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US20110065593A1 - Computer Methods and Devices for Detecting Kidney Damage - Google Patents

Computer Methods and Devices for Detecting Kidney Damage Download PDF

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Publication number
US20110065593A1
US20110065593A1 US12/852,202 US85220210A US2011065593A1 US 20110065593 A1 US20110065593 A1 US 20110065593A1 US 85220210 A US85220210 A US 85220210A US 2011065593 A1 US2011065593 A1 US 2011065593A1
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analyte
sample
microglobulin
alpha
concentrations
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US12/852,202
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Samuel T. Labrie
James P. Mapes
Ralph L. McDade
Dominic Eisinger
Karri L. Ballard
Michael D. Spain
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Myriad RBM Inc
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Rules Based Medicine Inc
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Assigned to RULES-BASED MEDICINE, INC. reassignment RULES-BASED MEDICINE, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: EISINGER, DOMINIC P., MCDADE, RALPH L., BALLARD, KARRI L., LABRIE, SAMUEL T., SPAIN, MICHAEL D., MAPES, JAMES P.
Publication of US20110065593A1 publication Critical patent/US20110065593A1/en
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Definitions

  • the invention encompasses methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal.
  • the present invention provides methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal.
  • the urinary system in particular the kidneys, perform several critical functions such as maintaining electrolyte balance and eliminating toxins from the bloodstream.
  • the pair of kidneys together process roughly 20% of the total cardiac output, amounting to about 1 L/min in a 70-kg adult male. Because compounds in circulation are concentrated in the kidney up to 1000-fold relative to the plasma concentration, the kidney is especially vulnerable to injury due to exposure to toxic compounds.
  • kidney injury is a major cause for delay during the development of candidate drugs.
  • regulatory agencies have required drug companies to provide results of blood urea nitrogen (BUN) and serum creatinine tests, two common diagnostic tests for renal function, to address concerns of potential kidney damage as part of the regulatory approval process.
  • BUN blood urea nitrogen
  • serum creatinine tests two common diagnostic tests for renal function, to address concerns of potential kidney damage as part of the regulatory approval process.
  • these diagnostic tests typically detect only late signs of kidney damage and provide little information as to the location of kidney damage.
  • kidney damage may also result from renal disorders such as kidney trauma, nephritis, kidney cancer, and kidney transplant rejection. Kidney damage may also occur as a secondary side effect of more systemic diseases such as diabetes, hypertension, and autoimmune diseases.
  • Existing diagnostic tests such as BUN and serum creatine tests typically detect only advanced stages of kidney damage.
  • Other diagnostic tests such as kidney tissue biopsies or CAT scans have the advantage of enhanced sensitivity to earlier stages of kidney damage, but these tests are also generally costly, slow, and/or invasive.
  • the detection of the early signs and locations of drug-induced kidney damage would be useful in guiding important decisions on lead compounds and dosage.
  • the early detection of kidney damage would help medical practitioners to diagnose and treat kidney damage more quickly and effectively.
  • the present invention provides computer methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal.
  • the present invention provides methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal.
  • One aspect of the present invention provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes providing a test sample that includes a sample of bodily fluid taken from the mammal, and determining the presence of a combination of three or more sample analytes in the test sample.
  • the analytes in the test sample may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • the combination of sample analytes is compared to the entries of a dataset in which each entry includes a combination of three or more diagnostic analytes reflective of a particular renal disorder.
  • the particular renal disorder of the mammal is identified as the renal disorder in the database having the combination of diagnostic analytes that essentially match the combination of sample analytes.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations for three or more sample analytes in the test sample.
  • the analytes may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • sample concentrations is compared to the entries of a dataset in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder.
  • Each minimum diagnostic concentration is the maximum concentration of a range of analyte concentrations for a healthy mammal.
  • a matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of calbindin, clusterin, CTGF, GST-alpha, KIM-1, and VEGF in the test sample.
  • the combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder.
  • a matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of beta-2 microglobulin, cystatin C, NGAL, osteopontin, and TIMP-1 in the test sample.
  • the combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder.
  • a matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of alpha-1 microglobulin, THP, and TFF-3 in the test sample.
  • the combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder.
  • a matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • a method for diagnosing, monitoring, or determining a renal disorder in a mammal includes providing a test sample comprising a sample of bodily fluid taken from the mammal and determining the concentrations of three or more sample analytes in a panel of biomarkers in the test sample.
  • the sample analytes may be selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • Diagnostic analytes are then identified in the test sample, wherein the diagnostic analytes are the sample analytes whose concentrations are statistically different from concentrations found in a control group of humans who do not suffer from a renal disorder.
  • the combination of diagnostic analytes are compared to a dataset comprising at least one entry, wherein each entry of the dataset comprises a combination of three or more diagnostic analytes reflective of a particular renal disorder.
  • the particular renal disorder in the list is identified as the renal disorder having the combination of diagnostic analytes that essentially match the combination of sample analytes.
  • An additional aspect provides a computer readable media encoded with an application that includes modules executable by a processor and configured to diagnose, monitor, or determine a renal disorder in a mammal.
  • An analyte input module receives three or more sample analyte concentrations that may include alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • a comparison module compares each sample analyte concentration to an entry of a renal disorder database, where each entry includes a list of minimum diagnostic concentrations reflective of a particular renal disorder.
  • An analysis module determines a most likely renal disorder by combining the particular renal disorders identified by the comparison module for all of the sample analyte concentrations.
  • Yet another aspect provides a system for diagnosing, monitoring, or determining a renal disorder in a mammal that includes a database to store a plurality of renal disorder database entries as well as a processing device that includes a renal disorder diagnosis application containing modules executable by the processing device.
  • the modules of the renal disorder diagnosis application include an analyte input module to receive three or more sample analyte concentrations selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • Another module compares each sample analyte concentration to an entry of the renal disorder database.
  • Each entry of the renal disorder database contains a list of minimum diagnostic concentrations reflective of a particular renal disorder.
  • An analysis module determines a most likely renal disorder by combining the particular renal disorders identified by the comparison module for all of the sample analyte concentrations.
  • An aspect provides a device for diagnosing, monitoring, or determining a renal disorder in a mammal that includes three or more antibodies and a plurality of indicators attached to each of the antibodies.
  • the antigenic determinants of the antibodies are analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • Another aspect provides a device for diagnosing, monitoring, or determining a renal disorder in a mammal that includes three or more capture antibodies, three or more capture agents, three or more detection antibodies, and three or more indicators.
  • the antigenic determinants of the capture antibodies are analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • One of the capture agents is attached to each of the capture antibodies, and includes an antigenic moiety.
  • the antigenic determinant of the detection antibodies is the antigenic moiety.
  • Each of the indicators is attached to one of the detection antibodies.
  • a final aspect provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes providing an analyte concentration measurement device that includes three or more detection antibodies, in which each detection antibody includes an antibody coupled to an indicator.
  • the antigenic determinants of the antibodies are sample analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • a test sample that contains three or more sample analytes and a bodily fluid taken from the mammal is provided and contacted with the detection antibodies.
  • the detection antibodies are allowed to bind to the sample analytes.
  • the concentrations of the sample analytes are determined by detecting the indicators of the detection antibodies bound to the sample analytes in the test sample.
  • the concentrations of each sample analyte are compared to a corresponding minimum diagnostic concentration reflective of a particular renal disorder.
  • FIG. 1 depicts four graphs comparing (A) the concentrations of alpha-1 microglobulin in the urine of normal controls, kidney cancer patients, and patients with other cancer types; (B) the concentrations of beta-2 microglobulin in the urine of normal controls, kidney cancer patients, and patients with other cancer types; (C) the concentrations of NGAL in the urine of normal controls, kidney cancer patients, and patients with other cancer types; and (D) the concentrations of THP in the urine of normal controls, kidney cancer patients, and patients with other cancer types.
  • FIG. 2 shows the four different disease groups from which samples were analyzed, and a plot of two different estimations on eGFR outlining the distribution within each group.
  • FIG. 3 is a number of scatter plots of results on selected proteins in urine and plasma. The various groups are indicated as follows—control: blue, AA: red, DN: green, GN: yellow, OU: orange.
  • A1M in plasma (B) cystatin C in plasma, (C) B2M in urine, (D) cystatin C in urine.
  • FIG. 4 depicts the multivariate analysis of the disease groups and their respective matched controls using plasma results. Relative importance shown using the random forest model.
  • FIG. 5 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish disease samples vs. normal samples.
  • FIG. 6 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish disease (AA+GN+ON+DN) samples vs. normal samples from plasma (A) and urine (B and C).
  • FIG. 7 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish analgesic abuse samples vs. normal samples. Abbreviations as in FIG. 4 .
  • FIG. 8 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish analgesic abuse samples vs. normal samples from plasma (A) and urine (B and C).
  • FIG. 9 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish analgesic abuse samples vs. diabetic nephropathy samples. Abbreviations as in FIG. 4 .
  • FIG. 10 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish analgesic abuse samples vs. diabetic nephropathy samples from plasma (A) and urine (B and C).
  • FIG. 11 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish glomerulonephritis samples vs. analgesic abuse samples. Abbreviations as in FIG. 4 .
  • FIG. 12 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish glomerulonephritis samples vs. analgesic abuse samples from plasma (A) and urine (B and C).
  • FIG. 13 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish obstructive uropathy samples vs. analgesic abuse samples. Abbreviations as in FIG. 4 .
  • FIG. 14 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish obstructive uropathy samples vs. analgesic abuse samples from plasma (A) and urine (B and C).
  • FIG. 15 is a block diagram of an emplary computing environment for implementing a renal disorder diagnostic system.
  • FIG. 16 is a block diagram that depicts an exemplary renal disorder diagnostic system.
  • FIG. 17 illustrates a method for diagnosing, monitoring, or determining a renal disorder in a mammal in accordance with an aspect of the renal disorder diagnostic system.
  • a multiplexed panel of up to 16 biomarkers may be used to detect early renal damage and pinpoint the location of renal damage within the kidney.
  • the biomarkers included in the multiplexed panel are analytes known in the art that may be detected in the urine, serum, plasma and other bodily fluids of mammals.
  • the analytes of the multiplexed panel may be readily extracted from the mammal in a test sample of bodily fluid.
  • the concentrations of the analytes within the test sample may be measured using known analytical techniques such as a multiplexed antibody-based immunological assay.
  • the combination of concentrations of the analytes in the test sample may be compared to empirically determined combinations of minimum diagnostic concentrations and combinations of diagnostic concentration ranges associated with healthy kidney function or one or more particular renal disorders to determine whether a renal disorder is indicated in the mammal.
  • the potentially large number of combinations of diagnostic analyte concentrations makes possible a wide range of diagnostic criteria that may be used to identify a variety of renal disorders and pinpoint the location in the kidney of a renal injury, using a single multiplexed assay to evaluate a single test sample.
  • the term “renal disorder” includes, but is not limited to glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, analgesic nephropathy, and acute tubular necrosis.
  • the multiplexed analyte panel identifies secondary kidney damaged caused by exposure to a toxic compound including but not limited to therapeutic drugs, recreational drugs, contrast agents, medical imaging contrast agents, and toxins.
  • therapeutic drugs may include an analgesic (e.g. aspirin, acetaminophen, ibuprofen, naproxen sodium), an antibiotic (e.g.
  • a chemotherapy agent e.g.
  • Cisplatin (Platinol®), Carboplatin (Paraplatin®), Cytarabine (Cytosar-U®), Gemtuzumab ozogamicin (Mylotarg®), Gemcitabine (Gemzar®), Melphalan (Alkeran®), Ifosfamide (Ifex®), Methotrexate (Rheumatrex®), Interleukin-2 (Proleukin®), Oxaliplatin (Eloxatin®), Streptozocin (Zanosar®), Pemetrexed (Alimta®), Plicamycin (Mithracin®), and Trimetrexate (Neutrexin®).
  • the kidney damage may be due to kidney stones, ischemia, liver transplantation, heart transplantation, lung transplantation, or hypovolemia.
  • the multiplexed analyte panel identifies kidney damage caused by disease including but not limited to diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to any infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate disease.
  • One embodiment of the present invention provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes determining the presence or concentration of a combination of three or more sample analytes in a test sample containing the bodily fluid of the mammal. The measured concentrations of the combination of sample analytes is compared to the entries of a dataset in which each entry contains the minimum diagnostic concentrations of a combination of three of more analytes reflective of a particular renal disorder.
  • Other embodiments provide computer-readable media encoded with applications containing executable modules, systems that include databases and processing devices containing executable modules configured to diagnose, monitor, or determine a renal disorder in a mammal.
  • Still other embodiments provide antibody-based devices for diagnosing, monitoring, or determining a renal disorder in a mammal.
  • analytes used as biomarkers in the multiplexed assay methods of diagnosing, monitoring, or determining a renal disorder using measurements of the analytes, systems and applications used to analyze the multiplexed assay measurements, and antibody-based devices used to measure the analytes are described in detail below.
  • One embodiment of the invention measures the concentrations of at least three, six, or preferably sixteen biomarker analytes within a test sample taken from a mammal and compares the measured analyte concentrations to minimum diagnostic concentrations to diagnose, monitor, or determine kidney damage in a mammal.
  • the biomarker analytes are known in the art to occur in the urine, plasma, serum and other bodily fluids of mammals.
  • the biomarker analytes are proteins that have known and documented associations with early kidney damage in humans.
  • the biomarker analytes may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, VEGF A, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, GST-mu, EGF, and cortisol.
  • a description of some of the biomarker analytes are given below.
  • Alpha-1 microglobulin (A1M, Swiss-Prot Accession Number P02760) is a 26 kDa glycoprotein synthesized by the liver and reabsorbed in the proximal tubules. Elevated levels of A1M in human urine are indicative of glomerulotubular dysfunction. A1M is a member of the lipocalin super family and is found in all tissues. Alpha-1-microglobulin exists in blood in both a free form and complexed with immunoglobulin A (IgA) and heme. Half of plasma A1M exists in a free form, and the remainder exists in complexes with other molecules including prothrombin, albumin, immunoglobulin A and heme.
  • IgA immunoglobulin A
  • Half of plasma A1M exists in a free form, and the remainder exists in complexes with other molecules including prothrombin, albumin, immunoglobulin A and heme.
