Assessment and Risk Prediction of Chronic Kidney Disease and Kidney Fibrosis Using Non-Invasive Biomarkers.

Effective management of chronic kidney disease (CKD), a major health problem worldwide, requires accurate and timely diagnosis, prognosis of progression, assessment of therapeutic efficacy, and, ideally, prediction of drug response. Multiple biomarkers and algorithms for evaluating specific aspects of CKD have been proposed in the literature, many of which are based on a small number of samples. Based on the evidence presented in relevant studies, a comprehensive overview of the different biomarkers applicable for clinical implementation is lacking. This review aims to compile information on the non-invasive diagnostic, prognostic, and predictive biomarkers currently available for the management of CKD and provide guidance on the application of these biomarkers. We specifically focus on biomarkers that have demonstrated added value in prospective studies or those based on prospectively collected samples including at least 100 subjects. Published data demonstrate that several valid non-invasive biomarkers of potential value in the management of CKD are currently available.

Early detection of organ involvement in Fabry disease by biomarker assessment in conjunction with LGE cardiac MRI: results from the SOPHIA study.

BACKGROUND: Initiation of enzyme replacement therapy (ERT) early in the Fabry disease course may facilitate better outcomes than in patients with advanced disease. Early diagnosis is often hindered by the heterogeneous nature of signs and symptoms, and by the presentation of atypical phenotypes. METHODS: The Sophisticated Assessment of Disease Burden in Patients with Fabry Disease study (SOPHIA; ClinicalTrials.gov, NCT01210196) evaluated clinical and diagnostic assessments for early detection of Fabry-related organ pathology in ERT-naïve patients with mild FD symptoms. Assessments included cardiac magnetic resonance imaging with late gadolinium enhancement (LGE-CMR), echocardiography, 24-h Holter electrocardiography, and biomarkers of FD and fibrosis. RESULTS: 35 patients with mean (SD) baseline age of 45.0 (10.2) years were included and assessed at baseline, 12 months, and (optionally) at 24 months. At baseline, LGE-CMR and elevated procollagen III N-terminal propeptide, sphingosine-1-phosphate, and globotriaosylsphingosine were the most prevalent indicators of early Fabry-related pathology. LGE was already present in 58.8% of patients with normal left ventricular mass index. 15.2% of patients showed grade 1 diastolic dysfunction. QRS duration increased from baseline to last observation, particularly in patients with severe baseline fibrosis. Fibrosis progressed from baseline to last observation, especially in patients with baseline LGE ≥ 2.50 mL (3.65 [1.14] mL vs 6.74 [1.10] mL). Statistically significant correlations were found between LGE volume and high-sensitivity troponin T, and between LGE volume and fragments of urinary collagen alpha-1 (I), (III), and (VII), and collagen alpha-3 (V). CONCLUSIONS: Fibrosis may become apparent before left ventricular hypertrophy occurs. LGE-CMR imaging is superior to conventional echocardiography for detecting early cardiomyopathy in FD and, in conjunction with biomarker tests, may help detect early organ involvement in mild FD.

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides.

Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.

Improving peptide relative quantification in MALDI-TOF MS for biomarker assessment.

Proteomic profiling by MALDI-TOF MS presents various advantages (speed of analysis, ease of use, relatively low cost, sensitivity, tolerance against detergents and contaminants, and possibility of automation) and is being currently used in many applications (e.g. peptide/protein identification and quantification, biomarker discovery, and imaging MS). Earlier studies by many groups indicated that moderate reproducibility in relative peptide quantification is a major limitation of MALDI-TOF MS. In the present work, we examined and demonstrate a clear effect, in cases apparently random, of sample dilution in complex samples (urine) on the relative quantification of peptides by MALDI-TOF MS. Results indicate that in urine relative abundance of peptides cannot be assessed with confidence based on a single MALDI-TOF MS spectrum. To account for this issue, we developed and propose a novel method of determining the relative abundance of peptides, taking into account that peptides have individual linear quantification ranges in relation to sample dilution. We developed an algorithm that calculates the range of dilutions at which each peptide responds in a linear manner and normalizes the received peptide intensity values accordingly. This concept was successfully applied to a set of urine samples from patients diagnosed with diabetes presenting normoalbuminuria (controls) and macroalbuminuria (cases).