Urine proteome analysis in heart failure with reduced ejection fraction complicated by chronic kidney disease: feasibility, and clinical and pathogenetic correlates

Development and Validation of a Capillary Electrophoresis Coupled to Mass Spectrometry Pipeline for Comparable Assessment of the Plasma Peptidome

Although capillary electrophoresis coupled to mass spectrometry (CE-MS) holds promise for urinary peptide profiling, only a limited number of studies have used CE-MS to study plasma peptides. Here we describe the establishment of a workflow, including sample preparation, CE-MS analysis, data processing and normalization optimized for the analysis of plasma peptides. Using 291 plasma samples from 136 patients with end stage kidney failure (including pre- and post-dialysis samples) and 20 patients with chronic kidney disease, we identified and quantified the abundance of 3920 unique plasma peptides. The repeatability and intermediate precision of the analysis were high (with a coefficient of variation of 5% on average for all peptides). Six hundred sixty-one out of 3920 peptides were sequenced by CE-MS/MS. These peptide fragments belonged to 135 parent proteins. Using the pipeline, we identified 169 sequenced plasma peptides with different plasma abundance pre- and post-dialysis. These peptides combined in a support vector machine (SVM) classifier successfully discriminated between pre- and post-dialysis samples in a blinded validation cohort of 45 dialysis patients. Enriched peptides post-dialyses were for the major part associated to inflammation and the coagulation contact systems that may serve as signatures for optimizing dialysis materials. In conclusion, this high-throughput strategy focuses on the plasma peptidome, an understudied component of the plasma, as a promising area for further exploration. Due to their close proximity to the vascular bed, plasma peptides hold significant potential to serve as reliable biomarkers for systemic complications associated with kidney disease.

Mortality Risk and Urinary Proteome Changes in Acute COVID-19 Survivors in the Multinational CRIT-COV-U Study

BACKGROUND/OBJECTIVES

Survival prospects following SARS-CoV-2 infection may extend beyond the acute phase, influenced by various factors including age, health conditions, and infection severity; however, this topic has not been studied in detail. Therefore, within this study, the mortality risk post-acute COVID-19 in the CRIT-COV-U cohort was investigated.

METHODS

Survival data from 651 patients that survived an acute phase of COVID-19 were retrieved and the association between urinary peptides and future death was assessed. Data spanning until December 2023 were collected from six countries, comparing mortality trends with age- and sex-matched COVID-19-negative controls. A death prediction classifier was developed and validated using pre-existing urinary peptidomic datasets.

RESULTS

Notably, 13.98% of post-COVID-19 patients succumbed during the follow-up, with mortality rates significantly higher than COVID-19-negative controls, particularly evident in younger individuals (<65 years). These data for the first time demonstrate that SARS-CoV-2 infection highly significantly increases the risk of mortality not only during the acute phase of the disease but also beyond for a period of about one year. In our study, we were further able to identify 201 urinary peptides linked to mortality. These peptides are fragments of albumin, alpha-2-HS-glycoprotein, apolipoprotein A-I, beta-2-microglobulin, CD99 antigen, various collagens, fibrinogen alpha, polymeric immunoglobulin receptor, sodium/potassium-transporting ATPase, and uromodulin and were integrated these into a predictive classifier (DP201). Higher DP201 scores, alongside age and BMI, significantly predicted death.

CONCLUSIONS

The peptide-based classifier demonstrated significant predictive value for mortality in post-acute COVID-19 patients, highlighting the utility of urinary peptides in prognosticating post-acute COVID-19 mortality, offering insights for targeted interventions. By utilizing these defined biomarkers in the clinic, risk stratification, monitoring, and personalized interventions can be significantly improved. Our data also suggest that mortality should be considered as one possible symptom or a consequence of post-acute sequelae of SARS-CoV-2 infection, a fact that is currently overlooked.

