Acute kidney injury (AKI) after cardiac surgery is associated with a 20 fold increase in mortality. Among patients who require dialysis, the risk of death is increased 70-fold. The magnitude of renal injury is variable and difficult to predict. Risk factors for kidney injury have been described but they do not predict if a given patient will develop renal failure. No accurate biomarkers currently exist to predict the prognosis of patients with kidney injury. The absence of predictive biomarkers is an impediment to studies of new therapies for AKI. Prognostic markers could identify the subset of patients in whom testing of new therapies should be performed. A significant effort is being made to identify diagnostic and early biomarkers. However, little progress has been achieved in identifying markers that predict the magnitude and course of the disease in AKI. The goal of this project is to identify biomarkers for prognosis in AKI after cardiac surgery. We have established a team of experts in AKI that will collect samples from patients at four sites. The proteomic, statistical and informatic collaborators have established collaborations with each other in which they have previously identified biomarkers. Urine will be collected from patients who develop AKI after cardiac surgery at four centers. The primary outcome variable is the requirement for renal replacement therapy. We will predict secondary outcomes that are either clinically useful or meaningful research outcomes. In the first aim we will measure candidate markers. The markers were chosen based on published literature and our own preliminary data. They include candidate markers for tubular injury, inflammatory response, tubular function, recovery of function, and progression to dialysis. In the second aim we will use two proteomic techniques, 2D electrophoresis with DIGE and MALDI polypeptide analysis to identify novel markers. The goal of these discovery studies is to find new markers that can be used in combination with the best markers from aim 1. We provide preliminary data with both techniques in which we have identified biomarkers. In the third aim we will select the candidate markers to be combined in a final assay using a second set of patients that is independent of the set used in the first two aims. Finally, we will validate the markers and the algorithm used to identify them in a third set consisting of 590 new patients. These studies will use a combination of hypothesis-driven and discovery based approaches to find the best combination of biomarkers to predict the course of acute kidney injury.
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| Alge, Joseph L; Arthur, John M (2015) Biomarkers of AKI: a review of mechanistic relevance and potential therapeutic implications. Clin J Am Soc Nephrol 10:147-55 |
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| Bhensdadia, Nishant M; Hunt, Kelly J; Lopes-Virella, Maria F et al. (2013) Urine haptoglobin levels predict early renal functional decline in patients with type 2 diabetes. Kidney Int 83:1136-43 |
| Alge, Joseph L; Karakala, Nithin; Neely, Benjamin A et al. (2013) Urinary angiotensinogen and risk of severe AKI. Clin J Am Soc Nephrol 8:184-93 |
| Alge, Joseph L; Karakala, Nithin; Neely, Benjamin A et al. (2013) Association of elevated urinary concentration of renin-angiotensin system components and severe AKI. Clin J Am Soc Nephrol 8:2043-52 |
| Alge, Joseph L; Karakala, Nithin; Neely, Benjamin A et al. (2013) Urinary angiotensinogen predicts adverse outcomes among acute kidney injury patients in the intensive care unit. Crit Care 17:R69 |
| Chawla, Lakhmir S; Davison, Danielle L; Brasha-Mitchell, Ermira et al. (2013) Development and standardization of a furosemide stress test to predict the severity of acute kidney injury. Crit Care 17:R207 |
| Schwacke, John H; Spainhour, John Christian G; Ierardi, Jessalyn L et al. (2013) Network modeling reveals steps in angiotensin peptide processing. Hypertension 61:690-700 |
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