Toward personalized risk assessment in patients with chronic heart failure: Detailed temporal patterns of NT-proBNP, troponin T, and CRP in the Bio-SHiFT study

Proteomic biomarkers related to obesity in heart failure with reduced ejection fraction and their associations with outcome

OBJECTIVE

Heart failure (HF) pathophysiology in patients with obesity may be distinct. To study these features, we identified obesity-related biomarkers from 4210 circulating proteins in patients with HF with reduced ejection fraction (HFrEF) and examined associations of these proteins with HF prognosis and biological mechanisms.

METHODS

In 373 patients with trimonthly blood sampling during a median follow-up of 2.1 (25th-75th percentile: 1.1-2.6) years, we applied an aptamer-based multiplex approach measuring 4210 proteins in baseline samples and the last two samples before study end. Associations between obesity (BMI > 30 kg/m2) and baseline protein levels were analyzed. Subsequently, associations of serially measured obesity-related proteins with biological mechanisms and the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, left ventricular assist device implantation, and heart transplantation) were examined.

RESULTS

Obesity was identified in 26% (96/373) of patients. A total of 30% (112/373) experienced a PEP (with obesity: 26% [25/96] vs. without obesity: 31% [87/277]). A total of 141/4210 proteins were linked to obesity, reflecting mechanisms of neuron projection development, cell adhesion, and muscle cell migration. A total of 50/141 proteins were associated with the PEP, of which 12 proteins related to atherosclerosis or hypertrophy provided prognostic information beyond clinical characteristics, N-terminal pro-B-type natriuretic peptide, and high-sensitivity troponin T.

CONCLUSIONS

Patients with HFrEF and obesity show distinct proteomic profiles compared to patients with HFrEF without obesity. Obesity-related proteins are independently associated with HF outcome. These proteins carry potential to improve management of obesity-related HF and could be leads for future research.

Serial cardiac biomarkers, pulmonary artery pressures and traditional parameters of fluid status in relation to prognosis in patients with chronic heart failure: Design and rationale of the BioMEMS study

AIMS

Heart failure (HF), a global pandemic affecting millions of individuals, calls for adequate predictive guidance for improved therapy. Congestion, a key factor in HF-related hospitalizations, further underscores the need for timely interventions. Proactive monitoring of intracardiac pressures, guided by pulmonary artery (PA) pressure, offers opportunities for efficient early-stage intervention, since haemodynamic congestion precedes clinical symptoms.

METHODS

The BioMEMS study, a substudy of the MONITOR-HF trial, proposes a multifaceted approach integrating blood biobank data with traditional and novel HF parameters. Two additional blood samples from 340 active participants in the MONITOR-HF trial were collected at baseline, 3-, 6-, and 12-month visits and stored for the BioMEMS biobank. The main aims are to identify the relationship between temporal biomarker patterns and PA pressures derived from the CardioMEMS-HF system, and to identify the biomarker profile(s) associated with the risk of HF events and cardiovascular death.

CONCLUSION

Since the prognostic value of single baseline measurements of biomarkers like N-terminal pro-B-type natriuretic peptide is limited, with the BioMEMS study we advocate a dynamic, serial approach to better capture HF progression. We will substantiate this by relating repeated biomarker measurements to PA pressures. This design rationale presents a comprehensive review on cardiac biomarkers in HF, and aims to contribute valuable insights into personalized HF therapy and patient risk assessment, advancing our ability to address the evolving nature of HF effectively.

Longitudinal NT-proBNP: Associations With Echocardiographic Changes and Outcomes in Heart Failure

BACKGROUND

The relationship of serial NT-proBNP (N-terminal pro-B-type natriuretic peptide) measurements with changes in cardiac features and outcomes in heart failure (HF) remains incompletely understood. We determined whether common clinical covariates impact these relationships.

METHODS AND RESULTS

In 2 nationwide observational populations with HF, the relationship of serial NT-proBNP measurements with serial echocardiographic parameters and outcomes was analyzed, further stratified by HF with reduced versus preserved left ventricular ejection fraction, inpatient versus outpatient enrollment, age, obesity, chronic kidney disease, atrial fibrillation, and attainment of ≥50% guideline-recommended doses of renin-angiotensin system inhibitors and β-blockers. Among 1911 patients (mean±SD age, 65.1±13.4 years; 26.6% women; 62% inpatient and 38% outpatient), NT-proBNP declined overall, with more rapid declines among inpatients, those with obesity, those with atrial fibrillation, and those attaining ≥50% guideline-recommended doses. Each doubling of NT-proBNP was associated with increases in left ventricular volume (by 6.1 mL), E/e' (transmitral to mitral annular early diastolic velocity ratio) (by 1.4 points), left atrial volume (by 3.6 mL), and reduced left ventricular ejection fraction (by -2.1%). The effect sizes of these associations were lower among patients with HF with preserved ejection fraction, atrial fibrillation, or advanced age (Pinteraction<0.001). A landmark analysis identified that an SD increase in NT-proBNP over 6 months was associated with a 27% increase in the risk of the composite event of HF hospitalization or all-cause death between 6 months and 2 years (adjusted hazard ratio, 1.27 [95% CI, 1.15-1.40]; P<0.001).

CONCLUSIONS

The relationships between NT-proBNP and structural/functional remodeling differed by age, presence of atrial fibrillation, and HF phenotypes. The association of increased NT-proBNP with increased risk of adverse outcomes was consistent in all subgroups.

Sex-specific cardiovascular protein levels and their link with clinical outcome in heart failure

AIMS

This study aims to provide insight into sex-specific cardiovascular protein profiles and their associations with adverse outcomes, which may contribute to a better understanding of heart failure (HF) pathophysiology and the optimal use of circulating proteins for prognostication in women and men.

METHODS AND RESULTS

In 250 stable patients with HF with reduced ejection fraction (HFrEF), we performed trimonthly blood sampling (median follow-up: 26 [17-30] months). We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization) or one sample closest to censoring and applied the Olink Cardiovascular III panel. We used linear regression to study sex-based differences in baseline levels and joint models to study differences in the prognostic value of serially measured proteins. In 66 women and 184 men (mean age of 66 and 67 years, respectively), 21% and 28% reached the PEP, respectively. Mean baseline levels of fatty acid-binding protein 4, secretoglobin family 3A member 2, paraoxonase 3, and trefoil factor 3 were higher in women (Pinteraction : 0.001, 0.007, 0.018, and 0.049, respectively), while matrix metalloproteinase-3, interleukin 1 receptor-like 1, and myoglobin were higher in men (Pinteraction : <0.001, 0.001, and 0.049, respectively), independent of clinical characteristics. No significant differences between sexes were observed in the longitudinal associations of proteins with the PEP. Only peptidoglycan recognition protein 1 showed a suggestive interaction with sex for the primary outcome (Pinteraction  = 0.028), without multiple testing correction, and was more strongly associated with adverse outcome in women {hazard ratio [HR] 3.03 [95% confidence interval (CI), 1.42 to 6.68], P = 0.008} compared with men [HR 1.18 (95% CI, 0.84 to 1.66), P = 0.347].