Integration of Urinary Peptidome and Fecal Microbiome to Explore Patient Clustering in Chronic Kidney Disease

Millions of people worldwide currently suffer from chronic kidney disease (CKD), requiring kidney replacement therapy at the end stage. Endeavors to better understand CKD pathophysiology from an omics perspective have revealed major molecular players in several sample sources. Focusing on non-invasive sources, gut microbial communities appear to be disturbed in CKD, while numerous human urinary peptides are also dysregulated. Nevertheless, studies often focus on isolated omics techniques, thus potentially missing the complementary pathophysiological information that multidisciplinary approaches could provide. To this end, human urinary peptidome was analyzed and integrated with clinical and fecal microbiome (16S sequencing) data collected from 110 Non-CKD or CKD individuals (Early, Moderate, or Advanced CKD stage) that were not undergoing dialysis. Participants were visualized in a three-dimensional space using different combinations of clinical and molecular data. The most impactful clinical variables to discriminate patient groups in the reduced dataspace were, among others, serum urea, haemoglobin, total blood protein, urinary albumin, urinary erythrocytes, blood pressure, cholesterol measures, body mass index, Bristol stool score, and smoking; relevant variables were also microbial taxa, including Roseburia, Butyricicoccus, Flavonifractor, Burkholderiales, Holdemania, Synergistaceae, Enterorhabdus, and Senegalimassilia; urinary peptidome fragments were predominantly derived from proteins of collagen origin; among the non-collagen parental proteins were FXYD2, MGP, FGA, APOA1, and CD99. The urinary peptidome appeared to capture substantial variation in the CKD context. Integrating clinical and molecular data contributed to an improved cohort separation compared to clinical data alone, indicating, once again, the added value of this combined information in clinical practice.

Characterizing the type 6 secretion system (T6SS) of E. cloacae SBP-8 and its role in pathogenesis and bacterial competition

Despite the relevance of E. cloacae as an opportunistic pathogen, very little is known about its pathogenicity mechanism and the factors influencing its virulence. The mechanism of E. cloacae pathogenicity appears to be complex and multifactorial, with the presence of different putative virulence factors whose role is still not clear in the development of the disease. In this study, we systematically investigated the role of T6SS (type six secretion system) of E. cloacae SBP-8, an environmental isolate, in eukaryotic and bacterial cell interaction. Analysis of the genome sequence of E. cloacae SBP-8 revealed the presence of sets of genes coding for the expression of one complete T6SS cluster, which is similar to T6SS-1 cluster of E. cloacae ATCC 13047 (clinical isolates). In addition, an Hcp effector protein was detected in the secretome, and this secretion depended on ClpV, an Atpase of T6SS, confirming that strain SBP-8 produces functional T6SS. Deletion of T6SS-associated gene clpV did not induce any significant change in the life span and rate of colonization in C. elegans. No major significant change was observed in the expression profiling of antimicrobial genes (clec-60, clec-85, clec-87 and lys-1) and toll-like receptor (toll-1) gene, involved in stimulating an immune response against the pathogen. No difference in the ability to invade and proliferate in intestinal cells and phagocytosis by macrophages was observed. In addition, we demonstrated that the ability of E. cloacae SBP-8 to out-compete Escherichia coli was reliant upon its T6SS in contact-dependent manner. Our results show that T6SS of the environmental isolates is required for interbacterial competition but not for invasion and proliferation inside host cells.

Identifying a urinary peptidomics profile for hypertension in young adults: The African-PREDICT study: Urinary peptidomics and hypertension: Urinary peptidomics and hypertension

Hypertension is one of the most important and complex risk factors for cardiovascular diseases (CVDs). By using urinary peptidomics analyses, we aimed to identify peptides associated with hypertension, building a framework for future research towards improved prediction and prevention of premature development of CVD. We included 78 hypertensive and 79 normotensive participants from the African-PREDICT study (aged 20-30 years), matched for sex (51% male) and ethnicity (49% black and 51% white). Urinary peptidomics data were acquired using capillary-electrophoresis-time-of-flight-mass-spectrometry. Hypertension-associated peptides were identified and combined into a support vector machine-based multidimensional classifier. When comparing the peptide data between the normotensive and hypertensive groups, 129 peptides were nominally differentially abundant (Wilcoxon p < 0.05). Nonetheless, only three peptides, all derived from collagen alpha-1(III), remained significantly different after rigorous adjustments for multiple comparisons. The 37 most significant peptides (all p ≤ 0.001) served as basis for the development of a classifier, with 20 peptides being combined into a unifying score, resulting in an AUC of 0.85 in the ROC analysis (p < 0.001), with 83% sensitivity at 80% specificity. Our study suggests potential value of urinary peptides in the classification of hypertension, which could enable earlier diagnosis and better understanding of the pathophysiology of hypertension and premature cardiovascular disease development.

