Evaluation of urinary biomarkers for coronary artery disease, diabetes, and diabetic kidney disease

Multiple urinary peptides display distinct sex-specific distribution

Previous studies have established the association of sex with gene and protein expression. This study investigated the association of sex with the abundance of endogenous urinary peptides, using capillary electrophoresis-coupled to mass spectrometry (CE-MS) datasets from 2008 healthy individuals and patients with type II diabetes, divided in one discovery and two validation cohorts. Statistical analysis using the Mann-Whitney test, adjusted for multiple testing, revealed 143 sex-associated peptides in the discovery cohort. Of these, 90 peptides were associated with sex in at least one of the validation cohorts and showed agreement in their regulation trends across all cohorts. The 90 sex-associated peptides were fragments of 29 parental proteins. Comparison with previously published transcriptomics data demonstrated that the genes encoding 16 of these parental proteins had sex-biased expression. The 143 sex-associated peptides were combined into a support vector machine-based classifier that could discriminate males from females in two independent sets of healthy individuals and patients with type II diabetes, with an AUC of 89% and 81%, respectively. Collectively, the urinary peptidome contains multiple sex-associated differences, which may enable a better understanding of sex-biased molecular mechanisms and the development of more accurate diagnostic, prognostic, or predictive classifiers for each individual sex.

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.

Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study

(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient's urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted.

A urinary peptidomics approach for early stages of cardiovascular disease risk: The African-PREDICT study

Cardiovascular disease (CVD) affects individuals across the lifespan, with multiple cardiovascular (CV) risk factors increasingly present in young populations. The underlying mechanisms in early cardiovascular disease development are complex and still poorly understood. We therefore employed urinary proteomics as a novel approach to gain better insight into early CVD-related molecular pathways based on a CVD risk stratification approach. This study included 964 apparently healthy (no self-reported chronic illnesses, free from clinical symptoms of CVD) black and white men and women (aged 20-30 years old) from the African Prospective study on the Early Detection and Identification of Cardiovascular disease and Hypertension (African-PREDICT) study. Cardiovascular risk factors used for stratification included obesity, physical inactivity, tobacco use, high alcohol intake, hyperglycemia, dyslipidemia and hypertension. Participants were divided into low (0 risk factors), medium (1-2 risk factors) and high (≥3 risk factors) CV risk groups. We analyzed urinary peptidomics by capillary electrophoresis time-of-flight mass spectrometry. After adjusting for ethnicity, sex and age, 65 sequenced urinary peptides were differentially expressed between the CV risk groups (all q-values ≤ 0.01). These peptides included a lower abundance of collagen type I- and III-derived peptides in the high compared to the low CV risk group. With regard to noncollagen peptides, we found a lower abundance of alpha-1-antitrypsin fragments in the high compared to the low CV risk group (all q-values ≤ 0.01). Our findings indicate lower abundances of collagen types I and III in the high compared to the low CV risk group, suggesting potential early alterations in the CV extracellular matrix.

Coronary Artery Disease and Aortic Valve Stenosis: A Urine Proteomics Study

Coronary artery disease (CAD) and the frequently coexisting aortic valve stenosis (AVS) are heart diseases accounting for most cardiac surgeries. These share many risk factors, such as age, diabetes, hypertension, or obesity, and similar pathogenesis, including endothelial disruption, lipid and immune cell infiltration, inflammation, fibrosis, and calcification. Unsuspected CAD and AVS are sometimes detected opportunistically through echocardiography, coronary angiography, and magnetic resonance. Routine biomarkers for early detection of either of these atherosclerotic-rooted conditions would be important to anticipate the diagnosis. With a noninvasive collection, urine is appealing for biomarker assessment. We conducted a shotgun proteomics exploratory analysis of urine from 12 CAD and/or AVS patients and 11 controls to identify putative candidates to differentiate these diseases from healthy subjects. Among the top 20 most dysregulated proteins, TIMP1, MMP2 and vWF stood out, being at least 2.5× increased in patients with CAD/AVS and holding a central position in a network of protein-protein interactions. Moreover, their assessment in an independent cohort (19 CAD/AVS and 10 controls) evidenced strong correlations between urinary TIMP1 and vWF levels and a common cardiovascular risk factor - HDL (r = 0.59, p < 0.05, and r = 0.64, p < 0.01, respectively).

Genetics of 35 blood and urine biomarkers in the UK Biobank

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.

Proteomic Evidence of Biological Aging in a Child with a Compound Heterozygous ZMPSTE24 Mutation

BACKGROUND

Progeria-like syndromes offer a unique insight into aging. Here the case of a boy affected with mandibuloacral dysplasia and compound heterozygous mutations in ZMPSTE24 is presented.

METHODS

Capillary electrophoresis-mass spectroscopy is used for proteome analysis to analyze peptides previously found to be differentially regulated in chronic kidney disease (273 peptides defining the CKD273 classifier), coronary artery disease (238 peptides defining the CAD238 classifier), and aging (116 peptides defining the AGE116 classifier).

RESULTS

No evidence of renal disease is identified. Although the boy has no overt cardiovascular disease other than a raised carotid intima media thickness relative to his age, a proteomic classifier for the diagnosis of coronary artery disease is mildly raised. The biological age based on the proteomic AGE116 classifier is 24 years compared to the chronological ages of 5 and 10 years. In contrast, a control group of healthy children has a significantly lower (p < 0.0001) calculated mean age of 13.

CONCLUSION

Urinary proteomic analysis is effective in confirming advanced biological age and to identify early evidence of renal or cardiovascular damage. This case highlights the value of proteomic approaches in aging research and may represent a method for non-invasive monitoring of the effects of early aging.