CONCLUSIONS

Although multiple cardiovascular-related proteins show sex differences at baseline, temporal associations with the adverse outcome do not differ between women and men with HFrEF.

Machine learning-based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure

Aims

Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF.

Methods and results

In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated c-index) and external validation (Henry Ford HF PharmacoGenomic Registry cohort) were performed. Nine proteins were selected in addition to the MAGGIC risk score, N-terminal pro-hormone B-type natriuretic peptide, and troponin T: suppression of tumourigenicity 2, tryptophanyl-tRNA synthetase cytoplasmic, histone H2A Type 3, angiotensinogen, deltex-1, thrombospondin-4, ADAMTS-like protein 2, anthrax toxin receptor 1, and cathepsin D. N-terminal pro-hormone B-type natriuretic peptide and angiotensinogen showed the strongest associations [hazard ratio (95% confidence interval): 1.96 (1.17-3.40) and 0.66 (0.49-0.88), respectively]. The multivariable model yielded a c-index of 0.85 upon internal validation and c-indices up to 0.80 upon external validation. The c-index was higher than that of a model containing established risk factors (P = 0.021).

Conclusion

Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively 'novel' biomarkers for prognostication.

Clinical Trial Registration

https://clinicaltrials.gov/ct2/show/NCT01851538?term=nCT01851538&draw=2&rank=1 24.

Exploring the diagnostic and prognostic value of the C-reactive protein/lymphocyte ratio for dilated cardiomyopathy based on a real-world study

To determine the risk factors for dilated cardiomyopathy (DCM) and construct a risk model for predicting HF in patients with DCM, We enrolled a total of 2122 patients, excluding those who did not meet the requirements. A total of 913 patients were included in the analysis (611 males and 302 females) from October 2012 to May 2020, and data on demographic characteristics, blood biochemical markers, and cardiac ultrasound results were collected. Patients were strictly screened for DCM based on the diagnostic criteria. First, these patients were evaluated using propensity score matching (PSM). Next, unconditional logistic regression was used to assess HF risk. Furthermore, receiver operating characteristic (ROC) curve analysis was conducted to determine diagnostic efficiency, and a nomogram was developed to predict HF. Finally, the Kaplan‒Meier survival curve was plotted. Of the initial 2122 patients, the ejection fraction (EF) in males was worse. We included 913 patients after the final DCM diagnosis. The results showed that the levels of NT-proBNP, WBC, PLT, neutrophils, lymphocytes, eosinophils, and IL-6, C-reactive protein (CRP) and the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and CRP/lymphocyte ratio (CLR) were higher in males than in females (P < 0.001-0.009). The nomogram showed that factors such as sex, WBC, neutrophils, PLR, and CLR could predict the risk of worsening cardiac function in patients with DCM before and after PSM (P < 0.05). The ROC curve showed that CLR with an 85.6% area demonstrated higher diagnostic efficacy than the NLR (77.0%) and PLR (76.6%, P < 0.05). Survival analysis showed a higher mortality risk in females with higher CLR levels (P < 0.001-0.009). However, high CLR levels indicated a higher mortality risk (P < 0.001) compared to sex. Male EF is lower in DCM patients. CLR could predict the risk of declined cardiac function in patients with DCM. The mortality in females with higher CLR levels was highest; however, the exact mechanism should be investigated.

Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine

Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.

HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms

BACKGROUND

HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment.

METHODS

In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1-2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated.

FINDINGS

We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1-4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76-6.69), and 2.88 (1.37-6.03), respectively).

INTERPRETATION

Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01851538https://clinicaltrials.gov/ct2/show/NCT01851538.

FUNDING

EU/EFPIA IMI2JU BigData@Heart grant n°116074, Jaap Schouten Foundation and Noordwest Academie.

Prognostic value of temporal patterns of left atrial reservoir strain in patients with heart failure with reduced ejection fraction

BACKGROUND

We investigated whether repeatedly measured left atrial reservoir strain (LASr) in heart failure with reduced ejection fraction (HFrEF) patients provides incremental prognostic value over a single baseline LASr value, and whether temporal patterns of LASr provide incremental prognostic value over temporal patterns of other echocardiographic markers and NT-proBNP.

METHODS

In this prospective observational study, 153 patients underwent 6-monthly echocardiography, during a median follow-up of 2.5 years. Speckle tracking echocardiography was used to measure LASr. Hazard ratios (HRs) were calculated for LASr from Cox models (baseline) and joint models (repeated measurements). The primary endpoint (PEP) comprised HF hospitalization, left ventricular assist device, heart transplantation, and cardiovascular death.

RESULTS

Mean age was 58 ± 11 years, 76% were men, 82% were in NYHA class I/II, mean LASr was 20.9% ± 11.3%, and mean LVEF was 29% ± 10%. PEP was reached by 50 patients. Baseline and repeated measurements of LASr (HR per SD change (95% CI) 0.20 (0.10-0.41) and (0.13 (0.10-0.29), respectively) were both significantly associated with the PEP, independent of both baseline and repeated measurements of other echo-parameters and NT-proBNP. Although LASr was persistently lower over time in patients with PEP, temporal trajectories did not diverge in patients with versus without the PEP as the PEP approached.

CONCLUSION

LASr was associated with adverse events in HFrEF patients, independent of baseline and repeated other echo-parameters and NT-proBNP. Temporal trajectories of LASr showed decreased but stable values in patients with the PEP, and do not provide incremental prognostic value for clinical practice compared to single measurements of LASr.

Sex-based differences in cardiovascular proteomic profiles and their associations with adverse outcomes in patients with chronic heart failure

BACKGROUND

Studies focusing on sex differences in circulating proteins in patients with heart failure with reduced ejection fraction (HFrEF) are scarce. Insight into sex-specific cardiovascular protein profiles and their associations with the risk of adverse outcomes may contribute to a better understanding of the pathophysiological processes involved in HFrEF. Moreover, it could provide a basis for the use of circulating protein measurements for prognostication in women and men, wherein the most relevant protein measurements are applied in each of the sexes.

