Proteomics in cardiovascular disease: recent progress and clinical implication and implementation

Introduction: Although multiple efforts have been initiated to shed light into the molecular mechanisms underlying cardiovascular disease, it still remains one of the major causes of death worldwide. Proteomic approaches are unequivocally powerful tools that may provide deeper understanding into the molecular mechanisms associated with cardiovascular disease and improve its management. Areas covered: Cardiovascular proteomics is an emerging field and significant progress has been made during the past few years with the aim of defining novel candidate biomarkers and obtaining insight into molecular pathophysiology. To summarize the recent progress in the field, a literature search was conducted in PubMed and Web of Science. As a result, 704 studies from PubMed and 320 studies from Web of Science were retrieved. Findings from original research articles using proteomics technologies for the discovery of biomarkers for cardiovascular disease in human are summarized in this review. Expert commentary: Proteins associated with cardiovascular disease represent pathways in inflammation, wound healing and coagulation, proteolysis and extracellular matrix organization, handling of cholesterol and LDL. Future research in the field should target to increase proteome coverage as well as integrate proteomics with other omics data to facilitate both drug development as well as clinical implementation of findings

Impact of a 6-wk olive oil supplementation in healthy adults on urinary proteomic biomarkers of coronary artery disease, chronic kidney disease, and diabetes (types 1 and 2): a randomized, parallel, controlled, double-blind study

Background: Olive oil (OO) consumption is associated with cardiovascular disease prevention because of both its oleic acid and phenolic contents. The capacity of OO phenolics to protect against low-density lipoprotein (LDL) oxidation is the basis for a health claim by the European Food Safety Authority. Proteomic biomarkers enable an early, presymptomatic diagnosis of disease, which makes them important and effective, but understudied, tools for primary prevention. Objective: We evaluated the impact of supplementation with OO, either low or high in phenolics, on urinary proteomic biomarkers of coronary artery disease (CAD), chronic kidney disease (CKD), and diabetes. Design: Self-reported healthy participants (n = 69) were randomly allocated (stratified block random assignment) according to age and body mass index to supplementation with a daily 20-mL dose of OO either low or high in phenolics (18 compared with 286 mg caffeic acid equivalents per kg, respectively) for 6 wk. Urinary proteomic biomarkers were measured at baseline and 3 and 6 wk alongside blood lipids, the antioxidant capacity, and glycation markers. Results: The consumption of both OOs improved the proteomic CAD score at endpoint compared with baseline (mean improvement: –0.3 for low-phenolic OO and −0.2 for high-phenolic OO; P < 0.01) but not CKD or diabetes proteomic biomarkers. However, there was no difference between groups for changes in proteomic biomarkers or any secondary outcomes including plasma triacylglycerols, oxidized LDL, and LDL cholesterol. Conclusion: In comparison with low-phenolic OO, supplementation for 6 wk with high-phenolic OO does not lead to an improvement in cardiovascular health markers in a healthy cohort. This trial was registered at www.controlled-trials.com as ISRCTN93136746

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p<0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p<0.0001 and ρ = −0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data

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 \u3c 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

Diabetic nephropathy: early detection and therapeutic strategies

The increasing global prevalence of diabetes poses a huge challenge to health services. The diagnosis is accompanied by a reduction in life expectancy, primarily due to cardiovascular disease which is inextricably linked to microvascular complications such as diabetic nephropathy (DN). Microalbuminuria (MA) is generally accepted as the primary clinical hallmark of DN, but despite widespread prescribing of agents blocking the renin angiotensin aldosterone system (RAAS) in these patients many continue to progress towards end-stage renal disease (ESRD). Clinical trials evaluating early initiation of RAAS blocking agents in untargeted, nonalbuminuric diabetic patients have shown potential for delaying disease progression but these effects are generally counterbalanced by side effects and adverse events associated with these therapies. Discovery of novel biomarkers to identify individuals at highest risk of DN who would stand to benefit most from targeted preclinical intervention would be a significant step towards implementation of personalised medicine in this population. One technique which shows promise is proteomics, based on the concept of separation and quantification of peptides in a biological sample to produce a disease-specific pattern. A panel of 273 urinary peptides (CKD273) has been shown to have potential for identification of nonalbuminuric diabetic patients who are at risk of progression to overt DN. However, many such novel biomarkers are described in the literature and to date none have successfully made the transition from research studies to routine clinical practice. In order to be considered for clinical implementation novel biomarkers are required to be subject to a rigorous evaluation process. In brief there are several key steps beginning with proof-of-concept studies; progressing through validation in independent populations to demonstration of incremental value beyond the current guideline-endorsed tests; thereafter proof of clinical applicability in determining treatment strategies and cost-effectiveness are required. The work contained within this thesis is designed to address each of these aspects with regard to use of the CKD273 proteomic panel as a biomarker for early detection of DN

