Urinary proteomic biomarkers to predict cardiovascular events

Prediction of coronary artery disease using urinary proteomics

AIMS

Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD.

METHODS AND RESULTS

Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly).

CONCLUSION

A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.

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.

Sample Processing Considerations for Protein Stability Studies of Low Concentration Biofluid Samples using Differential Scanning Calorimetry

Background:

The analysis of biofluid samples with low protein content (e.g., urine or saliva) can be challenging for downstream analysis methods with limited sensitivity. To circumvent this problem, sample processing methods are employed to increase the protein concentration in analyzed samples. However, for some techniques, like differential scanning calorimetry (DSC) that characterizes thermally-induced unfolding of biomolecules, sample processing must not affect native protein structure and stability.

Methods:

We evaluated centrifugal concentration and stirred cell ultrafiltration, two common methods of sample concentration characterized by a low risk of protein denaturation, with the goal of establishing a protocol for DSC analysis of low concentration biospecimens.

Results:

Our studies indicate that both methods can affect protein stability assessed by DSC and, even after optimization of several parameters, the obtained DSC profile (thermogram) suggested that sample processing affects the structure or intermolecular interactions of component proteins contributing to altered thermal stability detectable by DSC. We also found a relationship between changes in thermograms and low protein concentration, indicating that diluting biospecimens to concentrations below 0.1 mg/mL can perturb the intermolecular environment and affect the structure of proteins present in the solution.

Conclusions:

Dilution of samples below 0.1 mg/mL, as well as concentration of samples with low protein content, resulted in affected thermogram shapes suggesting changes in protein stability. This should be taken into account when concentrating dilute samples or employing techniques that lower the protein concentration (e.g., fractionation), when downstream applications include techniques, such as DSC, that require the preservation of native protein forms.

Urinary Proteomic Profile of Arterial Stiffness Is Associated With Mortality and Cardiovascular Outcomes

Background

The underlying mechanisms of arterial stiffness remain not fully understood. This study aimed to identify a urinary proteomic profile to illuminate its pathogenesis and to determine the prognostic value of the profile for adverse outcomes.

Methods and Results

We measured aortic stiffness using pulse wave velocity (PWV) and analyzed urinary proteome using capillary electrophoresis coupled with mass spectrometry in 669 randomly recruited Flemish patients (mean age, 50.2 years; 51.1% women). We developed a PWV‐derived urinary proteomic score (PWV‐UP) by modeling PWV with proteomics data at baseline through orthogonal projections to latent structures. PWV‐UP that consisted of 2336 peptides explained the 65% variance of PWV, higher than 36% explained by clinical risk factors. PWV‐UP was significantly associated with PWV (adjusted β=0.73 [95% CI, 0.67–0.79]; P<0.0001). Over 9.2 years (median), 36 participants died, and 75 experienced cardiovascular events. The adjusted hazard ratios (+1 SD) were 1.46 (95% CI, 1.08–1.97) for all‐cause mortality, 2.04 (95% CI, 1.07–3.87) for cardiovascular mortality, and 1.39 (95% CI, 1.11–1.74) for cardiovascular events (P≤0.031). For PWV, the corresponding estimates were 1.25 (95% CI, 0.97–1.60), 1.35 (95% CI, 0.85–2.15), and 1.22 (95% CI, 1.02–1.47), respectively (P≥0.033). Pathway analysis revealed that the peptides in PWV‐UP mostly involved multiple pathways, including collagen turnover, cell adhesion, inflammation, and lipid metabolism.

Conclusions

PWV‐UP was highly associated with PWV and could be used as a biomarker of arterial stiffness. PWV‐UP, but not PWV, was associated with all‐cause mortality and cardiovascular mortality, implying that PWV‐UP–associated peptides may be multifaceted and involved in diverse pathological processes beyond arterial stiffness.