METHODS

In 382 patients with HFrEF, we performed tri-monthly blood sampling (median follow-up: 25 [13-31] months). We selected all baseline samples and two samples closest to the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization) or censoring. We then applied an aptamer-based multiplex proteomic assay identifying 1105 proteins previously associated with cardiovascular disease. We used linear regression models and gene-enrichment analysis to study sex-based differences in baseline levels. We used time-dependent Cox models to study differences in the prognostic value of serially measured proteins. All models were adjusted for the MAGGIC HF mortality risk score and p-values for multiple testing.

RESULTS

In 104 women and 278 men (mean age 62 and 64 years, respectively) cumulative PEP incidence at 30 months was 25% and 35%, respectively. At baseline, 55 (5%) out of the 1105 proteins were significantly different between women and men. The female protein profile was most strongly associated with extracellular matrix organization, while the male profile was dominated by regulation of cell death. The association of endothelin-1 (P_interaction < 0.001) and somatostatin (P_interaction = 0.040) with the PEP was modified by sex, independent of clinical characteristics. Endothelin-1 was more strongly associated with the PEP in men (HR 2.62 [95%CI, 1.98, 3.46], p < 0.001) compared to women (1.14 [1.01, 1.29], p = 0.036). Somatostatin was positively associated with the PEP in men (1.23 [1.10, 1.38], p < 0.001), but inversely associated in women (0.33 [0.12, 0.93], p = 0.036).

CONCLUSION

Baseline cardiovascular protein levels differ between women and men. However, the predictive value of repeatedly measured circulating proteins does not seem to differ except for endothelin-1 and somatostatin.

Potential role of left atrial strain in estimation of left atrial pressure in patients with chronic heart failure

AIMS

In a large proportion of heart failure with reduced ejection fraction (HFrEF) patients, echocardiographic estimation of left atrial pressure (LAP) is not possible when the ratio of the peak early left ventricular filling velocity over the late filling velocity (E/A ratio) is not available, which may occur due to several potential causes. Left atrial reservoir strain (LASr) is correlated with LV filling pressures and may serve as an alternative parameter in these patients. The aim of this study was to determine whether LASr can be used to estimate LAP in HFrEF patients in whom E/A ratio is not available.

METHODS AND RESULTS

Echocardiograms of chronic HFrEF patients were analysed and LASr was assessed with speckle tracking echocardiography. LAP was estimated using the current ASE/EACVI algorithm. Patients were divided into those in whom LAP could be estimated using this algorithm (LAPe) and into those in whom this was not possible because E/A ratio was not available (LAPne). We assessed the prognostic value of LASr on the primary endpoint (PEP), which comprised the composite of hospitalization for the management of acute or worsened HF, left ventricular assist device implantation, cardiac transplantation, and cardiovascular death, whichever occurred first in time. We studied 153 patients with a mean age of 58 years of whom 76% men and 82% who were in NYHA class I-II. A total of 86 were in the LAPe group and 67 in the LAPne group. LASr was significantly lower in the LAPne group as compared with the LAPe group (15.8% vs. 23.8%, P < 0.001). PEP-free survival at a median follow-up of 2.5 years was 78% in LAPe versus 51% in LAPne patients. An increase in LASr was significantly associated with a reduced risk of the PEP in LAPne patients (adjusted hazard ratio: 0.91 per %, 95% confidence interval 0.84-0.98). An abnormal LASr (<18%) was associated with a five-fold increase in reaching the PEP.

CONCLUSIONS

In HFrEF patients in whom echocardiographic estimation of LAP is not possible due to due to unavailability of E/A ratio, assessing LASr potentially carries added clinical and prognostic value.

Joint modelling of longitudinal processes and time-to-event outcomes in heart failure: systematic review and exemplar examining the relationship between serum digoxin levels and mortality

BACKGROUND

Joint modelling combines two or more statistical models to reduce bias and increase efficiency. As the use of joint modelling increases it is important to understand how and why it is being applied to heart failure research.

METHODS

A systematic review of major medical databases of studies which used joint modelling within heart failure alongside an exemplar; joint modelling repeat measurements of serum digoxin with all-cause mortality using data from the Effect of Digoxin on Mortality and Morbidity in Patients with Heart Failure (DIG) trial.

RESULTS

Overall, 28 studies were included that used joint models, 25 (89%) used data from cohort studies, the remaining 3 (11%) using data from clinical trials. 21 (75%) of the studies used biomarkers and the remaining studies used imaging parameters and functional parameters. The exemplar findings show that a per unit increase of square root serum digoxin is associated with the hazard of all-cause mortality increasing by 1.77 (1.34-2.33) times when adjusting for clinically relevant covariates.

CONCLUSION

Recently, there has been a rise in publications of joint modelling being applied to heart failure. Where appropriate, joint models should be preferred over traditional models allowing for the inclusion of repeated measures while accounting for the biological nature of biomarkers and measurement error.

Bivariate joint models for survival and change of cognitive function

Changes in cognitive function over time are of interest in ageing research. A joint model is constructed to investigate. Generally, cognitive function is measured through more than one test, and the test scores are integers. The aim is to investigate two test scores and use an extension of a bivariate binomial distribution to define a new joint model. This bivariate distribution model the correlation between the two test scores. To deal with attrition due to death, the Weibull hazard model and the Gompertz hazard model are used. A shared random-effects model is constructed, and the random effects are assumed to follow a bivariate normal distribution. It is shown how to incorporate random effects that link the bivariate longitudinal model and the survival model. The joint model is applied to the English Longitudinal Study of Ageing data.

The role of serial cardiac biomarkers in prognostication and risk prediction of chronic heart failure: additional scientific insights with hemodynamic feedback

INTRODUCTION

Heart failure (HF) is considered as a chronic long-term and lethal disease and will continue to be a major public health problem. Studying (circulating) biomarkers is a promising field of research and could be the first step toward HF tailored prognostic strategies as well as understanding the response to HF drugs in CHF patients.

AREAS COVERED

In literature, there has been considerable research on elevated biomarker levels that are related to a poor prognosis for HF. Since biomarker levels change over time, it is important to study serial (repeated) biomarker measurements which may help us better understand the dynamic course of HF illness. However, the majority of research focuses predominantly on baseline values of biomarkers. Additionally, remote monitoring devices, like sensors, can be used to link hemodynamic information to freshen biomarker data in order to further ameliorate the management of HF.

EXPERT OPINION

Novel biomarkers and additional scientific insights with hemodynamic feedback strongly aid in the prognostication and risk prediction of chronic HF.

