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Final Progress Report

Grant Number: 5R01DK070910-05
Principal Investigator: John A Kellum, MD, University of Pittsburgh
Project Title: Biological Markers of Recovery for the Kidney
Project Period: 06/01/2010 – 05/31/2012

BioMaRK (R01DK070910) is a study of inflammatory mediators in the plasma and urine of adult patients with acute kidney injury (approximately 50% having sepsis). Of 819 participants, 298 (36.4%) were alive and free of dialysis at 60 days. After adjusting for demographics, comorbidity, and various acute illness measures, increased concentrations of various plasma mediators were associated with renal recovery and survival.

Inclusion of gender and minority study subjects is reported via the NIH gender and minority inclusion table attached to the end of this report.

Resource sharing will be executed via our CRISMA Center website via the BioMaRK Project home page once our planned analyses for this study are complete. Because all the clinical data analyzed in this project originated from the ARF Trial Network (ATN) study CSP530 – our parent study - the clinical data can only be requested via the NIDDK Central Database Repository website.

A link to our Data Request Form is posted in order that outside investigators may make requests for de-identified sample analysis data and BioMaRK samples. Once a request is approved by the Data and Publications Committee, a data use agreement (DUA) that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate computer technology; (3) a commitment to reporting analysis and/or publication progress prior to journal submission; and (4) a commitment to destroying or returning the data after analyses are completed will have to be put in place with the University of Pittsburgh and the requesting party. Once these formalities are executed, our data core will generate appropriate datasets in response to the specific needs of the proposed analysis. We will also post our main results and abstract publications, thus providing detailed descriptions of the available data to interested individuals. Documentation necessary to utilize the study data (data dictionary, calculated variables, SOPs) will also be shared with those who have received approval of their data request. It is our plan to freely allow access to the study data as soon as all the main study manuscripts are published. For any remaining BioMaRK samples, material transfers will be made with the Uniform Biological Materials Transfer Agreement (UBMTA) and without reach through requirements for public and non-profit entities via the University of Pittsburgh Office of Research. Traditional material transfer agreements may also be negotiated.

We have reported (or are in the process of reporting) our findings in manuscripts as follows and list a summary of findings for published as well as submitted and in-process manuscripts below:

1. Srisawat N, Wen X, Lee M, Long L, Elder M, Carter M, Unruh M, Finkel K, Vijayan A, Ramkumar M, Paginini E, Singbartl K, Palevsky P, Kellum JA. Urinary Biomarkers Predict Renal Recovery in Critically Ill Patients with Renal Support. CJASN 2011 Aug; 6(8): 1815-23; PMCID:PMC3156420.

Summary: Despite significant advances in the epidemiology of acute kidney injury (AKI), prognostication remains a major clinical challenge. Unfortunately, no reliable method to predict renal recovery exists. The discovery of biomarkers to aid in clinical risk prediction for recovery after AKI would represent a significant advance over current practice. Urine samples were collected on days 1, 7, and 14 from 76 patients who developed AKI and received renal replacement therapy (RRT) in the intensive care unit. We explored whether levels of urinary neutrophil gelatinase-associated lipocalin (uNGAL), urinary hepatocyte growth factor (uHGF), urinary cystatin C (uCystatin C), IL-18, neutrophil gelatinase-associated lipocalin/matrix metalloproteinase-9, and urine creatinine could predict subsequent renal recovery. We defined renal recovery as alive and free of dialysis at 60 days from the start of RRT. Patients who recovered had higher uCystatin C on day 1 (7.27 versus 6.60 ng/mg_creatinine) and lower uHGF on days 7 and 14 (2.97 versus 3.48 ng/mg_creatinine; 2.24 versus 3.40 ng/mg_creatinine). For predicting recovery, decreasing uNGAL and uHGF in the first 14 days was associated with greater odds of renal recovery. The most predictive model combined relative changes in biomarkers with clinical variables and resulted in an area under the receiver-operator characteristic curve of 0.94. In conclusion, we showed that a panel of urine biomarkers can augment clinical risk prediction for recovery after AKI.

