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Genomics of Septic Shock-Associated Kidney Injury

Genomics of Septic Shock-Associated Kidney Injury. Rajit K. Basu, MD Assistant Professor, Division of Critical Care Center for Acute Care Nephrology Cincinnati Children’s Hospital Medical Center. 1 st International Symposium on AKI in Children 7 th International Conference

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Genomics of Septic Shock-Associated Kidney Injury

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  1. Genomics of Septic Shock-Associated Kidney Injury Rajit K. Basu, MD Assistant Professor, Division of Critical Care Center for Acute Care Nephrology Cincinnati Children’s Hospital Medical Center 1st International Symposium on AKI in Children 7th International Conference Pediatric Continuous Renal Replacement Therapy September 2012

  2. Disclosures • Speaker is partially funded by the Gambro Renal Products for the TAKING-FOCUS clinical research study

  3. The problem of sepsis and AKI • Sepsis1 • Leading cause of death in critically ill adults (1/4) • Mortality of severe sepsis is 35%2, costs > $15 billion/yr • 42,000 pediatric cases/yr of septic shock in US2 • Mortality ~ 9%, ~ 4,400 deaths / yr, >$2 billion/yr • Acute kidney injury (AKI) • ~ 6% of all adult ICU patients (RIFLE)3 • ~2.5-10% of all pediatric ICU patients (pRIFLE)4 • Sepsis associated AKI (SA-AKI) • Most frequent etiology of AKI in adults (~ 33-50%)5 • Most frequent etiology of AKI in children (~25-50%)6 • Combined mortality ~50% (PICARD 2011) 1 – Angus, CCM 2001; 2 – Levy, CCM 2010; 3 – Watson, AJRCC 2003; 4 – Uchino, JAMA 2005; 5 – Schneider, CCM 2010; 6 – Bagshaw, CC 2008; 7 – Duzova, Peds Neph 2010

  4. The cardiac angina paradigm Detection  Improved outcomes? Acute Myocardial Infarction (AMI) Clinical Signs/Symptoms Ancillary Tests Classic Risk Factors Biomarkers Early Treatment (Thrombolytics, PCI) Early Recognition Early Recovery

  5. Identifying the renal troponin for SSAKI? Clinical Signs/Symptoms Ancillary Tests Classic Risk Factors Biomarkers Early Treatment (Thrombolytics, PCI) Early Recognition Early Recovery

  6. Markers of kidney function in SSAKI • No troponin-I for SSAKI currently exists • Common indices of kidney “function” inadequate for diagnosis and classification • Both urine and serum studies of “function” with marginal identification, prognosis, predictive power • Where could a potential SSAKI biomarker come from (that matches the diverse pathophysiology?) • Where do putative SSAKI biomarkers come from? • Majority developed in models of non-septic AKI • Ischemic AKI (including cardiopulmonary bypass) • Nephrotoxic AKI • Pathophysiology of SA-AKI is multifactorial • Combination of ischemic, inflammatory, nephrotoxic, apoptotic AKI • Studies of AKI biomarkers not stratified purely by “sepsis” etiology

  7. Biomarkers + Severe Sepsis Associated AKI (SSAKI) “Incidental” SSAKI biomarker studies • PROWESS • Study of drotrecogin-alfa (Activated Protein C) for sepsis • Biomarkers for sepsis also with notable performance for prediction of AKI (IL-6, APACHE-II score) (Chawla, CJASN 2007) • NORASEPT • Study of murine monoclonal Ab to tumor necrosis factor for treatment of sepsis • Association of TNF-a and inflammation with ↑rate of SSAKI (Iglesias, AJKD 2003) • PICARD • Prospective study examining the history, treatment, outcomes of ARF • ARF patients had higher pro-inflammatory markers (Simmons, KI 2004)

  8. Biomarkers + Severe Sepsis Associated AKI (SSAKI) • Where are the dedicated SSAKI biomarker studies? • Few and far between • Sepsis studies  highly heterogeneous given severity of illness differences (SOI) between patients • Barrier to proper study of biomarkers and therapy for sepsis • Complicates any study of SSAKI • NIDDK workshop regarding SSAKI trials (Molitoris, CJASN 2012) • Homogeneity of patients paramount • Classification/stratification of cohorts by SOI score • “Standard biomarkers” • “pNGAL is raised in patients with SIRS, severe sepsis, and septic shock and should be used with caution as a marker of AKI in ICU patients with septic shock” (Martensson, Intens Care Med 2010) • “The inflammatory response induced by sepsis has no impact on the levels of cystatin C in plasma during the first week in the ICU” – (Martensson, Neph Dial Trans 2012)

