Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury
- Kianoush Kashani1,
- Ali Al-Khafaji2,
- Thomas Ardiles3,
- Antonio Artigas4,
- Sean M Bagshaw5,
- Max Bell6,
- Azra Bihorac7,
- Robert Birkhahn8,
- Cynthia M Cely9,
- Lakhmir S Chawla10,
- Danielle L Davison10,
- Thorsten Feldkamp11,
- Lui G Forni12,
- Michelle Ng Gong13,
- Kyle J Gunnerson14,
- Michael Haase15,
- James Hackett16,
- Patrick M Honore17,
- Eric AJ Hoste18,
- Olivier Joannes-Boyau19,
- Michael Joannidis20,
- Patrick Kim21,
- Jay L Koyner22,
- Daniel T Laskowitz23,
- Matthew E Lissauer24,
- Gernot Marx25,
- Peter A McCullough26,
- Scott Mullaney27,
- Marlies Ostermann28,
- Thomas Rimmelé29,
- Nathan I Shapiro30,
- Andrew D Shaw31,
- Jing Shi32,
- Amy M Sprague33,
- Jean-Louis Vincent34,
- Christophe Vinsonneau35,
- Ludwig Wagner36,
- Michael G Walker32,
- R Gentry Wilkerson37,
- Kai Zacharowski38 and
- John A Kellum39Email author
© Kashani et al.; licensee BioMed Central Ltd. 2013
Received: 29 November 2012
Accepted: 16 January 2013
Published: 6 February 2013
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.
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.
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 <0.002), none of which achieved an AUC >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.
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.
ClinicalTrials.gov number NCT01209169.
Acute kidney injury (AKI) is a vexing clinical problem, in part, because it is difficult to identify before there is loss of organ function, which may then become irreversible . Patients developing AKI have a markedly increased risk of death prior to hospital discharge [2, 3] and survivors also appear to be at significant short- and long-term risk for complications [4, 5]. Available therapies are mainly predicated on supportive measures and the removal of nephrotoxic agents . Thus, risk assessment for AKI is recommended by clinical practice guidelines . However, risk stratification remains very difficult, mainly due to limited sensitivity and specificity of the available diagnostic tests for AKI . Prior efforts at identifying biomarkers for AKI have been hampered by the heterogeneous nature of the condition. Many different etiologies for AKI have been reported (for example sepsis, nephrotoxins, ischemia), and in any given patient the cause is typically thought to be multifactorial . Here we report the results of a prospective, multicenter investigation in which two novel biomarkers for AKI were identified in a discovery cohort of critically ill adult patients and subsequently validated using a clinical assay and compared to existing markers of AKI in an independent validation cohort of heterogeneous critically ill patients.
Materials and methods
The Sapphire study was designed and reported according to the STROBE guidelines . As shown in Figure 1, the Discovery study enrolled patients who were admitted to an intensive care unit (any type), were at least 18 years of age and typically had at least one recognized risk factor for AKI. The Sapphire (validation) study enrolled critically ill patients who were at least 21 years of age, admitted to the intensive care unit within 24 hours of enrollment, expected to remain in the ICU with a urinary catheter for at least 48 hours and were critically ill (respiratory or cardiovascular dysfunction). Patients with known existing moderate or severe AKI (KDIGO  stage 2 or 3) were excluded. Sample size for the Sapphire study was based on the results of the Discovery study and is explained in detail in Additional file 1.
Sample and data collection
Paired urine and blood samples were collected at enrollment and up to 18 hours later by standard methods and centrifuged. Plasma (EDTA), serum and urine supernatants were frozen, shipped on dry ice, stored at ≤-70°C and thawed immediately prior to analysis. Clinical data including patient demographics, prior health history, serum creatinine, and hourly urine output as available in the hospital record were collected. Samples were analyzed at Astute Medical by technicians blinded to clinical data. Password-protected, anonymized clinical data collected with electronic case-report forms resided on servers at independent sites (Acumen Healthcare Solutions, Plymouth, MN, USA and Medidata Solutions, New York, NY, USA for Discovery and Sapphire studies, respectively).
