Temporal evolution of liver function parameters predicts clinical outcome in chronic heart failure patients (Bio-SHiFT study).

BACKGROUND: Liver dysfunction contributes to worse clinical outcomes in heart failure (HF) patients. However, studies exploring temporal evolutions of liver function parameters in chronic HF (CHF) pa- tients, and their associations with clinical outcome, are scarce. Detailed temporal patterns of alkaline phosphatase (ALP), gamma glutamyl transpeptidase (GGTP), total bilirubin (TBIL) and albumin (ALB) were investigated, and their relation with clinical outcome, in patients with stable CHF with reduced ejection fraction. METHODS: Tri-monthly plasma samples were collected from 250 patients during 2.2 (1.4-2.5) years of follow-up. ALP, GGTP, ALB, and TBIL were measured in 749 selected samples and the relationship between repeatedly measured biomarker levels and the primary endpoint (PEP; composite of cardiovas- cular death, heart transplantation, left ventricular assist device implantation, and hospitalization for worsened HF) was evaluated by joint models. RESULTS: Mean age was 66 ± 13 years; 74% were men, 25% in New York Heart Association class III-IV. 66 (26%) patients reached the PEP. Repeatedly measured levels of TBIL, ALP, GGTP, and ALB were associated with the PEP after adjustment for N-terminal prohormone B-type natriuretic peptide and high sensitivity troponin T (hazard ratio [95% confidence interval] per doubling of biomarker level: 1.98 [1.32; 2.95], p = 0.002; 1.84 [1.09; 3.05], p = 0.018, 1.33 [1.08; 1.63], p = 0.006 and 1.14 [1.09; 1.20], p < 0.001, respectively). Serial levels of ALP and GGTP, and slopes of the temporal evolutions of ALB and TBIL, adjusted for clinical variables, were also significantly associated with the PEP. CONCLUSIONS: Changes in serum levels of TBIL, ALP, GGTP, and ALB precede adverse cardiovascular events in patients with CHF. These routine liver function parameters may provide additional prognostic information in heart failure with reduced ejection fraction patients in clinical practice.

Targeted proteomics and metabolomics for biomarker discovery in abdominal aortic aneurysm and post-EVAR sac volume.

BACKGROUND AND AIMS: Abdominal aortic aneurysm (AAA) patients undergo uniform surveillance programs both leading up to, and following surgery. Circulating biomarkers could play a pivotal role in individualizing surveillance. We applied a multi-omics approach to identify relevant biomarkers and gain pathophysiological insights. MATERIALS AND METHODS: In this cross-sectional study, 108 AAA patients and 200 post-endovascular aneurysm repair (post-EVAR) patients were separately investigated. We performed partial least squares regression and ingenuity pathway analysis on circulating concentrations of 96 proteins (92 Olink Cardiovascular-III panel, 4 ELISA-assays) and 199 metabolites (measured by LC-TQMS), and their associations with CT-based AAA/sac volume. RESULTS: The median (25th-75th percentile) maximal diameter was 50.0 mm (46.0, 53.0) in the AAA group, and 55.4 mm (45.0, 64.2) in the post-EVAR group. Correcting for clinical characteristics in AAA patients, the aneurysm volume Z-score differed 0.068 (95 %CI: (0.042, 0.093)), 0.066 (0.047, 0.085) and -0.051 (-0.064, -0.038) per Z-score valine, leucine and uPA, respectively. After correcting for clinical characteristics and orthogonalization in the post-EVAR group, the sac volume Z-score differed 0.049 (0.034, 0.063) per Z-score TIMP-4, -0.050 (-0.064, -0.037) per Z-score LDL-receptor, -0.051 (-0.062, -0.040) per Z-score 1-OG/2-OG and -0.056 (-0.066, -0.045) per Z-score 1-LG/2-LG. CONCLUSIONS: The branched-chain amino acids and uPA were related to AAA volume. For post-EVAR patients, LDL-receptor, monoacylglycerols and TIMP-4 are potential biomarkers for sac volume. Additionally, distinct markers for sac change were identified.