Novel Urinary Biomarkers For Improved Prediction Of Progressive Egfr Loss In Early Chronic Kidney Disease Stages And In High Risk Individuals Without Chronic Kidney Disease

Chronic kidney disease is associated with increased risk of CKD progression and death. Therapeutic approaches to limit progression are limited. Developing tools for the early identification of those individuals most likely to progress will allow enriching clinical trials in high risk early CKD patients. The CKD273 classifier is a panel of 273 urinary peptides that enables early detection of CKD and prognosis of progression. We have generated urine capillary electrophoresis-mass spectrometry-based peptidomics CKD273 subclassifiers specific for CKD stages to allow the early identification of patients at high risk of CKD progression. In the validation cohort, the CKD273 subclassifiers outperformed albuminuria and CKD273 classifier for predicting rapid loss of eGFR in individuals with baseline eGFR > 60 ml/min/1.73 m². In individuals with eGFR > 60 ml/min/1.73 m² and albuminuria <30 mg/day, the CKD273 subclassifiers predicted rapid eGFR loss with AUC ranging from 0.797 (0.743-0.844) to 0.736 (0.689-0.780). The association between CKD273 subclassifiers and rapid progression remained significant after adjustment for age, sex, albuminuria, DM, baseline eGFR, and systolic blood pressure. Urinary peptidomics CKD273 subclassifiers outperformed albuminuria and CKD273 classifier for predicting the risk of rapid CKD progression in individuals with eGFR > 60 ml/min/1.73 m². These CKD273 subclassifiers represented the earliest evidence of rapidly progressive CKD in non-albuminuric individuals with preserved renal function.

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis

Background

In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease.

Methods

We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers.

Results

For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology.

Conclusions

Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD.

A urinary proteome-based classifier for the early detection of decline in glomerular filtration

Background

Chronic kidney disease (CKD) progression is currently assessed by a decline in estimated glomerular filtration rate (eGFR) and/or an increase in urinary albumin excretion (UAE). However, these markers are considered either to be late-stage markers or to have low sensitivity or specificity. In this study, we investigated the performance of the urinary proteome-based classifier CKD273, compared with UAE, in a number of different narrow ranges of CKD severity, with each range separated by an eGFR of 10 mL/min/1.73 m 2 .

Methods

A total of 2672 patients with different CKD stages were included in the study. Of these, 394 individuals displayed a decline in eGFR of >5 mL/min/1.73 m 2 /year (progressors) and the remaining individuals were considered non-progressors. For all samples, UAE values and CKD273 classification scores were obtained. To assess UAE values and CKD273 scores at different disease stages, the cohort was divided according to baseline eGFRs of ≥80, 70-79, 60-69, 50-59, 40-49, 30-39 and <29 mL/min/1.73 m 2 . In addition, areas under the curve for CKD273 and UAE were calculated.

Results

In early stage CKD, the urinary proteome-based classifier performed significantly better than UAE in detecting progressors. In contrast, UAE performed better in patients with late-stage CKD. No significant difference in performance was found between CKD273 and UAE in patients with moderately reduced renal function.

Conclusions

These results suggest that urinary peptides, as combined in the CKD273 classifier, allow the detection of progressive CKD at early stages, a point where therapeutic intervention is more likely to be effective. However, late-stage disease, where irreversible damage of the kidney is already present, is better detected by UAE.

Detection of a Peptide Biomarker by Engineered Yeast Receptors

Directed evolution of membrane receptors is challenging as the evolved receptor must not only accommodate a non-native ligand, but also maintain the ability to transduce the detection of the new ligand to any associated intracellular components. The G-protein coupled receptor (GPCR) superfamily is the largest group of membrane receptors. As members of the GPCR family detect a wide range of ligands, GPCRs are an incredibly useful starting point for directed evolution of user-defined analytical tools and diagnostics. The aim of this study was to determine if directed evolution of the yeast Ste2p GPCR, which natively detects the α-factor peptide, could yield a GPCR that detects Cystatin C, a human peptide biomarker. We demonstrate a generalizable approach for evolving Ste2p to detect peptide sequences. Because the target peptide differs significantly from α-factor, a single evolutionary step was infeasible. We turned to a substrate walking approach and evolved receptors for a series of chimeric intermediates with increasing similarity to the biomarker. We validate our previous model as a tool for designing optimal chimeric peptide steps. Finally, we demonstrate the clinical utility of yeast-based biosensors by showing specific activation by a C-terminally amidated Cystatin C peptide in commercially sourced human urine. To our knowledge, this is the first directed evolution of a peptide GPCR.

Proteomics for Biomarker Identification and Clinical Application in Kidney Disease

Treatment effectiveness for kidney disease is limited by lack of accuracy, sensitivity, specificity of diagnostic, prognostic, and therapeutic biomarkers. The gold standard test renal biopsy along with serum creatinine and proteinuria is often necessary to establish a diagnosis, particularly in glomerular disease. Proteomics has become a powerful tool for novel biomarker discovery in kidney disease. Novel proteomics offer earlier and more accurate diagnosis of renal pathology than possible with traditional biomarkers such as serum creatinine and urine protein. In addition, proteomic biomarkers could also be useful to choose the most suitable therapeutic targets. This review focuses on the current status of proteomic biomarkers from animal models (5/6 nephrectomy, unilateral ureteral obstruction, and diabetic nephropathy) and human studies (chronic kidney disease, glomerular diseases, transplantation, dialysis, acute and drug-induced kidney injury) to assess relevant findings and clinical usefulness. Current issues and problems related to the discovery, validation, and clinical application of proteomic biomarkers are discussed. We also describe several proteomic strategies highlighting technologic advancements, specimen selection, data processing and analysis. This review might provide help in future proteomic studies to improve the diagnosis and management of kidney disease.