2. Murugan R, Wen X, Shah N, Lee M, Kong L, Unruh M, Finkel K, Vijayan A, Palevsky P, Paganini E, Carter M, Elder M, Kellum JA. Inflammatory and Apoptosis Markers Predict Dialysis Dependence and Death Among Critically Ill Patients Receiving Renal Replacement Therapy. (Under revision –Kidney Int)

Summary: Survivors of critical illness complicated by acute kidney injury requiring renal replacement therapy (RRT) are at an increased risk of dialysis dependence and death but the mechanisms are unknown. In a multicenter, prospective, cohort study of 819 critically ill patients receiving RRT, we examined association between plasma inflammatory, apoptosis, and growth factor biomarkers and renal recovery and mortality. Renal recovery was defined as alive and RRT independence at day 60. Of 819 participants, 36.4% were RRT independent, and 50.6% died. After adjusting for demographics, comorbid conditions; serum creatinine; nephrotoxin exposure; sepsis; presence of oliguria; mechanical ventilation; and severity of illness; increased concentrations of plasma interleukin (IL)-8,-18, and tumor necrosis factor receptor (TNFR)-I were independently associated with slower renal recovery (figure). Higher concentrations of IL-6,-8,-10,-18, macrophage migration inhibitory factor (MIF), TNFR-1, and death receptor-5 were associated with mortality. In an analysis of multiple markers simultaneously, increased IL-8 and -18 were associated with slower recovery, and increased IL-6,-8 and MIF concentrations were associated with mortality. Hence elevated plasma concentrations of inflammatory and apoptosis biomarkers are associated with RRT dependence and death. (Importantly these results were not significantly different between patients with sepsis compared to others suggesting the importance of chemokine signaling in multiple forms of critical illness.) Our study provides insight into mechanisms and suggests that future interventions should investigate broad-spectrum immune-modulation to improve outcomes.


 

3. Murugan R, Wen X, Shah N, Pike F, Weissfeld L, Unruh M, Finkel K, Vijayan A, Palevsky PM, Paganini E, Carter M, Elder M, Kellum JA. Effects of Intensity of Renal Replacement Therapy on markers of inflammation in Critically Ill Patients with Acute Kidney Injury. (In preparation)

Summary: Higher concentrations of inflammatory and apoptotic biomarkers are strongly associated with increased risk of death and non-recovery following acute kidney injury (AKI). Whether intensity of renal replacement therapy (RRT) influences circulating biomarker concentrations in patients is unknown.

Methods: Using a subset of 819 patients enrolled in the Acute Renal Failure Trial Network study, we measured 11 circulating inflammatory and apoptotic plasma biomarkers before (day 1) and after (day 8) RRT initiation. We examined whether higher intensity RRT was associated with lower circulating biomarker concentrations. With the exception of interleukin (IL)-8, we found no differences in any biomarker levels between intensive and less-intensive RRT arms on days 1 and 8. However, compared to less-intensive treatment, intensive treatment was associated with lower day 8 concentrations when day 1 levels were high. In contrast, intensive RRT was associated with a higher biomarker concentration on day 8 when day 1 levels were lower (Figure). Differences reached statistical significance for death receptor (DR)-5, IL-6, macrophage migration inhibitory factor (MIF) and tumor necrosis factor receptor (TNFR)-1, but similar trends were seen for all 11 markers. We conclude that intensity of RRT did not differ from normal RRT in terms of altering overall marker concentrations. However, high intensity RRT had differential effects on day 8 marker concentration depending on baseline (day 1) level. 4


 

4. We are currently working to develop a prediction model for recovery from AKI that combines clinical variables derived from the ATN trial and additional inflammatory biomarkers. The aim is to arrive at a clinically useful and parsimonious model using state of the art selection models. (This work is still underway).

5. We aim to use advanced clustering models to discern subgroups of patients that may benefit from different intensities of RRT by taking into account inflammatory profiles. We are searching for an appropriate funding mechanism to apply to in order to fund further analysis of the ATN-BioMaRK data to build these "personalized medicine" models. (This work is ongoing and additional funding is being sought).