Combination of tolvaptan and valsartan improves cardiac and renal functions in doxorubicin-induced heart failure in mice

Heart failure (HF) is often complicated by renal dysfunction. Tolvaptan and valsartan are two well-known agents for the treatment of HF. However, the role of tolvaptan/valsartan combination on HF with renal dysfunction remains unclear. To establish a mice model with HF with renal dysfunction, mice were intraperitoneally injected with doxorubicin (Dox). Echocardiogram was applied to assess the left ventricular function. Additionally, serum aldosterone (ALD) and angiotensin II (Ang II) level in mice were determined by ELISA. Meanwhile, western blot assay was used to evaluate the expressions of B cell lymphoma-2 (Bcl-2), Bcl-2 associated X (Bax) and cleaved caspase 3 in the heart and kidney tissues of mice. In this study, we found that compared to tolvaptan or valsartan alone treatment group, tolvaptan/valsartan combination obviously improved the left ventricular ejection fraction (LVEF) and the left ventricular fractional shortening (LVFS), and reduced serum ALD and Ang II level in Dox-treated mice. Additionally, tolvaptan/valsartan combination significantly prevented the inflammation and fibrosis of heart and kidney tissues in Dox-treated mice. Meanwhile, tolvaptan/valsartan combination notably inhibited the myocardial and renal cell apoptosis in Dox-treated mice via upregulation of Bcl-2 and downregulation of Bax and cleaved caspase 3, compared to the single drug treatment. Collectively, tolvaptan/valsartan combination could improve cardiac and renal functions, as well as prevent the fibrosis, inflammation and apoptosis of heart and kidney tissues in Dox-treated mice. Taken together, combining tolvaptan with valsartan might be a promising approach to achieve enhanced therapeutic effect for treatment of HF with renal dysfunction.

Vitamin D Binding Protein and Renal Injury in Acute Decompensated Heart Failure

Background

Renal function in acute decompensated heart faiulre (ADHF) is a strong predictor of disease evolution and poor outcome. Current biomarkers for early diagnostic of renal injury in the setting of ADHF are still controversial, and their association to early pathological changes needs to be established. By applying a proteomic approach, we aimed to identify early changes in the differential urine protein signature associated with development of renal injury in patients hospitalised due to ADHF.

Materials and Methods

Patients (71 [64–77] years old) admitted at the emergency room with ADHF and hospitalised were investigated (N = 64). Samples (urine/serum) were collected at hospital admission (day 0) and 72 h later (day 3). Differential serum proteome was analysed by two-dimensional electrophoresis and matrix-assisted laser desorption/ionisation-time of flight (MALDI-ToF/ToF). Validation studies were performed using ELISA.

Results

Proteomic analysis depicted urinary vitamin D binding protein (uVDBP) as a two spots protein with increased intensity in ADHF and significant differences depending on the glomerular filtration rate (GFR). Urinary VDBP in patients with ADHF at hospitalisation was &gt; threefold higher than in healthy subjects, with the highest levels in those patients with ADHF already presenting renal dysfunction. At day 3, urine VDBP levels in patients maintaining normal renal function dropped to normal values (P = 0.03 vs. day 0). In contrast, urine VDBP levels remained elevated in the group developing renal injury, with values twofold above the normal range (P &lt; 0.05), while serum creatinine and GF levels were within the physiological range in this group. Urinary VDBP in ADHF positively correlated with markers of renal injury such as cystatin C and Kidney Injury Molecule 1 (KIM-1). By ROC analysis, urinary VDBP, when added to cystatin C and KIM-1, improved the prediction of renal injury in patients with ADHF.

Conclusion

We showed increased urine VDBP in patients with ADHF at hospital admission and a differential uVDBP evolution pattern at early stage of renal dysfunction, before pathological worsening of GFR is evidenced.

Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure

Patients with heart failure (HF) are heterogeneous with various intrapersonal and interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and social determinants of health have been implicated in unequal treatment of patients with HF. Through several methodologies, artificial intelligence (AI) can provide models in HF prediction, prognostication, and provision of care, which may help prevent unequal outcomes. This review highlights AI as a strategy to address racial inequalities in HF; discusses key AI definitions within a health equity context; describes the current uses of AI in HF, strengths and harms in using AI; and offers recommendations for future directions.