Urinary peptide-based classifier CKD273: towards clinical application in chronic kidney disease

Capillary electrophoresis coupled with mass spectrometry (CE-MS) has been used as a platform for discovery and validation of urinary peptides associated with chronic kidney disease (CKD). CKD affects ∼ 10% of the population, with high associated costs for treatments. A urinary proteome-based classifier (CKD273) has been discovered and validated in cross-sectional and longitudinal studies to assess and predict the progression of CKD. It has been implemented in studies employing cohorts of > 1000 patients. CKD273 is commercially available as an in vitro diagnostic test for early detection of CKD and is currently being used for patient stratification in a multicentre randomized clinical trial (PRIORITY). The validity of the CKD273 classifier has recently been evaluated applying the Oxford Evidence-Based Medicine and Southampton Oxford Retrieval Team guidelines and a letter of support for CKD273 was issued by the US Food and Drug Administration. In this article we review the current evidence published on CKD273 and the challenges associated with implementation. Definition of a possible surrogate early endpoint combined with CKD273 as a biomarker for patient stratification currently appears as the most promising strategy to enable the development of effective drugs to be used at an early time point when intervention can still be effective.

Urine proteomics in the diagnosis of stable angina

Background

We have previously described a panel of 238 urinary polypeptides specific for established severe coronary artery disease (CAD). Here we studied this polypeptide panel in patients with a wider range of CAD severity.

Methods

We recruited 60 patients who underwent elective coronary angiography for investigation of stable angina. Patients were selected for either having angiographic evidence of CAD or not (NCA) following coronary angiography (n = 30/30; age, 55 ± 6 vs. 56 ± 7 years, P = 0.539) to cover the extremes of the CAD spectrum. A further 66 patients with severe CAD (age, 64 ± 9 years) prior to surgical coronary revascularization were added for correlation studies. The Gensini score was calculated from coronary angiograms as a measure of CAD severity. Urinary proteomic analyses were performed using capillary electrophoresis coupled online to micro time-of-flight mass spectrometry. The urinary polypeptide pattern was classified using a predefined algorithm and resulting in the CAD₂₃₈ score, which expresses the pattern quantitatively.

Results

In the whole cohort of patients with CAD (Gensini score 60 [40; 98]) we found a close correlation between Gensini scores and CAD₂₃₈ (ρ = 0.465, P < 0.001). After adjustment for age (β = 0.144; P = 0.135) the CAD₂₃₈ score remained a significant predictor of the Gensini score (β =0.418; P < 0.001). In those with less severe CAD (Gensini score 40 [25; 61]), however, we could not detect a difference in CAD₂₃₈ compared to patients with NCA (−0.487 ± 0.341 vs. −0.612 ± 0.269, P = 0.119).

Conclusions

In conclusion the urinary polypeptide CAD₂₃₈ score is associated with CAD burden and has potential as a new cardiovascular biomarker.

Electronic supplementary material

The online version of this article (doi:10.1186/s12872-016-0246-y) contains supplementary material, which is available to authorized users.

Body fluid peptide and protein signatures in diabetic kidney diseases

Body fluid protein-based biomarkers carry the hope of improving patient management in diabetes by enabling more accurate and earlier detection of diabetic kidney disease (DKD), but also of defining the most suitable therapeutic targets. We present the data on some of the best studied individual protein markers in body fluids and conclude that their potential in clinical application for assessing DKD is moderate. Proteome-based approaches aiming at the identification of panels of body fluid biomarkers might be a valid alternative. We discuss the past (first) clinical proteomics studies in DKD, stressing their drawbacks but also the lessons that could be learned from them, as well as the more recent studies that have a potential for actual clinical implementation. We also highlight relevant issues and current problems associated with clinical proteomics from discovery towards application, and give suggestions for solutions that may help guiding proteomic studies, thereby removing some of the current hurdles for implementation of potentially beneficial results.

Differential urinary glycoproteome analysis of type 2 diabetic nephropathy using 2D-LC-MS/MS and iTRAQ quantification

Background

Diabetic nephropathy (DN) is the leading cause of chronic kidney failure and end-stage kidney disease. More accurate and non-invasive test for the diagnosis and monitoring the progression of DN is urgently needed for the better care of such patients.

Methods

In this study we utilized urinary glycoproteome to discover the differential proteins during the course of type 2 DN. The urinary glycoproteins from normal controls, normalbuminuira, microalbuminura, and macroalbuminuria patients were enriched by concanavalin A (ConA) and analyzed by 2DLC/MS/MS and isobaric tags for relative and absolute quantitation quantification.

Results

A total of 478 proteins were identified and 408 were annotated as N-linked glycoproteins. A total of 72, 107 and 123 differential proteins were identified in normalbuminuria, microalbuminuria and macroalbuminuria, respectively. By bioinformatics analysis, in normalbuminruia state, cell proliferation and cell movement were activated, which might reflect the compensatory phase during the disease development. In micro- and macro-albuminuria, cell death and apoptosis was activated, which might reflect the de-compensatory phase. Pathway analysis showed acute phase proteins, the member of high density lipoprotein and low density lipoprotein proteins were changed, indicating the role of the inflammatory response and lipid metabolism abnormality in the pathogenesis of DN. Six selected differential proteins were validated by Western Blot. Alpha-1-antitrypsin (SERPINA1) and Ceruloplasmin are the two markers with excellent area under curve values (0.929 and 1.000 respectively) to distinguish the microalbuminuria and normalbuminuria. For the first time, we found pro-epidermal growth factor and prolactin-inducible protein were decreased in macroalbuminuria stage, which might reflect the inhibition of cell viability and the activation of cell death in kidney.

