Multimarker proteomic profiling for the prediction of cardiovascular mortality in patients with chronic heart failure

Proteomics for heart failure risk stratification: a systematic review

BACKGROUND

Heart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality.

METHODS

We searched four databases and two clinical trial registries for articles published from 2012 to 2023. HF proteomics studies measuring high numbers of proteins using aptamer or antibody-based affinity platforms on human plasma or serum with outcomes of all-cause or cardiovascular death were included. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. A third reviewer resolved conflicts. We assessed the risk of bias using the Risk Of Bias In Non-randomized Studies-of Exposure tool.

RESULTS

Out of 5131 unique articles identified, nine articles were included in the review. The nine studies were observational; three used the aptamer platform, and six used the antibody platform. We found considerable heterogeneity across studies in measurement panels, HF definitions, ejection fraction categorization, follow-up duration, and outcome definitions, and a lack of risk estimates for most protein associations. Hence, we proceeded with a systematic review rather than a meta-analysis. In two comparable aptamer studies in patients with HF with reduced ejection fraction, 21 proteins were identified in common for the association with all-cause death. Among these, one protein, WAP four-disulfide core domain protein 2 was also reported in an antibody study on HFrEF and for the association with CV death. We proposed standardized reporting criteria to facilitate the interpretation of future studies.

CONCLUSIONS

In this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.

Myocardial Recovery in Recent Onset Dilated Cardiomyopathy: Role of CDCP1 and Cardiac Fibrosis

BACKGROUND

Dilated cardiomyopathy (DCM) is a major cause of heart failure and carries a high mortality rate. Myocardial recovery in DCM-related heart failure patients is highly variable, with some patients having little or no response to standard drug therapy. A genome-wide association study may agnostically identify biomarkers and provide novel insight into the biology of myocardial recovery in DCM.

METHODS

A genome-wide association study for change in left ventricular ejection fraction was performed in 686 White subjects with recent-onset DCM who received standard pharmacotherapy. Genome-wide association study signals were subsequently functionally validated and studied in relevant cellular models to understand molecular mechanisms that may have contributed to the change in left ventricular ejection fraction.

RESULTS

The genome-wide association study identified a highly suggestive locus that mapped to the 5'-flanking region of the CDCP1 (CUB [complement C1r/C1s, Uegf, and Bmp1] domain containing protein 1) gene (rs6773435; P=7.12×10-7). The variant allele was associated with improved cardiac function and decreased CDCP1 transcription. CDCP1 expression was significantly upregulated in human cardiac fibroblasts (HCFs) in response to the PDGF (platelet-derived growth factor) signaling, and knockdown of CDCP1 significantly repressed HCF proliferation and decreased AKT (protein kinase B) phosphorylation. Transcriptomic profiling after CDCP1 knockdown in HCFs supported the conclusion that CDCP1 regulates HCF proliferation and mitosis. In addition, CDCP1 knockdown in HCFs resulted in significantly decreased expression of soluble ST2 (suppression of tumorigenicity-2), a prognostic biomarker for heart failure and inductor of cardiac fibrosis.

CONCLUSIONS

CDCP1 may play an important role in myocardial recovery in recent-onset DCM and mediates its effect primarily by attenuating cardiac fibrosis.

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling

BACKGROUND

Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE.

METHODS

In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001.

RESULTS

The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.

Comprehensive Proteomics Profiling Identifies Patients With Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in the Hypertrophic Cardiomyopathy Population

Introduction

In hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) represents myocardial fibrosis and is associated with sudden cardiac death. However, CMR requires particular expertise and is expensive and time-consuming. Therefore, it is important to specify patients with a high pre-test probability of having LGE as the utility of CMR is higher in such cases. The objective was to determine whether plasma proteomics profiling can distinguish patients with and without LGE on CMR in the HCM population.

Materials and Methods

We performed a multicenter case-control (LGE vs. no LGE) study of 147 patients with HCM. We performed plasma proteomics profiling of 4,979 proteins. Using the 17 most discriminant proteins, we performed logistic regression analysis with elastic net regularization to develop a discrimination model with data from one institution (the training set; n = 111) and tested the discriminative ability in independent samples from the other institution (the test set; n = 36). We calculated the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity.