AKI status was classified using the RIFLE  or AKIN criteria  together as described in the recent KDIGO international guideline  based on the serum creatinine (sCR) and urine output (UO) available in the hospital record. The primary endpoint for the Sapphire study was the development of moderate or severe AKI (KDIGO stage 2 or 3) within 12 hours of sample collection. The reference values for serum creatinine were obtained as follows: if at least five values were available the median of all values available from six months to six days prior to enrollment was used. Otherwise, the lowest value in the five days prior to enrollment was used. If no pre-enrollment creatinine was available, the creatinine value at the time of enrollment was used (see Additional file 1 for full details). We performed sensitivity analyses by repeating the primary analysis using several different methods of reference creatinine assignment. Details including the sensitivity analyses are given in Additional file 1. Secondary endpoints for the purpose of characterizing the patient population included renal replacement therapy at any time during hospitalization, survival and major adverse kidney events. We defined major adverse kidney events (MAKE30) as the composite of death, use of renal replacement therapy, or persistence of renal dysfunction (defined by serum creatinine ≥200% of reference) at hospital discharge truncated at 30 days .
Candidate biomarkers were identified through hypotheses based on AKI pathophysiology. Medline was searched from March 1995 to January 2011 for full reports of original research and review articles with the terms 'Acute kidney injury' OR 'Acute renal failure' AND/OR including one or more of the following terms: inflammation, apoptosis, necrosis, endothelial injury, cell-cell and cell-matrix adhesion, cytoprotection, oxidative processes and cell cycle. Abstracts were downloaded for all titles of potential relevance. Full papers were downloaded when the abstract was deemed relevant. A total of 340 candidate biomarkers were identified for analysis in the Discovery study. Proteins expressed in the kidney and peripherally (for example, in leukocytes) were included in the analyses. Biomarkers were ranked by ability to predict development of AKI RIFLE I or F within 12 to 36 hours. All possible combinations of two to four biomarkers (novel or previously described) were ranked to ensure that any biomarker that might contribute in top-performing combinations of biomarkers was retained.
Biomarkers were measured with single or multiplexed immunoassays using standard ELISA, Luminex 200 (Luminex, Austin, TX, USA), MSD SECTOR Imager 6000 (Meso Scale Discovery, Gaithersburg, MD, USA), or Astute140™ Meter (Astute Medical, San Diego, CA, USA) platforms. Immunoassays were either developed by Astute Medical or obtained from vendors and used as recommended by the vendor or modified to optimize performance. Novel biomarkers were measured with research assays (TIMP-2: R&D Systems, Minneapolis, MN, USA; IGFBP7: Millipore, Billerica, MA, USA) in the Discovery study and with the NephroCheck™ Test (Astute Medical, San Diego, CA, USA) in the Sapphire study. The NephroCheck Test was developed to simultaneously measure the two top-performing biomarkers (urine [TIMP-2]·[IGFBP7]) from the Discovery study using a platform that can be used clinically. Previously described biomarkers of AKI (including urine KIM-1, urine and plasma NGAL, plasma cystatin-C, urine IL-18, urine pi-GST, and urine L-FABP) were measured with commercially available assays (see Additional file 1).
The primary analysis was based on area under the receiver-operating characteristics curve (AUC) comparing [TIMP-2]·[IGFBP7] to previously described biomarkers for the development of the primary endpoint (KDIGO stage 2 to 3 within 12 hours of sample collection, for samples collected within 18 hours of enrollment). We also characterized the distributions of [TIMP-2]·[IGFBP7] values and several existing marker levels for AKI by severity and for various non-AKI conditions. We characterized risk for KDIGO stage 2 to 3 within 12 hours of sample collection and for MAKE30 by [TIMP-2]·[IGFBP7]. We calculated relative risk for KDIGO stage 2 to 3 by tertile. We computed the AUCs for novel and existing biomarkers in several subgroups of patients (see Additional file 1). We constructed a model based on the clinical variables found to be associated with the primary endpoint (P <0.1) and examined whether the addition of [TIMP-2]·[IGFBP7] improved risk prediction using time to event, integrated discrimination improvement (IDI), category-free net reclassification improvement (cfNRI) and risk assessment plot analyses (see Additional file 1). Statistical analyses and biomarker selection in the Discovery study were performed by Astute Medical. The primary statistical analyses for the Sapphire study were performed by a team of independent statisticians (MW, JS, and JH). Statistical analyses were performed using SAS 9.3 (SAS Institute, Cary, NC, USA) and R 2.12 . For all analyses, two-sided P values less than 0.05 were considered statistically significant. Categorical variables were analyzed using the Fisher exact test or logistic regression. AUC was calculated as empirical AUC with bootstrap confidence intervals to handle subjects with more than one sample collected within 18 hours of enrollment. Differences between AUCs were tested using bootstrap sampling. Time to event analyses used Cox proportional hazards regression with the log transform of [TIMP-2]·[IGFBP7] because the distribution was right-skewed. Tests of trend in relative risk across tertiles used the Jonckheere-Terpstra test .
