Biomarkers of Kidney Disease
454
Biomarkers of Kidney Disease
454eBook
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Overview
A biomarker is a molecule that is measured as a marker of normal biological processes, disease processes or the response to a diagnostic or therapeutic intervention. Kidney diseases like acute kidney injury, chronic kidney disease, diabetic nephropathy, glomerular disease, renal cancer and preeclampsia still have a high morbidity. Measurement of biomarkers in the blood or urine that detect patients at risk of kidney diseases or that detect kidney diseases in the earliest stage may ultimately result in preventative or earlier or more effective treatments for kidney diseases.
Biomarkers of Kidney Disease reviews the basic and clinical research on biomarkers of the common kidney diseases including acute kidney injury, chronic kidney disease, renal cancer, diabetic nephropathy, glomerular disease and preeclampsia. The characteristics of an ideal biomarker of kidney diseases and statistical analysis of biomarker studies is detailed. The latest techniques for biomarker detection like metabolomics and proteomics is covered in the book. This comprehensive book details the latest advances made in the field of biomarker research and development in kidney diseases. The book is an ideal companion for those interested in biomarker research and development, proteomics and metabolomics, kidney diseases, statistical analysis, transplantation and preeclampsia.
- Presents a comprehensive, translational source for all aspects of renal biomarkers in one reference work
- Experts in renal disease biomarkers (from all areas of academic and medical research) take readers from the bench research (cellular and molecular mechanism) to new therapeutic approaches
Product Details
| ISBN-13: | 9780123756732 |
|---|---|
| Publisher: | Elsevier Science |
| Publication date: | 10/27/2010 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 454 |
| File size: | 3 MB |
About the Author
Fellowships: University of Colorado (University Hospital) Program, Nephrology (1997)
Read an Excerpt
Biomarkers in KIDNEY DISEASE
Academic Press
Copyright © 2011 Elsevier Inc.All right reserved.
ISBN: 978-0-12-375673-2
Chapter One
Characteristics of an Ideal Biomarker of Kidney Diseases
Michael R. Bennett, Prasad Devarajan
Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
Contents
1. The Discovery of Biomarkers 1
2. Characteristics of an Ideal Biomarker 4
3. Biomarkers in Acute Kidney Injury 6
4. Biomarkers in Chronic Kidney Disease 11
5. The Example of NGAL as a Biomarker of Acute Kidney Injury 14
References 20
1. THE DISCOVERY OF BIOMARKERS
The quest for biomarkers is as old as medicine itself. From the earliest days of diagnostic medicine in ancient Egypt, to the misguided science of phrenology (the belief that skull measurements could predict personality traits), to the powerful discoveries of modern science, we have been searching for measurable biological cues that will allow us insight into the physiological workings of the human organism. In its simplest definition, a biomarker is anything that can be measured to extract information about a biological state or process. The NIH Biomarkers Definitions Working Group has defined a biological marker (biomarker) as "A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention."
Biomarkers appear in every form. Body temperature, in the form of a fever, can signal infection. Blood pressure and cholesterol levels can predict cardiovascular risk. Tracking biomarkers such as height and weight can give clues to normal human growth and development. Such general biomarkers have been used for decades or even centuries and have remained powerful tools for tracking general biological activity. However, the era of personalized medicine is well upon us. Ushered in by the remarkable genomic and proteomic advances in our understanding of health and disease, personalized medicine promises a more precise determination of disease predisposition, diagnosis and prognosis, earlier preventive and therapeutic interventions, a more efficient drug development process, and a safer and more fiscally responsive approach to medicine. Biomarkers are the essential tools for the implementation of personalized medicine. The quest for the advancement of personalized medicine pushes us further and further into the realm of molecular medicine to discover biomarkers with increasing sensitivity and specificity. For most of our history, biomarker discovery has relied on intimate knowledge of the pathophysiology of the diseases being studied. Biological substances that we knew were related to a disease state were investigated to see if they could serve as diagnostic markers, provide a target for therapy or lend further insight into the etiology of the disease. While this can be tedious, and relies heavily on prior knowledge of the disease mechanism, this hypothesis-driven method of research almost always provides useful scientific results, whether positive or negative.