Prognostic value of temporal patterns of global longitudinal strain in patients with chronic heart failure

Background

We investigated whether repeatedly measured global longitudinal strain (GLS) has incremental prognostic value over repeatedly measured left ventricular ejection fraction (LVEF) and N-terminal pro B-type natriuretic peptide (NT-proBNP), and a single "baseline" GLS value, in chronic heart failure (HF) patients.

Methods

In this prospective observational study, echocardiography was performed in 173 clinically stable chronic HF patients every six months during follow up. During a median follow-up of 2.7 years, a median of 3 (25th-75th percentile:2-4) echocardiograms were obtained per patient. The endpoint was a composite of HF hospitalization, left ventricular assist device, heart transplantation, cardiovascular death. We compared hazard ratios (HRs) for the endpoint from Cox models (used to analyze the first available GLS measurements) with HRs from joint models (which links repeated measurements to the time-to-event data).

Results

Mean age was 58 ± 11 years, 76% were men, 81% were in New York Heart Association functional class I/II, and all had LVEF < 50% (mean ± SD: 27 ± 9%). The endpoint was reached by 53 patients. GLS was persistently decreased over time in patients with the endpoint. However, temporal GLS trajectories did not further diverge in patients with versus without the endpoint and remained stable during follow-up. Both single measurements and temporal trajectories of GLS were significantly associated with the endpoint [HR per SD change (95%CI): 2.15(1.34-3.46), 3.54 (2.01-6.20)]. In a multivariable model, repeatedly measured GLS maintained its prognostic value while repeatedly measured LVEF did not [HR per SD change (95%CI): GLS:4.38 (1.49-14.70), LVEF:1.14 (0.41-3.23)]. The association disappeared when correcting for repeatedly measured NT-proBNP.

Conclusion

Temporal evolution of GLS was associated with adverse events, independent of LVEF but not independent of NT-proBNP. Since GLS showed decreased but stable values in patients with adverse prognosis, single measurements of GLS provide sufficient information for determining prognosis in clinical practice compared to repeated measurements, and temporal GLS patterns do not add prognostic information to NT-proBNP.

Heart failure subphenotypes based on repeated biomarker measurements are associated with clinical characteristics and adverse events (Bio-SHiFT study)

BACKGROUND

This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis.

METHODS

Clinical data and blood samples were collected tri-monthly until the primary endpoint (PEP) or censoring occurred, with a maximum of 11 visits. The Olink Cardiovascular III panel was measured in baseline samples and the last two samples before the PEP (in 66 PEP cases), or the last sample before censoring (in 184 PEP-free patients). The PEP comprised cardiovascular death, heart transplantation, Left Ventricular Assist Device implantation, and hospitalization for HF. Cluster analysis was performed on individual biomarker trajectories to identify subphenotypes. Then biomarker profiles and clinical characteristics were investigated, and survival analysis was conducted.

RESULTS

Clustering revealed three clinically diverse subphenotypes. Cluster 3 was older, with a longer duration of, and more advanced HF, and most comorbidities. Cluster 2 showed increasing levels over time of most biomarkers. In cluster 3, there were elevated baseline levels and increasing levels over time of 16 remaining biomarkers. Median follow-up was 2.2 (1.4-2.5) years. Cluster 3 had a significantly poorer prognosis compared to cluster 1 (adjusted event-free survival time ratio 0.25 (95%CI:0.12-0.50), p < 0.001). Repeated measurements clusters showed incremental prognostic value compared to clusters using single measurements, or clinical characteristics only.

CONCLUSIONS

Clustering based on repeated biomarker measurements revealed three clinically diverse subphenotypes, of which one has a significantly worse prognosis, therefore contributing to improved (individualized) prognostication.

Dynamic personalized risk prediction in chronic heart failure patients: a longitudinal, clinical investigation of 92 biomarkers (Bio-SHiFT study)

The aim of our observational study was to derive a small set out of 92 repeatedly measured biomarkers with optimal predictive capacity for adverse clinical events in heart failure, which could be used for dynamic, individual risk assessment in clinical practice. In 250 chronic HFrEF (CHF) patients, we collected trimonthly blood samples during a median of 2.2 years. We selected 537 samples for repeated measurement of 92 biomarkers with the Cardiovascular Panel III (Olink Proteomics AB). We applied Least Absolute Shrinkage and Selection Operator (LASSO) penalization to select the optimal set of predictors of the primary endpoint (PE). The association between repeatedly measured levels of selected biomarkers and the PE was evaluated by multivariable joint models (mvJM) with stratified fivefold cross validation of the area under the curve (cvAUC). The PE occurred in 66(27%) patients. The optimal set of biomarkers selected by LASSO included 9 proteins: NT-proBNP, ST2, vWF, FABP4, IGFBP-1, PAI-1, PON-3, transferrin receptor protein-1, and chitotriosidase-1, that yielded a cvAUC of 0.88, outperforming the discriminative ability of models consisting of standard biomarkers (NT-proBNP, hs-TnT, eGFR clinically adjusted) − 0.82 and performing equally well as an extended literature-based set of acknowledged biomarkers (NT-proBNP, hs-TnT, hs-CRP, GDF-15, ST2, PAI-1, Galectin 3) − 0.88. Nine out of 92 serially measured circulating proteins provided a multivariable model for adverse clinical events in CHF patients with high discriminative ability. These proteins reflect wall stress, remodelling, endothelial dysfunction, iron deficiency, haemostasis/fibrinolysis and innate immunity activation. A panel containing these proteins could contribute to dynamic, personalized risk assessment.

Clinical Trial Registration: 10/05/2013 https://clinicaltrials.gov/ct2/show/NCT01851538?term=nCT01851538&draw=2&rank=1.

Inflammation-Related Biomarkers Are Associated with Heart Failure Severity and Poor Clinical Outcomes in Patients with Non-Ischemic Dilated Cardiomyopathy

Inflammation-related biomarkers are associated with clinical outcomes in mixed-etiology chronic heart failure populations. Inflammation-related markers tend to be higher in ischemic than in non-ischemic dilated cardiomyopathy (NI-DCM) patients, which might impact their prognostic performance in NI-DCM patients. Therefore, we aimed to assess the association of inflammation-related biomarkers with heart failure severity parameters and adverse cardiac events in a pure NI-DCM patient cohort. Fifty-seven patients with NI-DCM underwent endomyocardial biopsy. Biopsies were evaluated by immunohistochemistry for CD3+, CD45ro+, CD68+, CD4+, CD54+, and HLA-DR+ cells. Blood samples were tested for high-sensitivity C-reactive protein (hs-CRP), interleukin-6, tumor necrosis factor-α (TNF-α), soluble urokinase-type plasminogen activator receptor and adiponectin. During a five-year follow-up, twenty-seven patients experienced at least one composite adverse cardiac event: left ventricle assist device implantation, heart transplantation or death. Interleukin-6, TNF-α and adiponectin correlated with heart failure severity parameters. Patients with higher levels of interleukin-6, TNF-α, adiponectin or hs-CRP, or a higher number of CD3+ or CD45ro+ cells, had lower survival rates. Interleukin-6, adiponectin, and CD45ro+ cells were independently associated with poor clinical outcomes. All patients who had interleukin-6, TNF-α and adiponectin concentrations above the threshold experienced an adverse cardiac event. Therefore, a combination of these cytokines can identify high-risk NI-DCM patients.