Reproducibility Evaluation of Urinary Peptide Detection Using CE-MS

In recent years, capillary electrophoresis coupled to mass spectrometry (CE-MS) has been increasingly applied in clinical research especially in the context of chronic and age-associated diseases, such as chronic kidney disease, heart failure and cancer. Biomarkers identified using this technique are already used for diagnosis, prognosis and monitoring of these complex diseases, as well as patient stratification in clinical trials. CE-MS allows for a comprehensive assessment of small molecular weight proteins and peptides (<20 kDa) through the combination of the high resolution and reproducibility of CE and the distinct sensitivity of MS, in a high-throughput system. In this study we assessed CE-MS analytical performance with regards to its inter- and intra-day reproducibility, variability and efficiency in peptide detection, along with a characterization of the urinary peptidome content. To this end, CE-MS performance was evaluated based on 72 measurements of a standard urine sample (60 for inter- and 12 for intra-day assessment) analyzed during the second quarter of 2021. Analysis was performed per run, per peptide, as well as at the level of biomarker panels. The obtained datasets showed high correlation between the different runs, low variation of the ten highest average individual log2 signal intensities (coefficient of variation, CV < 10%) and very low variation of biomarker panels applied (CV close to 1%). The findings of the study support the analytical performance of CE-MS, underlining its value for clinical application.

Comparison of the amniotic fluid and fetal urine peptidome for biomarker discovery in renal developmental disease

Production of amniotic fluid (AF) is view as predominately driven by excretion of fetal urine (FU). However, the origin of AF peptides, often considered as potential biomarkers of developmental diseases, has never been investigated. Here, we evaluated the FU origin of AF peptides and if the AF peptide content can be used as a surrogate of FU. The abundance of endogenous peptides was analyzed by capillary electrophoresis coupled to mass spectrometry in 216 AF and 64 FU samples. A total of 2668 and 3257 peptides was found in AF and FU respectively. The AF peptidome largely overlapped with the FU peptidome, ranging from 54% in the second pregnancy trimester to 65% in the third trimester. Examination of a subset of 16 paired AF and FU samples revealed that 67 peptides displayed a significant positively correlated abundance in AF and FU, strongly suggesting that their presence in AF was directly associated to FU excretion. As proof-of-concept we showed that measuring the AF abundance of these 67 peptides of FU origin allowed prediction of postnatal renal survival in fetuses with posterior urethral valves. These results demonstrate that the AF peptidome can be considered as a good surrogate of the FU peptidome.

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.

Differential urinary proteins to diagnose coronary heart disease based on iTRAQ quantitative proteomics

Coronary artery disease (CAD) is a manifestation of systemic atherosclerotic disease. It is assessed by intervention or traditional scoring risk factors. Diagnosis is limited by inaccurate and invasive methods. Developing noninvasive methods to screen for the risk of CAD is a major challenge. We aimed to identify urinary proteins associated with CAD. We utilized iTRAQ labeling followed by 2D LC-MS/MS to compare the urinary proteome of CAD patients to healthy cohorts. The multiple reaction monitoring (MRM) was used to verify the differential proteins. ROC analysis based on MRM data was used to evaluate the diagnostic application. A total of 876 proteins were quantified, and 100 differential proteins were found. Functional analysis revealed that the differential proteins were mainly associated with Liver X Receptor/Retinoid X Receptor (LXR/RXR) pathway activation, atherosclerosis signaling, production of nitric oxide and reactive oxygen species, and the top upstream regulator of the differential proteins by IPA analysis indicated to the APOE. Nineteen differential proteins were verified by MRM analysis. ROC based on MRM data revealed that the combination of two proteins (APOD and TFF1) could diagnose CAD with 85% sensitivity and 99% specificity (AUC 0.95). The urinary proteome might reflect the pathophysiological changes in CAD and be used for the clinical study of CAD.

Utilizing proteomics to understand and define hypertension: where are we and where do we go?

INTRODUCTION

Hypertension is a complex and multifactorial cardiovascular disorder. With different mechanisms contributing to a different extent to an individual's blood pressure, the discovery of novel pathogenetic principles of hypertension is challenging. However, there is an urgent and unmet clinical need to improve prevention, detection, and therapy of hypertension in order to reduce the global burden associated with hypertension-related cardiovascular diseases. Areas covered: Proteomic techniques have been applied in reductionist experimental models including angiotensin II infusion models in rodents and the spontaneously hypertensive rat in order to unravel mechanisms involved in blood pressure control and end organ damage. In humans proteomic studies mainly focus on prediction and detection of organ damage, particularly of heart failure and renal disease. While there are only few proteomic studies specifically addressing human primary hypertension, there are more data available in hypertensive disorders in pregnancy, such as preeclampsia. We will review these studies and discuss implications of proteomics on precision medicine approaches. Expert commentary: Despite the potential of proteomic studies in hypertension there has been moderate progress in this area of research. Standardized large-scale studies are required in order to make best use of the potential that proteomics offers in hypertension and other cardiovascular diseases.