The biomarker development process has typically been divided into five phases, as shown in Table 1.1. The preclinical discovery phase requires high-quality, well-characterized tissue or body fluid samples from carefully chosen animal or human models of the disease under investigation. In recent years, the ready availability of powerful tools to scan both the genome and the proteome of an organism have revolutionized and greatly accelerated biomarker discovery. Microarrays that can measure the entire complement of messenger RNA in a given sample type have yielded a number of promising biomarkers of kidney disease, such as neutrophil gelatinase-associated lipocalin (NGAL), an early predictor of acute kidney injury (AKI) and a powerful risk marker of chronic kidney disease (CKD) progression which we will discuss later, and have also led to the discovery of novel disease mechanisms in many fields. This approach can be combined with other techniques, such as laser capture microdissection, to target specific areas of diseased tissue to give mechanistic clues not possible just a decade ago. Even with this level of specificity, these techniques can yield a daunting array of data that must be sifted through for relevance. One example of this in human kidney disease was a study performed by Bennett et al in which the authors looked at gene expression profiles of laser captured glomeruli from kidney biopsies in patients with focal segmental glomerulosclerosis. The investigators were able to examine gene expression exclusively in the histological center of this disease, and still found well over 100 genes differentially expressed compared to glomeruli from control tissues. A shortcoming of such transcriptomic profiling approaches is that you cannot directly measure biological fluids. Another problem with this approach is that ultimately messenger RNA does not always reflect protein levels or activity and must be further confirmed at the protein level prior to larger validation studies.
Proteomic approaches move a step beyond genomic studies and screen the actual proteins and peptides present in a sample. This approach allows one to go beyond simple translation of mRNA into protein, and allows a look into protein regulation, post-translational modifications such as glycosylation and methylation, and even disease specific fragmentation. There are a number of proteomic approaches including gel electrophoresis and modern mass spectometry techniques such as matrix assisted laser desorption ionization time of flight (MALDI-TOF) mass spectrometry and surface enhanced laser desorption ionization time of flight (SELDI-TOF) mass spectrometry. These techniques are capable of identifying and quantifying proteins and peptides in exceedingly large numbers. The urinary proteome itself is quite large, with laboratories having identified over 1500 proteins to date. The blood proteome is even larger, with over 3000 non-redundant proteins identified in the plasma alone. Adding the proteome of the cellular component of blood will yield thousands more. To this end we have entered what has been termed an "open loop" or unbiased approach to biomarker discovery. This is in stark contrast to the hypothesis-driven approach of our past. With such a vast pool of potential biomarkers from readily available, non-invasive sources one must take care to plan and design the proper experimental approach to ensure parsimony.
2. CHARACTERISTICS OF AN IDEAL BIOMARKER
Prior to beginning the search for biomarkers of renal disease, one has to ask — What are the ideal characteristics of a renal biomarker? To be certain what constitutes an ideal biomarker is highly dependent upon the disease you are investigating. However, certain universal characteristics are important for any biomarker: (1) they should be non-invasive, easily measured, inexpensive, and produce rapid results; (2) they should be from readily available sources, such as blood or urine; (3) they should have a high sensitivity, allowing early detection, and no overlap in values between diseased patients and healthy controls; (4) they should have a high specificity, being greatly upregulated (or downregulated) specifically in the diseased samples and unaffected by comorbid conditions; (5) biomarker levels should vary rapidly in response to treatment; (6) biomarker levels should aid in risk stratification and possess prognostic value in terms of real outcomes; and (7) biomarkers should be biologically plausible and provide insight into the underlying disease mechanism.