Results

Overall, 82 of the 147 patients (56%) had LGE on CMR. The AUC of the 17-protein model was 0.83 (95% confidence interval [CI], 0.75–0.90) in the training set and 0.71 in the independent test set for validation (95% CI, 0.54–0.88). The sensitivity of the training model was 0.72 (95% CI, 0.61–0.83) and the specificity was 0.78 (95% CI, 0.66–0.90). The sensitivity was 0.71 (95% CI, 0.49–0.92) and the specificity was 0.74 (95% CI, 0.54–0.93) in the test set. Based on the discrimination model derived from the training set, patients in the test set who had high probability of having LGE had a significantly higher odds of having LGE compared to those who had low probability (odds ratio 29.6; 95% CI, 1.6–948.5; p = 0.03).

Conclusions

In this multi-center case-control study of patients with HCM, comprehensive proteomics profiling of 4,979 proteins demonstrated a high discriminative ability to distinguish patients with and without LGE. By identifying patients with a high pretest probability of having LGE, the present study serves as the first step to establishing a panel of circulating protein biomarkers to better inform clinical decisions regarding CMR utilization.

Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is caused by mutations in the genes coding for proteins essential in normal myocardial contraction. However, it remains unclear through which molecular pathways gene mutations mediate the development of HCM. The objectives were to determine plasma protein biomarkers of HCM and to reveal molecular pathways differentially regulated in HCM.

METHODS

We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma proteomics profiling of 1681 proteins. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based discrimination model with data from 1 institution (ie, the training set). We tested the discriminative ability in independent samples from the other institution (ie, the test set). As an exploratory analysis, we executed pathway analysis of significantly dysregulated proteins. Pathways with false discovery rate <0.05 were declared positive.

RESULTS

The study included 266 cases and 167 controls (n=308 in the training set; n=125 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.83-0.94) in the test set. Pathway analysis revealed that the Ras-MAPK (mitogen-activated protein kinase) pathway, along with its upstream and downstream pathways, was upregulated in HCM. Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the largest-scale investigation with the most comprehensive proteomics profiling in HCM, revealing circulating biomarkers and exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in HCM.

Apolipoprotein Proteomic Profiling for the Prediction of Cardiovascular Death in Patients with Heart Failure

PURPOSE

Risk stratification in chronic systolic heart failure (HF) is critical to identify the patients who may benefit from advanced therapies. It is aimed at identifying new biomarkers to improve prognosis evaluation and help to better understand HF physiopathology.

EXPERIMENTAL DESIGN

Prognostic evaluation is performed in 198 patients with chronic systolic HF: 99 patients who died from cardiovascular cause within three years are individually matched for age, sex, and HF etiology (ischemic vs not) with 99 patients who are alive after three years of HF evaluation. A proteomic profiling of 15 apolipoproteins (Apo) is performed: Apo-A1, -A2, -A4, -B100, -C1, -C2, -C3, -C4, -D, -E, -F, -H, -J, -L1, and -M using LC-MRM-MS.

RESULTS

In univariate analysis, the levels of Apo-B100 and -L1 are significantly lower and the levels of Apo-C1, -J, and -M are significantly higher in patients who died from cardiovascular cause as compared with patients alive. In the final statistical model, Apo-C1, Apo-J, and Apo-M improve individually the prediction of cardiovascular death. Ingenuity pathway analysis indicates these three Apo in a network associated with lipid metabolism, atherosclerosis signaling, and retinoid X receptor activation.

CONCLUSIONS

Proteomic profiling of apolipoproteins using LC-MRM-MS might be useful in clinical practice for risk stratification of HF patients.

Aptamer-Based Proteomic Platform Identifies Novel Protein Predictors of Incident Heart Failure and Echocardiographic Traits

BACKGROUND

We used a large-scale, high-throughput DNA aptamer-based discovery proteomic platform to identify circulating biomarkers of cardiac remodeling and incident heart failure (HF) in community-dwelling individuals.

