follow-up studies in non-elite and elite athletes are needed.

Soluble neprilysin retains catalytic activity in heart failure

931-3. 3. Vaseghi M, Lellouche N, Ritter H, et al. Mode and mechanisms of death after orthotopic heart transplantation. Heart Rhythm 2009;6:503-9. 4. Cogert GA, Shivkumar K, Patel JK, et al. Implantable cardioverter defibrillators in heart transplant patients at risk for sudden death: shocking news? J Heart Lung Transplant 2003;22:S178-9. 5. Marzoa-Rivas R, Perez-Alvarez L, Paniagua-Martin MJ, et al. Sudden cardiac death of two heart transplant patients with correctly functioning implantable cardioverter defibrillators. J Heart Lung Transplant 2009;28:412-4. 6. McDowell DL, Hauptman PJ. Implantable defibrillators and cardiac resynchronization therapy in heart transplant recipients: results of a national survey. J Heart Lung Transplant 2009;28:847-50.

Barcelona Bio-HF Calculator Version 2.0: incorporation of angiotensin II receptor blocker neprilysin inhibitor (ARNI) and risk for heart failure hospitalization

Estimating risk for an individual with heart failure (HF) is routine for the practising physician. This may sometimes be done using experience and clinical acumen, or by using a risk model. A number of prediction models with broad variation in terms of validation and output have been developed, but only a few are freely available as online calculators.1 The Barcelona (BCN) Bio-HF Calculator (www.bcnbiohfcalculator.org) (Figure 1),2 developed 3 years ago and discussed in the 2016 European Society of Cardiology HF guidelines,3 incorporates three biomarkers that reflect different facets of HF pathophysiology: N-terminal pro-B-type natriuretic peptide (NT-proBNP), a marker of myocardial stretch; high-sensitivity cardiac troponin T (hs-cTnT), a marker of myocyte injury, and high-sensitivity soluble ST2, which reflects myocardial fibrosis and remodelling. The calculator estimates the risk for all-cause death,2 has been externally validated,4 and was highlighted by Levy and Anand5 as a reference for the appropriate methodology for adding single or multiple variables to a risk model. The combination of clinical and treatment data plus routine laboratory data and biomarkers is also valuable for predicting HF-related hospitalization. Furthermore, the incorporation of novel drugs and devices into the HF armamentarium, notably sacubitril– valsartan, which have strong impacts on death and HF hospitalization,6 prompted an update of the BCN Bio-HF Calculator. The BCN Bio-HF Calculator Version 2.0 was derived from a cohort of 864 consecutive treated HF outpatients [72% men; mean age 68.2±12 years; New York Heart Association (NYHA) class I–II/III–IV 73%/27%, left ventricular ejection fraction (LVEF) 36%, ischaemic aetiology 52.2%].3 During followup of up to 5 years, 363 deaths and 210 first HF-related hospitalizations were recorded; 430 patients suffered at least one event of the composite endpoint. In the update, three new clinical variables (duration of HF in months, number of HF-related hospitalizations in the preceding year, and diabetes mellitus) and four new treatments [mineralocorticoid receptor antagonists, angiotensin II receptor blocker neprilysin inhibitors (ARNI), cardiac resynchronization therapy (CRT) and implantable cardioverter defibrillator (ICD)] were added to the original variables (age, sex, NYHA functional class, LVEF, serum sodium, estimated glomerular filtration rate, haemoglobin, loop diuretic dose, beta-blocker, angiotensin-converting enzyme inhibitor/angiotensin-II receptor blocker and statin treatments, and hs-cTnT, ST2 and NT-proBNP levels). Beta values for ARNI treatment were derived from the benefit observed in the PARADIGM-HF trial, which involved the largest and best characterized cohort of patients treated with ARNIs.6 HFrelated hospitalization was estimated taking into account competing risk for death. Model performance was evaluated using discrimination, calibration and reclassification tools. The C-statistics [area under the curve (AUC)] at 2 years for the model with biomarkers using logistic regression were 0.83 for all-cause death, 0.79 for HF-related hospitalization, and 0.80 for the composite endpoint. Discrimination was significantly better than that obtained in a model without biomarkers for risk for death (P= 0.001), risk for HF hospitalization (P< 0.05) and the composite endpoint (P= 0.001) (supplementary material online, Tables S1–S3). Calibration improved in the model with biomarkers, and reclassification with this model using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) was also highly significant (P< 0.001). Using NRI, the BCN Bio-HF Calculator Version 2.0 model with biomarkers reclassified in the correct direction 39% of patients for risk for death, and 42% for risk for the composite endpoint relative to the clinical model (supplementary material online, Tables S4 and S5). Validation for up to 2 years was possible in a subgroup of 1934 patients from the PARADIGM-HF study cohort6 for whom the three biomarkers were available. The C-statistics were 0.70 for both risk for death and risk for HF-related hospitalization at 2 years. Some variables and endpoints differed between the Barcelona derivation cohort and the PARADIGM-HF validation cohort. Indeed, risk prediction for the composite endpoint could not be validated because the composite endpoint in PARADIGM was cardiovascular death or HF-related hospitalization, rather than all-cause death. In a manner similar to the present efforts in a cohort of chronic ambulatory HF patients, the BIOSTAT-CHF study recently developed and validated three risk models to predict all-cause mortality, HF-related hospitalization and the composite endpoint in a cohort of worsening HF patients.7 These researchers obtained C-statistic values of 0.73, 0.69 and 0.71 for the three outcomes, respectively. Both methods are pioneers in their use of HF biomarkers and are appropriate in two different clinical scenarios. In conclusion, the updated version of the BCN Bio-HF Calculator incorporates new clinical variables and allows better individual prediction of all-cause death, HF-related hospitalization and the composite endpoint for up to 5 years. To the best of the present authors’ knowledge, this is the first online calculator to incorporate treatment with an ARNI in the prediction of risk in HF patients. Risk prediction is a cornerstone of HF management. Accurate prediction of risk for death and/or HF hospitalization may identify high-risk patients and candidates for intensified monitoring and treatment, such as drug dose increases, switches to ARNI,