Of course, very few biomarkers will meet all of the characteristics of an ideal marker, but let us discuss these characteristics in a little more detail. First, a biomarker should be non-invasive. For example, many chronic kidney diseases present with a range of proteinuria. Currently the preferred method for differentiating nephrotic syndrome-producing chronic kidney diseases such as focal segmental glomerulosclerosis, membranous nephropathy or minimal change disease is an invasive biopsy. In addition to the health risks, these procedures cause undue anxiety, especially in pediatric populations. While typically a safe procedure, there are associated risks, especially for those patients with contraindications to percutaneous renal biopsies who must elect for an "open", or operative renal biopsy. A recent study found major (cardiac arrest, stroke, sepsis) and minor (wound infection, pneumonia, arrhythmia, postoperative retroperitoneal bleed, deep vein thrombosis) complication rates of 6.1% and 27% in a group of 115 open biopsy patients from 1991 to 2006. While these are relatively rare occurrences, they illustrate the need for less invasive diagnostic procedures.
Regarding the source of biomarkers, the most readily available ones are urine and blood. These are substances obtained in the normal care of a patient, easily collected at the bedside, and associated with little to no health risks to the patient. Each source has desirable and negative characteristics. Urine is an excellent source of biomarkers produced in the kidney and thus may give better mechanistic insight into specific renal pathologies. Urine is less complex than serum and thus is easier to screen for potential biomarkers. Collection of urine is easy enough, and it can be readily employed in home testing kits. The handling of urine, however, greatly influences the stability of its proteins and measurements should be made immediately after collection or the urine should be promptly frozen at -80°C to avoid degradation. Finally, urinary biomarker studies typically adjust for urine creatinine to account for differences in urine concentration due to hydration status and medications such as diuretics. However, the utility of urine creatinine in biomarker correction has been questioned due to its variable excretion throughout the day and its dependence on normal renal function. Serum or plasma can also be a good source of biomarkers and is even available in anuric patients. Serum is less prone to bacterial contamination than urine and is considered more stable. Serum biomarkers, however, are more likely to represent a systemic response to disease, rather than an organ response. There are exceptions, such as the troponins in cardiac disease. The real problem with serum as a source of biomarkers lies in the discovery phase. Serum has a wide range of protein concentrations across several orders of magnitude, with a small number of proteins (such as albumin) accounting for a large percentage of the volume. This can be akin to trying to spot a single strand of cotton in a large tapestry. The more abundant proteins simply overwhelm the signal of those in less abundance. While there exist assays to remove these high abundance proteins from serum, many potential biomarkers have for example been shown to bind to albumin. Thus, when you deplete the albumin, the rest of the tapestry unravels with it and you may lose proteins relevant to your disease.
The sensitivity and specificity of a biomarker go hand in hand. The receiver operating characteristic (ROC) curve is a binary classification test, based on the sensitivity and specificity of a biomarker at certain cutoff points. ROC curves are often used to determine the clinical diagnostic value of a marker. The area under the ROC curve (AUC) is a common statistic derived from ROC curves. An AUC of 1.0 represents a perfect biomarker, while an AUC of 0.5 is a result that is no better than expected by chance. An AUC of 0.75 or greater is generally considered a good biomarker, while an AUC of 0.90 is considered an excellent biomarker. However, even a sensitive biomarker with what experimentally would be considered an excellent specificity of 90%, would still yield a false positive rate of 10%, which may be unacceptably high for clinical use as a stand alone marker. As a result, the best approach clinically may be to find multiple biomarkers that can be combined as part of a panel to achieve even higher specificity.
Lack of specificity and slow response to alterations in disease severity or treatment are primary reasons why serum creatinine is an unsatisfactory biomarker for renal disease, especially in cases of acute kidney injury (AKI). Firstly, serum creatinine levels change with factors unrelated to renal disease, such as age, gender, diet, muscle mass, muscle metabolism, race, strenuous exercise and hydration status. Creatinine levels are also influenced by certain drugs. Furthermore, in AKI, serum creatinine is not a real time indicator of kidney function, because the patients are not in steady state; so rises in serum creatinine occur long after the renal injury is sustained. In fact, serum creatinine concentrations may not change until approximately 50% of kidney function has been lost. This makes serum creatinine a poor diagnostic marker for AKI, since treatments need to be administered soon after injury to be effective. Animal studies have shown that treatments that can prevent or alleviate AKI need to begin well before the serum creatinine level begins to rise. Because so many variables affect creatinine levels, it also lacks precision in assessing disease progression or risk stratification. Finally, it is well known that significant renal disease such as fibrosis can exist with little or no change in creatinine because of renal reserve or enhanced tubular secretion of creatinine. Nephrology remains in the 1950s in its use of serum creatinine. Despite having few of the outlined characteristics of an ideal biomarker, serum creatinine remains in widespread use as an indicator of renal function and is the sole FDA approved diagnostic marker of AKI. The problems with creatinine have been evident for over thirty years, yet until recently little progress had been made in the search for replacement markers that will aid in earlier, more accurate and specific diagnosis of renal disease.