Circulating heart failure biomarkers beyond natriuretic peptides: review from the Biomarker Study Group of the Heart Failure Association (HFA), European Society of Cardiology (ESC)

New biomarkers are being evaluated for their ability to advance the management of patients with heart failure. Despite a large pool of interesting candidate biomarkers, besides natriuretic peptides virtually none have succeeded in being applied into the clinical setting. In this review, we examine the most promising emerging candidates for clinical assessment and management of patients with heart failure. We discuss high-sensitivity cardiac troponins (Tn), procalcitonin, novel kidney markers, soluble suppression of tumorigenicity 2 (sST2), galectin-3, growth differentiation factor-15 (GDF-15), cluster of differentiation 146 (CD146), neprilysin, adrenomedullin (ADM), and also discuss proteomics and genetic-based risk scores. We focused on guidance and assistance with daily clinical care decision-making. For each biomarker, analytical considerations are discussed, as well as performance regarding diagnosis and prognosis. Furthermore, we discuss potential implementation in clinical algorithms and in ongoing clinical trials.

Urine peptidome analysis in cardiorenal syndrome reflects molecular processes

The cardiorenal syndrome (CRS) is defined as the confluence of heart-kidney dysfunction. This study investigates the molecular differences at the level of the urinary peptidome between CRS patients and controls and their association to disease pathophysiology. The urinary peptidome of CRS patients (n = 353) was matched for age and sex with controls (n = 356) at a 1:1 ratio. Changes in the CRS peptidome versus controls were identified after applying the Mann-Whitney test, followed by correction for multiple testing. Proteasix tool was applied to investigate predicted proteases involved in CRS-associated peptide generation. Overall, 559 differentially excreted urinary peptides were associated with CRS patients. Of these, 193 peptides were specifically found in CRS when comparing with heart failure and chronic kidney disease urinary peptide profiles. Proteasix predicted 18 proteases involved in > 1% of proteolytic cleavage events including multiple forms of MMPs, proprotein convertases, cathepsins and kallikrein 4. Forty-four percent of the cleavage events were produced by 3 proteases including MMP13, MMP9 and MMP2. Pathway enrichment analysis supported that ECM-related pathways, fibrosis and inflammation were represented. Collectively, our study describes the changes in urinary peptides of CRS patients and potential proteases involved in their generation, laying the basis for further validation.

Proteomic Biomarkers in the Cardiorenal Syndrome: Toward Deciphering Molecular Pathophysiology

Cardiorenal syndrome (CRS) is defined by coexisting heart and renal dysfunctions. Malfunction of 1 organ may cause dysfunction of the other with variable causative disease that defines the type of CRS (1-5). Numerous studies showed that the prevalence of cardiovascular disease is increased in patients with chronic kidney disease (CKD). Similarly, CKD affects a large proportion of patients with heart failure. This overlap between primary heart or primary kidney disease blurs cause-effect inferences of the initiator/target organ. The classical subdivision of CRS in 5 categories does not provide pathophysiological suggestions for targeted intervention. It seems timely to revisit the value of CRS biomarkers in a pathophysiology-centered approach. We systematically reviewed the literature in CRS, which revealed 53 clinical studies describing the use of 44 biomarkers and 4 proteomic panels. All biomarkers are involved in at least one of the CRS comorbidities. Among the pathways affected, inflammation, aberrant glucose metabolism, neurohormonal activation, and oxidative stress are well described. There is growing evidence that fibrosis may be the "cornerstone" that unifies most of the pathways leading to CRS. Formation of excess fibrous connective tissue antedates CRS in many cases. This review highlights that biomarkers reflecting fibrosis may be of substantial clinical value in the early detection, prognostication, and guiding treatment of CRS. Biomarkers detecting changes in collagen turnover in the extracellular matrix of heart and kidney appear able to depict subclinical changes in the fibrotic remodeling of tissues and constitute a promising approach toward personalized intervention in CRS.

Patient profiling in heart failure for tailoring medical therapy. A consensus document of the Heart Failure Association of the European Society of Cardiology