Subject characteristics and event rates
Baseline characteristics for Sapphire study patients.
Chronic kidney disease
Congestive heart failure
Coronary artery disease
Chronic obstructive pulmonary disease
Coronary care unit
Reason for ICU admission 2
Enrollment serum creatinine 1,3
APACHE III 1,4
Novel biomarker performance
Comparison of biomarker performance to previously described AKI biomarkers
Risk of AKI and MAKE30 by [TIMP-2]·[IGFBP7] result
Additional information from biomarkers over clinical variables
We also examined whether [TIMP-2]·[IGFBP7] enhances predictive ability over clinical variables. [TIMP-2]·[IGFBP7] significantly improved risk prediction when added to a nine-parameter clinical model (including serum creatinine at matched time points with biomarkers) for the primary endpoint, using time to event, IDI, cfNRI and risk assessment plot analyses (Tables S4-S6 in Additional file 1 and Figure S3 in Additional file 1). All analyses showed significant enhancement by the addition of [TIMP-2]·[IGFBP7] with [TIMP-2]·[IGFBP7] remaining strongly associated with AKI in all models.
Finally, we performed a variety of sensitivity analyses (Table S7 in Additional file 1). We examined several methods of assigning the serum creatinine reference value. We also examined the effect of including or excluding patients who had (unbeknownst to the investigators at the time) reached the endpoint prior to sample collection and including only the enrollment sample (see Additional file 1). For all sensitivity analyses, our conclusions were unchanged and the [TIMP-2]·[IGFBP7] AUC was not different from the primary analysis (point estimate for AUC within the 95% confidence interval) and was higher than the AUC of all previously described biomarkers tested.
To our knowledge this is the first report of an AKI biomarker study that used a development-validation approach with separate patient cohorts in the context of a large prospective multicenter trial framework. Our results are striking not only in terms of identifying new robust markers that have improved performance characteristics when directly compared with existing methods for detecting risk for AKI, but also provide significant additional information over clinical data as evidenced by IDI, cfNRI and Cox models. Furthermore, these molecules are known to be associated with mechanisms recently implicated in the pathogenesis of AKI [15–17]. Thus, our results are important on two levels, development of new diagnostics and bolstering understanding of the mechanism of disease.
AKI poses both unique opportunities and challenges for development of biomarkers to aid in risk assessment. The ability to sample fluid 'proximal' to the site of injury, (that is urine), is an important advantage. However, AKI is also challenging because traditional methods of biomarker discovery often rely on model systems where pathogenesis is well understood or on tissues taken from patients with disease  and, since biopsies are rarely obtained from patients with AKI, these tissues are not easy to obtain. Further challenges exist because AKI is not a single disease but a complex syndrome with multiple underlying etiologies [6, 19]. Animal models are usually the source of tissue for many biomarker discovery programs, but these rarely, if ever, exemplify the full complexity of human AKI . For these reasons, we chose to discover potential biomarkers in critically ill humans with and without AKI as opposed to relying on animal models. This approach has the distinct advantage of being immediately relevant because the discovered biomarkers are active within the same context of disease encountered in clinical practice. Rather than force our current understanding of the disease mechanisms on the discovery process, we required candidate markers to discriminate risk class (that is, high or low risk of moderate to severe AKI in 12 to 36 hours). Once the best-performing markers had been identified, we tested their performance in a second group of adult critical care patients, thus requiring them to show robust utility across multiple institutions and patient subtypes. Finally, we subjected the new markers to analyses that tested their ability to enhance discrimination over robust clinical models. For these reasons we believe that these markers are the most promising early markers of AKI reported to date.