  9. Biomarkers + Severe Sepsis Associated AKI (SSAKI) • Human “models” • Association of SSAKI and ↑inflammatory phenotype • HLA genotype associated with “severe AKI” (Payen, PLoS One 2012) • TGF-b, TNF-a, IL-6, KC, MIP-1a, MCP-1 all linked to ↑rates of AKI • Animal models • Initial ischemic models led to identification of prominent biomarkers (Devarajan, Mol Med 2003) • Models of sepsis in animals are JUST as heterogeneous as human patients • Degree of sepsis variable • “observed variability in susceptibility to septic AKI in our models replicates that of human disease” – (Benes, Crit Care 2011) • Rates of AKI after sepsis inconsistent • Meprin – 1- a elevated (though AKI was variable) (Holly, KI 2006) • Later reports indicate no correlation between Meprin -1 and AKI

  10. Biomarkers + Severe Sepsis Associated AKI (SSAKI) AKI = [Cr] > 2 mg/dl = BUN > 100 mg/dl = dialysis NGAL performance: Sens = 86% Spec = 39% PPV = 39% NPV = 94% ROC : 0.68 (0.56-0.79) Wheeler (PCCM, 2008) • There is a need to identify AKI biomarkers • Specific to patients with SSAKI • Especially in pediatrics • Limited number of studies AKI Markers in SSAKI: Poor Specificity Poor Discrimination Poor Precision

  11. Microarray  biomarkers for SSAKI METHODS: • Inclusion: • Age < 10, diagnosis of septic shock • Controls – from ambulatory departments • Whole blood derived RNA, 1st 24 hours of presentation • Microarray using Human Genome U133 Plus 2.0 GeneChip • Hybridization vs. 80,000 gene probes • 53 normal controls used for normalization • SSAKI • Defined as > 2x creatinine persistent to 7 days (“resolved” creatinine elevations not included) • Patients with mortality before 7 days were included • Outcomes • SSAKI : Morbidity and mortality tracked to 28 days Basu, Crit Care, 2011

  12. Microarray  biomarkers for SSAKI Basu, Crit Care, 2011

  13. Microarray  biomarkers for SSAKI Basu, Crit Care, 2011

  14. Testing the prediction of each patient for SSAKI or no SSAKI using gene expression Leave-one-out cross validation procedure for derivation cohort (148 without SSAKI, 31 with SSAKI) Basu, Crit Care, 2011

  15. Microarray  biomarkers for SSAKI Basu, Crit Care, 2011

  16. Microarray  biomarkers for SSAKI • Differentially regulated probes analyzed for readily measurable products • Protein expression readily measured in serum • Matrix metalloproteinase-8 (MMP-8) • Neutrophil elastase-2 (Ela-2) • Tested serum MMP-8 and Ela-2 expression versus development of SSAKI in derivation cohort • 150 samples analyzed (84%) • 132 no SSAKI (88%), 18 with SSAKI (12%)

  17. Microarray  biomarkers for SSAKI Basu, Crit Care, 2011

  18. Microarray  biomarkers for SSAKI Basu, Crit Care, 2011

  19. Microarray  biomarkers for SSAKI Basu, Crit Care, 2011

  20. Microarray  biomarkers for SSAKI Basu, Crit Care, 2011

  21. Genomics  SSAKI biomarkers • 1st attempt to characterize biomarkers for SA-AKI (vs. all cause-AKI) • 1st 24 hours – expression of 21 gene probes demonstrate high reliability for prediction of persistent AKI • Protein products of two gene probes from list measured in serum carry high sensitivity and negative predictive value • Biological links of MMP-8 and Ela-2 to SSAKI are unclear • MMP-8 association with sepsis being investigated (Solan, CCM 2012) • Gene expression micro-array can be leveraged to identify putative biomarkers of SSAKI

  22. Conclusions • Biomarkers for SSAKI will need to come from select patients properly stratified • Genomics offer a potential avenue for biomarker identification • Still in its infancy • Will allow for • Stratification of patients by severity of SSAKI • Patient specific decision making • Potential outcome variable

  23. Acknowledgements • Cincinnati Children’s Hospital • Hector R. Wong • Stuart L. Goldstein • Prasad Devarajan • Center for Acute Care Nephrology • Division of Critical Care • Collaborators (Multiple Institutions) • Stephen Standage • Natalie Cvijanovich • Geoffrey Allen • Neal Thomas • Robert Freishtat • Nick Anas • Keith Meyer • Paul Checchia • Richard Lin • Thomas Shanley • Mike Bigham

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