Sex-specific temporal evolution of circulating biomarkers in patients with chronic heart failure with reduced ejection fraction

BACKGROUND

We aimed to assess differences in clinical characteristics, prognosis, and the temporal evolution of circulating biomarkers in male and female patients with HFrEF.

METHODS

We included 250 patients (66 women) with chronic heart failure (CHF) between 2011 and 2013 and performed trimonthly blood sampling during a median follow-up of 2.2 years [median (IQR) of 8 (5-10) urine and 9 (5-10) plasma samples per patient]. After completion of follow-up we measured 8 biomarkers. The primary endpoint (PE) was the composite of cardiac death, cardiac transplantation, left ventricular assist device implantation, and hospitalization due to acute or worsened CHF. Joint models were used to determine whether there were differences in the temporal patterns of the biomarkers between men and women as the PE approached.

RESULTS

A total of 66 patients reached the PE of which 52 (78.8%) were male and 14 (21.2%) were female. The temporal patterns of all studied biomarkers were associated with the PE, and overall showed disadvantageous changes as the PE approached. For NT-proBNP, HsTnT, and CRP, women showed higher levels over the entire follow-up duration and concomitant numerically higher hazard ratios [NT-proBNP: women: HR(95%CI) 7.57 (3.17-21.93), men: HR(95%CI) 3.14 (2.09-4.79), p for interaction = 0.104, HsTnT: women: HR(95%CI) 6.38 (2.18-22.46), men: HR(95%CI) 4.91 (2.58-9.39), p for interaction = 0.704, CRP: women: HR(95%CI) 7.48 (3.43-19.53), men: HR(95%CI) 3.29 [2.27-5.44], p for interaction = 0.106). In contrast, temporal patterns of glomerular and tubular renal markers showed similar associations with the PE in men and women.

CONCLUSION

Although interaction terms are not statistically significant, the associations of temporal patterns of NT-proBNP, HsTnT, and CRP appear more outspoken in women than in men with HFrEF, whereas associations seem similar for temporal patterns of creatinine, eGFR, Cystatin C, KIM-1 and NAG. Larger studies are needed to confirm these potential sex differences.

Personalized screening intervals for kidney function in patients with chronic heart failure: a modeling study

Background

High mortality and rehospitalization rates demonstrate that improving risk assessment in heart failure patients remains challenging. Individual temporal evolution of kidney biomarkers is associated with poor clinical outcome in these patients and hence may carry the potential to move towards a personalized screening approach.

Methods

In 263 chronic heart failure patients included in the prospective Bio-SHiFT cohort study, glomerular and tubular biomarker measurements were serially obtained according to a pre-scheduled, fixed trimonthly scheme. The primary endpoint (PE) comprised cardiac death, cardiac transplantation, left ventricular assist device implantation or heart failure hospitalization. Personalized scheduling of glomerular and tubular biomarker measurements was compared to fixed scheduling in individual patients by means of a simulation study, based on clinical characteristics of the Bio-SHiFT study. For this purpose, repeated biomarker measurements and the PE were jointly modeled. For personalized scheduling, using this fitted joint model, we determined the optimal time point of the next measurement based on the patient’s individual risk profile as estimated by the joint model and the maximum information gain on the patient’s prognosis. We compared the schedule’s capability of enabling timely intervention before the occurrence of the PE and number of measurements needed.

Results

As compared to a pre-defined trimonthly scheduling approach, personalized scheduling of glomerular and tubular biomarker measurements showed similar performance with regard to prognostication, but required a median of 0.4–2.7 fewer measurements per year.

Conclusion

Personalized scheduling is expected to reduce the number of patient visits and healthcare costs. Thus, it may contribute to efficient monitoring of chronic heart failure patients and could provide novel opportunities for timely adaptation of treatment.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40620-021-01014-0.

Associations of serially measured PCSK9, LDLR and MPO with clinical outcomes in heart failure

Aim: To investigate the temporal evolution of plasma proprotein convertase subtilisin/kexin type 9 (PCSK9), low-density lipoprotein receptor (LDLR) and myeloperoxidase (MPO) in relation to clinical outcome in chronic heart failure (CHF). Methodology & results: Trimonthly blood sampling was performed during a median follow-up of 2.2 (IQR 1.4-2.5) years in 263 CHF patients. Seventy patients reached the primary end point (PE) (cardiovascular death, heart transplantation, left ventricular assist device implantation or HF-hospitalization). MPO level was independently associated with the PE; the adjusted (for clinical factors) hazard ratio (aHR) per standard deviation difference in MPO was 1.71 (95% CI: 1.23-2.43) at any time during follow-up. PCSK9 level (HR: 1.45 [1.04-2.06]) and LDLR (HR: 0.66 [0.49-0.87]) were statistical significantly associated with the PE but only in unadjusted analyses. Slope of temporal MPO evolution (aHR: 1.34 [1.12-1.76] per 0.1 standard deviation/year difference in slope) and LDLR (aHR: 0.78 [0.61-0.90]) however, were associated with PE. Conclusion: Temporal patterns of MPO and LDLR are independently associated with clinical outcome in CHF, which illustrates the importance of assessing temporal evolutions. Clinical trial registration information: registered in ClinicalTrials.gov, number NCT01851538. https://clinicaltrials.gov/ct2/show/NCT01851538.

Plasma proteome profiling of cardiotoxicity in patients with diffuse large B-cell lymphoma

BACKGROUND

Cardiovascular toxicity is a notorious complication of doxorubicin (DXR) therapy for diffuse large B-cell lymphoma (DLBCL). Although surveillance of well-known biological markers for cardiovascular disease (CVD) as NTproBNP and Troponins may be helpful, there are no established markers to monitor for evolving CVD during treatment. New possibilities have arisen with the emergence of newer techniques allowing for analysis of plasma proteins that can be associated with cardiovascular disease. Proximity Extension Assay is one of them.