Genome-Wide Association with Select Biomarker Traits in the Framingham Heart Study

BACKGROUND: Systemic biomarkers provide insights into disease pathogenesis, diagnosis, and risk stratification. Many systemic biomarker concentrations are heritable phenotypes. Genome-wide association studies (GWAS) provide mechanisms to investigate the genetic contributions to biomarker variability unconstrained by current knowledge of physiological relations. METHODS: We examined the association of Affymetrix 100K GeneChip single nucleotide polymorphisms (SNPs) to 22 systemic biomarker concentrations in 4 biological domains: inflammation/oxidative stress; natriuretic peptides; liver function; and vitamins. Related members of the Framingham Offspring cohort (n = 1012; mean age 59 ± 10 years, 51% women) had both phenotype and genotype data (minimum-maximum per phenotype n = 507–1008). We used Generalized Estimating Equations (GEE), Family Based Association Tests (FBAT) and variance components linkage to relate SNPs to multivariable-adjusted biomarker residuals. Autosomal SNPs (n = 70,987) meeting the following criteria were studied: minor allele frequency ≥ 10%, call rate ≥ 80% and Hardy-Weinberg equilibrium p ≥ 0.001. RESULTS: With GEE, 58 SNPs had p < 10-6: the top SNPs were rs2494250 (p = 1.00*10-14) and rs4128725 (p = 3.68*10-12) for monocyte chemoattractant protein-1 (MCP1), and rs2794520 (p = 2.83*10-8) and rs2808629 (p = 3.19*10-8) for C-reactive protein (CRP) averaged from 3 examinations (over about 20 years). With FBAT, 11 SNPs had p < 10-6: the top SNPs were the same for MCP1 (rs4128725, p = 3.28*10-8, and rs2494250, p = 3.55*10-8), and also included B-type natriuretic peptide (rs437021, p = 1.01*10-6) and Vitamin K percent undercarboxylated osteocalcin (rs2052028, p = 1.07*10-6). The peak LOD (logarithm of the odds) scores were for MCP1 (4.38, chromosome 1) and CRP (3.28, chromosome 1; previously described) concentrations; of note the 1.5 support interval included the MCP1 and CRP SNPs reported above (GEE model). Previous candidate SNP associations with circulating CRP concentrations were replicated at p < 0.05; the SNPs rs2794520 and rs2808629 are in linkage disequilibrium with previously reported SNPs. GEE, FBAT and linkage results are posted at . CONCLUSION: The Framingham GWAS represents a resource to describe potentially novel genetic influences on systemic biomarker variability. The newly described associations will need to be replicated in other studies.National Heart, Lung, and Blood Institute's Framingham Heart Study (N01-HC25195); National Institutes of Health National Center for Research Resources Shared Instrumentation grant (1S10RR163736-01A1); National Institutes of Health (HL064753, HL076784, AG028321, HL71039, 2 K24HL04334, 1K23 HL083102); Doris Duke Charitable Foundation; American Diabetes Association Career Developement Award; National Center for Research Resources (GCRC M01-RR01066); US Department of Agriculture Agricultural Research Service (58-1950-001, 58-1950-401); National Institute of Aging (AG14759

QUANTITATIVE PROTEOMIC ANALYSES OF HUMAN PLASMA: APPLICATION OF MASS SPECTROMETRY FOR THE DISCOVERY OF CLINICAL DELIRIUM BIOMARKERS