Conclusions

Above data indicated that urinary glycoproteome could be useful to distinguish the differences in protein profiles in different stages in DN, which will help better individualized care of patients in DN.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0712-9) contains supplementary material, which is available to authorized users.

Proteomic biomarkers in kidney disease: issues in development and implementation

Proteomic biomarkers offer the hope of improving the management of patients with kidney diseases by enabling more accurate and earlier detection of renal pathology than is possible with currently available biomarkers, serum creatinine and urinary albumin. In addition, proteomic biomarkers could also be useful to define the most suitable therapeutic targets in a given patient or disease setting. This Review describes the current status of proteomic and protein biomarkers in the context of kidney diseases. The valuable lessons learned from early clinical studies of potential proteomic biomarkers in kidney disease are presented to give context to the newly identified biomarkers, which have potential for actual clinical implementation. This article also includes an overview of protein-based biomarker candidates that are undergoing development for use in nephrology, focusing on those with the greatest potential for clinical implementation. Relevant issues and problems associated with the discovery, validation and clinical application of proteomic biomarkers are discussed, along with suggestions for solutions that might help to guide the design of future proteomic studies. These improvements might remove some of the current obstacles to the utilization of proteomic biomarkers, with potentially beneficial results.

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.

Advances in urinary protein biomarkers for urogenital and non-urogenital pathologies

The discovery of protein biomarkers that reflect the biological state of the body is of vital importance to disease management. Urine is an ideal source of biomarkers that provides a non-invasive approach to diagnosis, prognosis and prediction of diseases. Consequently, the study of the human urinary proteome has increased dramatically over the last 10 years, with many studies being published. This review focuses on urinary protein biomarkers that have shown potential, in initial studies, for diseases affecting the urogenital tract, specifically chronic kidney disease and prostate cancer, as well as other non-urogenital pathologies such as breast cancer, diabetes, atherosclerosis and osteoarthritis. PubMed was searched for peer-reviewed literature on the subject, published in the last 10 years. The keywords used were "urine, biomarker, protein, and/or prostate cancer/breast cancer/chronic kidney disease/diabetes/atherosclerosis/osteoarthritis". Original studies on the subject, as well as a small number of reviews, were analysed including the strengths and weaknesses, and we summarized the performance of biomarkers that demonstrated potential. One of the biggest challenges found is that biomarkers are often shared by several pathologies so are not specific to one disease. Therefore, the trend is shifting towards implementing a panel of biomarkers, which may increase specificity. Although there have been many advances in urinary proteomics, these have not resulted in similar advancements in clinical practice due to high costs and the lack of large data sets. In order to translate these potential biomarkers to clinical practice, vigorous validation is needed, with input from industry or large collaborative studies.

Quantitative proteomics reveals novel therapeutic and diagnostic markers in hypertension

Hypertension is a prevalent disorder in the world representing one of the major risk factors for heart attack and stroke. These risks are increased in salt sensitive individuals. Hypertension and salt sensitivity are complex phenotypes whose pathophysiology remains poorly understood and, remarkably, salt sensitivity is still laborious to diagnose.

Here we present a urinary proteomic study specifically designed to identify urinary proteins relevant for the pathogenesis of hypertension and salt sensitivity. Despite previous studies that underlined the association of UMOD gene variants with hypertension, this work provides novel evidence showing different uromodulin protein level in the urine of hypertensive patients compared to healthy individuals. Notably, we also show that patients with higher level of uromodulin are homozygous for UMOD risk variant and display a decreased level of salt excretion, highlighting the essential role of UMOD in the regulation of salt reabsorption in hypertension. Additionally, we found that urinary nephrin 1, a marker of glomerular slit diaphragm, may predict a salt sensitive phenotype and positively correlate with increased albuminuria associated with this type of hypertension.

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We identified urinary proteins differently excreted in hypertensive patients.

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Nephrin 1 might predict salt sensitive phenotype and glomerular complications.

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Uromodulin impacts salt homeostasis in hypertension.

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We provide new insights into the pathogenesis of hypertension and salt sensitivity.

Excretion of urinary orosomucoid 1 protein is elevated in patients with chronic heart failure

Easily screening markers for early detection of chronic heart failure (CHF) are lacking. We identified twenty differently expressed proteins including orosomucoid 1(ORM1) in urine between patients with CHF and normal controls by proteomic methods. Bioinformatics analyses suggested ORM1 could be used for further analysis. After verification by western blotting, the urinary levels of ORM1 were quantified with enzyme-linked immunosorbent assay (ELISA) by correcting for creatinine expression. The ORM1-Cr was significantly elevated in CHF patients than normal controls (6498.83±4300.21 versus 2102.26±1069.24 ng/mg). Furthermore, a Spearman analysis indicated that the urinary ORM1 levels had a high positive correlation with the classification of CHF, and the multivariate analysis suggested that the urinary ORM1 content was associated with the plasma amino-terminal pro- brain natriuretic peptide (NT-proBNP) (OR: 2.106, 95% CI: 1.213–3.524, P = 0.002) and the New York Heart Association (NYHA) classification (OR: 3.019, 95% CI: 1.329–4.721, P<0.001). In addition, receiving operating curve (ROC) analyses suggested that an optimum cut-off value of 2484.98 ng/mg with 90.91% sensitivity and 85.48% specificity, respectively, could be used for the diagnosis of CHF. To sum up, our findings indicate that ORM1 could be a potential novel urinary biomarker for the early detection of CHF.