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk.

BACKGROUND: We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long-term mortality risk. METHODS AND RESULTS: BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high-frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker-based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all-cause death were evaluated using accelerated failure time models (median follow-up, 9.1 years; 141 deaths). Three biomarker-based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long-term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44-0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39-1.32; P=0.281) compared with patients with a repeat ACS. CONCLUSIONS: Patients with subphenotypes of post-ACS with different all-cause mortality risks during long-term follow-up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.

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.

Circulating biomarkers of cardiovascular disease are related to aneurysm volume in abdominal aortic aneurysm.

BACKGROUND: Surveillance programs in abdominal aortic aneurysms (AAA) are mainly based on imaging and leave room for improvement to timely identify patients at risk for AAA growth. Many biomarkers are dysregulated in patients with AAA, which fuels interest in biomarkers as indicators of disease progression. We examined associations of 92 cardiovascular disease (CVD)-related circulating biomarkers with AAA and sac volume. METHODS: In a cross-sectional analysis, we separately investigated (1) 110 watchful waiting (WW) patients (undergoing periodic surveillance imaging without planned intervention) and (2) 203 patients after endovascular aneurysm repair (EVAR). The Cardiovascular Panel III (Olink Proteomics AB, Sweden) was used to measure 92 CVD-related circulating biomarkers. We used cluster analyses to investigate protein-based subphenotypes, and linear regression to examine associations of biomarkers with AAA and sac volume on CT scans. RESULTS: Cluster analyses revealed two biomarker-based subgroups in both WW and EVAR patients, with higher levels of 76 and 74 proteins, respectively, in one subgroup versus the other. In WW patients, uPA showed a borderline significant association with AAA volume. Adjusting for clinical characteristics, there was a difference of -0.092 (-0.148, -0.036) loge mL in AAA volume per SD uPA. In EVAR patients, after multivariable adjustment, four biomarkers remained significantly associated with sac volume. The mean effects on sac volume per SD difference were: LDLR: -0.128 (-0.212, -0.044), TFPI: 0.139 (0.049, 0.229), TIMP4: 0.110 (0.023, 0.197), IGFBP-2: 0.103 (0.012, 0.194). CONCLUSION: LDLR, TFPI, TIMP4, and IGFBP-2 were independently associated with sac volume after EVAR. Subgroups of patients with high levels of the majority of CVD-related biomarkers emphasize the intertwined relationship between AAA and CVD.ClinicalTrials.gov Identifier: NCT03703947.

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.

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 (Pinteraction < 0.001) and somatostatin (Pinteraction = 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.

Serially measured high-sensitivity cardiac troponin T, N-terminal-pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 for risk assessment after acute coronary syndrome: the BIOMArCS cohort.

AIMS: Evidence regarding the role of serial measurements of biomarkers for risk assessment in post-acute coronary syndrome (ACS) patients is limited. The aim was to explore the prognostic value of four, serially measured biomarkers in a large, real-world cohort of post-ACS patients. METHODS AND RESULTS: BIOMArCS is a prospective, multi-centre, observational study in 844 post-ACS patients in whom 12 218 blood samples (median 17 per patient) were obtained during 1-year follow-up. The longitudinal patterns of high-sensitivity cardiac troponin T (hs-cTnT), N-terminal-pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), and growth differentiation factor 15 (GDF-15) were analysed in relation to the primary endpoint (PE) of cardiovascular mortality and recurrent ACS using multivariable joint models. Median age was 63 years, 78% were men and the PE was reached by 45 patients. The average biomarker levels were systematically higher in PE compared with PE-free patients. After adjustment for 6-month post-discharge Global Registry of Acute Coronary Events score, 1 standard deviation increase in log[hs-cTnT] was associated with a 61% increased risk of the PE [hazard ratio (HR) 1.61, 95% confidence interval (CI) 1.02-2.44, P = 0.045], while for log[GDF-15] this was 81% (HR 1.81, 95% CI 1.28-2.70, P = 0.001). These associations remained significant after multivariable adjustment, while NT-proBNP and hs-CRP were not. Furthermore, GDF-15 level showed an increasing trend prior to the PE (Structured Graphical Abstract). CONCLUSION: Longitudinally measured hs-cTnT and GDF-15 concentrations provide prognostic value in the risk assessment of clinically stabilized patients post-ACS. CLINICAL TRIAL REGISTRATION: The Netherlands Trial Register. Currently available at URL https://trialsearch.who.int/; Unique Identifiers: NTR1698 and NTR1106.