The biomarker discovery pipeline is a multi-step endeavor to identify potential diagnostic or prognostic markers of a disease. Although the advent of modern mass spectrometers has revolutionized the initial discovery phase, a significant bottleneck still exists when validating discovered biomarkers. In this doctoral research, I demonstrate that the discovery, verification and validation of biomarkers can all be performed using mass spectrometry and apply the biomarker pipeline to the context of clinical delirium. First, a systematic review of recent literature provided a birds-eye view of untargeted, discovery proteomic attempts for biomarkers of delirium in the geriatric population. Here, a comprehensive search from five databases yielded 1172 publications, from which eight peer-reviewed studies met our defined inclusion criteria. Despite the paucity of published studies that applied systems- biology approaches for biomarker discovery on the subject, lessons learned and insights from this review was instrumental in the study designing and proteomics analyses of plasma sample in our cohort. We then performed a targeted study on four biomarkers for their potential mediation role in the occurrence of delirium after high-dose intra-operative oxygen treatment. Although S100B calcium binding protein (S100B), gamma enolase (ENO2), chitinase-3-like protein 1 (CHI3L1) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) have well-documented associations with delirium, we did not find any such associations in our cohort. Of note, this study demonstrates that the use of targeted approaches for the purposes of biomarker discovery, rather than an untargeted, systems-biology approach, is unavoidably biased and may lead to misleading conclusions. Lastly, we applied lessons learned and comprehensively profiled the plasma samples of delirium cases and non-delirium cases, at both pre- and post-surgical timepoints. We found 16 biomarkers as signatures of cardiopulmonary bypass, and 11 as potential diagnostic candidates of delirium (AuROC = 93%). We validated the discovered biomarkers on the same mass spectrometry platform without the use of traditional affinity-based validation methods. Our discovery of novel biomarkers with no know association with delirium such as serum amyloid A1 (SAA1) and A2 (SAA2), pepsinogen A3 (PEPA3) and cathepsin B (CATB) shed new lights on possible neuronal pathomechanisms

