[show abstract][hide abstract] ABSTRACT: Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The 'Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial' (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273).
In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier.
We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16-89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found.
We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial.
[show abstract][hide abstract] ABSTRACT: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is one curative treatment for hematologic malignancies, but is compromised by life threatening complications, such as severe acute graft-versus-host disease (aGvHD). Prediction of severe aGvHD as early as possible is crucial to allow timely initiation of treatment. Here we report on a multicentre validation of an aGvHD-specific urinary proteomic classifier (aGvHD_MS17) in 423 patients. Samples (n=1106) were collected prospectively between day +7 and day +130 and analyzed using capillary electrophoresis coupled on-line to mass spectrometry (CE-MS). Integration of aGvHD_MS17 analysis with demographic and clinical variables using a logistic regression model led to correct classification of patients developing severe aGvHD 14 days prior to any clinical signs with 82.4% sensitivity and 77.3% specificity. Multivariate regression analysis showed that aGvHD_MS17 positivity was the only strong predictor for aGvHD grade III or IV (P<0.0001). The classifier consists of 17 peptides derived from albumin, β2-microglobulin, CD99, fibronectin, and various collagen α chains, indicating inssflammation, activation of T-cells, and changes in the extracellular matrix as early signs of GvHD-induced organ damage. This study is currently the largest demonstration of accurate and investigator-independent prediction of patients at risk for severe aGvHD, thus allowing preemptive therapy based on proteomic profiling.Leukemiaaccepted article preview online, 11 July 2013; doi:10.1038/leu.2013.210.
Leukemia: official journal of the Leukemia Society of America, Leukemia Research Fund, U.K 07/2013; · 10.16 Impact Factor
[show abstract][hide abstract] ABSTRACT: AIMS/HYPOTHESIS: Microalbuminuria is considered the first clinical sign of kidney dysfunction and is associated with a poor renal and cardiovascular prognosis in type 2 diabetes. Detection of patients who are prone to develop micro- or macroalbuminuria may represent an effective strategy to start or optimise therapeutic intervention. Here we assessed the value of a urinary proteomic-based risk score (classifier) in predicting the development and progression of microalbuminuria. METHODS: We conducted a prospective case-control study. Cases (n = 44) and controls (n = 44) were selected from the PREVEND (Prevention of Renal and Vascular End-stage Disease) study and from the Steno Diabetes Center (Gentofte, Denmark). Cases were defined by transition from normo- to microalbuminuria or from micro- to macroalbuminuria over a follow-up of 3 years. Controls with no transitions in albuminuria were pair-matched for age, sex and albuminuria status. A model for the progression of albuminuria was built using a proteomic classifier based on 273 urinary peptides. RESULTS: The proteomic classifier was independently associated with transition to micro- or macroalbuminuria (OR 1.35 [95% CI 1.02, 1.79], p = 0.035). The classifier predicted the development and progression of albuminuria on top of albuminuria and estimated GFR (eGFR, area under the receiver operating characteristic [ROC] curve increase of 0.03, p = 0.002; integrated discrimination index [IDI]: 0.105, p = 0.002). Fragments of collagen and α-2-HS-glycoprotein showed significantly different expression between cases and controls. CONCLUSIONS/INTERPRETATION: Although limited by the relatively small sample size, these results suggest that analysis of a urinary biomarker set enables early renal risk assessment in patients with diabetes. Further work is required to confirm the role of urinary proteomics in the prevention of renal failure in diabetes.
[show abstract][hide abstract] ABSTRACT: Non-invasive detection of diseases, based on urinary proteomics, is becoming an increasingly important area of research, especially in the area of chronic kidney disease (CKD). Different platforms have been used in independent studies, mostly capillary-electrophoresis coupled ESI-MS (CE-MS), liquid chromatography coupled mass spectrometry, and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We have compared the performance of CE-MS with MALDI-MS in detecting CKD, based on a cohort of 137 urine samples (62 cases and 75 controls). Data cross-talk between the two platforms was established for the comparison of detected biomarkers. The results demonstrate superior performance of the CE-MS approach in terms of peptide resolution and obtained disease prediction accuracy rates. However, the data also demonstrate the ability of the MALDI-MS approach to separate CKD patients from controls, at slightly reduced accuracy, but expected reduced cost and time. As a consequence, a practical approach can be foreseen where MALDI-MS is employed as an inexpensive, fast, and robust screening tool to detect probable CKD. In a second step, high resolution CE-MS could be used in those patients only that scored negative for CKD in the MALDI-MS analysis, reducing costs and time of such a program.