Prediction of remission after metabolic surgery using a novel scoring system in type 2 diabetes - a retrospective cohort study

Background

Remission of diabetes is seen in more than 60% of patients after bariatric surgery. There is extensive variability in the remission rates between different surgical procedures. We analyzed our database and aimed to develop an easy scoring system to predict the probability of diabetes remission after two surgical procedures i.e. Ileal Interposition coupled with Sleeve Gastrectomy (IISG) or Diverted Sleeve Gastrectomy (IIDSG).

Methods

In this retrospective study, we analyzed records pertaining to patients who underwent IISG (n = 46) and IIDSG (n = 29). The primary outcome measure was diabetes remission (A1c <6.5% and not requiring hypoglycemic drugs). We identified seven preoperative clinical variables (age, duration of diabetes, body mass index, micro and macrovascular complications, use of insulin and stimulated C-peptide) based on our previous reports to be included in the diabetes remission score (DRS). The DRS score (7 – 14) was compared between the patients with and without remission in both the surgery groups.

Results

Mean DRS in patients who underwent IISG was 9.2 ± 1.4. Twenty one (46%) had a remission in diabetes. DRS was significantly lower in patients with remission than patients without remission (8.1 ± 0.8 versus 10.2 ± 0.9, p < 0.0001). Mean DRS in patients who underwent IIDSG was 10.4 ± 1.3. Twenty one (72%) had a remission in diabetes. DRS was significantly lower in patients with remission than patients without remission (9.7 ± 0.8 versus 12.0 ± 0.5, p < 0.0001). Patients with a DRS ≥ 10 in IISG group and more than 12 in IIDSG group did not get into remission.

Conclusion

Preoperative DRS can be a useful tool to select the type of surgical procedure and to predict the postoperative diabetes remission.

Trial registration

NCT00834626.

Early diabetic nephropathy in type 1 diabetes: new insights

PURPOSE OF REVIEW

Despite improvements in glycemic and blood pressure control in patients with type 1 diabetes, diabetic nephropathy remains the most common cause of chronic kidney disease worldwide. A major challenge in preventing diabetic nephropathy is the inability to identify high-risk patients at an early stage, emphasizing the importance of discovering new therapeutic targets and implementation of clinical trials to reduce diabetic nephropathy risk.

RECENT FINDINGS

Limitations of managing patients with diabetic nephropathy with renin-angiotensin-aldosterone system blockade have been identified in recent clinical trials, including the failure of primary prevention studies in T1D and the demonstration of harm with dual renin-angiotensin-aldosterone system blockade. Fortunately, several new targets, including serum uric acid, insulin sensitivity, vasopressin, and sodium-glucose cotransporter-2 inhibition, are promising in the prevention and treatment of diabetic nephropathy.

SUMMARY

Diabetic nephropathy is characterized by a long clinically silent period without signs or symptoms of disease. There is an urgent need for improved methods of detecting early mediators of renal injury, to ultimately prevent the initiation and progression of diabetic nephropathy. In this review, we will focus on early diabetic nephropathy and summarize potential new therapeutic targets.

CKD273, a new proteomics classifier assessing CKD and its prognosis

National Kidney Foundation CKD staging has allowed uniformity in studies on CKD. However, early diagnosis and predicting progression to end stage renal disease are yet to be improved. Seventy six patients with different levels of CKD, including outpatients and dialysed patients were studied for transcriptome, metabolome and proteome description. High resolution urinary proteome analysis was blindly performed in the 53 non-anuric out of the 76 CKD patients. In addition to routine clinical parameters, CKD273, a urinary proteomics-based classifier and its peptides were quantified. The baseline values were analyzed with regard to the clinical parameters and the occurrence of death or renal death during follow-up (3.6 years) as the main outcome measurements. None of the patients with CKD273<0.55 required dialysis or died while all fifteen patients that reached an endpoint had a CKD273 score >0.55. Unsupervised clustering analysis of the CKD273 peptides separated the patients into two main groups differing in CKD associated parameters. Among the 273 biomarkers, peptides derived from serum proteins were relatively increased in patients with lower glomerular filtration rate, while collagen-derived peptides were relatively decreased (p<0.05; Spearman). CKD273 was different in the groups with different renal function (p<0.003). The CKD273 classifier separated CKD patients according to their renal function and informed on the likelihood of experiencing adverse outcome. Recently defined in a large population, CKD273 is the first proteomic-based classifier successfully tested for prognosis of CKD progression in an independent cohort.

Experiences of continuous subcutaneous insulin infusion in pregnant women with type 1 diabetes during delivery from four Italian centers: a retrospective observational study

OBJECTIVES

An optimized metabolic control during delivery is mandatory to prevent maternal-neonatal complications. The primary aim of this study was to evaluate the efficacy and safety of continuous subcutaneous insulin infusion (CSII) during delivery in pregnant women with type 1 diabetes. The secondary aim was to assess the impact of real-time continuous glucose monitoring (RT-CGM) added to CSII versus CSII alone.

RESEARCH DESIGN AND METHODS

This was a multicenter observational retrospective study. A standardized protocol, to use CSII throughout pregnancy and delivery, foresaw three different insulin basal rates according to blood glucose level: profile A, the last basal rate in use; profile B, preventive 50% reduction of the last basal rate in use; and profile C, 0.1-0.2 U/h for blood glucose level <70 mg/dL, activated just before anesthesia or at the beginning of active labor. An alternative intravenous protocol (IVP) was given in case of complications and relevant metabolic deterioration. Blood glucose in the target range (70-140 mg/dL) throughout delivery and percentage of activation of the IVP were primary outcomes.