METHODS

We evaluated 1895 FHS (Framingham Heart Study) participants (age 55±10 years, 54% women) who underwent proteomic profiling and echocardiography. Plasma levels of 1305 proteins were related to echocardiographic traits and to incident HF using multivariable regression. Statistically significant protein-HF associations were replicated in the HUNT (Nord-Trøndelag Health) study (n=2497, age 63±10 years, 43% women), and results were meta-analyzed. Genetic variants associated with circulating protein levels (pQTLs) were related to echocardiographic traits in the EchoGen (n=30 201) and to incident HF in the CHARGE (n=20 926) consortia.

RESULTS

Seventeen proteins associated with echocardiographic traits in cross-sectional analyses (false discovery rate <0.10), and 8 of these proteins had pQTLs associated with echocardiographic traits in EchoGen (P<0.0007). In Cox models adjusted for clinical risk factors, 29 proteins demonstrated associations with incident HF in FHS (174 HF events, mean follow-up 19 [limits, 0.2-23.7] years). In meta-analyses of FHS and HUNT, 6 of these proteins were associated with incident HF (P<3.8×10-5; 3 with higher risk: NT-proBNP [N-terminal proB-type natriuretic peptide], TSP2 [thrombospondin-2], MBL [mannose-binding lectin]; and 3 with lower risk: ErbB1 [epidermal growth factor receptor], GDF-11/8 [growth differentiation factor-11/8], and RGMC [hemojuvelin]). For 5 of the 6 proteins, pQTLs were associated with echocardiographic traits (P<0.0006) in EchoGen, and for RGMC, a protein quantitative trait loci was associated with incident HF (P=0.001).

CONCLUSIONS

A large-scale proteomics approach identified new predictors of cardiac remodeling and incident HF. Future studies are warranted to elucidate how biological pathways represented by these proteins may mediate cardiac remodeling and HF risk and to assess if these proteins can improve HF risk prediction.

Circulating proteomic signature of early death in heart failure patients with reduced ejection fraction

Heart failure (HF) remains a main cause of mortality worldwide. Risk stratification of patients with systolic chronic HF is critical to identify those who may benefit from advanced HF therapies. The aim of this study is to identify plasmatic proteins that could predict the early death (within 3 years) of HF patients with reduced ejection fraction hospitalized in CHRU de Lille. The subproteome targeted by an aptamer-based technology, the Slow Off-rate Modified Aptamer (SOMA) scan assay of 1310 proteins, was profiled in blood samples from 168 HF patients, and 203 proteins were significantly modulated between patients who died of cardiovascular death and patients who were alive after 3 years of HF evaluation (Wilcoxon test, FDR 5%). A molecular network was built using these 203 proteins, and the resulting network contained 2281 molecules assigned to 34 clusters annotated to biological pathways by Gene Ontology. This network model highlighted extracellular matrix organization as the main mechanism involved in early death in HF patients. In parallel, an adaptive Least Absolute Shrinkage and Selection Operator (LASSO) was performed on these 203 proteins, and six proteins were selected as candidates to predict early death in HF patients: complement C3, cathepsin S and F107B were decreased and MAPK5, MMP1 and MMP7 increased in patients who died of cardiovascular causes compared with patients living 3 years after HF evaluation. This proteomic signature of 6 circulating plasma proteins allows the identification of systolic HF patients with a risk of early death.

Targeted Metabolomic Profiling of Plasma and Survival in Heart Failure Patients

OBJECTIVES

This study sought to derive and validate plasma metabolite associations with survival in heart failure (HF) patients.

BACKGROUND

Profiling of plasma metabolites to predict the course of HF appears promising, but validation and incremental value of these profiles are less established.

