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 &gt; 30 kg/m 2) 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. (Figure presented.).</p

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: NCT01851538 https://clinicaltrials.gov/ct2/show/NCT01851538. Funding: EU/ EFPIA IMI2JU BigData@Heart grant n° 116074, Jaap Schouten Foundation and Noordwest Academie.</p

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.</p

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

Identifying plasma proteomic signatures from health to heart failure, across the ejection fraction spectrum

Circulating proteins may provide insights into the varying biological mechanisms involved in heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). We aimed to identify specific proteomic patterns for HF, by comparing proteomic profiles across the ejection fraction spectrum. We investigated 4210 circulating proteins in 739 patients with normal (Stage A/Healthy) or elevated (Stage B) filling pressures, HFpEF, or ischemic HFrEF (iHFrEF). We found 2122 differentially expressed proteins between iHFrEF-Stage A/Healthy, 1462 between iHFrEF-HFpEF and 52 between HFpEF-Stage A/Healthy. Of these 52 proteins, 50 were also found in iHFrEF vs. Stage A/Healthy, leaving SLITRK6 and NELL2 expressed in lower levels only in HFpEF. Moreover, 108 proteins, linked to regulation of cell fate commitment, differed only between iHFrEF-HFpEF. Proteomics across the HF spectrum reveals overlap in differentially expressed proteins compared to stage A/Healthy. Multiple proteins are unique for distinguishing iHFrEF from HFpEF, supporting the capacity of proteomics to discern between these conditions.</p

Identifying plasma proteomic signatures from health to heart failure, across the ejection fraction spectrum

Circulating proteins may provide insights into the varying biological mechanisms involved in heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). We aimed to identify specific proteomic patterns for HF, by comparing proteomic profiles across the ejection fraction spectrum. We investigated 4210 circulating proteins in 739 patients with normal (Stage A/Healthy) or elevated (Stage B) filling pressures, HFpEF, or ischemic HFrEF (iHFrEF). We found 2122 differentially expressed proteins between iHFrEF-Stage A/Healthy, 1462 between iHFrEF-HFpEF and 52 between HFpEF-Stage A/Healthy. Of these 52 proteins, 50 were also found in iHFrEF vs. Stage A/Healthy, leaving SLITRK6 and NELL2 expressed in lower levels only in HFpEF. Moreover, 108 proteins, linked to regulation of cell fate commitment, differed only between iHFrEF-HFpEF. Proteomics across the HF spectrum reveals overlap in differentially expressed proteins compared to stage A/Healthy. Multiple proteins are unique for distinguishing iHFrEF from HFpEF, supporting the capacity of proteomics to discern between these conditions

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.</p

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

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

Abstract 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

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