RESULTS

Sixty-five pregnant women with diabetes included in the study (56-86% cesarean section; 9-14% spontaneous/stimulated vaginal delivery). Mean blood glucose level was 102 ± 31 mg/dL at 0 min, 109 ± 42 mg/dL at 30 min, 120 ± 48 mg/dL at 60 min, and 99 ± 34 mg/dL at 24 h. Mean basal rate during delivery was 0.6 ± 0.4 U/h (profile B). Mean capillary blood glucose (CBG) level was lower in the RT-CGM group relative to the CSII-alone group: 80 ± 14 mg/dL versus 111 ± 32 mg/dL at 0 min (P<0.01), 79 ± 11 mg/dL versus 109 ± 42 mg/dL at 30 min (P<0.02), and 98 ± 20 mg/dL versus 125 ± 51 mg/dL at 60 min (difference not significant). Eleven newborns experienced transient neonatal hypoglycemia. None of the women switched to IVP. No major differences were observed according to delivery procedure.

CONCLUSIONS

CSII is possible and safe in different types of delivery in selected and educated women. RT-CGM helps to obtain better outcomes in terms of maternal peripartum CBG level.

Urinary proteomic biomarkers for diagnosis and risk stratification of autosomal dominant polycystic kidney disease: a multicentric study

Treatment options for autosomal dominant polycystic kidney disease (ADPKD) will likely become available in the near future, hence reliable diagnostic and prognostic biomarkers for the disease are strongly needed. Here, we aimed to define urinary proteomic patterns in ADPKD patients, which aid diagnosis and risk stratification. By capillary electrophoresis online coupled to mass spectrometry (CE-MS), we compared the urinary peptidome of 41 ADPKD patients to 189 healthy controls and identified 657 peptides with significantly altered excretion, of which 209 could be sequenced using tandem mass spectrometry. A support-vector-machine based diagnostic biomarker model based on the 142 most consistent peptide markers achieved a diagnostic sensitivity of 84.5% and specificity of 94.2% in an independent validation cohort, consisting of 251 ADPKD patients from five different centers and 86 healthy controls. The proteomic alterations in ADPKD included, but were not limited to markers previously associated with acute kidney injury (AKI). The diagnostic biomarker model was highly specific for ADPKD when tested in a cohort consisting of 481 patients with a variety of renal and extrarenal diseases, including AKI. Similar to ultrasound, sensitivity and specificity of the diagnostic score depended on patient age and genotype. We were furthermore able to identify biomarkers for disease severity and progression. A proteomic severity score was developed to predict height adjusted total kidney volume (htTKV) based on proteomic analysis of 134 ADPKD patients and showed a correlation of r = 0.415 (p<0.0001) with htTKV in an independent validation cohort consisting of 158 ADPKD patients. In conclusion, the performance of peptidomic biomarker scores is superior to any other biochemical markers of ADPKD and the proteomic biomarker patterns are a promising tool for prognostic evaluation of ADPKD.

Recent advances in capillary electrophoresis-based proteomic techniques for biomarker discovery

Due to the inherent disadvantage of biomarker dilution in complex biological fluids such as serum/plasma, urine, and saliva, investigative studies directed at tissues obtained from the primary site of pathology probably afford the best opportunity for the discovery of disease biomarkers. Still, the large variation of protein relative abundances with clinical specimens often exceeds the dynamic range of currently available proteomic techniques. Furthermore, since the sizes of human tissue biopsies are becoming significantly smaller due to the advent of minimally invasive methods and early detection and treatment of lesions, a more effective discovery-based proteomic technology is critically needed to enable comprehensive and comparative studies of protein profiles that will have diagnostic and therapeutic relevance.This review therefore focuses on the most recent advances in capillary electrophoresis-based single and multidimensional separations coupled with mass spectrometry for performing comprehensive proteomic analysis of clinical specimens. In addition to protein identification, monitoring quantitative changes in protein expression is essential for the discovery of disease-associated biomarkers. Comparative proteomics involving measurements in changes of biological pathways or functional processes are further expected to provide relevant markers and networks, molecular relationships among different stages of disease, and molecular mechanisms that drive the progression of disease.

Novel biomarkers in type 1 diabetes

Biomarkers are useful tools for research into type 1 diabetes (T1D) for a number of purposes, including elucidation of disease pathogenesis, risk prediction, and therapeutic monitoring. Susceptibility genes and islet autoantibodies are currently the most useful biomarkers for T1D risk prediction. However, these markers do not fully meet the needs of scientists and physicians for several reasons. First, improvement of the specificity and sensitivity is still desirable to achieve better positive predictive values. Second, autoantibodies appear relatively late in the disease process, thus limiting their value in early disease prediction. Third, the currently available biomarkers are not useful for assessing therapeutic outcomes because some are not involved in the disease process (autoantibodies) and others do not change during disease progression (susceptibility genes). Therefore, considerable effort has been devoted to the discovery of novel T1D biomarkers in the last three decades. The advent of high-throughput technologies for genetic, transcriptomic, and proteomic studies has allowed genome-wide examinations of genetic polymorphisms, global gene changes, and protein expression changes in T1D patients and prediabetic subjects. These large-scale studies resulted in the discovery of a large number of susceptibility genes and changes in gene and protein expression. While these studies have provided a number of novel biomarker candidates, their clinical benefits remain to be evaluated in prospective studies, and no new "star biomarker" has been identified until now. Previous studies suggest that significant improvements in study design and analytical methodologies have to be made to identify clinically relevant biomarkers. In this review, we discuss progress, opportunities, challenges, and future directions in the development of T1D biomarkers, mainly by focusing on the genetic, transcriptomic, and proteomic aspects.