Proteomic, circulating and functional biomarkers of cardiovascular disease

Cardiovascular disease is the leading cause of morbidity and mortality in the Western world, mainly through cerebrovascular and coronary artery related events. Cardiovascular disease is a chronic progressive disease with different stages. These stages can be assessed by a variety of biomarkers. Biomarker quantification can be used for different purposes: screening, prediction of disease recurrence, therapeutic monitoring, diagnosis and prognostication. Noninvasive, inexpensive diagnostic tests currently applied in clinical practice have a relative high rate of false positive and false negative results. Therefore further refinement of the diagnostic process could improve clinical care. Regarding prognostication the need for improvement also remains as current risk models only predict a small quantity of occurring cardiovascular events. The concept of the cardiovascular continuum postulates that cardiovascular disease consists of a chain of events, is initiated by numerous cardiovascular risk factors and subsequently progresses through pathophysiological processes, ultimately leading to end-stage heart failure. For that reason cardiovascular diseases are chronic progressive conditions and can be divided into different stages, such as early tissue dysfunction or subclinical atherosclerosis prior to development of clinically overt disease. Biomarkers suitable for prognostication and diagnosis can differ at each stage. The general aim of this thesis was therefore the investigation of a variety of biomarkers in diagnosis and prediction of cardiovascular disease at different stages of the cardiovascular continuum, as covered by three different study cohorts contributing to this thesis. This included several approaches: the comparison of central and peripheral pulse pressure in middle aged hypertensive patients in regards of their prognostic potential; the application of established circulating, functional and structural biomarkers to the diagnostic process of coronary artery disease in stable angina patients; the development/refinement of a urinary proteomic biomarker for coronary artery disease and the examination of its diagnostic potential in stable angina patients. Biomarkers successful in the diagnosis of coronary artery disease were included in multiple biomarker models. Aside from biomarker development for the general population, investigations of specific cohorts, such as patients with certain diseases and belonging to certain age groups or sharing specific biochemical features provided advances in the past. To estimate the potential of a biomarker in risk prediction association studies with surrogate biomarkers are applicable. We collected a cohort of middle-aged hypertensive patients to assess if central pulse pressure, derived from non-invasive assessment of arterial stiffness, could improve risk prediction. Central pulse pressure has been previously shown to have prognostic value in populations with end-stage renal failure, coronary artery disease and high prevalence of diabetes mellitus. Considering the prognostic information of peripheral pulse pressure in the elderly, the hypothesis that central pulse pressure could improve risk prediction is comprehensive and was investigated as part of this thesis. This was accomplished by comparing the strength of correlation between central or peripheral pulse pressure and these surrogate biomarkers. When compared to peripheral pulse pressure, central pulse pressure had stronger associations with aortic pulse wave velocity, carotid intima-media thickness, and left ventricular mass index, but equal association with the albumin:creatinine ratio. In contrast, after adjustment for age, mean arterial pressure, heart rate and hypertension status there was no significant difference between central and peripheral pulse pressure for prediction of listed surrogate biomarkers in multivariate analysis. These results suggested that central pulse pressure is unlikely to provide more prognostic information than peripheral pulse pressure in middle-aged hypertensive patients. The diagnosis of coronary artery disease is clinically relevant in symptomatic patients, either acute or stable. The diagnosis of stable flow limiting coronary artery disease is especially challenging as non-cardiac as well as other cardiac conditions can mimic symptoms. Non-invasive diagnostic tools have either moderate sensitivities or specificities, or are not widely available. Therefore new biomarkers for the diagnosis of flow limiting coronary artery disease have the potential to improve current diagnostic strategies. This could be accomplished adjacent to existing biomarkers or by replacement of such, due to cost effectiveness, better discriminatory etc. As part of this thesis, a biomarker identification and validation study was conducted into urinary proteomics of coronary artery disease. First we tried to replicate a study conducted by our research group in the past. Therein, an established coronary artery disease specific polypeptide pattern was unable to differentiate between patients with severe coronary artery disease and healthy controls despite strong cohort similarities to the original study. We therefore recalibrated the urinary polypeptide pattern using an enlarged biomarker discovery cohort and adjusted the pattern for lipid lowering and angiotensin converting enzyme inhibitor treatment effects. We calculated a score from the resulting polypeptide pattern, which identified coronary artery disease patients with a sensitivity of 79% and a specificity of 88% in a biomarker validation cohort. As the next step of biomarker development we performed a diagnostic validation study. The investigated clinical cohort consisted of stable angina patients with or without coronary artery disease. The new polypeptide pattern score was unable to differentiate between these two groups. The score however correlated strongly with coronary artery disease extent as measured by the Gensini score, implying that urinary proteomics in the diagnosis of coronary artery disease is promising, yet requires further effort before clinical employment. In addition to the urinary proteomic biomarker development second diagnostic approach was selected. As coronary artery disease is a complex chronic disease, the combination of different biomarkers should result in a better discrimination between stable angina patients with or without coronary artery disease. This approach attempts to position the individual as precisely as possible on the cardiovascular continuum including serologic, functional vascular and imaging biomarkers of subclinical atherosclerosis. Serologic markers thereby present a plasma proteomic approach covering pathophysiological processes with known correlation or causative for coronary artery disease. Functional and structural changes of the peripheral vasculature resemble the coronary artery system. We investigated circulating biomarkers and vascular biomarkers separately. A variety of circulating biomarkers differentiated patients with severe coronary artery disease from healthy control subjects. When patients with stable angina and with or without coronary artery disease as diagnosed by coronary angiography were investigated no statistically significant differences could be detected for circulating biomarkers. In the same study a microvascular biomarker, the reactive hyperaemia index, and a macrovascular biomarker, the carotide plaque score, were able to differentiated between cases and controls. Both markers either added separately or together improved the risk classification of exercise treadmill test results. This suggests that a multiple biomarker approach in the diagnosis of coronary artery disease in stable angina patients could be successful. Different aspects of the cardiovascular continuum can be applied to diagnosis and prognostication of cardiovascular disease. In this regard we were able to show, that early processes such as endothelial dysfunction or later processes such as plaque formation can support the diagnostic process. However, randomly collected circulating biomarkers might be unable to do this. Our finding that central pulse pressure is unlikely to have more prognostic value in middle aged hypertensive patients underlines that biomarkers can be useful in specific patient collectives but not necessarily in all cohorts. Instead of applying established biomarkers, also new biomarkers can be developed. Urine proteomics showed great promise in this regard, as specific polypeptide patterns reflect coronary artery disease and are strongly correlated to its extent