METHODS

Patients (n = 1,032) who met Framingham HF criteria with a history of reduced ejection fraction were randomly divided into derivation and validation cohorts (n = 516 each). Amino acids, organic acids, and acylcarnitines were quantified using mass spectrometry in fasting plasma samples. We derived a prognostic metabolite profile (PMP) in the derivation cohort using Lasso-penalized Cox regression. Validity was assessed by 10-fold cross validation in the derivation cohort and by standard testing in the validation cohort. The PMP was analyzed as both a continuous variable (PMPscore) and dichotomized at the median (PMPcat), in univariate and multivariate models adjusted for clinical risk score and N-terminal pro-B-type natriuretic peptide.

RESULTS

Overall, 48% of patients were African American, 35% were women, and the average age was 69 years. After a median follow-up of 34 months, there were 256 deaths (127 and 129 in derivation and validation cohorts, respectively). Optimized modeling defined the 13 metabolite PMPs, which was cross validated as both the PMPscore (hazard ratio [HR]: 3.27; p < 2 × 10^-16) and PMPcat (HR: 3.04; p = 2.93 × 10^-8). The validation cohort showed similar results (PMPscore HR: 3.9; p < 2 × 10^-16 and PMPcat HR: 3.99; p = 3.47 × 10^-9). In adjusted models, PMP remained associated with mortality in the cross-validated derivation cohort (PMPscore HR: 1.63; p = 0.0029; PMPcat HR: 1.47; p = 0.081) and the validation cohort (PMPscore HR: 1.54; p = 0.037; PMPcat HR: 1.69; p = 0.043).

CONCLUSIONS

Plasma metabolite profiles varied across HF subgroups and were associated with survival incremental to conventional predictors. Additional investigation is warranted to define mechanisms and clinical applications.

Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction

Background

Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF.

Methods and Results

Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972) discriminated between HFrEF patients (N = 94, sensitivity = 93.6%) and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%). Interestingly, HFrEF103 showed low sensitivity (12.6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin.

Conclusion

CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure.

Increased level of phosphorylated desmin and its degradation products in heart failure

Although several risk factors such as infarct size have been identified, the progression/severity of heart failure (HF) remains difficult to predict in clinical practice. Using an experimental rat model of ischemic HF and phosphoproteomic technology, we found an increased level of phosphorylated desmin in the left ventricle (LV) of HF-rats. The purpose of the present work is to assess whether desmin is a circulating or only a tissue biomarker of HF. We used several antibodies in order to detect desmin, its proteolytic fragments and its phosphorylated form in LV and plasma by western blot, phosphate affinity electrophoresis, mass spectrometry and immunofluorescence. Plasma was treated with combinatorial peptide ligand library or depleted for albumin and immunoglobulins to increase the sensitivity of detection. We found a 2-fold increased serine-desmin phosphorylation in the LV of HF-rats, mainly in the insoluble fraction, suggesting the formation of desmin aggregates. Desmin cleavage products were also detected in the LV of HF rats, indicating that the increased phosphorylation of desmin results in more susceptibility to proteolytic activity, likely mediated by calpain activity. The native desmin and its degradation products were undetectable in the plasma of rat, mouse or human. These data suggest the potential of serine-phosphorylated form of desmin and its degradation products, but not of desmin itself, as tissue but not circulating biomarkers of HF.

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Desmin is mainly expressed in insoluble fraction of rat left ventricle.

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In experimental heart failure, desmin is highly phosphorylated in serine.

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Desmin and its degradation products are not detected in plasma.

[Potential of non-coding RNA as biomarkers in heart failure]

Heart failure is a major public health problem with an estimated prevalence of 1-2% in the adult population in developed countries. Despite some progress, the prognosis of heart failure is associated with high mortality. There is a need for prognostic markers for heart failure, such as circulating biomarkers, in order to better identify individual heart failure patients with high risk of early death, and to improve the selection of patients for invasive treatment with limited availability such as heart transplantation. Among the non coding RNA family, we investigated the potentiality of long non coding RNAs (lncRNA) as potential biomarkers in heart disease. We observed that the plasmatic levels of the lncRNA named LIPCAR may predict early mortality in patients with heart failure independently of the etiology and may improve clinical-decision making for a more personalized treatment of "high-risk" patients.