BioMaRK Funded Publications

Papers

1. Srisawat N, Lawsin L, Uchino S, Bellomo R, Kellum J. Cost of Acute Renal Replacement Therapy in the Intensive Care Unit: Results from the Beginning and Ending Supportive Therapy for the Kidney (BEST Kidney) Study. Crit Care 2010 Mar 26;14(2):R46. PMCID:PMC2887158.  

2. Srisawat N, Wen X, Lee M, Long L, Elder M, Carter M, Unruh M, Finkel K, Vijayan A, Ramkumar M, Paginini E, Singbartl K, Palevsky P, Kellum JA. Urinary Biomarkers Predict Renal Recovery in Critically Ill Patients with Renal Support. CJASN 2011 Aug; 6(8): 1815-23; PMCID:PMC3156420.

3. Singbartl K, Bishop JV, Wen X, Murugan R, Chandra S, Filippi MD, Kellum JA. Differential Effects of Kidney-lung Cross-talk During Acute Kidney Injury and Bacterial Pneumonia. Kidney Int. 2011 Sep;80(6):633-44. PMCID:PMC3164921.

4. Peng ZY, Wang HZ, Srisawat N, Wen X, Rimmele T, Bishop J, Singbartl K, Murugan R, Kellum JA. Bacterial Antibiotics Temporarily Increase Inflammation and Worsen Acute Kidney Injury in Experimental Sepsis. Crit Care Med. 2012 Feb;40(2):538-43. PMCID:PMC3254710

5. Srisawat N, Murugan R, Lee M, Kong L, Carter M, Angus D C, Kellum J, Plasma Neutrophil Gelatinase-associated Llipocalin Predicts Recovery from Acute Kidney Injury Following Community-acquired Pneumonia. Kidney International 2011 Sep; 80(5):545-52. Epub 2011 Jun 15. PMCID:PMC3257035.

6. Murugan R, Wen X, Shah N, Lee M, Kong L, Unruh M, Finkel K, Vijayan A, Palevsky P, Paganini E, Carter M, Elder M, Kellum JA. Inflammatory and Apoptosis Markers Predict Dialysis Dependence and Death Among Critically Ill Patients Receiving Renal Replacement Therapy. Under revision –Kidney Int.

7. Srisawat N, Murugan R, Wen X, Singbartl K, Clermont G, Eiam-Ong S, Kellum JA. Recovery From Acute Kidney Injury: Determinants and Predictors. Contrib Nephrol. 2012,165:284-91. Epub 2010 Apr 20.

8. Wen X, Murugan R, Peng Z, Kellum JA. Pathophysiology of Acute Kidney Injury: A New Perspective. Contrib Nephrol. 2010;165:39-45. Epub 2010 Apr 20.


Abstracts

1. Srisawat N, Wen X, Lee M, Kong L, Elder M, Carter M, Unruh M, Finkel K, Vijayan A, Ramkumar M, Paganini E, Singbartl K, Palevsky P, Kellum JA. Urinary Biomarkers Predict Renal Recovery in Critically Ill Patients with Renal Support: Result from BioMaRK Study. 2010 ASN Week. November 16-21, 2010. Abstract.

2. Murugan R, Weissfeld L, Yende S, Singbartl K, Angus D, Kellum J. Statin Use Not Protective For Sepsis-induced Acute Kidney Injury. 2011 SCCM. January 15-19, 2011. Abstract.


Posters

1. Wen X. Differential Effects of Renal Replacement Therapy on Plasma Inflammatory and Apoptotic Biomarkers. ASN; Nov 4, 2011.


Oral Presentations

1. Wen X, Murugan R, Kong L, Shah N, Pked F, Weissfeld L, Lee MJ, Carter M, Elder M, Palevsky P, Unruh M, Kellum JA. Higher Concentrations of Inflammatory and Apoptotic Biomarkers are Associated with Mortality and Non-recovery of Kidney Function. ASN; Nov 5, 2011.
 

Targeted / Planned Enrollment Table