  • A1M A1M in human urine
  • proximal tubular cells Nearly all of the free A1M in human urine is reabsorbed by the megalin receptor in proximal tubular cells, where it is then catabolized. Small amounts of A1M are excreted in the urine of healthy humans. Increased A1M concentrations in human urine may be an early indicator of renal damage, primarily in the proximal tubule.
  • Beta-2 Microglobulin (b) Beta-2 Microglobulin (B2M)
  • Beta-2 microglobulin (B2M, Swiss-Prot Accession Number P61769) is a protein found on the surfaces of all nucleated cells and is shed into the blood, particularly by tumor cells and lymphocytes. Due to its small size, B2M passes through the glomerular membrane, but normally less than 1% is excreted due to reabsorption of B2M in the proximal tubules of the kidney. Therefore, high plasma levels of B2M occur as a result of renal failure, inflammation, and neoplasms, especially those associated with B-lymphocytes.
  • Calbindin (Calbindin D-28K, Swiss-Prot Accession Number P05937) is a Ca-binding protein belonging to the troponin C superfamily. It is expressed in the kidney, pancreatic islets, and brain. Calbindin is found predominantly in subpopulations of central and peripheral nervous system neurons, in certain epithelial cells involved in Ca2+ transport such as distal tubular cells and cortical collecting tubules of the kidney, and in enteric neuroendocrine cells.
  • Clusterin (Swiss-Prot Accession Number P10909) is a highly conserved protein that has been identified independently by many different laboratories and named SGP2, S35-S45, apolipoprotein J, SP-40, 40, ADHC-9, gp80, GPIII, and testosterone-repressed prostate message (TRPM-2).
  • SGP2 S35-S45
  • apolipoprotein J SP-40
  • 40 ADHC-9
  • gp80 gp80
  • GPIII testosterone-repressed prostate message
  • TRPM-2 testosterone-repressed prostate message
  • clusterin protein has also been implicated in physiological processes that do not involve apoptosis, including the control of complement-mediated cell lysis, transport of beta-amyloid precursor protein, shuttling of aberrant beta-amyloid across the blood-brain barrier, lipid scavenging, membrane remodeling, cell aggregation, and protection from immune detection and tumor necrosis factor induced cell death.
  • CTGF Connective Tissue Growth Factor
  • CTGF Connective tissue growth factor
  • P29279 Connective tissue growth factor
  • Creatinine is a metabolite of creatine phosphate in muscle tissue, and is typically produced at a relatively constant rate by the body. Creatinine is chiefly filtered out of the blood by the kidneys, though a small amount is actively secreted by the kidneys into the urine. Creatinine levels in blood and urine may be used to estimate the creatinine clearance, which is representative of the overall glomerular filtration rate (GFR), a standard measure of renal function. Variations in creatinine concentrations in the blood and urine, as well as variations in the ratio of urea to creatinine concentration in the blood, are common diagnostic measurements used to assess renal function.
  • GFR overall glomerular filtration rate
  • Cystatin C (Cyst C, Swiss-Prot Accession Number P01034) is a 13 kDa protein that is a potent inhibitor of the C1 family of cysteine proteases. It is the most abundant extracellular inhibitor of cysteine proteases in testis, epididymis, prostate, seminal vesicles and many other tissues. Cystatin C, which is normally expressed in vascular wall smooth muscle cells, is severely reduced in both atherosclerotic and aneurismal aortic lesions.
  • Glutathione S-transferase alpha (GST-alpha, Swiss-Prot Accession Number P08263) belongs to a family of enzymes that utilize glutathione in reactions contributing to the transformation of a wide range of compounds, including carcinogens, therapeutic drugs, and products of oxidative stress. These enzymes play a key role in the detoxification of such substances.
  • Kidney Injury Molecule-1 Kidney Injury Molecule-1 (KIM-1)
  • Kidney injury molecule-1 (KIM-1, Swiss-Prot Accession Number Q96D42) is an immunoglobulin superfamily cell-surface protein highly upregulated on the surface of injured kidney epithelial cells. It is also known as TIM-1 (T-cell immunoglobulin mucin domain-1), as it is expressed at low levels by subpopulations of activated T-cells and hepatitis A virus cellular receptor-1 (HAVCR-1). KIM-1 is increased in expression more than any other protein in the injured kidney and is localized predominantly to the apical membrane of the surviving proximal epithelial cells.
  • TIM-1 T-cell immunoglobulin mucin domain-1
  • HAVCR-1 hepatitis A virus cellular receptor-1
  • Albumin is the most abundant plasma protein in humans and other mammals. Albumin is essential for maintaining the osmotic pressure needed for proper distribution of body fluids between intravascular compartments and body tissues. Healthy, normal kidneys typically filter out albumin from the urine. The presence of albumin in the urine may indicate damage to the kidneys. Albumin in the urine may also occur in patients with long-standing diabetes, especially type 1 diabetes. The amount of albumin eliminated in the urine has been used to differentially diagnose various renal disorders. For example, nephrotic syndrome usually results in the excretion of about 3.0 to 3.5 grams of albumin in human urine every 24 hours. Microalbuminuria, in which less than 300 mg of albumin is eliminated in the urine every 24 hours, may indicate the early stages of diabetic nephropathy.
  • Neutrophil gelatinase-associated lipocalin (NGAL, Swiss-Prot Accession Number P80188) forms a disulfide bond-linked heterodimer with MMP-9. It mediates an innate immune response to bacterial infection by sequestrating iron. Lipocalins interact with many different molecules such as cell surface receptors and proteases, and play a role in a variety of processes such as the progression of cancer and allergic reactions.
  • Osteopontin (OPN, Swiss-Prot Accession Number P10451) is a cytokine involved in enhancing production of interferon-gamma and IL-12, and inhibiting the production of IL-10.
  • OPN is essential in the pathway that leads to type I immunity.
  • OPN appears to form an integral part of the mineralized matrix.
  • OPN is synthesized within the kidney and has been detected in human urine at levels that may effectively inhibit calcium oxalate crystallization. Decreased concentrations of OPN have been documented in urine from patients with renal stone disease compared with normal individuals.
  • Tamm-Horsfall protein (THP, Swiss-Prot Accession Number P07911), also known as uromodulin, is the most abundant protein present in the urine of healthy subjects and has been shown to decrease in individuals with kidney stones.
  • THP is secreted by the thick ascending limb of the loop of Henley.
  • THP is a monomeric glycoprotein of ⁇ 85 kDa with ⁇ 30% carbohydrate moiety that is heavily glycosylated.
  • THP may act as a constitutive inhibitor of calcium crystallization in renal fluids.
  • Tissue Inhibitor of Metalloproteinase-1 (n) Tissue Inhibitor of Metalloproteinase-1 (TIMP-1)
  • Tissue inhibitor of metalloproteinase-1 (TIMP-1, Swiss-Prot Accession Number P01033) is a major regulator of extracellular matrix synthesis and degradation.
  • a certain balance of MMPs and TIMPs is essential for tumor growth and health. Fibrosis results from an imbalance of fibrogenesis and fibrolysis, highlighting the importance of the role of the inhibition of matrix degradation role in renal disease.
  • Trefoil factor 3 (TFF3, Swiss-Prot Accession Number Q07654), also known as intestinal trefoil factor, belongs to a small family of mucin-associated peptides that include TFF1, TFF2, and TFF3.
  • TFF3 exists in a 60-amino acid monomeric form and a 118-amino acid dimeric form. Under normal conditions TFF3 is expressed by goblet cells of the intestine and the colon. TFF3 expression has also been observed in the human respiratory tract, in human goblet cells and in the human salivary gland. In addition, TFF3 has been detected in the human hypothalamus.
  • VEGF Vascular Endothelial Growth Factor
  • VEGF Vascular endothelial growth factor
  • the method for diagnosing, monitoring, or determining kidney damage involves determining the presence or concentrations of a combination of sample analytes in a test sample.
  • the combinations of sample analytes are any group of three or more analytes selected from the biomarker analytes, including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • the combination of analytes may be selected to provide a group of analytes associated with a wide range of potential types of kidney damage.
  • the combination of analytes may be selected to provide a group of analytes associated with a particular type of kidney damage or region of renal injury.
  • the combination of sample analytes may be any three of the biomarker analytes. In other embodiments, the combination of sample analytes may be any four, any five, any six, any seven, any eight, any nine, any ten, any eleven, any twelve, any thirteen, any fourteen, any fifteen, or all sixteen of the sixteen biomarker analytes. In some embodiments, the combination of sample analytes comprises alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1. In another embodiment, the combination of sample analytes may comprise a combination listed in Table A.
  • the combination of sample analytes may include creatinine, KIM-1, and THP.
  • the combination of sample analytes may include microalbumin, creatinine, and KIM-1.
  • the combination of sample analytes may include creatinine, TIMP-1, and THP.
  • the combination of sample analytes may include creatinine, microalbumin, and THP.
  • test sample is an amount of bodily fluid taken from a mammal.
  • bodily fluids include urine, blood, plasma, serum, saliva, semen, perspiration, tears, mucus, and tissue lysates.
  • the bodily fluid contained in the test sample is urine, plasma, or serum.
  • a mammal as defined herein, is any organism that is a member of the class Mammalia.
  • mammals appropriate for the various embodiments include humans, apes, monkeys, rats, mice, dogs, cats, pigs, and livestock including cattle and oxen.
  • the mammal is a human.
  • the bodily fluids of the test sample may be taken from the mammal using any known device or method so long as the analytes to be measured by the multiplexed assay are not rendered undetectable by the multiplexed assay.
  • devices or methods suitable for taking bodily fluid from a mammal include urine sample cups, urethral catheters, swabs, hypodermic needles, thin needle biopsies, hollow needle biopsies, punch biopsies, metabolic cages, and aspiration.
  • the test sample may be diluted to reduce the concentration of the sample analytes prior to analysis.
  • the degree of dilution may depend on a variety of factors including but not limited to the type of multiplexed assay used to measure the analytes, the reagents utilized in the multiplexed assay, and the type of bodily fluid contained in the test sample.
  • the test sample is diluted by adding a volume of diluent ranging from about 1 ⁇ 2 of the original test sample volume to about 50,000 times the original test sample volume.
  • test sample is human urine and the multiplexed assay is an antibody-based capture-sandwich assay
  • the test sample is diluted by adding a volume of diluent that is about 100 times the original test sample volume prior to analysis.
  • test sample is human serum and the multiplexed assay is an antibody-based capture-sandwich assay
  • the test sample is diluted by adding a volume of diluent that is about 5 times the original test sample volume prior to analysis.
  • test sample is human plasma and the multiplexed assay is an antibody-based capture-sandwich assay
  • the test sample is diluted by adding a volume of diluent that is about 2,000 times the original test sample volume prior to analysis.
  • the diluent may be any fluid that does not interfere with the function of the multiplexed assay used to measure the concentration of the analytes in the test sample.
  • suitable diluents include deionized water, distilled water, saline solution, Ringer's solution, phosphate buffered saline solution, TRIS-buffered saline solution, standard saline citrate, and HEPES-buffered saline.
  • the concentration of a combination of sample analytes is measured using a multiplexed assay device capable of measuring the concentrations of up to sixteen of the biomarker analytes.
  • a multiplexed assay device as defined herein, is an assay capable of simultaneously determining the concentration of three or more different sample analytes using a single device and/or method. Any known method of measuring the concentration of the biomarker analytes may be used for the multiplexed assay device.
  • Non-limiting examples of measurement methods suitable for the multiplexed assay device include electrophoresis, mass spectrometry, protein microarrays, and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, and particle-based capture-sandwich immunoassays.
  • electrophoresis mass spectrometry
  • protein microarrays protein microarrays
  • immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, and particle-based capture-sandwich immunoassays.
  • the concentration of a combination of sample analytes is measured using a multiplexed assay device capable of measuring the concentrations of up to 189 of the biomarker analytes.
  • a multiplexed assay device as defined herein, is an assay capable of simultaneously determining the concentration of three or more different sample analytes using a single device and/or method. Any known method of measuring the concentration of the biomarker analytes may be used for the multiplexed assay device.
  • Non-limiting examples of measurement methods suitable for the multiplexed assay device include electrophoresis, mass spectrometry, protein microarrays, and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, vibrational detection using MicroElectroMagnetic Devices (MEMS), and particle-based capture-sandwich immunoassays.
  • electrophoresis mass spectrometry
  • protein microarrays and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, vibrational detection using MicroElectroMagnetic Devices (MEMS), and particle-based capture-sandwich immunoassays.
  • ELISA enzyme-linked immunosorbent assay
  • MEMS MicroElectroMagnetic Devices
  • the multiplexed immunoassay device includes three or more capture antibodies.
  • Capture antibodies as defined herein, are antibodies in which the antigenic determinant is one of the biomarker analytes.
  • Each of the at least three capture antibodies has a unique antigenic determinant that is one of the biomarker analytes.
  • the capture antibodies form antigen-antibody complexes in which the analytes serve as antigens.
  • the capture antibodies may be attached to a substrate in order to immobilize any analytes captured by the capture antibodies.
  • suitable substrates include paper or cellulose strips, polystyrene or latex microspheres, and the inner surface of the well of a microtitration tray.
  • an indicator is attached to each of the three or more capture antibodies.
  • the indicator as defined herein, is any compound that registers a measurable change to indicate the presence of one of the sample analytes when bound to one of the capture antibodies.
  • Non-limiting examples of indicators include visual indicators and electrochemical indicators.
  • Visual indicators are compounds that register a change by reflecting a limited subset of the wavelengths of light illuminating the indicator, by fluorescing light after being illuminated, or by emitting light via chemiluminescence.
  • the change registered by visual indicators may be in the visible light spectrum, in the infrared spectrum, or in the ultraviolet spectrum.
  • Non-limiting examples of visual indicators suitable for the multiplexed immunoassay device include nanoparticulate gold, organic particles such as polyurethane or latex microspheres loaded with dye compounds, carbon black, fluorophores, phycoerythrin, radioactive isotopes, nanoparticles, quantum dots, and enzymes such as horseradish peroxidase or alkaline phosphatase that react with a chemical substrate to form a colored or chemiluminescent product.
  • Electrochemical indicators are compounds that register a change by altering an electrical property.
  • the changes registered by electrochemical indicators may be an alteration in conductivity, resistance, capacitance, current conducted in response to an applied voltage, or voltage required to achieve a desired current.
