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      Predicting acute kidney injury: current status and future challenges

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          Abstract

          Acute kidney injury (AKI) is characterized by an acute decline in renal function and is associated to increased mortality rate, hospitalization time, and total health-related costs. The severity of this ‘fearsome’ clinical complication might depend on, or even be worsened by, the late detection of AKI, when the diagnosis is based on the elevation of serum creatinine (SCr). For these reasons, in recent years a great number of new tools, biomarkers and predictive models have been proposed to clinicians in order to improve diagnosis and prevent the development of AKI. The purpose of this narrative paper is to review the current state of the art in prediction and early detection of AKI and outline future challenges.

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          Most cited references163

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          Metabolomics--the link between genotypes and phenotypes.

          Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
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            Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury

            Introduction Acute kidney injury (AKI) can evolve quickly and clinical measures of function often fail to detect AKI at a time when interventions are likely to provide benefit. Identifying early markers of kidney damage has been difficult due to the complex nature of human AKI, in which multiple etiologies exist. The objective of this study was to identify and validate novel biomarkers of AKI. Methods We performed two multicenter observational studies in critically ill patients at risk for AKI - discovery and validation. The top two markers from discovery were validated in a second study (Sapphire) and compared to a number of previously described biomarkers. In the discovery phase, we enrolled 522 adults in three distinct cohorts including patients with sepsis, shock, major surgery, and trauma and examined over 300 markers. In the Sapphire validation study, we enrolled 744 adult subjects with critical illness and without evidence of AKI at enrollment; the final analysis cohort was a heterogeneous sample of 728 critically ill patients. The primary endpoint was moderate to severe AKI (KDIGO stage 2 to 3) within 12 hours of sample collection. Results Moderate to severe AKI occurred in 14% of Sapphire subjects. The two top biomarkers from discovery were validated. Urine insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinases-2 (TIMP-2), both inducers of G1 cell cycle arrest, a key mechanism implicated in AKI, together demonstrated an AUC of 0.80 (0.76 and 0.79 alone). Urine [TIMP-2]·[IGFBP7] was significantly superior to all previously described markers of AKI (P 0.72. Furthermore, [TIMP-2]·[IGFBP7] significantly improved risk stratification when added to a nine-variable clinical model when analyzed using Cox proportional hazards model, generalized estimating equation, integrated discrimination improvement or net reclassification improvement. Finally, in sensitivity analyses [TIMP-2]·[IGFBP7] remained significant and superior to all other markers regardless of changes in reference creatinine method. Conclusions Two novel markers for AKI have been identified and validated in independent multicenter cohorts. Both markers are superior to existing markers, provide additional information over clinical variables and add mechanistic insight into AKI. Trial registration ClinicalTrials.gov number NCT01209169.
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              Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis.

              Serum cystatin C (Cys C) has been proposed as a simple, accurate, and rapid endogenous marker of glomerular filtration rate (GFR) in research and clinical practice. However, there are conflicting reports regarding the superiority of Cys C over serum creatinine (Cr), with a few studies suggesting no significant difference. We performed a meta-analysis of available data from various studies to compare the accuracy of Cys C and Cr in relation to a reference standard of GFR. A bibliographic search showed 46 articles until December 31, 2001. We also retrieved data from eight other studies presented and published in abstract form. The overall correlation coefficient for the reciprocal of serum Cys C (r = 0.816; 95% confidence interval [CI], 0.804 to 0.826) was superior to that of the reciprocal of serum Cr (r = 0.742; 95% CI, 0.726 to 0.758; P < 0.001). Similarly, receiver operating characteristic (ROC)-plot area under the curve (AUC) values for 1/Cys C had greater identity with the reference test for GFR (mean ROC-plot AUC for Cys C, 0.926; 95% CI, 0.892 to 0.960) than ROC-plot AUC values for 1/Cr (mean ROC-plot AUC for serum Cr, 0.837; 95% CI, 0.796 to 0.878; P < 0.001). Immunonephelometric methods of Cys C assay produced significantly greater correlations than other assay methods (r = 0.846 versus r = 0.784; P < 0.001). In this meta-analysis using currently available data, serum Cys C is clearly superior to serum Cr as a marker of GFR measured by correlation or mean ROC-plot AUC. Copyright 2002 by the National Kidney Foundation, Inc.
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                Author and article information

                Contributors
                simonini.marco@hsr.it
                Journal
                J Nephrol
                J. Nephrol
                Journal of Nephrology
                Springer International Publishing (Cham )
                1121-8428
                1724-6059
                17 June 2017
                17 June 2017
                2018
                : 31
                : 2
                : 209-223
                Affiliations
                GRID grid.15496.3f, Chair of Nephrology - IRCCS San Raffaele Scientific Institute, Genomics of Renal Diseases and Hypertension Unit, , Università Vita Salute San Raffaele, ; Via Olgettina 60, 20132 Milan, Italy
                Article
                416
                10.1007/s40620-017-0416-8
                5829133
                28624882
                3d5e2d4e-7f07-4baf-9e59-4cc30a8a3bc8
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 2 December 2016
                : 27 May 2017
                Categories
                Review
                Custom metadata
                © Italian Society of Nephrology 2018

                acute kidney injury,prediction,biomarkers,genetics,new omics

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