Longitudinal profile of circulating endothelial cells in post-acute coronary syndrome patients.

IntroductionPatients who have experienced an acute coronary syndrome (ACS) are at risk of a recurrent event, but their level of risk varies. Because of their close temporal relationship with vascular injury, longitudinal measurements of circulating endothelial cells (CECs) carry potential to improve individual risk assessment.MethodsWe conducted an explorative nested case-control study within our multicenter, prospective, observational biomarker study (BIOMArCS) of 844 ACS patients. Following an index ACS, high-frequency blood sampling was performed during 1-year follow-up. CECs were identified using flow cytometric analyses in 15 cases with recurrent event, and 30 matched controls.ResultsCases and controls had a median (25th-75thpercentile) age of 64.1 (58.1-75.1) years and 80% were men. During the months preceding the endpoint, the mean (95%CI) CEC concentration in cases was persistently higher than in controls (12.8 [8.2-20.0] versus 10.0 [7.0-14.4] cells/ml), although this difference was non-significant (P = 0.339). In controls, the mean cell concentration was significantly (P = 0.030) lower in post 30-day samples compared to samples collected within one day after index ACS: 10.1 (7.5-13.6) versus 17.0 (10.8-26.6) cells/ml. Similar results were observed for CEC subsets co-expressing CD133 and CD309 (VEGFR-2) or CD106 (VCAM-1).ConclusionDespite their close relation to vascular damage, no increase in cell concentrations were found prior to the occurrence of a secondary adverse cardiac event.

Multimarker Analysis of Serially Measured GDF-15, NT-proBNP, ST2, GAL-3, cTnI, Creatinine, and Prognosis in Acute Heart Failure.

BACKGROUND: Studies on serially measured GDF-15 (growth differentiation factor 15) in acute heart failure (HF) are limited. Moreover, several pathophysiological pathways contribute to HF. Therefore, we aimed to explore the (additional) prognostic value of serially measured GDF-15 using a multi-marker approach to more accurately predict HF risk. METHODS: TRIUMPH (Translational Initiative on Unique and Novel Strategies for Management of Patients With Heart Failure) is a prospective cohort of 496 patients with acute HF who were enrolled in 14 hospitals in the Netherlands between 2009 and 2014. Blood sampling was scheduled at 7 moments during 1-year follow-up. GDF-15, NT-proBNP (N-terminal pro-B-type natriuretic peptide), ST2 (suppression of tumorigenicity 2), galectin-3, troponin I, and creatinine were measured in a central laboratory. We associated repeated measurements of these biomarkers with the composite primary end point of all-cause mortality and HF rehospitalization, using multivariable joint modeling. RESULTS: Median age was 74 years, and 37% were women. Median baseline GDF-15 was 4632 pg/mL. The primary end point was reached in 188 (40%) patients. The average estimated GDF-15 level increased weeks before the primary end point was reached. The hazard ratio per 1 SD difference in log-GDF-15 was 2.14 (95% CI, 1.78-2.57) unadjusted, 1.96 (1.49-2.53) after adjustment for clinical confounders and 1.44 (1.05-1.91) when jointly modeled with all biomarkers. The adjusted HRs for NT-proBNP were 2.38 (1.78-3.33) and 1.52 (1.15-2.08), respectively. The multimarker model combining GDF-15, NT-proBNP, and troponin I provided a favorable risk discrimination (area under the curve=0.785). CONCLUSIONS: Sequentially measured GDF-15 independently and dynamically predicts risk of adverse outcomes during 1-year follow-up after index admission for acute HF. NT-proBNP remains a robust predictor among potential candidates. Multiple biomarkers should be considered for stratification in clinical practice. REGISTRATION: URL: https://www.trialregister.nl/trial/1783; Unique Identifier: NTR1893. (The trial can be found temporarily at https://trialsearch.who.int/Trial2.aspx?TrialID=NTR1893.).