  • Non-limiting examples of electrochemical indicators include redox species such as ascorbate (vitamin C), vitamin E, glutathione, polyphenols, catechols, quercetin, phytoestrogens, penicillin, carbazole, murranes, phenols, carbonyls, benzoates, and trace metal ions such as nickel, copper, cadmium, iron and mercury.
  • test sample containing a combination of three or more sample analytes is contacted with the capture antibodies and allowed to form antigen-antibody complexes in which the sample analytes serve as the antigens.
  • concentrations of the three or more analytes are determined by measuring the change registered by the indicators attached to the capture antibodies.
  • the indicators are polyurethane or latex microspheres loaded with dye compounds and phycoerythrin.
  • the multiplexed immunoassay device has a sandwich assay format.
  • the multiplexed sandwich immunoassay device includes three or more capture antibodies as previously described. However, in this embodiment, each of the capture antibodies is attached to a capture agent that includes an antigenic moiety. The antigenic moiety serves as the antigenic determinant of a detection antibody, also included in the multiplexed immunoassay device of this embodiment. In addition, an indicator is attached to the detection antibody.
  • the test sample is contacted with the capture antibodies and allowed to form antigen-antibody complexes in which the sample analytes serve as antigens.
  • the detection antibodies are then contacted with the test sample and allowed to form antigen-antibody complexes in which the capture agent serves as the antigen for the detection antibody. After removing any uncomplexed detection antibodies the concentration of the analytes are determined by measuring the changes registered by the indicators attached to the detection antibodies.
  • the concentrations of each of the sample analytes may be determined using any approach known in the art.
  • a single indicator compound is attached to each of the three or more antibodies.
  • each of the capture antibodies having one of the sample analytes as an antigenic determinant is physically separated into a distinct region so that the concentration of each of the sample analytes may be determined by measuring the changes registered by the indicators in each physically separate region corresponding to each of the sample analytes.
  • each antibody having one of the sample analytes as an antigenic determinant is marked with a unique indicator.
  • a unique indicator is attached to each antibody having a single sample analyte as its antigenic determinant.
  • all antibodies may occupy the same physical space. The concentration of each sample analyte is determined by measuring the change registered by the unique indicator attached to the antibody having the sample analyte as an antigenic determinant.
  • the multiplexed immunoassay device is a microsphere-based capture-sandwich immunoassay device.
  • the device includes a mixture of three or more capture-antibody microspheres, in which each capture-antibody microsphere corresponds to one of the biomarker analytes.
  • Each capture-antibody microsphere includes a plurality of capture antibodies attached to the outer surface of the microsphere.
  • the antigenic determinant of all of the capture antibodies attached to one microsphere is the same biomarker analyte.
  • the microsphere is a small polystyrene or latex sphere that is loaded with an indicator that is a dye compound.
  • the microsphere may be between about 3 ⁇ m and about 5 ⁇ m in diameter.
  • Each capture-antibody microsphere corresponding to one of the biomarker analytes is loaded with the same indicator. In this manner, each capture-antibody microsphere corresponding to a biomarker analyte is uniquely color-coded.
  • the multiplexed immunoassay device further includes three or more biotinylated detection antibodies in which the antigenic determinant of each biotinylated detection antibody is one of the biomarker analytes.
  • the device further includes a plurality of streptaviden proteins complexed with a reporter compound.
  • a reporter compound as defined herein, is an indicator selected to register a change that is distinguishable from the indicators used to mark the capture-antibody microspheres.
  • the concentrations of the sample analytes may be determined by contacting the test sample with a mixture of capture-antigen microspheres corresponding to each sample analyte to be measured.
  • the sample analytes are allowed to form antigen-antibody complexes in which a sample analyte serves as an antigen and a capture antibody attached to the microsphere serves as an antibody. In this manner, the sample analytes are immobilized onto the capture-antigen microspheres.
  • the biotinylated detection antibodies are then added to the test sample and allowed to form antigen-antibody complexes in which the analyte serves as the antigen and the biotinylated detection antibody serves as the antibody.
  • the streptaviden-reporter complex is then added to the test sample and allowed to bind to the biotin moieties of the biotinylated detection antibodies.
  • the antigen-capture microspheres may then be rinsed and filtered.
  • the concentration of each analyte is determined by first measuring the change registered by the indicator compound embedded in the capture-antigen microsphere in order to identify the particular analyte. For each microsphere corresponding to one of the biomarker analytes, the quantity of analyte immobilized on the microsphere is determined by measuring the change registered by the reporter compound attached to the microsphere.
  • the indicator embedded in the microspheres associated with one sample analyte may register an emission of orange light
  • the reporter may register an emission of green light
  • a detector device may measure the intensity of orange light and green light separately. The measured intensity of the green light would determine the concentration of the analyte captured on the microsphere, and the intensity of the orange light would determine the specific analyte captured on the microsphere.
  • Any sensor device may be used to detect the changes registered by the indicators embedded in the microspheres and the changes registered by the reporter compound, so long as the sensor device is sufficiently sensitive to the changes registered by both indicator and reporter compound.
  • suitable sensor devices include spectrophotometers, photosensors, colorimeters, cyclic coulometry devices, and flow cytometers.
  • the sensor device is a flow cytometer.
  • the multiplexed immunoassay device has a vibrational detection format using a MEMS.
  • the immunoassay device uses capture antibodies as previously described.
  • the capture antibodies are attached to a microscopic silicon microcantilever beam structure.
  • the microcantilevers are micromechanical beams that are anchored at one end, such as diving spring boards that can be readily fabricated on silicon wafers and other materials.
  • the microcantilever sensors are physical sensors that respond to surface stress changes due to chemical or biological processes. When fabricated with very small force constants, they can measure forces and stresses with extremely high sensitivity. The very small force constant of a cantilever allows detection not surface stress variation due to the binding of an analyte to the capture antibody on the microcantilever.
  • Binding of the analyte results in a differential surface stress due to adsorption-induced forces, which manifests as a deflection which can be measured.
  • the vibrational detection may be multiplexed.
  • a method for diagnosing, monitoring, or determining a renal disorder includes providing a test sample, determining the concentration of a combination of three or more a sample analytes, comparing the measured concentrations of the combination of sample analytes to the entries of a dataset, and identifying a particular renal disorder based on the comparison between the concentrations of the sample analytes and the minimum diagnostic concentrations contained within each entry of the dataset.
  • the concentrations of the sample analytes are compared to the entries of a dataset.
  • each entry of the dataset includes a combination of three or more minimum diagnostic concentrations indicative of a particular renal disorder.
  • a minimum diagnostic concentration is the concentration of an analyte that defines the limit between the concentration range corresponding to normal, healthy renal function and the concentration reflective of a particular renal disorder.
  • each minimum diagnostic concentration is the maximum concentration of the range of analyte concentrations for a healthy, normal individual.
  • the minimum diagnostic concentration of an analyte depends on a number of factors including but not limited to the particular analyte and the type of bodily fluid contained in the test sample. As an illustrative example, Table 1 lists the expected normal ranges of the biomarker analytes in human plasma, serum, and urine.
  • the high values shown for each of the biomarker analytes in Table 1 for the analytic concentrations in human plasma, sera and urine are the minimum diagnostics values for the analytes in human plasma, sera, and urine, respectively.
  • the minimum diagnostic concentration in human plasma of alpha-1 microglobulin is about 16 ⁇ g/ml
  • beta-2 microglobulin is about 2.2 ⁇ g/ml
  • calbindin is greater than about 5 ng/ml
  • clusterin is about 134 ⁇ g/ml
  • CTGF is about 16 ng/ml
  • cystatin C is about 1170 ng/ml
  • GST-alpha is about 62 ng/ml
  • KIM-1 is about 0.57 ng/ml
  • NGAL is about 375 ng/ml
  • osteopontin is about 25 ng/ml
  • THP is about 0.052 ⁇ g/ml
  • TIMP-1 is about 131 ng/ml
  • TFF-3 is about 0.49 ⁇ g
  • the minimum diagnostic concentration in human sera of alpha-1 microglobulin is about 17 ⁇ g/ml
  • beta-2 microglobulin is about 2.6 ⁇ g/ml
  • calbindin is greater than about 2.6 ng/ml
  • clusterin is about 152 ⁇ g/ml
  • CTGF is greater than about 8.2 ng/ml
  • cystatin C is about 1250 ng/ml
  • GST-alpha is about 52 ng/ml
  • KIM-1 is greater than about 0.35 ng/ml
  • NGAL is about 822 ng/ml
  • osteopontin is about 12 ng/ml
  • THP is about 0.053 ⁇ g/ml
  • TIMP-1 is about 246 ng/ml
  • TFF-3 is about 0.17 ⁇ g/ml
  • VEGF is about 1630 ⁇ g/ml.
  • the minimum diagnostic concentration in human urine of alpha-1 microglobulin is about 233 ⁇ g/ml
  • beta-2 microglobulin is greater than about 0.17 ⁇ g/ml
  • calbindin is about 233 ng/ml
  • clusterin is greater than about 0.089 ⁇ g/ml
  • CTGF is greater than about 0.90 ng/ml
  • cystatin C is about 1170 ng/ml
  • GST-alpha is greater than about 26 ng/ml
  • KIM-1 is about 0.67 ng/ml
  • NGAL is about 81 ng/ml
  • osteopontin is about 6130 ng/ml
  • THP is about 2.6 ⁇ g/ml
  • TIMP-1 is greater than about 3.9 ng/ml
  • TFF-3 is greater than about 21 ⁇ g/ml
  • VEGF is about 517 ⁇ g/ml.
  • the minimum diagnostic concentrations represent the maximum level of analyte concentrations falling within an expected normal range.
  • a renal disorder may be indicated if the concentration of an analyte is higher than the minimum diagnostic concentration for the analyte.
  • the minimum diagnostic concentration may not be an appropriate diagnostic criterion for identifying the particular renal disorder indicated by the sample analyte concentrations.
  • a maximum diagnostic concentration may define the limit between the expected normal concentration range for the analyte and a sample concentration reflective of a renal disorder. In those cases in which a maximum diagnostic concentration is the appropriate diagnostic criterion, sample concentrations that fall below a maximum diagnostic concentration may indicate a particular renal disorder.
  • a critical feature of the method of the multiplexed analyte panel is that a combination of sample analyte concentrations may be used to diagnose a renal disorder.
  • the analytes may be algebraically combined and compared to corresponding diagnostic criteria.
  • two or more sample analyte concentrations may be added and/or subtracted to determine a combined analyte concentration.
  • two or more sample analyte concentrations may be multiplied and/or divided to determine a combined analyte concentration.
  • the combined analyte concentration may be compared to a diagnostic criterion in which the corresponding minimum or maximum diagnostic concentrations are combined using the same algebraic operations used to determine the combined analyte concentration.
  • the analyte concentration measured from a test sample containing one type of body fluid may be algebraically combined with an analyte concentration measured from a second test sample containing a second type of body fluid to determine a combined analyte concentration.
  • the ratio of urine calbindin to plasma calbindin may be determined and compared to a corresponding minimum diagnostic urine:plasma calbindin ratio to identify a particular renal disorder.
  • any sample concentration falling outside the expected normal range indicates a renal disorder.
  • the multiplexed analyte panel may be used to evaluate the analyte concentrations in test samples taken from a population of patients having a particular renal disorder and compared to the normal expected analyte concentration ranges.
  • any sample analyte concentrations that are significantly higher or lower than the expected normal concentration range may be used to define a minimum or maximum diagnostic concentration, respectively.
  • sample analyte concentrations of a population of patients exposed to varying dosages of a potentially drug may be compared to each other and to the expected normal analyte concentrations. Any sample analyte concentrations falling significantly outside the expected normal analyte concentration range may be used to define diagnostic criteria.
  • sample analyte concentrations may be correlated to the dosage of the potentially toxic drug in order to define a diagnostic criteria used to determine the severity of a particular renal disorder based on the sample analyte concentration.
  • kidney damage identified by the multiplexed analyte panel include, but are not limited to glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, and acute tubular necrosis.
  • the multiplexed analyte panel identifies secondary kidney damaged caused by exposure to agents including but not limited to therapeutic drugs, recreational drugs, medical imaging contrast agents, toxins, kidney stones, ischemia, liver transplantation, heart transplantation, lung transplantation, and hypovolemia.
  • the multiplexed analyte panel identifies kidney damage caused by disease including but not limited to diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to any infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate disease.
  • diseases including lupus, Wegener's granulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to any infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate disease.
  • LDD least detectable doses
  • LLOQ lower limits of quantitation
  • the concentrations of the analytes were measured using a capture-sandwich assay using antigen-specific antibodies. For each analyte, a range of standard sample dilutions ranging over about four orders of magnitude of analyte concentration were measured using the assay in order to obtain data used to construct a standard dose response curve.
  • the dynamic range for each of the analytes defined herein as the range of analyte concentrations measured to determine its dose response curve, is presented below.
  • a filter-membrane microtiter plate was pre-wetted by adding 100 ⁇ L wash buffer, and then aspirated using a vacuum manifold device. The contents of the wells of the hard-bottom plate were then transferred to the corresponding wells of the filter-membrane plate. All wells of the hard-bottom plate were vacuum-aspirated and the contents were washed twice with 100 ⁇ L of wash buffer. After the second wash, 100 ⁇ L of wash buffer was added to each well, and then the washed microspheres were resuspended with thorough mixing. The plate was then analyzed using a Luminex 100 Analyzer (Luminex Corporation, Austin, Tex., USA). Dose response curves were constructed for each analyte by curve-fitting the median fluorescence intensity (MFI) measured from the assays of diluted standard samples containing a range of analyte concentrations.
  • MFI median fluorescence intensity
  • the least detectable dose was determined by adding three standard deviations to the average of the MFI signal measured for 20 replicate samples of blank standard solution (i.e. standard solution containing no analyte).
  • the MFI signal was converted to an LDD concentration using the dose response curve and multiplied by a dilution factor of 2.
  • the lower limit of quantification defined herein as the point at which the coefficient of variation (CV) for the analyte measured in the standard samples was 30%, was determined by the analysis of the measurements of increasingly diluted standard samples.
  • the standard solution was diluted by 2 fold for 8 dilutions.
  • samples were assayed in triplicate, and the CV of the analyte concentration at each dilution was calculated and plotted as a function of analyte concentration.
  • the LLOQ was interpolated from this plot and multiplied by a dilution factor of 2.
  • the results of this experiment characterized the least detectible dose and the lower limit of quantification for fourteen analytes associated with various renal disorders using a capture-sandwich assay.