Journal of proteomics 07/2012; 75(18):5888-97. · 5.07 Impact Factor
[show abstract][hide abstract] ABSTRACT: 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.
[show abstract][hide abstract] ABSTRACT: (Full text is available at http://www.manu.edu.mk/prilozi). Acute kidney injury (AKI) comprises several syndromes that are associated with a sudden decrease in renal function. AKI is a common condition especially among critically ill patients. It is typically multifactorial and of great prognostic significance. The incidence of AKI has increased while the associated mortality rate has remained unchanged over the last years. Recent definitions of AKI, namely the Risk, Injury, Failure, Loss of renal function and End-stage kidney disease (RIFLE) classifycation or the Acute Kidney Injury Network (AKIN) criteria, incorporate serum creatinine and urine output as the principal markers to define and detect AKI. However, elevated serum creatinine or oliguria were demonstrated to detect AKI at late stages of renal injury when preventive strategies may be less effective. Therefore, there has recently been a great scientific interest in obtainng valuable markers for early AKI detection. In the last 5 years numerous new markers such as neutrophil-gelatinase associated lipo-calin, interleukin-18, cystatin C and kidney injury molecule 1 in the urine and/or serum have been studied and proposed as early detection markers of AKI. Persistently, these markers performed well in initial pilot trials. However, these promising results could often not be confirmed in later, larger multicentre trials and limitation of these biomarkers in the early diagnosis of renal injury were discovered. Furthermore, as AKI is multi-factorial and heterogeneous in origin, it seems likely that not one single marker but a panel of biomarkers will be required to detect all subtypes of AKI early during their evolution. This has initiated proteomic studies to develop panels of biomarkers which may facilitate early detection of AKI. The present review will focus on the most important clinical studies evaluating the ability of single AKI biomarkers and on those in clinical proteomics that attempted to establish panels of biomarkers in urine for early and accurate AKI diagnosis and prognosis. Key words: acute kidney injury, diagnosis, prognosis, biomarkers, proteomics.
Prilozi / Makedonska akademija na naukite i umetnostite, Oddelenie za biološki i medicinski nauki = Contributions / Macedonian Academy of Sciences and Arts, Section of Biological and Medical Sciences 07/2012; 33(1):27-48.
[show abstract][hide abstract] ABSTRACT: Purpose: Severe ureteropelvic junction obstruction is treated surgically. However,
for milder cases most clinical teams adopt a watchful waiting approach and
only operate in the presence of significant decline of renal function combined with
severe hydronephrosis. Little is known about the long-term consequences of
ureteropelvic junction obstruction.
Materials and Methods: Using capillary electrophoresis coupled with mass spectrometry,
we analyzed the urinary proteome of 42 patients with ureteropelvic
junction obstruction 5 years postoperatively or 5 years following spontaneous
Results: At 5-year followup urinary proteomes were similar between patients
with early surgical correction of ureteropelvic junction obstruction and age
matched controls. In contrast, urinary proteomes differed significantly between
conservatively followed patients and controls. Analyses of the proteomic differences
suggested ongoing renal or ureteral remodeling in the conservatively followed
patients that was not visible clinically.
Conclusions: Long-term followup studies are warranted in patients with ureteropelvic
junction obstruction, especially those followed conservatively, to determine
whether the observed changes in the urinary proteomes become clinically
relevant at a later stage.