How to get proteomics to the clinic? Issues in clinical proteomics, exemplified by CE-MS

Clinical proteomics is defined as application of proteome analysis aiming at improving the current clinical situation. As such, the success of clinical proteomics should be assessed based on the clinical impact following implementation of the findings. While we have experienced significant technological advancements in mass spectrometry in the last years, based on the above measure, this has not at all resulted in similar advancements in clinical proteomics. Although a large number of proteomic biomarkers have been described, most of them were not subsequently validated, and certainly have had no impact in clinical decision making as yet. Under the current conditions, it appears likely that the situation will not change significantly: we will be flooded by reports on biomarkers, but not see any implementation. In this article, some key issues in proteomic biomarker research are pinpointed, based on the experience with CE-MS, likely also holding true for biomarkers resulting from other analysis domains.

Urinary proteomics to support diagnosis of stroke

Accurate diagnosis in suspected ischaemic stroke can be difficult. We explored the urinary proteome in patients with stroke (n = 69), compared to controls (n = 33), and developed a biomarker model for the diagnosis of stroke. We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Potentially disease-specific peptides were identified and a classifier based on these was generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. We developed two biomarker-based classifiers, employing 14 biomarkers (nominal p-value <0.004) or 35 biomarkers (nominal p-value <0.01). When tested on a blinded test set of 47 independent samples, the classification factor was significantly different between groups; for the 35 biomarker model, median value of the classifier was 0.49 (−0.30 to 1.25) in cases compared to −1.04 (IQR −1.86 to −0.09) in controls, p<0.001. The 35 biomarker classifier gave sensitivity of 56%, specificity was 93% and the AUC on ROC analysis was 0.86. This study supports the potential for urinary proteomic biomarker models to assist with the diagnosis of acute stroke in those with mild symptoms. We now plan to refine further and explore the clinical utility of such a test in large prospective clinical trials.

Novel urinary protein biomarkers predicting the development of microalbuminuria and renal function decline in type 1 diabetes

OBJECTIVE

To define a panel of novel protein biomarkers of renal disease.

RESEARCH DESIGN AND METHODS

Adults with type 1 diabetes in the Coronary Artery Calcification in Type 1 Diabetes study who were initially free of renal complications (n = 465) were followed for development of micro- or macroalbuminuria (MA) and early renal function decline (ERFD, annual decline in estimated glomerular filtration rate of ≥3.3%). The label-free proteomic discovery phase was conducted in 13 patients who progressed to MA by the 6-year visit and 11 control subjects, and four proteins (Tamm-Horsfall glycoprotein, α-1 acid glycoprotein, clusterin, and progranulin) identified in the discovery phase were measured by enzyme-linked immunosorbent assay in 74 subjects: group A, normal renal function (n = 35); group B, ERFD without MA (n = 15); group C, MA without ERFD (n = 16); and group D, both ERFD and MA (n = 8).

RESULTS

In the label-free analysis, a model of progression to MA was built using 252 peptides, yielding an area under the curve (AUC) of 84.7 ± 5.3%. In the validation study, ordinal logistic regression was used to predict development of ERFD, MA, or both. A panel including Tamm-Horsfall glycoprotein (odds ratio 2.9, 95% CI 1.3-6.2, P = 0.008), progranulin (1.9, 0.8-4.5, P = 0.16), clusterin (0.6, 0.3-1.1, P = 0.09), and α-1 acid glycoprotein (1.6, 0.7-3.7, P = 0.27) improved the AUC from 0.841 to 0.889.

CONCLUSIONS

A panel of four novel protein biomarkers predicted early renal damage in type 1 diabetes. These findings require further validation in other populations for prediction of renal complications and treatment monitoring.

Urinary proteomics in the assessment of chronic kidney disease

PURPOSE OF REVIEW

Urinary proteomics has emerged as an approach that could deliver relevant clinical information. In this review, we aim at highlighting the recent developments, especially with respect to clinical implementation. We review several of the recent publications reporting on larger cohorts, focusing on those that aim at qualification and/or validation of urinary proteomics biomarkers.

RECENT FINDINGS

Several components of the urinary proteome, especially its low molecular weight fraction (sometimes referred to as the 'peptidome'), have been significantly associated with chronic kidney disease (CKD). Independent studies, encompassing sometimes close to 1000 independent samples, indicate that specific peptides from extracellular matrix (ECM) proteins encompass a major component of the urinary proteome. Highly significant changes in the abundance of some of these peptides are associated with CKD indicating that alterations in ECM, reflected via the urinary proteome, may represent an early stage in CKD pathology. These peptides may serve as specific early biomarkers, and interference with pathological ECM accumulation may be a valuable new therapeutic approach in CKD.

SUMMARY

Urinary proteomic biomarkers have emerged as clinically relevant variables. First studies involving several hundred individuals indicate a potential added benefit of urinary proteomic biomarkers. First large clinical trials are being initiated to employ urinary proteomics in clinical decision making.

Multiplexed quantification of 63 proteins in human urine by multiple reaction monitoring-based mass spectrometry for discovery of potential bladder cancer biomarkers

Three common urological diseases are bladder cancer, urinary tract infection, and hematuria. Seventeen bladder cancer biomarkers were previously discovered using iTRAQ - these findings were verified by MRM-MS in this current study. Urine samples from 156 patients with hernia (n=57, control), bladder cancer (n=76), or urinary tract infection/hematuria (n=23) were collected and subjected to multiplexed LC-MRM/MS to determine the concentrations of 63 proteins that are normally considered to be plasma proteins, but which include proteins found in our earlier iTRAQ study. Sixty-five stable isotope-labeled standard proteotypic peptides were used as internal standards for 63 targeted proteins. Twelve proteins showed higher concentrations in the bladder cancer group than in the hernia and the urinary tract infection/hematuria groups, and thus represent potential urinary biomarkers for detection of bladder cancer. Prothrombin had the highest AUC (0.796), with 71.1% sensitivity and 75.0% specificity for differentiating bladder cancer (n=76) from non-cancerous (n=80) patients. The multiplexed MRM-MS data was used to generate a six-peptide marker panel. This six-peptide panel (afamin, adiponectin, complement C4 gamma chain, apolipoprotein A-II precursor, ceruloplasmin, and prothrombin) can discriminate bladder cancer subjects from non-cancerous subjects with an AUC of 0.814, with a 76.3% positive predictive value, and a 77.5% negative predictive value. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.