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background

The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown.

Methods

Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years.

Results

CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model.

Conclusion

A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users.

Precision Medicine and Personalized Medicine in Cardiovascular Disease

Precision medicine aims to offer "the right treatment to the right patient at the right time." In cardiovascular medicine the potential of precision medicine applies to all stages of the disease development and includes risk prediction, preventative measures, and targeted therapeutic approaches. Precision medicine will benefit from new developments in the area of genomics and other omics but equally heavily depends on established biomarkers, functional tests, and imaging. Cardiovascular medicine often relies on noninvasive diagnostic procedures and symptom-based disease management. In contrast, other clinical disciplines including oncology and immunology have already moved to molecular diagnostics that lend themselves to precision medicine approaches. There are opportunities to implement precision medicine approaches by focusing on common diseases such as hypertension, conditions with diagnostic and prognostic uncertainty such as angina, and conditions that are associated with high mortality and involve costly and potentially harmful interventions such as dilated cardiomyopathy and cardiac resynchronization therapy. Sex and gender issues have not yet been fully explored in precision medicine although the opportunity to use molecular data to more accurately manage men and women with cardiovascular disease has been acknowledged. A mindshift is required in order to fully exploit the potential of precision medicine to tackle the global burden of cardiovascular diseases.

Current state of the art for enhancing urine biomarker discovery

INTRODUCTION

Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state.

AREAS COVERED

High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Expert commentary: Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.

Does urinary peptide content differ between COPD patients with and without inherited alpha-1 antitrypsin deficiency?

Differentiating between chronic obstructive pulmonary disease (COPD) patients with normal (PiMM) or deficient (PiZZ) genetic variants of alpha-1 antitrypsin (A1AT) is important not only for understanding the pathobiology of disease progression but also for improving personalized therapies. This pilot study aimed to investigate whether urinary peptides reflect the A1AT-related phenotypes of COPD. Urine samples from 19 clinically stable COPD cases (7 PiMM and 12 PiZZ A1AT) were analyzed by capillary electrophoresis coupled to mass spectrometry. We identified 66 peptides (corresponding to 36 unique proteins) that differed between PiZZ and PiMM COPD. Among these, peptides from the collagen family were the most abundant and divergent. A logistic regression model based on COL1A1 or COL5A3 peptides enabled differentiation between PiMM and PiZZ groups, with a sensitivity of 100% and specificity of 85.71% for COL1A1 and a sensitivity of 91.67% and specificity of 85.71% for COL5A3. Furthermore, patients with PiZZ presented low levels of urinary peptides involved in lipoproteins/lipids and retinoic acid metabolism, such as apolipoprotein A-I and C4, retinol-binding protein 4 and prostaglandin-H2 D-isomerase. However, peptides of MDS1 and EVII complex locus, gelsolin and hemoglobin alpha were found in the urine of COPD cases with PiZZ, but not with PiMM. These capillary electrophoresis coupled to mass spectrometry-based results provide the first evidence that urinary peptide content differs between PiMM and PiZZ patients with COPD.