3. BIOMARKERS IN ACUTE KIDNEY INJURY
AKI is a serious clinical problem and is increasing in incidence, lacks satisfactory therapeutic options and presents an enormous financial burden to society. Conservative estimates have placed the annual health care expenditures attributable to hospital-acquired AKI at greater than 10 billion dollars in the United States alone. AKI is a major side effect of other medical procedures and can result from insults ranging from ischemia reperfusion injury (IRI) following cardiopulmonary bypass surgery or renal transplant to damage from nephrotoxic agents such as contrast used in CT or cysplatin used in chemotherapy. Although many new insights into the mechanisms of AKI have been advanced in recent years and novel interventions in animal models have shown promise, translational efforts in humans have been disappointing. There are many plausible reasons for this lack of success, among them is a paucity of early diagnostic markers of AKI leading to delayed initiation of therapy and incomplete pathophysiological understanding of the disease process.
Another major hindrance to the successful implementation of new therapies is the lack of a consensus definition of AKI (previously known as acute renal failure, or ARF). In fact, the Acute Dialysis Quality Initiative (ADQI) workgroup found that over 30 definitions for ARF were used in the literature. The definitions varied from a 25% increase over baseline serum creatinine to the need for dialysis. The term AKI is of relatively recent origin and was proposed to better account for the diverse spectrum of molecular, biochemical and structural processes that characterize the AKI syndrome. In order to better classify AKI, the RIFLE classification system (Table 1.2) was developed (Riske—Injurye—Failuree—Losse—End stage renal disease). The first three classes represent degrees of injury and the last two are outcome measures. This system has shown to correlate well with mortality rates. In order to further refine the definition of AKI, the Acute Kidney Injury Network (AKIN) was created, which proposed a modified version of the RIFLE classification, known as the AKIN criteria. The AKIN criteria define AKI as an abrupt (within 48 h) reduction in kidney function as measured by an absolute increase in serum creatinine ≥ 0.3 mg/dL, a percentage increase in serum creatinine ≥ 50%, or documented oliguria (<0.5 mL/kg/h) for more than 6 h. Minor modifications of the RIFLE criteria (Table 1.3) include broadening the "risk" category of RIFLE to include an increase in serum creatinine of at least 0.3 mg/dL in order to increase the sensitivity of RIFLE for detecting AKI at an earlier time point. In addition, the AKIN criteria sets a window on first documentation of any criteria to 48 h and categorizes patients in the "failure" category of RIFLE if they are treated with renal replacement therapy, regardless of either changes in creatinine or urine output. Finally, AKIN replaces the three levels of severity R, I and F with stages 1, 2 and 3.
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Table of Contents
1. Characteristics of an Ideal Biomarker of Kidney Diseases 2. Statistical Analysis of Biomarker Studies 3. Metabolonomics: Screening for Biomarkers of Kidney Disease 4. Proteomics: Screening for Biomarkers of Kidney Disease 5. Biomarkers of Acute Kidney Injury 6. Biomarkers of Kidney Transplant Rejection 7. Cystatin C-A Biomarker of Risk in Chronic Kidney Disease 8. Prognostic Biomarkers in Renal Cell Cancer 9. Biomarkers in Diabetic Nephropathy 10. Biomarkers in Glomerular Diseases 11. Diagnostic Markers and Therapeutic Approaches in Preeclampsia
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The first comprehensive translational reference focusing on the biomarkers of kidney diseases, outlining how preclinical (basic) research has been taken to the bedside