We chose to assess risk of moderate to severe AKI rather than all AKI because this severity (corresponding to KDIGO stage 2 and 3) has been shown to be associated with a significantly increased incidence of clinically important outcomes such as need for renal replacement therapy, in hospital death, and persistent renal dysfunction [2, 3].
Markers of cell-cycle arrest such as TIMP-2 and IGFBP7 may signal that the renal epithelium has been stressed and has shut down function but may still be able to recover without permanent injury to the organ. Importantly, both TIMP-2 and IGFBP7 appear to be able to signal in autocrine and paracrine fashions [24, 27–30] thus spreading the 'alarm' from the site of injury. In terms of timing, this signal could be ideal as it may be early enough that treatment can still alter the outcome - further study will be required to test this hypothesis. Finally, TIMP-2 and IGFBP7 are known to be involved in the response to a wide variety of insults (inflammation, oxidative stress, ultraviolet radiation, drugs, and toxins) [16, 23, 24]. This may help explain why they correspond to risk for AKI, a syndrome known for its multiple etiologies even in the same patient.
Our study has important limitations. Although we measured more than 300 candidates in our discovery study, many taken from unbiased 'omics' approaches, our list is by no means exhaustive. Furthermore, because we felt that the most important unanswered question was early risk stratification, we chose to study patients without evidence of AKI and sought to predict its clinical manifestation over the next 12 hours. Thus, we emphasized molecules with a rapid response to injury. We recognize that progression of disease and recovery are also important clinical questions and our results do not directly address these areas.
Urine TIMP-2, and IGFBP7, two novel biomarkers for risk stratification of AKI, were discovered and validated in more than 1,000 critically ill patients. These markers performed better than any other biomarker reported to date, showed significant enhancement over clinical variables, are mechanistically relevant, and can be easily measured with existing technology. Indeed, we chose to validate the two-marker panel ([TIMP-2]·[IGFBP7]) using a clinical rather than a research assay so as to facilitate rapid translation into clinical practice. The introduction of this new test should significantly improve the ability of physicians caring for critically ill patients to identify risk of impending AKI; and also facilitate future AKI research by permitting more accurate identification of high-risk patients for enrollment into intervention trials.
Urine insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinases-2 (TIMP-2) are new biomarkers for AKI and perform better than existing markers for predicting the development of moderate or severe AKI (KDIGO stage 2 or 3) within 12 hours of sample collection.
[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.
Risk for major adverse kidney events (death, dialysis or persistent renal dysfunction) within 30 days (MAKE30) elevated sharply for [TIMP-2]·[IGFBP7] above 0.3 and doubled when values were >2.0.
Both IGFBP7 and TIMP-2 are inducers of G1 cell-cycle arrest, a key mechanism implicated in AKI.
Acute kidney injury
area under the receiver-operating characteristics curve
category-free net reclassification improvement
integrated discrimination improvement
insulin-like growth factor-binding protein 7
kidney injury molecule-1
liver fatty acid-binding protein
major adverse kidney event
neutrophil gelatinase-associated lipocalin
tissue inhibitor of metalloproteinases-2.
The authors would like to acknowledge the many staff, coordinators, and investigators whose work was essential to completion of this study. A complete list of Sapphire investigators and support personnel is provided in the online appendix and is available online .
This study was funded by Astute Medical. For the Discovery study, the Mayo cohort was paid based on patients enrolled while the Vienna and Duke cohorts were paid a research grant per annum. Sapphire study sites were paid based on patient enrollment and data entry.
The study sponsor (Astute Medical) was primarily responsible for trial design, data collection, data analysis, and data interpretation, with crucial assistance from the trial lead investigators (JK, LC, KK) and the independent biostatisticians (MW, JS, JH).
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