Prognostic value of syntax score, intravascular ultrasound and near-infrared spectroscopy to identify low-risk patients with coronary artery disease 5-year results from the ATHEROREMO and IBIS-3 cohorts.

The prognostic value of SYNTAX score (SS), intravascular ultrasound (IVUS)-derived plaque burden (PB) and near-infrared spectroscopy (NIRS)-derived lipid core burden index(LCBI) for identification of high-risk patients for major adverse cardiovascular events (MACE) has been proven in previous studies. The majority of patients presenting in the cathlab however do not endure MACE over time, and identification of low-risk groups has remained underexposed. This study evaluates the combined prognostic value of SS, PB and LCBI in identifying patients with low MACE risk. This post-hoc analysis combines the ATHEROREMO and IBIS-3 studies and included 798 patients undergoing coronary angiography. Anatomical SS was calculated (N = 617) and ≥40mm non-stenotic segment of a non-target vessel was investigated with IVUS (N = 645) and NIRS (N = 273) to determine PB and maximum 4mm LCBI (LCBI4mm). During five-year follow-up, 191 MACE were observed. Patients with PB ≤70%, LCBI4mm ≤227 (median), or SS ≤8 (median) had lower MACE incidence than their counterparts with higher values. Combined into one model, LCBI4mm ≤227 (adjusted hazard ratio [aHR] 0.49, 95% confidence interval [CI] 0.30-0.78; p-value = 0.003) and SS ≤8 (aHR 0.67, 95%CI 0.48-0.96, p-value = 0.027) were independently associated with (lower) MACE rate, but PB was not. Additionally, negative predictive value (NPV) of this model was high (SS<8: 0.80, PB<70%: 0.77, LCBI4mm<227: 0.79). In this cohort, SS and LCBI4mm proved to be independent predictors of MACE-free survival during five-year follow-up. Combination of SS and LCBI4mm is useful to identify a low-risk population. Furthermore, NPV of SS, PB and LCBI4mm for prediction of MACE is high.

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 .

Relation of Iron Status to Prognosis After Acute Coronary Syndrome.

Iron deficiency has been extensively researched and is associated with adverse outcomes in heart failure. However, to our knowledge, the temporal evolution of iron status has not been previously investigated in patients with acute coronary syndrome (ACS). Therefore, we aimed to explore the temporal pattern of repeatedly measured iron, ferritin, transferrin, and transferrin saturation (TSAT) in relation to prognosis post-ACS. BIOMArCS (BIOMarker study to identify the Acute risk of a Coronary Syndrome) is a prospective, multicenter, observational cohort study conducted in The Netherlands between 2008 and 2015. A total of 844 patients with post-ACS were enrolled and underwent high-frequency (median 17) blood sampling during 1 year follow-up. Biomarkers of iron status were measured batchwise in a central laboratory. We analyzed 3 patient subsets, including the case-cohort (n = 187). The primary endpoint (PE) was a composite of cardiovascular mortality and repeat nonfatal ACS, including unstable angina pectoris requiring revascularization. The association between iron status and the PE was analyzed using multivariable joint models. Mean age was 63 years; 78% were men, and >50% had iron deficiency at first sample in the case-cohort. After adjustment for a broad range of clinical variables, 1 SD decrease in log-iron was associated with a 2.2-fold greater risk of the PE (hazard ratio 2.19, 95% confidence interval 1.34 to 3.54, p = 0.002). Similarly, 1 SD decrease in log-TSAT was associated with a 78% increased risk of the PE (hazard ratio 1.78, 95% confidence interval 1.17 to 2.65, p = 0.006). Ferritin and transferrin were not associated with the PE. Repeated measurements of iron and TSAT predict risk of adverse outcomes in patients with post-ACS during 1 year follow-up. Trial Registration: The Netherlands Trial Register. Unique identifiers: NTR1698 and NTR1106. Registered at https://www.trialregister.nl/trial/1614 and https://www.trialregister.nl/trial/1073.