  • the analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • A1M alpha-1 microglobulin
  • B2M beta-2 microglobulin
  • calbindin clusterin
  • CTGF cystatin C
  • GST-alpha cystatin C
  • KIM-1 NGAL
  • osteopontin OPN
  • THP TIMP-1
  • TFF-3 vascular endothelialpha
  • the results of this experiment characterized the precision of a capture-sandwich assay for fourteen analytes associated with various renal disorders over a wide range of analyte concentrations.
  • the precision of the assay varied between about 1% and about 15% error within a given run, and between about 5% and about 15% error between different runs.
  • the percent errors summarized in Table 2 provide information concerning random error to be expected in an assay measurement caused by variations in technicians, measuring instruments, and times of measurement.
  • analytes spiked into urine, serum, and plasma samples were assessed by an assay used to measure the concentration of analytes associated with renal disorders.
  • the analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • the concentrations of the analytes in the samples were measured using the methods described in Example 1.
  • the average % recovery was calculated as the proportion of the measurement of analyte spiked into the urine, serum, or plasma sample (observed) to the measurement of analyte spiked into the standard solution (expected).
  • the results of the spike recovery analysis are summarized in Table 5.
  • the sandwich-type assay is reasonably sensitive to the presence of all analytes measured, whether the analytes were measured in standard samples, urine samples, plasma samples, or serum samples.
  • the analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • A1M alpha-1 microglobulin
  • B2M beta-2 microglobulin
  • calbindin clusterin
  • CTGF cystatin C
  • cystatin C GST-alpha
  • KIM-1 NGAL
  • osteopontin OPN
  • THP TIMP-1
  • TFF-3 VEGF
  • Matrix interference was assessed by spiking hemoglobin, bilirubin, and triglyceride into standard analyte samples and measuring analyte concentrations using the methods described in Example 1. A % recovery was determined by calculating the ratio of the analyte concentration measured from the spiked sample (observed) divided by the analyte concentration measured form the standard sample (expected). The results of the matrix interference analysis are summarized in Table 6.
  • analytes spiked into urine, serum, and plasma samples were assessed to assess the ability of analytes spiked into urine, serum, and plasma samples to tolerate freeze-thaw cycles.
  • the analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • A1M alpha-1 microglobulin
  • B2M beta-2 microglobulin
  • calbindin clusterin
  • CTGF cystatin C
  • GST-alpha cystatin C
  • KIM-1 NGAL
  • osteopontin osteopontin
  • THP TIMP-1
  • TFF-3 VEGF
  • the concentrations of the analytes in the samples were measured using the methods described in Example 1 after the initial addition of the analyte, and after one, two and three cycles of freezing and thawing.
  • analyte concentrations in urine, serum and plasma samples were measured immediately after the addition of the analyte to the samples as well as after storage at room temperature for two hours and four hours, and after storage at 4° C. for 2 hours, four hours, and 24 hours.
  • KIM-1 Control 1.5 100 0.23 100 0.24 100 (ng/mL) 2 hr @ 1.2 78 0.2 86 0.22 90 room temp 2 hr. @ 1.6 106 0.23 98 0.21 85 4° C. 4 hr @ 1.3 84 0.19 82 0.2 81 room temp 4 hr. @ 1.4 90 0.22 93 0.19 80 4° C. 24 hr. @ 1.1 76 0.18 76 0.23 94 4° C.
  • VEGF Control 851 100 1215 100 670 100 (pg/mL) 2 hr @ 793 93 1055 87 622 93 room temp 2 hr. @ 700 82 1065 88 629 94 4° C.
  • Cys- Control 52 100 819 100 476 100 tatin 2 hr @ 50 96 837 102 466 98 C room temp (ng/mL) 2 hr. @ 44 84 884 108 547 115 4° C. 4 hr @ 49 93 829 101 498 105 room temp 4 hr. @ 46 88 883 108 513 108 4° C. 24 hr. @ 51 97 767 94 471 99 4° C.
  • NGAL Control 857 100 302 100 93 100 (ng/mL) 2 hr @ 888 104 287 95 96 104 room temp 2 hr. @ 923 108 275 91 92 100 4° C.
  • TIMP-1 Control 17 100 349 100 72 100 (ng/mL) 2 hr @ 17 98 311 89 70 98 room temp 2 hr. @ 16 94 311 89 68 95 4° C. 4 hr @ 17 97 306 88 68 95 room temp 4 hr. @ 16 93 329 94 74 103 4° C. 24 hr. @ 18 105 349 100 72 100 4° C.
  • Urine concentrations of analytes included in a human kidney toxicity panel were measured by the assay, including alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • FIG. 1 summarizes the urine concentrations of those analytes that differed significantly from control urine concentrations.
  • the urine concentrations of A1M, NGAL, and THP were slightly elevated for the renal cancer patient group and more significantly elevated for the “other” cancer patient group.
  • Urine B2M concentrations appeared to be elevated for both the renal cancer and “other” cancer patient groups, although the BRM concentrations exhibited more variability than the other analyte concentrations shown in FIG. 1 .
  • a screen for potential protein biomarkers in relation to kidney toxicity/damage was performed using a panel of biomarkers, in a set of urine and plasma samples from patients with documented renal damage.
  • the investigated patient groups included diabetic nephropathy (DN), obstructive uropathy (OU), analgesic abuse (AA) and glomerulonephritis (GN) along with age, gender and BMI matched control groups.
  • DN diabetic nephropathy
  • OU obstructive uropathy
  • AA analgesic abuse
  • GN glomerulonephritis
  • Multiplexed immunoassays were applied in order to quantify the following protein analytes: Alpha-1 Microglobulin ( ⁇ 1M), KIM-1, Microalbumin, Beta-2-Microglobulin ( ⁇ 2M), Calbindin, Clusterin, CystatinC, TreFoilFactor-3 (TFF-3), CTGF, GST-alpha, VEGF, Calbindin, Osteopontin, Tamm-HorsfallProtein (THP), TIMP-1 and NGAL.
  • Li-Heparin plasma and mid-stream spot urine samples were collected from four different patient groups. Samples were also collected from age, gender and BMI matched control subjects. 20 subjects were included in each group resulting in a total number of 160 urine and plasma samples. All samples were stored at ⁇ 80° C. before use. Glomerular filtration rate for all samples was estimated using two different estimations (Modification of Diet in Renal Disease or MDRD, and the Chronic Kidney Disease Epidemiology Collaboration or CKD-EPI) to outline the eGFR (estimated glomerular filtration rate) distribution within each patient group ( FIG. 2 ). Protein analytes were quantified in human plasma and urine using multiplexed immunoassays in the Luminex xMAPTM platform.
  • microsphere-based multiplex immunoassays consist of antigen-specific antibodies and optimized reagents in a capture-sandwich format. Output data was given as g/ml calculated from internal standard curves. Because urine creatinine (uCr) correlates with renal filtration rate, data analysis was performed without correction for uCr. Univariate and multivariate data analysis was performed comparing all case vs. control samples as well as cases vs. control samples for the various disease groups.
  • Urine and plasma samples were taken from 80 normal control group subjects and 20 subjects from each of four disorders: analgesic abuse, diabetic nephropathy, glomerulonephritis, and obstructive uropathy.
  • the samples were analyzed for the quantity and presence of 16 different proteins (alpha-1 microglobulin ( ⁇ 1M), beta-2 microglobulin ( ⁇ 2M), calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF) as described in Example 1 above.
  • the goal was to determine the analytes that distinguish between a normal sample and a diseased sample, a normal sample and an obstructive uropathy (OU) sample, and finally, an glomerulonephritis sample from the other disease samples (diabetic nephropathy (DN), analgesic abuse (AA), and glomerulonephritis (GN)).
  • DN diabetic nephropathy
  • AA analgesic abuse
  • GN glomerulonephritis
  • the mean error rates and AUROC were calculated from urine and AUROC was calculated from plasma for 100 runs of the above method for each of the following comparisons: disease (AA+GN+OU+DN) vs. normal ( FIG. 5 , Table 11), AA vs. normal ( FIG. 7 , Table 13), DN vs. AA ( FIG. 9 , Table 15, AA vs. GN ( FIG. 11 , Table 17), and AA vs. OU ( FIG. 13 , Table 19).
  • FIG. 15 is a block diagram of an exemplary computing environment 1500 for diagnosing, monitoring, and/or determining a renal disorder in a mammal.
  • the computing environment 1500 includes sample input device 1502 , a renal disorder diagnostics system (RDSS) 1504 , and a data source 1506 .
  • RDSS renal disorder diagnostics system
  • sample input device 1502 is a computer or processing device 1508 , such as a personal computer, a server computer, or a mobile processing device.
  • the computer 1508 may include a display such as a computer monitor, for viewing data, and an input device, such as a keyboard or a pointing device (e.g., a mouse, trackball, pen, touch pad, or other device), for entering data.
  • the computer 1508 is used by a user to enter analyte concentrations of a test sample for processing by the RDSS 1504 .
  • the user uses the keyboard to interact with an analyte concentration entry form (not shown) on the display to enter test sample analyte data that includes, for example, three or more analyte concentrations.
  • test sample analyte concentrations are collected and then transmitted to the RDSS 1504 via an analyte measurement/sensor device 1510 (e.g., multiplexed immunoassay device) that measures the sample analyte concentration.
  • the analyte measurement/sensor device 1510 communicates the measured sample analyte concentrations data to the RDSS 1504 via a data cable, infrared signal, wireless connection, or other methods of data transmission known in the art.
  • the RDDS 1504 executes a renal disorder determining application 1512 in response to test sample analyte concentration data received from the received from the sample input device 102 .
  • the renal disorder determining application (RDDA) 1512 analyzes the analyte concentration data for the test sample and determines whether the received analyte concentration data is indicative of renal disorder and, if so, a type of renal disorder.
  • the renal disorder determining application 1512 displays whether the result of the analysis is positive or negative for a renal disorder and, if applicable, the type of renal disorder.
  • the RDDS 1904 retrieves concentration threshold data and/or disorder threshold data from the data source 1506 to determine whether the received analyte concentration data is indicative of one or more renal disorders.
  • the data source 1506 is, for example, a computer system, a database, or another data system that stores data, electronic documents, records, other documents, and/or other data.
  • the data source 1506 may include memory and one or more processors or processing systems to receive, process, and transmit communications and store and retrieve data.
  • the data source 1506 includes a diagnostic analytic concentrations database 1514 that stores normal ranges of biomarker analytes for human plasma, serum, and urine, such described above in connection with Table 1.
  • the entries of the diagnostic analytic concentrations database 1514 may also include additional minimum diagnostic concentrations to further define diagnostic criteria including but not limited to minimum diagnostic concentrations for additional types of bodily fluids, additional types of mammals, and severities of a particular disorder. As described above, if the measured concentration for a particular analyte of a sample of plasma exceeds the high value in Table 1, then the measured concentration of that particular may be indicative of a renal disorder or disease in the subject from with the test sample was collected.
  • the disorder database 1516 includes various data tables index by disorder or disease type.
  • Each data table corresponds to a specific disorder/disease type and identifies a list of minimum diagnostic concentrations that are indicative of that particular disease.
  • diabetic nephropathy data table indicates by sample type (i.e., plasma, urine, serum) the minimum concentration required, if any, for each of sixteen analyte biomarkers described above in connection with Table 1.
  • the data source is illustrated in FIG. 15 as being integrated with the RDDS 1504 , it is contemplated that in other aspects the data source 1506 may be separate and/or remote from the RDDS 1504 .
  • the RDDS 1504 communicates with the data source 1506 over a communication network, such as the Internet, an intranet, an Ethernet network, a wireline network, a wireless network, and/or another communication network, to identify relevant images, electronic documents, records, other documents, and/or other data to retrieve from the data source 1506 .
  • the sample input device 1502 communicates with the RDDS 1904 through the communication network.
  • the RDDS 1504 communicates with the data source 1506 through a direct connection.
  • FIG. 16 is a block diagram that depicts an exemplary RDDS 1504 .
  • the RDDS 1504 includes a processing system 1602 that executes the RDDA 1512 to determine whether the received analyte concentration data is indicative of renal disorder and, if so, the type of renal disorder.
  • the processing system 1602 includes memory and one or more processors, and the processing system 1602 can reside on a computer or other processing system.
  • the data source 1506 is not shown and is, for example, located remotely from the RDDS 1504 .
  • the RDDA 1512 includes instructions or modules that are executable by the processing system 1602 to manage the retrieval of renal disorder diagnostic data, including a record, from the data source 1506 .
  • the RDDS 1504 includes computer readable media 1604 configured with the RDDA 1512 .
  • Computer readable medium (CRM) 1604 may include volatile media, nonvolatile media, removable media, non-removable media, and/or another available medium that can be accessed by the RDDS 1504 .
  • computer readable medium 1604 comprises computer storage media and communication media.
  • Computer storage media includes memory, volatile media, nonvolatile media, removable media, and/or non-removable media implemented in a method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.
  • Communication media may embody computer readable instructions, data structures, program modules, or other data and include an information delivery media or system.
  • An analyte input module 1606 receives three or more sample analyte concentrations that include the biomarker analytes.
  • the sample analyte concentrations are entered as input by a user of the computer 1508 .
  • the sample analyte concentrations are received directly from analyte measure/sensor device 1510 , such as a multiplexed immunoassay device.
  • the analyte input module 1606 receives sample analyte concentrations for at least six biomarker analytes.
  • the at least six biomarker analytes include alpha 1 microglobulin, cystatin C, KIM-1, Tamm-Horsfall, Beta 2-microglobulin, and TIMP-1.
  • the analyte input module 1606 receives sample analyte concentrations for sixteen biomarker analytes.
  • the sixteen biomarker analytes include the analyte types shown in Table 1.
  • a comparison module 1608 compares each analyte concentration of a sample received from the analyte input module 1606 to a corresponding analyte entry in the diagnostic analyte database to determine if one or more concentrations for a particular analyte of the sample are exceed the minimum diagnostic value for that particular analyte. For example, referring briefly to Table 1, if the sample concentrations are obtained from plasma and the particular analyte is calbindin, the comparison module compares the measured calbindin analyte concentration to the sample to the corresponding high concentration value for plasma to determine if it is greater than about 5 ng/ml.
  • a measured calbindin analyte concentration less than about 5 ng/ml indicates is not indicative of renal disorder.
  • a measured calbindin analyte concentration that is greater than about 5 ng/ml is indicative of a renal disorder.