Prediction of acute coronary syndromes by urinary proteome analysis

Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

The Future of "Omics" in Hypertension

Despite decades of research and clinical practice, the pathogenesis of hypertension remains incompletely understood, and blood pressure is often suboptimally controlled. “Omics” technologies allow the description of a large number of molecular features and have the potential to identify new factors that contribute to blood pressure regulation and how they interact. In this review, we focus on the potential of genomics, transcriptomics, proteomics, and metabolomics and explore their roles in unraveling the pathophysiology and diagnosis of hypertension, the prediction of organ damage and treatment response, and monitoring treatment effect. Substantial progress has been made in the area of genomics, in which genome-wide association studies have identified > 50 blood pressure–related, single-nucleotide polymorphisms, and sequencing studies (especially in secondary forms of hypertension) have discovered novel regulatory pathways. In contrast, other omics technologies, despite their ability to provide detailed insights into the physiological state of an organism, have only more recently demonstrated their impact on hypertension research and clinical practice. The majority of current proteomic studies focus on organ damage resulting from hypertension and may have the potential to help us understand the link between blood pressure and organ failure but also serve as biomarkers for early detection of cerebrovascular or coronary disease. Examples include signatures for early detection of left ventricular dysfunction or albuminuria. Metabolomic studies have the potential to integrate environmental and intrinsic factors and are particularly suited to monitor the response to treatment. We discuss examples of omics studies in hypertension and explore the challenges related to these novel technologies.

A capillary electrophoresis coupled to mass spectrometry pipeline for long term comparable assessment of the urinary metabolome

Although capillary electrophoresis coupled to mass spectrometry (CE-MS) has potential application in the field of metabolite profiling, very few studies actually used CE-MS to identify clinically useful body fluid metabolites. Here we present an optimized CE-MS setup and analysis pipeline to reproducibly explore the metabolite content of urine. We show that the use of a beveled tip capillary improves the sensitivity of detection over a flat tip. We also present a novel normalization procedure based on the use of endogenous stable urinary metabolites identified in the combined metabolome of 75 different urine samples from healthy and diseased individuals. This method allows a highly reproducible comparison of the same sample analyzed nearly 130 times over a range of 4 years. To demonstrate the use of this pipeline in clinical research we compared the urinary metabolome of 34 newborns with ureteropelvic junction (UPJ) obstruction and 15 healthy newborns. We identified 32 features with differential urinary abundance. Combination of the 32 compounds in a SVM classifier predicted with 76% sensitivity and 86% specificity UPJ obstruction in a separate validation cohort of 24 individuals. Thus, this study demonstrates the feasibility to use CE-MS as a tool for the identification of clinically relevant urinary metabolites.

Urine proteome analysis as a discovery tool in patients with deep vein thrombosis and pulmonary embolism

PURPOSE

Early and accurate detection of deep vein thrombosis (DVT) is an important clinical need. Based on the hypothesis that urinary peptides may hold information on DVT in conjunction with pulmonary embolism (PE), the study was aimed at identifying such peptide biomarkers using capillary electrophoresis coupled mass spectrometry.

EXPERIMENTAL DESIGN

Patients with symptoms of unprovoked/idiopathic DVT and/or PE were examined by doppler-sonography or angio-computed tomography. Urinary proteome analysis allowed for identification of respective peptide biomarkers. To confirm their biological relevance, we induced PE in mice and assessed human ex vivo thrombi.

RESULTS

We identified 62 urinary peptides as DVT-specific biomarkers, i.e. fragments of collagen type I and a fragment of fibrinogen β-chain. The presence of fibrinogen α/β in the acute thrombus, and collagen type I and osteopontin in the older, organized thrombus was demonstrated. The classifier DVT62 established through support vector machine (SVM) modeling based on the 62 identified peptides was validated in an independent cohort of 47 subjects (six cases and 41 controls) with a sensitivity of 100% and specificity of 83%.

CONCLUSIONS AND CLINICAL RELEVANCE

Urine proteome analysis enabled the detection of DVT-specific peptides, which were validated in human and mouse tissue. Furthermore, it allowed for the establishment of an urinary-proteome based classifier that is relatively specific for DVT. The data provide the basis for assessment of these biomarkers in a prospective clinical study.

Implementation of CE-MS-identified proteome-based biomarker panels in drug development and patient management

The recent advancements in clinical proteomics enabled identification of biomarker panels for a large range of diseases. A number of CE-MS-identified biomarker panels were verified and implemented in clinical studies. Despite multiple challenges, accumulating evidence supports the value and the need for proteome-based biomarker panels. In this perspective, we provide an overview of clinical studies indicating the added value of CE-MS biomarker panels over traditional diagnostics and monitoring methods. We outline apparent advantages of applying novel proteomic biomarker panels for disease diagnosis, prognosis, staging, drug development and patient management. Facing the plethora of benefits associated with the use of CE-MS biomarker panels, we envision their implementation into the medical practice in the near future.