A pilot study on the effect of short-term consumption of a polyphenol rich drink on biomarkers of coronary artery disease defined by urinary proteomics

Polyphenol rich diets have been associated with a reduced risk of cardiovascular disease. We examined the effect of a polyphenol rich (P-R) drink on biomarkers assessed by urinary proteomics. Thirty nine middle aged and overweight subjects were randomized to P-R drink (n = 20) or placebo (n = 19) in addition to their normal diet. After two weeks urine samples were obtained for assessment of the urinary proteome using capillary electrophoresis coupled to a mass spectrometer. A total of 93 polypeptides were found to be candidates for differential distribution with a nominal p-value <0.05, though these differences did not reach significance when multiple testing was accounted for. Sequences were determined in 19 of these demonstrating that they originate from alpha-1 antitrypsin, collagens, fibrinogen alpha and IgG kappa. Levels of 27 polypeptides were greater than 4-fold different between the two groups. Of these, 7 were previously found to be part of a coronary artery disease (CAD) specific urinary biomarker pattern. Their direction of expression was closer to the healthy state in the P-R drink group and closer to CAD state in the placebo group. Our data suggest that the P-R drink may have beneficial effects on urinary biomarkers of CAD. The data encourage the planning of future prospective studies, aimed at investigating significant effects of polyphenol rich dietary products.

Biomarkers for Diabetes Complications: The Results of Several Clinical Studies

Diabetes is a common metabolic disorder. Its microvascular and macrovascular complications contribute to death, disabilities, and reduction in life expectancy in diabetes. It is a costly disease, and affects not only the patient and family, but also the public health, communities and society. It takes an increasing proportion of the national health care expenditure. The prevention of the development of diabetes and its complications is a major concern. Biomarkers have been investigated for understanding the mechanisms of the development and progression of diabetic complications. In this paper, the biomarkers which are recommended in the clinical practice and laboratory medicine guidelines, and which have been investigated for prediction or diagnosis of diabetes complications, have been reviewed. The results of several clinical studies will be summarized.

Human urinary peptide database for multiple disease biomarker discovery

PURPOSE

Human urine is an ideal candidate for use in clinical diagnostics. It is easily available, as untrained personnel can collect it. It correlates well with the pathophysiology of a number of diseases, making it a useful source for clinical proteomics.

EXPERIMENTAL DESIGN

In this article, we give an update of the human urinary peptide database derived from over 13,000 data sets of CE-MS by now.

RESULTS

Urine samples from both patients and healthy subjects were analyzed by CE-MS; these included 47 different pathophysiological conditions. Besides defining biomarkers by their experimental parameters, information on their sequences provides fundamental data into the pathological pathways of diseases. Therefore, we have sequenced 953 urinary peptides by using state-of-the-art top-down MS/MS. Identified biomarkers of all clinical proteomic CE-MS studies including their regulation are also listed in this work.

CONCLUSIONS AND CLINICAL RELEVANCE

Biomarker discovery can be used in the management of a wide range of diseases, by combining these data sets of the database. Taking this approach, we can reveal details, at a molecular level, on the pathogenesis of a number of diseases, in particular those associated with urine production and excretion.

Urinary proteomics in cardiovascular disease: Achievements, limits and hopes

Cardiovascular disease (CVD) is the major cause of mortality and morbidity worldwide. Diagnosis of CVD and risk stratification of patients with CVD remains challenging despite the availability of a wealth of non-invasive and invasive tests. Clinical proteomics analyses a large number of peptides and proteins in biofluids. For clinical applications, the urinary proteome appears particularly attractive due to the relative low complexity compared with the plasma proteome and the noninvasive collection of urine. In this article, we review the results from pilot studies into urinary proteomics of coronary artery disease and discuss the potential of urinary proteomics in the context of pathogenesis of CVD.

Urinary Proteomics for Prediction of Preeclampsia

Preeclampsia is a major determinant of fetal and maternal morbidity and mortality. We used a proteomic strategy to identify urinary biomarkers that predict preeclampsia before the onset of disease. We prospectively collected urine samples from women throughout pregnancy. Samples from gestational weeks 12 to 16 (n=45), 20 (n=50), and 28 (n=18) from women who subsequently had preeclampsia develop were matched to controls (n=86, n=49, and n=17, respectively). We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Disease-specific peptide patterns were generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. From comparison with nonpregnant controls, we defined a panel of 284 pregnancy-specific proteomic biomarkers. Subsequently, we developed a model of 50 biomarkers from specimens obtained at week 28 that was associated with future preeclampsia (classification factor in cases, 1.032±0.411 vs controls, −1.038±0.432; P <0.001). Classification factor increased markedly from week 12 to 16 to 28 in women who subsequently had preeclampsia develop (n=16; from −0.392±0.383 to 1.070±0.383; P <0.001) and decreased slightly in controls (n=16; from −0.647±0.437 to −1.024±0.433; P =0.043). Among the biomarkers are fibrinogen alpha chain, collagen alpha chain, and uromodulin fragments. The markers appear to predict preeclampsia at gestational week 28 with good confidence but not reliably at earlier time points (weeks 12–16 and 20). After prospective validation in other cohorts, these markers may contribute to better prediction, monitoring, and accurate diagnosis of preeclampsia.