[show abstract][hide abstract] ABSTRACT: (Full text is available at http://www.manu.edu.mk/prilozi). The technology platforms for proteome analysis have advanced considerably over the last few years. Due to these improvements the number of studies on the analysis of the proteome/peptidome with the aim of defining biomarkers has escalated. In this review, we will summarise the technical aspects that relate to the proteomics field targeting the discovery of biomarkers for disease diagnosis. We will describe the course from biomarker discovery or 'potential' biomarkers to those that are clinically important. We also present several examples of successful proteomic studies that have defined 'biomarker patterns' for clinical applications, focussed on urine as a material source and capillary electrophoresis coupled mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed. Key words: proteomics, urine biomarkers, clinical applications.
Prilozi / Makedonska akademija na naukite i umetnostite, Oddelenie za biološki i medicinski nauki = Contributions / Macedonian Academy of Sciences and Arts, Section of Biological and Medical Sciences 07/2011; 32(1):13-44.
[show abstract][hide abstract] ABSTRACT: Early detection of malignant biliary tract diseases, especially cholangiocarcinoma (CC) in
patients with primary sclerosing cholangitis (PSC), is very difficult and often comes too late
to give the patient a therapeutic benefit. We hypothesize that bile proteomic analysis distinguishes
CC from nonmalignant lesions. We used capillary electrophoresis mass spectrometry
(CE-MS) to identify disease-specific peptide patterns in patients with choledocholithiasis
(n 5 16), PSC (n 5 18), and CC (n 5 16) in a training set. A model for differentiation of
choledocholithiasis from PSC and CC (PSC/CC model) and another model distinguishing
CC from PSC (CC model) were subsequently validated in independent cohorts (choledocholithiasis
[n 5 14], PSC [n 5 18] and CC [n 5 25]). Peptides were characterized by sequencing.
Application of the PSC/CC model in the independent test cohort resulted in correct
exclusion of 12/14 bile samples from patients with choledocholithiasis and identification of
40/43 patients with PSC or CC (86% specificity, 93% sensitivity). The corresponding
receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of
0.93 (95% confidence interval [CI]: 0.82-0.98, P 5 0.0001). The CC model succeeded in an
accurate detection of 14/18 bile samples from patients with PSC and 21/25 samples with
CC (78% specificity, 84% sensitivity) in the independent cohort, resulting in an AUC value
of 0.87 (95% CI: 0.73-0.95, P 5 0.0001) in ROC analysis. Eight out of 10 samples of
patients with CC complicating PSC were identified. Conclusion: Bile proteomic analysis discriminates
benign conditions from CC accurately. This method may become a diagnostic
tool in future as it offers a new possibility to diagnose malignant bile duct disease and thus
enables efficient therapy particularly in patients with PSC.
Hepatology International 03/2011; 3(3-5):875-884. · 2.64 Impact Factor
[show abstract][hide abstract] ABSTRACT: Proteome analysis, the key technology for biomarker discovery, continues to gain importance in clinical diagnosis and follow-up. In this review we describe proteome analysis in the context of allogeneic, hematopoietic stem cell transplantation concentrating on capillary electrophoresis coupled on-line to mass spectrometry.
Mini Reviews in Medicinal Chemistry 06/2009; 9(5):627-3. · 2.87 Impact Factor
[show abstract][hide abstract] ABSTRACT: Aging is closely related to the onset of chronic diseases, such as coronary artery disease, diabetic nephropathy or different types of malignancies, reflecting the demand for novel biomarkers to manage theses diseases.
The analysis of the human proteome for biomarkers has made considerable advances in the last years.
We describe the main technological approaches taken, their advantages and disadvantages.
We will review the different clinical sources of material and attempt to highlight the different challenges and approaches associated with these. Age-related changes in the proteome have been described and were found to be highly similar to changes associated with chronic diseases. We will give several examples on the successful application of proteomics in the diagnosis, prognosis and therapy of these chronic diseases.
A boost in disease-related proteomic information is expected in the very near future, and will also result in its broad clinical application. However, this view appears to be dependent on the strict adherence to proper technological/analytical parameters, correct statistics, and large databases that allow comparison of datasets provided by different scientists. Clearly, the proteome is by far too complex to be tackled by one laboratory on its own.