Despite guideline recommendations and available evidence, implementation of treatment in heart failure (HF) is poor. The majority of patients are not prescribed drugs at target doses that have been proven to positively impact morbidity and mortality. Among others, tolerability issues related to low blood pressure, heart rate, impaired renal function or hyperkalaemia are responsible. Chronic kidney disease plays an important role as it affects up to 50% of patients with HF. Also, dynamic changes in estimated glomerular filtration rate may occur during the course of HF, resulting in inappropriate dose reduction or even discontinuation of decongestive or neurohormonal modulating therapy in clinical practice. As patients with HF are rarely naïve to pharmacologic therapies, the challenge is to adequately prioritize or select the most appropriate up-titration schedule according to patient profile. In this consensus document, we identified nine patient profiles that may be relevant for treatment implementation in HF patients with a reduced ejection fraction. These profiles take into account heart rate (<60 bpm or >70 bpm), the presence of atrial fibrillation, symptomatic low blood pressure, estimated glomerular filtration rate (<30 or >30 mL/min/1.73 m² ) or hyperkalaemia. The pre-discharge patient, frequently still congestive, is also addressed. A personalized approach, adjusting guideline-directed medical therapy to patient profile, may allow to achieve a better and more comprehensive therapy for each individual patient than the more traditional, forced titration of each drug class before initiating treatment with the next.

Value of Urine Peptides in Assessing Kidney and Cardiovascular Disease

Urinary peptides gained significant attention as potential biomarkers especially in the context of kidney and cardiovascular disease. In this manuscript the recent literature since 2015 on urinary peptide investigation in human kidney and cardiovascular disease is reviewed. The technology most commonly used in this context is capillary electrophoresis coupled mass spectrometry, in part owed to the large database available and the well-defined dataspace. Several studies based on over 1000 subjects are reported in the recent past, especially examining CKD273, a classifier for assessment of chronic kidney disease based on 273 urine peptides. Interestingly, the most abundant urinary peptides are generally collagen fragments, which may have gone undetected for some time as they are typically modified via proline hydroxylation. The data available suggest that urinary peptides specifically depict inflammation and fibrosis, and may serve as a non-invasive tool to assess fibrosis, which appears to be a key driver in kidney and cardiovascular disease. The recent successful completion of the first urinary peptide guided intervention trial, PRIORITY, is expected to further spur clinical application of urinary peptidomics, aiming especially at early detection of chronic diseases, prediction of progression, and prognosis of drug response.

Discovery, validation and sequencing of urinary peptides for diagnosis of liver fibrosis-A multicentre study

Background

Liver fibrosis is a consequence of chronic inflammation and is associated with protein changes within the hepatocytes structure. In this study, we aimed to investigate if this is reflected by the urinary proteome and can be explored to diagnose liver fibrosis in patients with chronic liver disease.

Methods

In a multicentre combined cross-sectional and prospective diagnostic test validation study, 129 patients with varying degrees of liver fibrosis and 223 controls without liver fibrosis were recruited. Additionally, 41 patients with no liver, but kidney fibrosis were included to evaluate interference with expressions of kidney fibrosis. Urinary low molecular weight proteome was analysed by capillary electrophoresis coupled to mass spectrometry (CE-MS) and a support vector machine marker model was established by integration of peptide markers for liver fibrosis.

Findings

CE-MS enabled identification of 50 urinary peptides associated with liver fibrosis. When combined into a classifier, LivFib-50, it separated patients with liver fibrosis (N = 31) from non-liver disease controls (N = 123) in cross-sectional diagnostic phase II evaluation with an area under the curve (AUC) of 0.94 (95% confidence intervals (CI): 0.89–0.97, p<0.0001). When adjusted for age, LivFib-50 demonstrated an AUC of 0.94 (95% CI: 0.89–0.97, p<0.0001) in chronic liver disease patients with (N = 19) or without (N = 17) liver fibrosis progression. In this prospective diagnostic phase III validation set, age-adjusted LivFib-50 showed 84.2% sensitivity (95% CI: 60.4–96.6) and 82.4% specificity (95% CI: 56.6–96.2) for detection of liver fibrosis. The sequence-identified peptides are mainly fragments of collagen chains, uromodulin and Na/K-transporting ATPase subunit γ. We also identified ten putative proteolytic cleavage sites, eight were specific for matrix metallopeptidases and two for cathepsins.

Interpretation

In liver fibrosis, urinary peptides profiling offers potential diagnostic markers and leads to discovery of proteolytic sites that could be targets for developing anti-fibrotic therapy.

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

Artificial Intelligence for Cardiac Imaging-Genetics Research

Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.

Plasma proteomic approach in patients with heart failure: insights into pathogenesis of disease progression and potential novel treatment targets

AIMS

To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis.

METHODS AND RESULTS

The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two-dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI-MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up-regulated and 46 down-regulated with more than two-fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein-protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised.

CONCLUSIONS

Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure.