  • An analysis module 1610 determines a most likely renal disorder as a function of the particular measured analyte concentrations identified as indicative of a renal disorder by the comparison module. For example, the analysis module 1610 compares the particular measured analyte concentrations to entries in the disorder tables stored in the renal disorder database 1516 to identify the most likely type renal disorder.
  • Each disorder table includes, for example, the minimum concentrations or threshold concentrations for each of the sixteen analytes types shown in Table 1 that are associated with the diagnosis of a particular renal disorder or disease. It is also contemplated that the analyte types listed in a disorder table for particular renal disorder or disease may be different from the analyte types listed in another disorder table for a different renal disorder or disease.
  • the most likely renal disorder is the particular renal disorder type in the disorder database 1516 having the most minimum diagnostic concentrations that are less than the corresponding sample analyte concentrations.
  • the most likely disorder is identified from the disorder table that includes the most threshold concentrations that are exceeded by the sample analyte concentrations. For example, consider that five of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in the disorder table for a first renal disorder, such as analgesic abuse. Also, consider that four of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a second renal disorder, such as obstructive uropathy. In this example, the most likely renal disorder is analgesic abuse.
  • the most likely renal disorder is the particular renal disorder type in the disorder database 1516 having the most minimum diagnostic concentrations that are less than the corresponding sample analyte concentrations.
  • the most likely disorder is identified from the disorder table that includes the most threshold concentrations that are exceeded by the sample analyte concentrations. For example, consider that five of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a first renal disorder, such as analgesic abuse. Also, consider that four of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a second renal disorder, such as obstructive uropathy. In this example, the most likely renal disorder is analgesic abuse.
  • the most likely renal disorder is the particular renal disorder from the database entry having minimum diagnostic concentrations that are all less than the corresponding sample analyte concentrations.
  • the analysis module 1610 combines the sample analyte concentrations algebraically to calculate a combined sample analyte concentration that is compared to a combined minimum diagnostic concentration calculated from the corresponding minimum diagnostic criteria using the same algebraic operations. See Table A for example combinations. Other combinations of sample analyte concentrations from within the same test sample, or combinations of sample analyte concentrations from two or more different test samples containing two or more different bodily fluids may be used to determine a particular renal disorder in still other embodiments.
  • An output module 1612 generates a display of analyte types and corresponding concentrations for each of the measured analytes identified as indicative of a renal disorder by the comparison module.
  • the output module 1612 also generates a display of the most likely renal disorder determined by the analysis module 1610 .
  • FIG. 17 illustrates a method for diagnosing, monitoring, or determining a renal disorder in a mammal in accordance with an aspect of the RDDS 1504 .
  • analyte concentrations read by an assay device or defined via user input at a computer are communicated to the renal disorder determining application 1512 .
  • the sample analyte concentrations are transferred to the RDSS 1504 for processing.
  • the concentration of each analyte type in the sample is compared to a corresponding threshold analyte concentration in a diagnostic analyte database at 1706 .
  • the threshold analyte concentrations in the diagnostic analyte database correspond to analyte concentration for various sample types that have been previous determined to be indicative of one or more renal disorders or diseases. If none of the analyte concentrations for the sample are determined to be greater than the corresponding threshold analyte concentrations at 1708 . The one or more of the analyte concentrations and/or a message indicating the concentrations are within normal range is generated for display via the computer at 1710 .
  • the one or more analyte concentrations for the sample are then compared to disorder threshold analyte concentrations in a disorder database at 1712 .
  • the disorder threshold analyte concentrations correspond to minimum analyte concentrations associated with a particular renal disorder or disease.
  • the particular disorder that corresponds to the disorder table that has the most disorder threshold analyte concentrations exceeded by the sample analyte concentrations is identified as the most likely renal disorder.
  • the one or more of the analyte concentrations for the sample and the most likely renal disorder type is generated for display via the computer at 1716 .

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Abstract

Methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal are described. In particular, methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal are described.

Description

    RELATED APPLICATIONS
  • This application takes priority to U.S. Provisional Patent Application No. 61/327,389, filed Apr. 23, 2010 and U.S. Provisional Patent Application No. 61/232,091, filed Aug. 7, 2009, and both entitled Methods and Devices for Detecting Kidney Damage, the entire contents of which are incorporated herein by reference, and is related to U.S. Patent Application Nos. [Not Yet Assigned], entitled Methods and Devices for Detecting Obstructive Uropathy and Associated Disorders, Methods and Devices for Detecting Kidney Damage, Devices for Detecting Renal Disorders, Methods and Devices for Detecting Kidney Transplant Rejection, Methods and Devices for Detecting Diabetic Nephropathy and Associated Disorders, and Methods and Devices for Detecting Glomerulonephritis and Associated Disorders, Attorney Docket Nos. 060075-, filed on the same date as this application, the entire contents of which are incorporated herein by reference.
    • FIELD OF THE INVENTION
  • The invention encompasses methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal. In particular, the present invention provides methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal.
    • BACKGROUND OF THE INVENTION
  • The urinary system, in particular the kidneys, perform several critical functions such as maintaining electrolyte balance and eliminating toxins from the bloodstream. In the human body, the pair of kidneys together process roughly 20% of the total cardiac output, amounting to about 1 L/min in a 70-kg adult male. Because compounds in circulation are concentrated in the kidney up to 1000-fold relative to the plasma concentration, the kidney is especially vulnerable to injury due to exposure to toxic compounds.
  • In the pharmaceutical industry, drug-induced kidney injury is a major cause for delay during the development of candidate drugs. Historically, regulatory agencies have required drug companies to provide results of blood urea nitrogen (BUN) and serum creatinine tests, two common diagnostic tests for renal function, to address concerns of potential kidney damage as part of the regulatory approval process. However, these diagnostic tests typically detect only late signs of kidney damage and provide little information as to the location of kidney damage.
  • In addition to injuries resulting from exposure to drugs or other toxic compounds, kidney damage may also result from renal disorders such as kidney trauma, nephritis, kidney cancer, and kidney transplant rejection. Kidney damage may also occur as a secondary side effect of more systemic diseases such as diabetes, hypertension, and autoimmune diseases. Existing diagnostic tests such as BUN and serum creatine tests typically detect only advanced stages of kidney damage. Other diagnostic tests such as kidney tissue biopsies or CAT scans have the advantage of enhanced sensitivity to earlier stages of kidney damage, but these tests are also generally costly, slow, and/or invasive.
  • A need exists in the art for a fast, simple, reliable, and sensitive method of detecting a renal disorder. The detection of the early signs and locations of drug-induced kidney damage would be useful in guiding important decisions on lead compounds and dosage. In a clinical setting, the early detection of kidney damage would help medical practitioners to diagnose and treat kidney damage more quickly and effectively.
    • SUMMARY OF THE INVENTION
  • The present invention provides computer methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal. In particular, the present invention provides methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal.
  • One aspect of the present invention provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes providing a test sample that includes a sample of bodily fluid taken from the mammal, and determining the presence of a combination of three or more sample analytes in the test sample. The analytes in the test sample may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. The combination of sample analytes is compared to the entries of a dataset in which each entry includes a combination of three or more diagnostic analytes reflective of a particular renal disorder. The particular renal disorder of the mammal is identified as the renal disorder in the database having the combination of diagnostic analytes that essentially match the combination of sample analytes.
  • In another aspect, a method for diagnosing, monitoring, or determining a renal disorder in a mammal is provided that includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations for three or more sample analytes in the test sample. The analytes may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. The combination of sample concentrations is compared to the entries of a dataset in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder. Each minimum diagnostic concentration is the maximum concentration of a range of analyte concentrations for a healthy mammal. A matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • In yet another aspect, a method for diagnosing, monitoring, or determining a renal disorder in a mammal is provided that includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of calbindin, clusterin, CTGF, GST-alpha, KIM-1, and VEGF in the test sample. The combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder. A matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • In still another aspect, a method for diagnosing, monitoring, or determining a renal disorder in a mammal is provided that includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of beta-2 microglobulin, cystatin C, NGAL, osteopontin, and TIMP-1 in the test sample. The combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder. A matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • In an additional aspect, a method for diagnosing, monitoring, or determining a renal disorder in a mammal is provided that includes providing a test sample that includes a sample of bodily fluid taken from the mammal and determining a combination of sample concentrations consisting of the concentrations of alpha-1 microglobulin, THP, and TFF-3 in the test sample. The combination of sample concentrations is compared to the entries of a data set in which each entry includes a particular renal disorder and a list of three or more minimum diagnostic concentrations indicative of the particular renal disorder. A matching entry is determined in which all minimum diagnostic concentrations are less than the corresponding sample concentrations, and an indicated renal disorder is identified as the particular renal disorder of the matching entry.
  • In yet another aspect, a method for diagnosing, monitoring, or determining a renal disorder in a mammal is provided. The method includes providing a test sample comprising a sample of bodily fluid taken from the mammal and determining the concentrations of three or more sample analytes in a panel of biomarkers in the test sample. The sample analytes may be selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. Diagnostic analytes are then identified in the test sample, wherein the diagnostic analytes are the sample analytes whose concentrations are statistically different from concentrations found in a control group of humans who do not suffer from a renal disorder. The combination of diagnostic analytes are compared to a dataset comprising at least one entry, wherein each entry of the dataset comprises a combination of three or more diagnostic analytes reflective of a particular renal disorder. The particular renal disorder in the list is identified as the renal disorder having the combination of diagnostic analytes that essentially match the combination of sample analytes.
  • An additional aspect provides a computer readable media encoded with an application that includes modules executable by a processor and configured to diagnose, monitor, or determine a renal disorder in a mammal. An analyte input module receives three or more sample analyte concentrations that may include alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. A comparison module compares each sample analyte concentration to an entry of a renal disorder database, where each entry includes a list of minimum diagnostic concentrations reflective of a particular renal disorder. An analysis module determines a most likely renal disorder by combining the particular renal disorders identified by the comparison module for all of the sample analyte concentrations.
  • Yet another aspect provides a system for diagnosing, monitoring, or determining a renal disorder in a mammal that includes a database to store a plurality of renal disorder database entries as well as a processing device that includes a renal disorder diagnosis application containing modules executable by the processing device. The modules of the renal disorder diagnosis application include an analyte input module to receive three or more sample analyte concentrations selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. Another module, the comparison module, compares each sample analyte concentration to an entry of the renal disorder database. Each entry of the renal disorder database contains a list of minimum diagnostic concentrations reflective of a particular renal disorder. An analysis module determines a most likely renal disorder by combining the particular renal disorders identified by the comparison module for all of the sample analyte concentrations.
  • An aspect provides a device for diagnosing, monitoring, or determining a renal disorder in a mammal that includes three or more antibodies and a plurality of indicators attached to each of the antibodies. The antigenic determinants of the antibodies are analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF.
  • Another aspect provides a device for diagnosing, monitoring, or determining a renal disorder in a mammal that includes three or more capture antibodies, three or more capture agents, three or more detection antibodies, and three or more indicators. The antigenic determinants of the capture antibodies are analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. One of the capture agents is attached to each of the capture antibodies, and includes an antigenic moiety. The antigenic determinant of the detection antibodies is the antigenic moiety. Each of the indicators is attached to one of the detection antibodies.
  • A final aspect provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes providing an analyte concentration measurement device that includes three or more detection antibodies, in which each detection antibody includes an antibody coupled to an indicator. The antigenic determinants of the antibodies are sample analytes associated with a renal disorder including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. A test sample that contains three or more sample analytes and a bodily fluid taken from the mammal is provided and contacted with the detection antibodies. The detection antibodies are allowed to bind to the sample analytes. The concentrations of the sample analytes are determined by detecting the indicators of the detection antibodies bound to the sample analytes in the test sample. The concentrations of each sample analyte are compared to a corresponding minimum diagnostic concentration reflective of a particular renal disorder.
  • Other aspects and iterations of the invention are described in more detail below.
    • DESCRIPTION OF FIGURES
  • FIG. 1 depicts four graphs comparing (A) the concentrations of alpha-1 microglobulin in the urine of normal controls, kidney cancer patients, and patients with other cancer types; (B) the concentrations of beta-2 microglobulin in the urine of normal controls, kidney cancer patients, and patients with other cancer types; (C) the concentrations of NGAL in the urine of normal controls, kidney cancer patients, and patients with other cancer types; and (D) the concentrations of THP in the urine of normal controls, kidney cancer patients, and patients with other cancer types.
  • FIG. 2 shows the four different disease groups from which samples were analyzed, and a plot of two different estimations on eGFR outlining the distribution within each group.
  • FIG. 3 is a number of scatter plots of results on selected proteins in urine and plasma. The various groups are indicated as follows—control: blue, AA: red, DN: green, GN: yellow, OU: orange. (A) A1M in plasma, (B) cystatin C in plasma, (C) B2M in urine, (D) cystatin C in urine.
  • FIG. 4 depicts the multivariate analysis of the disease groups and their respective matched controls using plasma results. Relative importance shown using the random forest model.
  • FIG. 5 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish disease samples vs. normal samples. Disease encompasses analgesic abuse (AA), glomerulonephritis (GN), obstructive uropathy (OU), and diabetic nephropathy (DN). Normal=NL.
  • FIG. 6 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish disease (AA+GN+ON+DN) samples vs. normal samples from plasma (A) and urine (B and C).
  • FIG. 7 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish analgesic abuse samples vs. normal samples. Abbreviations as in FIG. 4.
  • FIG. 8 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish analgesic abuse samples vs. normal samples from plasma (A) and urine (B and C).
  • FIG. 9 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish analgesic abuse samples vs. diabetic nephropathy samples. Abbreviations as in FIG. 4.
  • FIG. 10 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish analgesic abuse samples vs. diabetic nephropathy samples from plasma (A) and urine (B and C).
  • FIG. 11 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish glomerulonephritis samples vs. analgesic abuse samples. Abbreviations as in FIG. 4.
  • FIG. 12 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish glomerulonephritis samples vs. analgesic abuse samples from plasma (A) and urine (B and C).
  • FIG. 13 depicts three graphs showing the mean AUROC and its standard deviation (A) for plasma samples, and mean error rates (B) and mean AUROC (C) from urine samples for each classification method used to distinguish obstructive uropathy samples vs. analgesic abuse samples. Abbreviations as in FIG. 4.