OBJECTIVES

We aimed to illustrate the incidence of CVD in DLBCL patients treated with DXR and to establish whether there are plasma proteins associated with pre-existing or emerging CVD.

METHODS

In 95 patients, 182 different proteins from OLINK panels, NTproBNP, Troponin I and CRP were assessed prior to, during and after treatment. For comparison, samples from controls were analyzed.

RESULTS

In the DLBCL cohort, 33.3% had pre-treatment CVD compared to 5.0% in the controls and 23.2% developed new CVD. Of the 32.6% who died during follow up, CVD was the cause in 4 patients. Spondin-1 (SPON-1) correlated to pre-treatment CVD (1.22 fold change, 95% CI 1.10-1.35, p = 0.00025, q = 0.045). Interleukin-1 receptor type 1 (IL-1RT1) was associated to emerging CVD (1.24 fold change, 95% CI 1.10-1.39, p = 0.00044, q = 0.082).

CONCLUSION

We observed a higher prevalence of CVD in DLBCL patients compared to controls prior to DXR therapy. Two proteins, SPON-1 and IL-1RT1, were related to pre-existing and emerging CVD in DXR treated patients. If confirmed in larger cohorts, IL-1RT1 may emerge as a reliable biomarker for unfolding CVD in DLBCL.

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders

Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.

Prognostic relevance of Doppler echocardiographic re-assessment in HFrEF patients

BACKGROUND

Current guidelines do not recommend periodically repeating echocardiograms in the follow-up of stable heart failure patients with reduced ejection fraction (HFrEF). The objective of the study was to verify the additional prognostic information provided by a comprehensive re-assessment of their cardiac function and hemodynamic profile at Doppler echocardiography in HFrEF patients.

METHODS

Retrospective analysis of 769 stable HFrEF outpatients who underwent two complete echocardiograms, at baseline and at re-assessment. Main candidate predictors of prognosis were: left ventricular (LV) filling pattern, pulmonary artery systolic pressure (PASP) and right ventricular function (TAPSE). Age, LV ejection fraction, mitral regurgitation severity, NYHA class, brain natriuretic peptide plasma levels at baseline, and their changes at 12 months, were used as covariates. Median follow-up was 30 months. All-cause death was the study end-point.

RESULTS

At baseline, restrictive filling pattern and low TAPSE were significant predictors of poor prognosis. At re-evaluation, persistently restrictive/worsened filling pattern, persistently-low/worsened TAPSE and worsened PASP, were associated with poorer survival. A significant interaction between changes in TAPSE, PASP and LV filling pattern was observed: in the restrictive pattern subgroup, survival was poorer in worsened/persistently low TAPSE (p < 0.01); in non-restrictive pattern subgroup, survival was poorer in worsened/persistently elevated PASP (p = 0.01). The re-assessment model improved the C-index from 0.69 to 0.74 (P < 0.01) compared to baseline model.

CONCLUSIONS

Doppler echocardiographic re-assessment of LV filling pattern, PASP and TAPSE allows a better prognostic stratification of HFrEF outpatients than baseline evaluation and is additional to changes in BNP and NYHA class.

Prognostic value of C-reactive protein in adults with congenital heart disease

BACKGROUND

High-sensitivity C reactive protein (hs-CRP) has been associated with outcomes in adult congenital heart disease (ACHD). However, its prognostic value beyond N-terminal pro B type natriuretic peptide (NT-proBNP) or troponin T remains unknown. We studied the temporal evolution of hs-CRP, as well as the relation between hs-CRP and adverse clinical outcomes independent of NT-proBNP and troponin T in patients with ACHD.

METHODS

In this prospective cohort study, we enrolled 602 patients with ACHD (2011-2013) who underwent baseline and thereafter annual blood sampling during 4 years. Hs-CRP, hs-troponin T and NT-proBNP were measured. The primary endpoint was composed of death or heart failure (HF). Cox regression and Joint Modelling was used to relate 2log hs-CRP levels with the endpoint, with adjustment for baseline characteristics and (repeated) hs-troponin T and NT-proBNP measurements.

RESULTS

Hs-CRP was measured at baseline in 591 patients, median age 33 years, 58% men, 90% New York Heart Association I with an average of 4.3 measurements per patient. Median follow-up was 5.9 (IQR 5.3-6.3) years (99.2% complete) and 69 patients met the endpoint. Higher baseline hs-CRP was independently associated with higher risk of death or HF (HR 1.36, 95% CI 1.19 to 1.55). Hs-CRP increased over time prior to death or HF, and repeated hs-CRP measurements were associated with the endpoint, independent of repeated NT-proBNP and hs-troponin T (HR 1.54, 95% CI 1.24 to 1.98).

CONCLUSIONS

Hs-CRP carries incremental prognostic value for the risk of death or HF, beyond NT-proBNP and hs-troponin T. Hs-CRP increased prior to the occurrence of HF or death, supporting the role of inflammation in the clinical deterioration of patients with ACHD.

Serially Measured Cytokines and Cytokine Receptors in Relation to Clinical Outcome in Patients With Stable Heart Failure

In this prospective cohort study of 250 stable heart failure patients with trimonthly blood sampling, we investigated associations of 17 repeatedly measured cytokines and cytokine receptors with clinical outcome during a median follow-up of 2.2 (25th-75th percentile, 1.4-2.5) years. Sixty-six patients reached the primary end point (composite of cardiovascular mortality, heart failure hospitalization, heart transplantation, left ventricular assist device implantation). Repeatedly measured levels of 8 biomarkers correlated with clinical outcomes independent of clinical characteristics. Rates of change over time (slopes of biomarker evolutions) remained independently associated with outcome for 15 biomarkers. Thus, temporal patterns of cytokines and cytokine receptors, in particular tumour necrosis factor ligand superfamily member 13B and interleukin-1 receptor type 1, might contribute to personalized risk assessment.

Prognostic Value of Serial High-Sensitivity Troponin T Measurements in Adults With Congenital Heart Disease

BACKGROUND

Single high-sensitivity troponin T (hs-TnT) measurement is predictive of cardiac events in adults with congenital heart disease (ACHD). We aimed to study the prognostic value of serial hs-TnT measurements in stable patients with ACHD.

METHODS

In total, 602 consecutive patients with ACHD were enrolled in this prospective study (2011-2013). Blood sampling was performed at enrollment and thereafter yearly during scheduled visits, up to 4 years. Hs-TnT, N-terminal pro B-type natriuretic peptide (NT-proBNP), and estimated glomerular filtration rate (eGFR) were measured. The composite primary endpoint was defined as all-cause mortality, heart failure, arrhythmia, hospitalization, cardiac (re)interventions, or thromboembolic events. The relationship between changes in serial hs-TnT and the primary endpoint was studied by joint models with adjustment for repeated NT-proBNP and eGFR.