Non-natriuretic peptide biomarkers in heart failure with preserved and reduced ejection fraction

Heart failure (HF) with reduced and preserved ejection fraction constitutes two entities with distinct pathogenetic backgrounds sharing common features. Beyond natriuretic peptides, several novel biomarkers have been proven useful in the diagnosis, prognosis and treatment of HF. Biomarkers of myocardial fibrosis have a low diagnostic yield in subjects with acute HF but may add prognostic information, especially in patients with HF and preserved ejection fraction. Biomarkers of renal impairment identify subjects with worse prognosis independently of left ventricle ejection fraction while inflammatory markers have not been proven useful in patients with systolic or diastolic impairment. In this review article, we summarize the main differences and application of non-natriuretic peptide biomarkers in HF patients with preserved and reduced ejection fraction.

Circulating proteins as predictors of incident heart failure in the elderly

AIMS

To identify novel risk markers for incident heart failure using proteomic profiling of 80 proteins previously associated with cardiovascular pathology.

METHODS AND RESULTS

Proteomic profiling (proximity extension assay) was performed in two community-based prospective cohorts of elderly individuals without heart failure at baseline: the Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS, n = 901, median age 70.2 (interquartile range 70.0-70.3) years, 80 events]; and the Uppsala Longitudinal Study of Adult Men [ULSAM, n = 685, median age 77.8 (interquartile range 76.9-78.1) years, 90 events]. Twenty-nine proteins were associated with incident heart failure in the discovery cohort PIVUS after adjustment for age and sex, and correction for multiple testing. Eighteen associations replicated in ULSAM. In pooled analysis of both cohorts, higher levels of nine proteins were associated with incident heart failure after adjustment for established risk factors: growth differentiation factor 15 (GDF-15), T-cell immunoglobulin and mucin domain 1 (TIM-1), tumour necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL-R2), spondin-1 (SPON1), matrix metalloproteinase-12 (MMP-12), follistatin (FS), urokinase-type plasminogen activator surface receptor (U-PAR), osteoprotegerin (OPG), and suppression of tumorigenicity 2 (ST2). Of these, GDF-15, U-PAR, MMP-12, TRAIL-R2, SPON1 and FS were associated with worsened echocardiographic left ventricular systolic function at baseline, while only TIM-1 was positively associated with worsened diastolic function (P < 0.02 for all).

CONCLUSION

Proteomic profiling identified several novel associations between proteins involved in apoptosis, inflammation, matrix remodelling, and fibrinolysis with incident heart failure in elderly individuals. Our results encourage additional studies investigating the underlying mechanisms and the clinical utility of our findings.

Urinary levels of novel kidney biomarkers and risk of true worsening renal function and mortality in patients with acute heart failure

AIMS

Recent studies indicate the need to redefine worsening renal function (WRF) in acute heart failure (AHF), linking a rise in creatinine with clinical status to identify patients who develop 'true WRF'. We evaluated the usefulness of serial assessment of urinary levels of neutrophil gelatinase-associated lipocalin (uNGAL), kidney injury molecule-1 (uKIM-1), and cystatin C (uCysC) for prediction of 'true WRF'.

METHODS AND RESULTS

In 132 patients with AHF, uNGAL, uKIM-1, and uCysC were measured using a highly sensitive immunoassay based on a single-molecule counting technology (Singulex, Alameda, CA, USA) at baseline, day 2, and day 3. Patients who developed WRF (a ≥0.3 mg/dL increase in serum creatinine or a >25% decrease in the estimated glomerular filtration rate from the baseline value) were differentiated into those 'true WRF' (presence of deterioration/no improvement in clinical status during hospitalization) vs. 'pseudo-WRF' (uneventful clinical course). 'True WRF' occurred in 13 (10%), 'pseudo-WRF' in 15 (11%), whereas the remaining 104 (79%) patients did not develop WRF. Patients with 'true WRF' were more often females, had higher levels of NT-proBNP, creatinine, and urea on admission, higher urine albumin to creatinine ratio at day 2, higher uNGAL at baseline, day 2, and day 3, and higher KIM-1 at day 2 (vs. pseudo-WRF vs. without WRF, all P < 0.05). Patients with pseudo-WRF did not differ from those without WRF. In the multivariable model, elevated uNGAL at all time points and uKIM-1 at day 2 remained independent predictors of 'true WRF'.

CONCLUSION

Elevated levels of uNGAL and uKIM-1 may predict development of 'true WRF' in AHF.