Serum Neprilysin and Recurrent Admissions in Patients With Heart Failure

Background Our aim was to evaluate the association between the soluble form of neprilysin (sNEP) levels and long‐term all‐cause, cardiovascular, and acute heart failure (AHF) recurrent admissions in an ambulatory cohort of patients with heart failure. sNEP has emerged as a new biomarker with promising implications for prognosis and therapy in patients with heart failure. Reducing the recurrent admission rate of heart failure patients has become an important target of public health planning strategies. Methods and Results We measured sNEP levels in 1021 consecutive ambulatory heart failure patients. End points were the number of all‐cause, cardiovascular, and AHF hospitalizations during follow‐up. We used covariate‐adjusted incidence rate ratios to identify associations. At a median follow‐up of 3.4 years (interquartile range: 1.8–5.7), 391 (38.3%) patients died, 477 (46.7%) patients had 1901 all‐cause admissions, 324 (31.7%) patients had 770 cardiovascular admissions, and 218 (21.4%) patients had 488 AHF admissions. The medians for sNEP and amino‐terminal pro‐brain natriuretic peptide were 0.64 ng/mL (interquartile range: 0.39–1.22) and 1248 pg/mL (interquartile range: 538–2825), respectively. In a multivariate setting, the adjusted incidence rate ratios for the top (>1.22 ng/mL) versus the bottom (≤0.39 ng/mL) quartiles of sNEP were 1.37 (95% confidence interval: 1.03–1.82), P=0.032; 1.51 (95% confidence interval: 1.10–2.06), P=0.010; and 1.51 (95% confidence interval: 1.05–2.16), P=0.026 for all‐cause, cardiovascular, and AHF admissions, respectively. Conclusions Elevated sNEP levels predicted an increased risk of recurrent all‐cause, cardiovascular, and AHF admissions in ambulatory patients with heart failure.

Bio-profiling and bio-prognostication of chronic heart failure with mid-range ejection fraction

BACKGROUND Recent ESC guidelines on heart failure (HF) have introduced a new phenotype based on left ventricular ejection fraction (LVEF), called the mid-range (HFmrEF). This phenotype falls between the classical reduced (HFrEF) and preserved (HFpEF) HF phenotypes. We aimed to characterize the HFmrEF biomarker profile and outcomes. METHODS 1069 consecutive ambulatory patients were included in the study (age 66.2 ± 12.8 years); 800 with HFrEF (74.8%), 134 with HFmrEF (12.5%), and 135 with HFpEF (12.5%). We measured serum concentrations of N-terminal pro-brain natriuretic peptide (NT-proBNP), high-sensitivity troponin T (hs-TnT), soluble suppression of tumorigenicity (ST2), galectin-3, high-sensitivity C-reactive protein, cystatin-C, neprilysin, and soluble transferrin receptor, during 4.9 ± 2.8 years of follow-up. The primary end-point was the composite: cardiovascular death or HF-related hospitalization. We also examined all-cause, cardiovascular death, and the composite: all-cause death or HF-related hospitalization. RESULTS NTproBNP levels in HFmrEF were similar to levels in HFpEF, but significantly lower than levels in HFrEF. No other studied biomarkers were different between HFmrEF and HFrEF. All biomarkers, except neprilysin, showed higher risk prediction capabilities in HFmrEF than in HFrEF or HFpEF. The largest difference between HFrEF and HFmrEF was the hs-TnT level (hazard ratio [HR]: 4.72, 95% CI: 2.81-7.94 vs. HR: 1.67, 95%CI: 1.74-1.89; all p < 0.001). CONCLUSIONS Although HFmrEF is acknowledged as an intermediate phenotype between HFrEF and HFpEF, from a multi-biomarker point of view, HFmrEF was similar to HFrEF, except that NTproBNP levels were lower. Biomarkers commonly used for HFrEF risk prediction are more valuable for HFmrEF risk stratification.