RESULTS

In 601 patients (median age, 33 [interquartile range, 25-41] years, 42% women, 90% NYHA I), at least 1 hs-TnT measurement was performed; a mean of 4.3 hs-TnT measurements per patient were collected. After a median follow-up of 5.8 [interquartile range, 5.3-6.3] years, 229 (38.1%) patients reached the primary endpoint. On average, hs-TnT levels increased over time, and more in patients who reached the primary endpoint (P < 0.001). A 2-fold higher hs-TnT was associated with the primary endpoint (unadjusted hazard ratio, 1.62; 95% confidence interval, 1.44-1.82; P < 0.001). The association remained after adjustment for repeated eGFR but not when adjusted for repeated NT-proBNP; repeated NT-proBNP remained associated with the primary endpoint.

CONCLUSION

In stable patients with ACHD, hs-TnT levels increased before the occurrence of an event and repeated hs-TnT was associated with the risk of adverse cardiac events. However, repeated hs-TnT was not superior to repeated NT-proBNP.

Temporal Patterns of 14 Blood Biomarker candidates of Cardiac Remodeling in Relation to Prognosis of Patients With Chronic Heart Failure-The Bio- SH i FT Study

Background

Remodeling biomarkers carry high potential for predicting adverse events in chronic heart failure (CHF) patients. However, temporal patterns during the course of CHF, and especially the trajectory before an adverse event, are unknown. We studied the prognostic value of temporal patterns of 14 cardiac remodeling biomarker candidates in stable patients with CHF from the Bio‐SHiFT (Serial Biomarker Measurements and New Echocardiographic Techniques in Chronic Heart Failure Patients Result in Tailored Prediction of Prognosis) study.

Methods and Results

In 263 CHF patients, we performed trimonthly blood sampling during a median follow‐up of 2.2 years. For the analysis, we selected all baseline samples, the 2 samples closest to the primary end point (PE), or the last sample available for end point–free patients. Thus, in 567 samples, we measured suppression of tumorigenicity‐2, galectin‐3, galectin‐4, growth differentiation factor‐15, matrix metalloproteinase‐2, 3, and 9, tissue inhibitor metalloproteinase‐4, perlecan, aminopeptidase‐N, caspase‐3, cathepsin‐D, cathepsin‐Z, and cystatin‐B. The PE was a composite of cardiovascular mortality, heart transplantation, left ventricular assist device implantation, and HF hospitalization. Associations between repeatedly measured biomarker candidates and the PE were investigated by joint modeling. Median age was 68 (interquartile range: 59–76) years with 72% men; 70 patients reached the PE. Repeatedly measured suppression of tumorigenicity‐2, galectin‐3, galectin‐4, growth differentiation factor‐15, matrix metalloproteinase‐2 and 9, tissue inhibitor metalloproteinase‐4, perlecan, cathepsin‐D, and cystatin‐B levels were significantly associated with the PE, and increased as the PE approached. The slopes of biomarker trajectories were also predictors of clinical outcome, independent of their absolute level. Associations persisted after adjustment for clinical characteristics and pharmacological treatment. Suppression of tumorigenicity‐2 was the strongest predictor (hazard ratio: 7.55 per SD difference, 95% CI: 5.53–10.30), followed by growth differentiation factor‐15 (4.06, 2.98–5.54) and matrix metalloproteinase‐2 (3.59, 2.55–5.05).

Conclusions

Temporal patterns of remodeling biomarker candidates predict adverse clinical outcomes in CHF.

Clinical Trial Registration

URL: http://www.clinicaltrials.gov. Unique identifier: NCT01851538.

Circulating Biomarkers of Cell Adhesion Predict Clinical Outcome in Patients with Chronic Heart Failure

Cardiovascular inflammation and vascular endothelial dysfunction are involved in chronic heart failure (CHF), and cellular adhesion molecules are considered to play a key role in these mechanisms. We evaluated temporal patterns of 12 blood biomarkers of cell adhesion in patients with CHF. In 263 ambulant patients, serial, tri-monthly blood samples were collected during a median follow-up of 2.2 (1.4-2.5) years. The primary endpoint (PE) was a composite of cardiovascular mortality, HF hospitalization, heart transplantation and implantation of a left ventricular assist device and was reached in 70 patients. We selected the baseline blood samples in all patients, the two samples closest to a PE, or, for event-free patients, the last sample available. In these 567 samples, associations between biomarkers and PE were investigated by joint modelling. The median age was 68 (59-76) years, with 72% men and 74% New York Heart Association class I-II. Repeatedly measured levels of Complement component C1q receptor (C1qR), Cadherin 5 (CDH5), Chitinase-3-like protein 1 (CHI3L1), Ephrin type-B receptor 4 (EPHB4), Intercellular adhesion molecule-2 (ICAM-2) and Junctional adhesion molecule A (JAM-A) were independently associated with the PE. Their rates of change also predicted clinical outcome. Level of CHI3L1 was numerically the strongest predictor with a hazard ratio (HR) (95% confidence interval) of 2.27 (1.66-3.16) per SD difference in level, followed by JAM-A (2.10, 1.42-3.23) and C1qR (1.90, 1.36-2.72), adjusted for clinical characteristics. In conclusion, temporal patterns of C1qR, CDH5, CHI3L1, EPHB4, ICAM2 and JAM-A are strongly and independently associated with clinical outcome in CHF patients.

[GDF-15 in plasma and circulating mononuclear cells and NT-proBNP for diagnosis of chronic heart failure and predicting cardiovascular disease events]

OBJECTIVE

To investigate the diagnostic value of plasma growth and differentiation factor-15 (GDF-15) level, GDF-15 mRNA expression in circulating mononuclear cells (MNCs), and plasma pro-B-type natriuretic peptide (NT-proBNP) level for heart failure in patients with different underlying cardiac diseases, namely dilated cardiomyopathy (DCM) and coronary artery heart disease (CAD), and assess their value in predicting the severity of heart failure and long-term cardiovascular disease (CVD) events.

METHODS

Fasting venous blood samples were collected from 261 patients with DCM and 251 patients with CAD admitted in our hospital between January, 2018 and January, 2019, with 132 healthy individuals serving as the control group. The plasma level of GDF-15 was measured by enzyme-linked immunosorbent assay (ELISA), and the expression of GDF-15 mRNA in the MNCs was measured by real-time PCR. We also analyzed the expression of GDF-15 in patients with different NYHA classes, and the ROC curve was used to evaluate the predictive power of GDF-15 mRNA for CVD events.