  • FIG. 14 depicts three graphs showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (A and B) or boosting (C) to distinguish obstructive uropathy samples vs. analgesic abuse samples from plasma (A) and urine (B and C).
  • FIG. 15 is a block diagram of an emplary computing environment for implementing a renal disorder diagnostic system.
  • FIG. 16 is a block diagram that depicts an exemplary renal disorder diagnostic system.
  • FIG. 17 illustrates a method for diagnosing, monitoring, or determining a renal disorder in a mammal in accordance with an aspect of the renal disorder diagnostic system.
    •  DETAILED DESCRIPTION OF THE INVENTION
  • It has been discovered that a multiplexed panel of up to 16 biomarkers may be used to detect early renal damage and pinpoint the location of renal damage within the kidney. The biomarkers included in the multiplexed panel are analytes known in the art that may be detected in the urine, serum, plasma and other bodily fluids of mammals. As such, the analytes of the multiplexed panel may be readily extracted from the mammal in a test sample of bodily fluid. The concentrations of the analytes within the test sample may be measured using known analytical techniques such as a multiplexed antibody-based immunological assay. The combination of concentrations of the analytes in the test sample may be compared to empirically determined combinations of minimum diagnostic concentrations and combinations of diagnostic concentration ranges associated with healthy kidney function or one or more particular renal disorders to determine whether a renal disorder is indicated in the mammal. The potentially large number of combinations of diagnostic analyte concentrations makes possible a wide range of diagnostic criteria that may be used to identify a variety of renal disorders and pinpoint the location in the kidney of a renal injury, using a single multiplexed assay to evaluate a single test sample.
  • As used herein, the term “renal disorder” includes, but is not limited to glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, analgesic nephropathy, and acute tubular necrosis. In another embodiment, the multiplexed analyte panel identifies secondary kidney damaged caused by exposure to a toxic compound including but not limited to therapeutic drugs, recreational drugs, contrast agents, medical imaging contrast agents, and toxins. Non-limiting examples of therapeutic drugs may include an analgesic (e.g. aspirin, acetaminophen, ibuprofen, naproxen sodium), an antibiotic (e.g. an aminoglycoside, a beta lactam (cephalosporins, penicillins, penems), rifampin, vancomycin, a sulfonamide, a fluoroquinolone, and a tetracycline), or a chemotherapy agent (e.g. Cisplatin (Platinol®), Carboplatin (Paraplatin®), Cytarabine (Cytosar-U®), Gemtuzumab ozogamicin (Mylotarg®), Gemcitabine (Gemzar®), Melphalan (Alkeran®), Ifosfamide (Ifex®), Methotrexate (Rheumatrex®), Interleukin-2 (Proleukin®), Oxaliplatin (Eloxatin®), Streptozocin (Zanosar®), Pemetrexed (Alimta®), Plicamycin (Mithracin®), and Trimetrexate (Neutrexin®). In yet another embodiment, the kidney damage may be due to kidney stones, ischemia, liver transplantation, heart transplantation, lung transplantation, or hypovolemia. In still another embodiment, the multiplexed analyte panel identifies kidney damage caused by disease including but not limited to diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to any infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate disease.
  • One embodiment of the present invention provides a method for diagnosing, monitoring, or determining a renal disorder in a mammal that includes determining the presence or concentration of a combination of three or more sample analytes in a test sample containing the bodily fluid of the mammal. The measured concentrations of the combination of sample analytes is compared to the entries of a dataset in which each entry contains the minimum diagnostic concentrations of a combination of three of more analytes reflective of a particular renal disorder. Other embodiments provide computer-readable media encoded with applications containing executable modules, systems that include databases and processing devices containing executable modules configured to diagnose, monitor, or determine a renal disorder in a mammal. Still other embodiments provide antibody-based devices for diagnosing, monitoring, or determining a renal disorder in a mammal.
  • The analytes used as biomarkers in the multiplexed assay, methods of diagnosing, monitoring, or determining a renal disorder using measurements of the analytes, systems and applications used to analyze the multiplexed assay measurements, and antibody-based devices used to measure the analytes are described in detail below.
    • I. Analytes in Multiplexed Assay
  • One embodiment of the invention measures the concentrations of at least three, six, or preferably sixteen biomarker analytes within a test sample taken from a mammal and compares the measured analyte concentrations to minimum diagnostic concentrations to diagnose, monitor, or determine kidney damage in a mammal. In this aspect, the biomarker analytes are known in the art to occur in the urine, plasma, serum and other bodily fluids of mammals. The biomarker analytes are proteins that have known and documented associations with early kidney damage in humans. As defined herein, the biomarker analytes may include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, VEGF A, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, GST-mu, EGF, and cortisol. A description of some of the biomarker analytes are given below.
    • (a) Alpha-1 Microglobulin (A1M)
  • Alpha-1 microglobulin (A1M, Swiss-Prot Accession Number P02760) is a 26 kDa glycoprotein synthesized by the liver and reabsorbed in the proximal tubules. Elevated levels of A1M in human urine are indicative of glomerulotubular dysfunction. A1M is a member of the lipocalin super family and is found in all tissues. Alpha-1-microglobulin exists in blood in both a free form and complexed with immunoglobulin A (IgA) and heme. Half of plasma A1M exists in a free form, and the remainder exists in complexes with other molecules including prothrombin, albumin, immunoglobulin A and heme. Nearly all of the free A1M in human urine is reabsorbed by the megalin receptor in proximal tubular cells, where it is then catabolized. Small amounts of A1M are excreted in the urine of healthy humans. Increased A1M concentrations in human urine may be an early indicator of renal damage, primarily in the proximal tubule.
    • (b) Beta-2 Microglobulin (B2M)
  • Beta-2 microglobulin (B2M, Swiss-Prot Accession Number P61769) is a protein found on the surfaces of all nucleated cells and is shed into the blood, particularly by tumor cells and lymphocytes. Due to its small size, B2M passes through the glomerular membrane, but normally less than 1% is excreted due to reabsorption of B2M in the proximal tubules of the kidney. Therefore, high plasma levels of B2M occur as a result of renal failure, inflammation, and neoplasms, especially those associated with B-lymphocytes.
    • (c) Calbindin
  • Calbindin (Calbindin D-28K, Swiss-Prot Accession Number P05937) is a Ca-binding protein belonging to the troponin C superfamily. It is expressed in the kidney, pancreatic islets, and brain. Calbindin is found predominantly in subpopulations of central and peripheral nervous system neurons, in certain epithelial cells involved in Ca2+ transport such as distal tubular cells and cortical collecting tubules of the kidney, and in enteric neuroendocrine cells.
    • (d) Clusterin
  • Clusterin (Swiss-Prot Accession Number P10909) is a highly conserved protein that has been identified independently by many different laboratories and named SGP2, S35-S45, apolipoprotein J, SP-40, 40, ADHC-9, gp80, GPIII, and testosterone-repressed prostate message (TRPM-2). An increase in clusterin levels has been consistently detected in apoptotic heart, brain, lung, liver, kidney, pancreas, and retinal tissue both in vivo and in vitro, establishing clusterin as a ubiquitous marker of apoptotic cell loss. However, clusterin protein has also been implicated in physiological processes that do not involve apoptosis, including the control of complement-mediated cell lysis, transport of beta-amyloid precursor protein, shuttling of aberrant beta-amyloid across the blood-brain barrier, lipid scavenging, membrane remodeling, cell aggregation, and protection from immune detection and tumor necrosis factor induced cell death.
    • (e) Connective Tissue Growth Factor (CTGF)
  • Connective tissue growth factor (CTGF, Swiss-Prot Accession Number P29279) is a 349-amino acid cysteine-rich polypeptide belonging to the CCN family. In vitro studies have shown that CTGF is mainly involved in extracellular matrix synthesis and fibrosis. Up-regulation of CTGF mRNA and increased CTGF levels have been observed in various diseases, including diabetic nephropathy and cardiomyopathy, fibrotic skin disorders, systemic sclerosis, biliary atresia, liver fibrosis and idiopathic pulmonary fibrosis, and nondiabetic acute and progressive glomerular and tubulointerstitial lesions of the kidney. A recent cross-sectional study found that urinary CTGF may act as a progression promoter in diabetic nephropathy.
    • (f) Creatinine
  • Creatinine is a metabolite of creatine phosphate in muscle tissue, and is typically produced at a relatively constant rate by the body. Creatinine is chiefly filtered out of the blood by the kidneys, though a small amount is actively secreted by the kidneys into the urine. Creatinine levels in blood and urine may be used to estimate the creatinine clearance, which is representative of the overall glomerular filtration rate (GFR), a standard measure of renal function. Variations in creatinine concentrations in the blood and urine, as well as variations in the ratio of urea to creatinine concentration in the blood, are common diagnostic measurements used to assess renal function.
    • (g) Cystatin C (Cyst C)
  • Cystatin C (Cyst C, Swiss-Prot Accession Number P01034) is a 13 kDa protein that is a potent inhibitor of the C1 family of cysteine proteases. It is the most abundant extracellular inhibitor of cysteine proteases in testis, epididymis, prostate, seminal vesicles and many other tissues. Cystatin C, which is normally expressed in vascular wall smooth muscle cells, is severely reduced in both atherosclerotic and aneurismal aortic lesions.
    • (h) Glutathione S-Transferase Alpha (GST-Alpha)
  • Glutathione S-transferase alpha (GST-alpha, Swiss-Prot Accession Number P08263) belongs to a family of enzymes that utilize glutathione in reactions contributing to the transformation of a wide range of compounds, including carcinogens, therapeutic drugs, and products of oxidative stress. These enzymes play a key role in the detoxification of such substances.
    • (i) Kidney Injury Molecule-1 (KIM-1)
  • Kidney injury molecule-1 (KIM-1, Swiss-Prot Accession Number Q96D42) is an immunoglobulin superfamily cell-surface protein highly upregulated on the surface of injured kidney epithelial cells. It is also known as TIM-1 (T-cell immunoglobulin mucin domain-1), as it is expressed at low levels by subpopulations of activated T-cells and hepatitis A virus cellular receptor-1 (HAVCR-1). KIM-1 is increased in expression more than any other protein in the injured kidney and is localized predominantly to the apical membrane of the surviving proximal epithelial cells.
    • (j) Microalbumin
  • Albumin is the most abundant plasma protein in humans and other mammals. Albumin is essential for maintaining the osmotic pressure needed for proper distribution of body fluids between intravascular compartments and body tissues. Healthy, normal kidneys typically filter out albumin from the urine. The presence of albumin in the urine may indicate damage to the kidneys. Albumin in the urine may also occur in patients with long-standing diabetes, especially type 1 diabetes. The amount of albumin eliminated in the urine has been used to differentially diagnose various renal disorders. For example, nephrotic syndrome usually results in the excretion of about 3.0 to 3.5 grams of albumin in human urine every 24 hours. Microalbuminuria, in which less than 300 mg of albumin is eliminated in the urine every 24 hours, may indicate the early stages of diabetic nephropathy.
    • (k) Neutrophil Gelatinase-Associated Lipocalin (NGAL)
  • Neutrophil gelatinase-associated lipocalin (NGAL, Swiss-Prot Accession Number P80188) forms a disulfide bond-linked heterodimer with MMP-9. It mediates an innate immune response to bacterial infection by sequestrating iron. Lipocalins interact with many different molecules such as cell surface receptors and proteases, and play a role in a variety of processes such as the progression of cancer and allergic reactions.
    • (I) Osteopontin (OPN)
  • Osteopontin (OPN, Swiss-Prot Accession Number P10451) is a cytokine involved in enhancing production of interferon-gamma and IL-12, and inhibiting the production of IL-10. OPN is essential in the pathway that leads to type I immunity. OPN appears to form an integral part of the mineralized matrix. OPN is synthesized within the kidney and has been detected in human urine at levels that may effectively inhibit calcium oxalate crystallization. Decreased concentrations of OPN have been documented in urine from patients with renal stone disease compared with normal individuals.
    • (m) Tamm-Horsfall Protein (THP)
  • Tamm-Horsfall protein (THP, Swiss-Prot Accession Number P07911), also known as uromodulin, is the most abundant protein present in the urine of healthy subjects and has been shown to decrease in individuals with kidney stones. THP is secreted by the thick ascending limb of the loop of Henley. THP is a monomeric glycoprotein of ˜85 kDa with ˜30% carbohydrate moiety that is heavily glycosylated. THP may act as a constitutive inhibitor of calcium crystallization in renal fluids.
    • (n) Tissue Inhibitor of Metalloproteinase-1 (TIMP-1)
  • Tissue inhibitor of metalloproteinase-1 (TIMP-1, Swiss-Prot Accession Number P01033) is a major regulator of extracellular matrix synthesis and degradation. A certain balance of MMPs and TIMPs is essential for tumor growth and health. Fibrosis results from an imbalance of fibrogenesis and fibrolysis, highlighting the importance of the role of the inhibition of matrix degradation role in renal disease.
    • (o) Trefoil Factor 3 (TFF3)
  • Trefoil factor 3 (TFF3, Swiss-Prot Accession Number Q07654), also known as intestinal trefoil factor, belongs to a small family of mucin-associated peptides that include TFF1, TFF2, and TFF3. TFF3 exists in a 60-amino acid monomeric form and a 118-amino acid dimeric form. Under normal conditions TFF3 is expressed by goblet cells of the intestine and the colon. TFF3 expression has also been observed in the human respiratory tract, in human goblet cells and in the human salivary gland. In addition, TFF3 has been detected in the human hypothalamus.
    • (p) Vascular Endothelial Growth Factor (VEGF)
  • Vascular endothelial growth factor (VEGF, Swiss-Prot Accession Number P15692) is an important factor in the pathophysiology of neuronal and other tumors, most likely functioning as a potent promoter of angiogenesis. VEGF may also be involved in regulating blood-brain-barrier functions under normal and pathological conditions. VEGF secreted from the stromal cells may be responsible for the endothelial cell proliferation observed in capillary hemangioblastomas, which are typically composed of abundant microvasculature and primitive angiogenic elements represented by stromal cells.
    • II. Combinations of Analytes Measured by Multiplexed Assay
  • The method for diagnosing, monitoring, or determining kidney damage involves determining the presence or concentrations of a combination of sample analytes in a test sample. The combinations of sample analytes, as defined herein, are any group of three or more analytes selected from the biomarker analytes, including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. In one embodiment, the combination of analytes may be selected to provide a group of analytes associated with a wide range of potential types of kidney damage. In another embodiment, the combination of analytes may be selected to provide a group of analytes associated with a particular type of kidney damage or region of renal injury.