Persistently elevated levels of sST2 after acute coronary syndrome are associated with recurrent cardiac events.

PURPOSE: Higher soluble ST2 (sST2) levels at admission are associated with adverse outcome in acute coronary syndrome (ACS) patients. We studied the dynamics of sST2 over time in post-ACS patients prior to a recurrent ACS or cardiac death. METHODS: We used the BIOMArCS case cohort, consisting of 187 patients who underwent serial blood sampling during one-year follow-up post-ACS. sST2 was batch-wise quantified after completion of follow-up in a median of 8 (IQR: 5-11) samples per patient. Joint modelling was used to investigate the association between longitudinally measured sST2 and the endpoint, adjusted for gender, GRACE risk score and history of cardiovascular diseases. RESULTS: Median age was 64 years and 79% were men. The 36 endpoint patients had systematically higher sST2 levels than those that remained endpoint free (mean value 29.6 ng/ml versus 33.7 ng/ml, p-value 0.052). The adjusted hazard ratio for the endpoint per standard deviation increase of sST2 was 1.64 (95% confidence interval: 1.09-2.34; p = 0.019) at any time point. We could not identify a steady or sudden increase of sST2 in the run-up to the combined endpoint. CONCLUSION: Asymptomatic post-ACS patients with persistently higher sST2 levels are at higher risk of recurrent ACS or cardiac death during one-year follow-up.

Determinants of quality of life in acute heart failure patients with and without comorbidities: a prospective, observational study.

AIMS: The relation between non-cardiac comorbidities and health-related quality of life (HRQoL) in patients with heart failure (HF) has been studied to a limited extent. To investigate the HRQoL and their determinants among HF patients with and without comorbidities. METHODS AND RESULTS: TRIUMPH (TRanslational Initiative on Unique and novel strategies for Management of Patients with Heart failure) is a Dutch prospective, multicentre study enrolling 496 acute HF patients between 2009 and 2014. We included 334 patients who had completed the HRQoL questionnaires at baseline. The HRQoL was measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) en EuroQuality-of-life five Dimensions (EQ-5D). Comorbidity was defined as having a history of at least one of the following comorbidities: chronic kidney disease, diabetes mellitus, chronic obstructive pulmonary disease (COPD), and/or cerebrovascular accident. Patients with comorbidity (n = 205, 61%) had lower scores on the physical limitation scale and clinical summary score of the KCCQ (P = 0.03 and P = 0.01, respectively). Female sex, COPD, previous HF, increasing body mass index (BMI), elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP), high systolic blood pressure, and the presence of anxiety and/or depression negatively influenced the HRQoL among HF patients with comorbidity. Besides anxiety and depression, we hardly found any other determinant of HRQoL in patients without comorbidity. CONCLUSION: Heart failure patients without comorbidity had better HRQoL than patients with comorbidity. Sex, previous HF, BMI, COPD, systolic blood pressure, NT-proBNP levels, and also anxiety and depression were determinants of HRQoL in patients with comorbidity. In those without comorbidity, apart from anxiety and depression, no further determinants of HRQoL were found.

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.