RESULTS

The plasma levels of GDF-15 and GDF-15 mRNA in the MNCs were significantly higher in patients with DCM and CAD than in the control group (P < 0.01). Plasma GDF-15 levels were significantly higher in NYHA class Ⅳ patients than in class Ⅱ and Ⅲ patients, and GDF-15 mRNA expressions in the MNCs were much higher in class Ⅲ and Ⅳ patients than class Ⅱ patients (P < 0.01). ROC curve analysis showed that for predicting CVD events, the area under the curve (AUC) was 0.73 (95% CI: 0.69-0.77, P < 0.001) for NT-proBNP alone, as compared with 0.83 (95% CI: 0.79-0.86, P < 0.001) for GDF-15 mRNA in the MNCs combined with NT-proBNP.

CONCLUSIONS

Plasma GDF-15 level and GDF-15 mRNA expression level in the MNSc can both be used as biomarkers for heart failure. Plasma level of GDF-15 is more sensitive for predicting NYHA class Ⅳ patients with heart failure, while GDF-15 mRNA level in the MNCs better predicts class Ⅱ patients. The combination of NT-proBNP with GDF-15 mRNAlevel in the MNCs can more accurately predict the risk of long-term CVD events.

Cardiac Biomarkers in Advanced Heart Failure: How Can They Impact Our Pre-transplant or Pre-LVAD Decision-making

PURPOSE OF REVIEW

Decision-making in advanced heart failure (HF) is a complex process that involves careful consideration of competing tradeoffs of risks and benefits in regard to heart transplantation (HT) or left ventricular assist device (LVAD) placement. The purpose of this review is to discuss how biomarkers may affect decision-making for HT or LVAD implantation.

RECENT FINDINGS

N-Terminal probrain natriuretic peptide, soluble suppression of tumorigenicity-2, galectin-3, copeptin, and troponin T levels are associated with HF survival and can help identify the appropriate timing for advanced HF therapies. Patients at risk of right ventricular failure after LVAD implantation can be identified with preimplant biomarkers of extracellular matrix turnover, neurohormonal activation, and inflammation. There is limited data on the adoption of biomarker measurement for decision-making in the allocation of advanced HF therapies. Nonetheless, biomarkers can improve risk stratification and prognostication thereby optimizing patient selection for HT and LVAD implantation.

Longitudinal patterns of N-terminal pro B-type natriuretic peptide, troponin T, and C-reactive protein in relation to the dynamics of echocardiographic parameters in heart failure patients

Aims

To further elucidate the nature of the association between N-terminal pro-B type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), C-reactive protein (CRP), and clinical outcome, we examined the relationship between serial simultaneous measurements of echocardiographic parameters and these biomarkers in chronic heart failure (CHF) patients.

Methods and results

In 117 CHF patients with ejection fraction ≤50%, NT-proBNP, hs-TnT, and CRP were measured simultaneously with echocardiographic evaluation at 6-month intervals until the end of 30 months follow-up or until an adverse clinical event occurred. Linear mixed effects models were used for data-analysis. Median follow-up was 2.2 years (interquartile range 1.5–2.6). We performed up to six follow-up evaluations with 55% of patients having at least three evaluations performed. A model containing all three biomarkers revealed that doubling of NT-proBNP was associated with a decrease in left ventricular ejection fraction by 1.83 (95% confidence interval −2.63 to −1.03)%, P &lt; 0.0001; relative increase in mitral E/e′ ratio by 12 (6–18)%, P &lt; 0.0001; relative increase in mitral E/A ratio by 16 (9–23)%, P &lt; 0.0001; decrease in tricuspid annular plane systolic excursion by 0.66 (−1.27 to −0.05) mm, P = 0.03; rise in tricuspid regurgitation peak systolic gradient by 2.74 (1.43–4.05) mmHg, P = 0.001; and increase in left ventricular and atrial dimensions, P &lt; 0.05. Hs-TnT and CRP showed significant associations with some echocardiographic parameters after adjustment for clinical covariates, but after adjustment for the other biomarkers the associations were not significant.

Conclusion

Serum NT-proBNP independently reflects changes in echocardiographic parameters of systolic function, left ventricular filling pressures, estimated pulmonary pressure, and chamber dimensions. Our results support further studies on NT-proBNP as a surrogate marker for haemodynamic congestion and herewith support its potential value for therapy guidance.

Cardiometabolic Biomarkers and Their Temporal Patterns Predict Poor Outcome in Chronic Heart Failure (Bio-SHiFT Study)

PURPOSE

Multiple hormonal and metabolic alterations occur in chronic heart failure (CHF), but their proper monitoring during clinically silent progression of CHF remains challenging. Hence, our objective was to explore whether temporal patterns of six emerging cardiometabolic biomarkers predict future adverse clinical events in stable patients with CHF.

METHODS

In 263 patients with CHF, we determined the risk of a composite end point of heart failure hospitalization, cardiac death, left ventricular assist device implantation, and heart transplantation in relation to serially assessed blood biomarker levels and slopes (i.e., rate of biomarker change per year). During 2.2 years of follow-up, we repeatedly measured IGF binding proteins 1, 2, and 7 (IGFBP-1, IGFBP-2, IGFBP-7), adipose fatty acid binding protein 4 (FABP-4), resistin, and chemerin (567 samples in total).

RESULTS

Serially measured IGFBP-1, IGFBP-2, IGFBP-7, and FABP-4 levels predicted the end point [univariable hazard ratio (95% CI) per 1-SD increase: 3.34 (2.43 to 4.87), 2.86 (2.10 to 3.92), 2.45 (1.91 to 3.13), and 2.46 (1.88 to 3.24), respectively]. Independently of the biomarkers' levels, their slopes were also strong clinical predictors [per 0.1-SD increase: 1.20 (1.11 to 1.31), 1.27 (1.14 to 1.45), 1.23 (1.11 to 1.37), and 1.27 (1.12 to 1.48)]. All associations persisted after multivariable adjustment for patient baseline characteristics, baseline N-terminal pro-hormone brain natriuretic peptide and cardiac troponin T, and pharmacological treatment during follow-up.

MAIN CONCLUSIONS

The temporal patterns of IGFBP-1, IGFBP-2, IGFBP-7, and adipose FABP-4 predict adverse clinical outcomes during outpatient follow-up of patients with CHF and may be clinically relevant as they could help detect more aggressive CHF forms and assess patient prognosis, as well as ultimately aid in designing more effective biomarker-guided therapy.