  • In one embodiment, the combination of sample analytes may be any three of the biomarker analytes. In other embodiments, the combination of sample analytes may be any four, any five, any six, any seven, any eight, any nine, any ten, any eleven, any twelve, any thirteen, any fourteen, any fifteen, or all sixteen of the sixteen biomarker analytes. In some embodiments, the combination of sample analytes comprises alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1. In another embodiment, the combination of sample analytes may comprise a combination listed in Table A.
  • TABLE A
    alpha-1 microglobulin beta-2 microglobulin calbindin
    alpha-1 microglobulin beta-2 microglobulin clusterin
    alpha-1 microglobulin beta-2 microglobulin CTGF
    alpha-1 microglobulin beta-2 microglobulin creatinine
    alpha-1 microglobulin beta-2 microglobulin cystatin C
    alpha-1 microglobulin beta-2 microglobulin GST-alpha
    alpha-1 microglobulin beta-2 microglobulin KIM-1
    alpha-1 microglobulin beta-2 microglobulin microalbumin
    alpha-1 microglobulin beta-2 microglobulin NGAL
    alpha-1 microglobulin beta-2 microglobulin osteopontin
    alpha-1 microglobulin beta-2 microglobulin THP
    alpha-1 microglobulin beta-2 microglobulin TIMP-1
    alpha-1 microglobulin beta-2 microglobulin TFF-3
    alpha-1 microglobulin beta-2 microglobulin VEGF
    alpha-1 microglobulin calbindin clusterin
    alpha-1 microglobulin calbindin CTGF
    alpha-1 microglobulin calbindin creatinine
    alpha-1 microglobulin calbindin cystatin C
    alpha-1 microglobulin calbindin GST-alpha
    alpha-1 microglobulin calbindin KIM-1
    alpha-1 microglobulin calbindin microalbumin
    alpha-1 microglobulin calbindin NGAL
    alpha-1 microglobulin calbindin osteopontin
    alpha-1 microglobulin calbindin THP
    alpha-1 microglobulin calbindin TIMP-1
    alpha-1 microglobulin calbindin TFF-3
    alpha-1 microglobulin calbindin VEGF
    alpha-1 microglobulin clusterin CTGF
    alpha-1 microglobulin clusterin creatinine
    alpha-1 microglobulin clusterin cystatin C
    alpha-1 microglobulin clusterin GST-alpha
    alpha-1 microglobulin clusterin KIM-1
    alpha-1 microglobulin clusterin microalbumin
    alpha-1 microglobulin clusterin NGAL
    alpha-1 microglobulin clusterin osteopontin
    alpha-1 microglobulin clusterin THP
    alpha-1 microglobulin clusterin TIMP-1
    alpha-1 microglobulin clusterin TFF-3
    alpha-1 microglobulin clusterin VEGF
    alpha-1 microglobulin CTGF creatinine
    alpha-1 microglobulin CTGF cystatin C
    alpha-1 microglobulin CTGF GST-alpha
    alpha-1 microglobulin CTGF KIM-1
    alpha-1 microglobulin CTGF microalbumin
    alpha-1 microglobulin CTGF NGAL
    alpha-1 microglobulin CTGF osteopontin
    alpha-1 microglobulin CTGF THP
    alpha-1 microglobulin CTGF TIMP-1
    alpha-1 microglobulin CTGF TFF-3
    alpha-1 microglobulin CTGF VEGF
    alpha-1 microglobulin creatinine cystatin C
    alpha-1 microglobulin creatinine GST-alpha
    alpha-1 microglobulin creatinine KIM-1
    alpha-1 microglobulin creatinine microalbumin
    alpha-1 microglobulin creatinine NGAL
    alpha-1 microglobulin creatinine osteopontin
    alpha-1 microglobulin creatinine THP
    alpha-1 microglobulin creatinine TIMP-1
    alpha-1 microglobulin creatinine TFF-3
    alpha-1 microglobulin creatinine VEGF
    alpha-1 microglobulin cystatin C GST-alpha
    alpha-1 microglobulin cystatin C KIM-1
    alpha-1 microglobulin cystatin C microalbumin
    alpha-1 microglobulin cystatin C NGAL
    alpha-1 microglobulin cystatin C osteopontin
    alpha-1 microglobulin cystatin C THP
    alpha-1 microglobulin cystatin C TIMP-1
    alpha-1 microglobulin cystatin C TFF-3
    alpha-1 microglobulin cystatin C VEGF
    alpha-1 microglobulin GST-alpha KIM-1
    alpha-1 microglobulin GST-alpha microalbumin
    alpha-1 microglobulin GST-alpha NGAL
    alpha-1 microglobulin GST-alpha osteopontin
    alpha-1 microglobulin GST-alpha THP
    alpha-1 microglobulin GST-alpha TIMP-1
    alpha-1 microglobulin GST-alpha TFF-3
    alpha-1 microglobulin GST-alpha VEGF
    alpha-1 microglobulin KIM-1 microalbumin
    alpha-1 microglobulin KIM-1 NGAL
    alpha-1 microglobulin KIM-1 osteopontin
    alpha-1 microglobulin KIM-1 THP
    alpha-1 microglobulin KIM-1 TIMP-1
    alpha-1 microglobulin KIM-1 TFF-3
    alpha-1 microglobulin KIM-1 VEGF
    alpha-1 microglobulin microalbumin NGAL
    alpha-1 microglobulin microalbumin osteopontin
    alpha-1 microglobulin microalbumin THP
    alpha-1 microglobulin microalbumin TIMP-1
    alpha-1 microglobulin microalbumin TFF-3
    alpha-1 microglobulin microalbumin VEGF
    alpha-1 microglobulin NGAL osteopontin
    alpha-1 microglobulin NGAL THP
    alpha-1 microglobulin NGAL TIMP-1
    alpha-1 microglobulin NGAL TFF-3
    alpha-1 microglobulin NGAL VEGF
    alpha-1 microglobulin osteopontin THP
    alpha-1 microglobulin osteopontin TIMP-1
    alpha-1 microglobulin osteopontin TFF-3
    alpha-1 microglobulin osteopontin VEGF
    alpha-1 microglobulin THP TIMP-1
    alpha-1 microglobulin THP TFF-3
    alpha-1 microglobulin THP VEGF
    alpha-1 microglobulin TIMP-1 TFF-3
    alpha-1 microglobulin TIMP-1 VEGF
    alpha-1 microglobulin TFF-3 VEGF
    beta-2 microglobulin calbindin clusterin
    beta-2 microglobulin calbindin CTGF
    beta-2 microglobulin calbindin creatinine
    beta-2 microglobulin calbindin cystatin C
    beta-2 microglobulin calbindin GST-alpha
    beta-2 microglobulin calbindin KIM-1
    beta-2 microglobulin calbindin microalbumin
    beta-2 microglobulin calbindin NGAL
    beta-2 microglobulin calbindin osteopontin
    beta-2 microglobulin calbindin THP
    beta-2 microglobulin calbindin TIMP-1
    beta-2 microglobulin calbindin TFF-3
    beta-2 microglobulin calbindin VEGF
    beta-2 microglobulin clusterin CTGF
    beta-2 microglobulin clusterin creatinine
    beta-2 microglobulin clusterin cystatin C
    beta-2 microglobulin clusterin GST-alpha
    beta-2 microglobulin clusterin KIM-1
    beta-2 microglobulin clusterin microalbumin
    beta-2 microglobulin clusterin NGAL
    beta-2 microglobulin clusterin osteopontin
    beta-2 microglobulin clusterin THP
    beta-2 microglobulin clusterin TIMP-1
    beta-2 microglobulin clusterin TFF-3
    beta-2 microglobulin clusterin VEGF
    beta-2 microglobulin CTGF creatinine
    beta-2 microglobulin CTGF cystatin C
    beta-2 microglobulin CTGF GST-alpha
    beta-2 microglobulin CTGF KIM-1
    beta-2 microglobulin CTGF microalbumin
    beta-2 microglobulin CTGF NGAL
    beta-2 microglobulin CTGF osteopontin
    beta-2 microglobulin CTGF THP
    beta-2 microglobulin CTGF TIMP-1
    beta-2 microglobulin CTGF TFF-3
    beta-2 microglobulin CTGF VEGF
    beta-2 microglobulin creatinine cystatin C
    beta-2 microglobulin creatinine GST-alpha
    beta-2 microglobulin creatinine KIM-1
    beta-2 microglobulin creatinine microalbumin
    beta-2 microglobulin creatinine NGAL
    beta-2 microglobulin creatinine osteopontin
    beta-2 microglobulin creatinine THP
    beta-2 microglobulin creatinine TIMP-1
    beta-2 microglobulin creatinine TFF-3
    beta-2 microglobulin creatinine VEGF
    beta-2 microglobulin cystatin C GST-alpha
    beta-2 microglobulin cystatin C KIM-1
    beta-2 microglobulin cystatin C microalbumin
    beta-2 microglobulin cystatin C NGAL
    beta-2 microglobulin cystatin C osteopontin
    beta-2 microglobulin cystatin C THP
    beta-2 microglobulin cystatin C TIMP-1
    beta-2 microglobulin cystatin C TFF-3
    beta-2 microglobulin cystatin C VEGF
    beta-2 microglobulin GST-alpha KIM-1
    beta-2 microglobulin GST-alpha microalbumin
    beta-2 microglobulin GST-alpha NGAL
    beta-2 microglobulin GST-alpha osteopontin
    beta-2 microglobulin GST-alpha THP
    beta-2 microglobulin GST-alpha TIMP-1
    beta-2 microglobulin GST-alpha TFF-3
    beta-2 microglobulin GST-alpha VEGF
    beta-2 microglobulin KIM-1 microalbumin
    beta-2 microglobulin KIM-1 NGAL
    beta-2 microglobulin KIM-1 osteopontin
    beta-2 microglobulin KIM-1 THP
    beta-2 microglobulin KIM-1 TIMP-1
    beta-2 microglobulin KIM-1 TFF-3
    beta-2 microglobulin KIM-1 VEGF
    beta-2 microglobulin microalbumin NGAL
    beta-2 microglobulin microalbumin osteopontin
    beta-2 microglobulin microalbumin THP
    beta-2 microglobulin microalbumin TIMP-1
    beta-2 microglobulin microalbumin TFF-3
    beta-2 microglobulin microalbumin VEGF
    beta-2 microglobulin NGAL osteopontin
    beta-2 microglobulin NGAL THP
    beta-2 microglobulin NGAL TIMP-1
    beta-2 microglobulin NGAL TFF-3
    beta-2 microglobulin NGAL VEGF
    beta-2 microglobulin osteopontin THP
    beta-2 microglobulin osteopontin TIMP-1
    beta-2 microglobulin osteopontin TFF-3
    beta-2 microglobulin osteopontin VEGF
    beta-2 microglobulin THP TIMP-1
    beta-2 microglobulin THP TFF-3
    beta-2 microglobulin THP VEGF
    beta-2 microglobulin TIMP-1 TFF-3
    beta-2 microglobulin TIMP-2 VEGF
    beta-2 microglobulin TFF-3 VEGF
    calbindin clusterin CTGF
    calbindin clusterin creatinine
    calbindin clusterin cystatin C
    calbindin clusterin GST-alpha
    calbindin clusterin KIM-1
    calbindin clusterin microalbumin
    calbindin clusterin NGAL
    calbindin clusterin osteopontin
    calbindin clusterin THP
    calbindin clusterin TIMP-1
    calbindin clusterin TFF-3
    calbindin clusterin VEGF
    calbindin CTGF creatinine
    calbindin CTGF cystatin C
    calbindin CTGF GST-alpha
    calbindin CTGF KIM-1
    calbindin CTGF microalbumin
    calbindin CTGF NGAL
    calbindin CTGF osteopontin
    calbindin CTGF THP
    calbindin CTGF TIMP-1
    calbindin CTGF TFF-3
    calbindin CTGF VEGF
    calbindin creatinine cystatin C
    calbindin creatinine GST-alpha
    calbindin creatinine KIM-1
    calbindin creatinine microalbumin
    calbindin creatinine NGAL
    calbindin creatinine osteopontin
    calbindin creatinine THP
    calbindin creatinine TIMP-1
    calbindin creatinine TFF-3
    calbindin creatinine VEGF
    calbindin cystatin C GST-alpha
    calbindin cystatin C KIM-1
    calbindin cystatin C microalbumin
    calbindin cystatin C NGAL
    calbindin cystatin C osteopontin
    calbindin cystatin C THP
    calbindin cystatin C TIMP-1
    calbindin cystatin C TFF-3
    calbindin cystatin C VEGF
    calbindin GST-alpha KIM-1
    calbindin GST-alpha microalbumin
    calbindin GST-alpha NGAL
    calbindin GST-alpha osteopontin
    calbindin GST-alpha THP
    calbindin GST-alpha TIMP-1
    calbindin GST-alpha TFF-3
    calbindin GST-alpha VEGF
    calbindin KIM-1 microalbumin
    calbindin KIM-1 NGAL
    calbindin KIM-1 osteopontin
    calbindin KIM-1 THP
    calbindin KIM-1 TIMP-1
    calbindin KIM-1 TFF-3
    calbindin KIM-1 VEGF
    calbindin microalbumin NGAL
    calbindin microalbumin osteopontin
    calbindin microalbumin THP
    calbindin microalbumin TIMP-1
    calbindin microalbumin TFF-3
    calbindin microalbumin VEGF
    calbindin NGAL osteopontin
    calbindin NGAL THP
    calbindin NGAL TIMP-1
    calbindin NGAL TFF-3
    calbindin NGAL VEGF
    calbindin osteopontin THP
    calbindin osteopontin TIMP-1
    calbindin osteopontin TFF-3
    calbindin osteopontin VEGF
    calbindin THP TIMP-1
    calbindin THP TFF-3
    calbindin THP VEGF
    calbindin TIMP-1 TFF-3
    calbindin TIMP-1 VEGF
    calbindin TFF-3 VEGF
    clusterin CTGF creatinine
    clusterin CTGF cystatin C
    clusterin CTGF GST-alpha
    clusterin CTGF KIM-1
    clusterin CTGF microalbumin
    clusterin CTGF NGAL
    clusterin CTGF osteopontin
    clusterin CTGF THP
    clusterin CTGF TIMP-1
    clusterin CTGF TFF-3
    clusterin CTGF VEGF
    clusterin creatinine cystatin C
    clusterin creatinine GST-alpha
    clusterin creatinine KIM-1
    clusterin creatinine microalbumin
    clusterin creatinine NGAL
    clusterin creatinine osteopontin
    clusterin creatinine THP
    clusterin creatinine TIMP-1
    clusterin creatinine TFF-3
    clusterin creatinine VEGF
    clusterin cystatin C GST-alpha
    clusterin cystatin C KIM-1
    clusterin cystatin C microalbumin
    clusterin cystatin C NGAL
    clusterin cystatin C osteopontin
    clusterin cystatin C THP
    clusterin cystatin C TIMP-1
    clusterin cystatin C TFF-3
    clusterin cystatin C VEGF
    clusterin GST-alpha KIM-1
    clusterin GST-alpha microalbumin
    clusterin GST-alpha NGAL
    clusterin GST-alpha osteopontin
    clusterin GST-alpha THP
    clusterin GST-alpha TIMP-1
    clusterin GST-alpha TFF-3
    clusterin GST-alpha VEGF
    clusterin KIM-1 microalbumin
    clusterin KIM-1 NGAL
    clusterin KIM-1 osteopontin
    clusterin KIM-1 THP
    clusterin KIM-1 TIMP-1
    clusterin KIM-1 TFF-3
    clusterin KIM-1 VEGF
    clusterin microalbumin NGAL
    clusterin microalbumin osteopontin
    clusterin microalbumin THP
    clusterin microalbumin TIMP-1
    clusterin microalbumin TFF-3
    clusterin microalbumin VEGF
    clusterin NGAL osteopontin
    clusterin NGAL THP
    clusterin NGAL TIMP-1
    clusterin NGAL TFF-3
    clusterin NGAL VEGF
    clusterin osteopontin THP
    clusterin osteopontin TIMP-1
    clusterin osteopontin TFF-3
    clusterin osteopontin VEGF
    clusterin THP TIMP-1
    clusterin THP TFF-3
    clusterin THP VEGF
    clusterin TIMP-1 TFF-3
    clusterin TIMP-1 VEGF
    clusterin TFF-3 VEGF
    CTGF creatinine cystatin C
    CTGF creatinine GST-alpha
    CTGF creatinine KIM-1
    CTGF creatinine microalbumin
    CTGF creatinine NGAL
    CTGF creatinine osteopontin
    CTGF creatinine THP
    CTGF creatinine TIMP-1
    CTGF creatinine TFF-3
    CTGF creatinine VEGF
    CTGF cystatin C GST-alpha
    CTGF cystatin C KIM-1
    CTGF cystatin C microalbumin
    CTGF cystatin C NGAL
    CTGF cystatin C osteopontin
    CTGF cystatin C THP
    CTGF cystatin C TIMP-1
    CTGF cystatin C TFF-3
    CTGF cystatin C VEGF
    CTGF GST-alpha KIM-1
    CTGF GST-alpha microalbumin
    CTGF GST-alpha NGAL
    CTGF GST-alpha osteopontin
    CTGF GST-alpha THP
    CTGF GST-alpha TIMP-1
    CTGF GST-alpha TFF-3
    CTGF GST-alpha VEGF
    CTGF KIM-1 microalbumin
    CTGF KIM-1 NGAL
    CTGF KIM-1 osteopontin
    CTGF KIM-1 THP
    CTGF KIM-1 TIMP-1
    CTGF KIM-1 TFF-3
    CTGF KIM-1 VEGF
    CTGF microalbumin NGAL
    CTGF microalbumin osteopontin
    CTGF microalbumin THP
    CTGF microalbumin TIMP-1
    CTGF microalbumin TFF-3
    CTGF microalbumin VEGF
    CTGF NGAL osteopontin
    CTGF NGAL THP
    CTGF NGAL TIMP-1
    CTGF NGAL TFF-3
    CTGF NGAL VEGF
    CTGF osteopontin THP
    CTGF osteopontin TIMP-1
    CTGF osteopontin TFF-3
    CTGF osteopontin VEGF
    CTGF THP TIMP-1
    CTGF THP TFF-3
    CTGF THP VEGF
    CTGF TIMP-1 TFF-3
    CTGF TIMP-1 VEGF
    CTGF TFF-3 VEGF
    creatinine cystatin C GST-alpha
    creatinine cystatin C KIM-1
    creatinine cystatin C microalbumin
    creatinine cystatin C NGAL
    creatinine cystatin C osteopontin
    creatinine cystatin C THP
    creatinine cystatin C TIMP-1
    creatinine cystatin C TFF-3
    creatinine cystatin C VEGF
    creatinine GST-alpha KIM-1
    creatinine GST-alpha microalbumin
    creatinine GST-alpha NGAL
    creatinine GST-alpha osteopontin
    creatinine GST-alpha THP
    creatinine GST-alpha TIMP-1
    creatinine GST-alpha TFF-3
    creatinine GST-alpha VEGF
    creatinine KIM-1 microalbumin
    creatinine KIM-1 NGAL
    creatinine KIM-1 osteopontin
    creatinine KIM-1 THP
    creatinine KIM-1 TIMP-1
    creatinine KIM-1 TFF-3
    creatinine KIM-1 VEGF
    creatinine microalbumin NGAL
    creatinine microalbumin osteopontin
    creatinine microalbumin THP
    creatinine microalbumin TIMP-1
    creatinine microalbumin TFF-3
    creatinine microalbumin VEGF
    creatinine NGAL osteopontin
    creatinine NGAL THP
    creatinine NGAL TIMP-1
    creatinine NGAL TFF-3
    creatinine NGAL VEGF
    creatinine osteopontin THP
    creatinine osteopontin TIMP-1
    creatinine osteopontin TFF-3
    creatinine osteopontin VEGF
    creatinine THP TIMP-1
    creatinine THP TFF-3
    creatinine THP VEGF
    creatinine TIMP-1 TFF-3
    creatinine TIMP-1 VEGF
    creatinine TFF-3 VEGF
    cystatin C GST-alpha KIM-1
    cystatin C GST-alpha microalbumin
    cystatin C GST-alpha NGAL
    cystatin C GST-alpha osteopontin
    cystatin C GST-alpha THP
    cystatin C GST-alpha TIMP-1
    cystatin C GST-alpha TFF-3
    cystatin C GST-alpha VEGF
    cystatin C KIM-1 microalbumin
    cystatin C KIM-1 NGAL
    cystatin C KIM-1 osteopontin
    cystatin C KIM-1 THP
    cystatin C KIM-1 TIMP-1
    cystatin C KIM-1 TFF-3
    cystatin C KIM-1 VEGF
    cystatin C microalbumin NGAL
    cystatin C microalbumin osteopontin
    cystatin C microalbumin THP
    cystatin C microalbumin TIMP-1
    cystatin C microalbumin TFF-3
    cystatin C microalbumin VEGF
    cystatin C NGAL osteopontin
    cystatin C NGAL THP
    cystatin C NGAL TIMP-1
    cystatin C NGAL TFF-3
    cystatin C NGAL VEGF
    cystatin C osteopontin THP
    cystatin C osteopontin TIMP-1
    cystatin C osteopontin TFF-3
    cystatin C osteopontin VEGF
    cystatin C THP TIMP-1
    cystatin C THP TFF-3
    cystatin C THP VEGF
    cystatin C TIMP-1 TFF-3
    cystatin C TIMP-1 VEGF
    cystatin C TFF-3 VEGF
    GST-alpha KIM-1 microalbumin
    GST-alpha KIM-1 NGAL
    GST-alpha KIM-1 osteopontin
    GST-alpha KIM-1 THP
    GST-alpha KIM-1 TIMP-1
    GST-alpha KIM-1 TFF-3
    GST-alpha KIM-1 VEGF
    GST-alpha microalbumin NGAL
    GST-alpha microalbumin osteopontin
    GST-alpha microalbumin THP
    GST-alpha microalbumin TIMP-1
    GST-alpha microalbumin TFF-3
    GST-alpha microalbumin VEGF
    GST-alpha NGAL osteopontin
    GST-alpha NGAL THP
    GST-alpha NGAL TIMP-1
    GST-alpha NGAL TFF-3
    GST-alpha NGAL VEGF
    GST-alpha osteopontin THP
    GST-alpha osteopontin TIMP-1
    GST-alpha osteopontin TFF-3
    GST-alpha osteopontin VEGF
    GST-alpha THP TIMP-1
    GST-alpha THP TFF-3
    GST-alpha THP VEGF
    GST-alpha TIMP-1 TFF-3
    GST-alpha TIMP-1 VEGF
    GST-alpha TFF-3 VEGF
    KIM-1 microalbumin NGAL
    KIM-1 microalbumin osteopontin
    KIM-1 microalbumin THP
    KIM-1 microalbumin TIMP-1
    KIM-1 microalbumin TFF-3
    KIM-1 microalbumin VEGF
    KIM-1 NGAL osteopontin
    KIM-1 NGAL THP
    KIM-1 NGAL TIMP-1
    KIM-1 NGAL TFF-3
    KIM-1 NGAL VEGF
    KIM-1 osteopontin THP
    KIM-1 osteopontin TIMP-1
    KIM-1 osteopontin TFF-3
    KIM-1 osteopontin VEGF
    KIM-1 THP TIMP-1
    KIM-1 THP TFF-3
    KIM-1 THP VEGF
    KIM-1 TIMP-1 TFF-3
    KIM-1 TIMP-1 VEGF
    KIM-1 TFF-3 VEGF
    microalbumin NGAL osteopontin
    microalbumin NGAL THP
    microalbumin NGAL TIMP-1
    microalbumin NGAL TFF-3
    microalbumin NGAL VEGF
    microalbumin osteopontin THP
    microalbumin osteopontin TIMP-1
    microalbumin osteopontin TFF-3
    microalbumin osteopontin VEGF
    microalbumin THP TIMP-1
    microalbumin THP TFF-3
    microalbumin THP VEGF
    microalbumin TIMP-1 TFF-3
    microalbumin TIMP-1 VEGF
    microalbumin TFF-3 VEGF
    NGAL osteopontin THP
    NGAL osteopontin TIMP-1
    NGAL osteopontin TFF-3
    NGAL osteopontin VEGF
    NGAL THP TIMP-1
    NGAL THP TFF-3
    NGAL THP VEGF
    NGAL TIMP-1 TFF-3
    NGAL TIMP-1 VEGF
    NGAL TFF-3 VEGF
    osteopontin THP TIMP-1
    osteopontin THP TFF-3
    osteopontin THP VEGF
    osteopontin TIMP-1 TFF-3
    osteopontin TIMP-1 VEGF
    osteopontin TFF-3 VEGF
    THP TIMP-1 TFF-3
    THP TIMP-1 VEGF
    THP TFF-3 VEGF
    TIMP-1 TFF-3 VEGF
  • In one exemplary embodiment, the combination of sample analytes may include creatinine, KIM-1, and THP. In another exemplary embodiment, the combination of sample analytes may include microalbumin, creatinine, and KIM-1. In yet another exemplary embodiment, the combination of sample analytes may include creatinine, TIMP-1, and THP. In still another exemplary embodiment, the combination of sample analytes may include creatinine, microalbumin, and THP.
    • III. Test Sample
  • The method for diagnosing, monitoring, or determining a renal disorder involves determining the presence of sample analytes in a test sample. A test sample, as defined herein, is an amount of bodily fluid taken from a mammal. Non-limiting examples of bodily fluids include urine, blood, plasma, serum, saliva, semen, perspiration, tears, mucus, and tissue lysates. In an exemplary embodiment, the bodily fluid contained in the test sample is urine, plasma, or serum.
    • (a) Mammals
  • A mammal, as defined herein, is any organism that is a member of the class Mammalia. Non-limiting examples of mammals appropriate for the various embodiments include humans, apes, monkeys, rats, mice, dogs, cats, pigs, and livestock including cattle and oxen. In an exemplary embodiment, the mammal is a human.
  • (b) Devices and Methods of Taking Bodily Fluids from Mammals
  • The bodily fluids of the test sample may be taken from the mammal using any known device or method so long as the analytes to be measured by the multiplexed assay are not rendered undetectable by the multiplexed assay. Non-limiting examples of devices or methods suitable for taking bodily fluid from a mammal include urine sample cups, urethral catheters, swabs, hypodermic needles, thin needle biopsies, hollow needle biopsies, punch biopsies, metabolic cages, and aspiration.
  • In order to adjust the expected concentrations of the sample analytes in the test sample to fall within the dynamic range of the multiplexed assay, the test sample may be diluted to reduce the concentration of the sample analytes prior to analysis. The degree of dilution may depend on a variety of factors including but not limited to the type of multiplexed assay used to measure the analytes, the reagents utilized in the multiplexed assay, and the type of bodily fluid contained in the test sample. In one embodiment, the test sample is diluted by adding a volume of diluent ranging from about ½ of the original test sample volume to about 50,000 times the original test sample volume.
  • In one exemplary embodiment, if the test sample is human urine and the multiplexed assay is an antibody-based capture-sandwich assay, the test sample is diluted by adding a volume of diluent that is about 100 times the original test sample volume prior to analysis. In another exemplary embodiment, if the test sample is human serum and the multiplexed assay is an antibody-based capture-sandwich assay, the test sample is diluted by adding a volume of diluent that is about 5 times the original test sample volume prior to analysis. In yet another exemplary embodiment, if the test sample is human plasma and the multiplexed assay is an antibody-based capture-sandwich assay, the test sample is diluted by adding a volume of diluent that is about 2,000 times the original test sample volume prior to analysis.
  • The diluent may be any fluid that does not interfere with the function of the multiplexed assay used to measure the concentration of the analytes in the test sample. Non-limiting examples of suitable diluents include deionized water, distilled water, saline solution, Ringer's solution, phosphate buffered saline solution, TRIS-buffered saline solution, standard saline citrate, and HEPES-buffered saline.
    • IV. Multiplexed Assay Device
  • In one embodiment, the concentration of a combination of sample analytes is measured using a multiplexed assay device capable of measuring the concentrations of up to sixteen of the biomarker analytes. A multiplexed assay device, as defined herein, is an assay capable of simultaneously determining the concentration of three or more different sample analytes using a single device and/or method. Any known method of measuring the concentration of the biomarker analytes may be used for the multiplexed assay device. Non-limiting examples of measurement methods suitable for the multiplexed assay device include electrophoresis, mass spectrometry, protein microarrays, and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, and particle-based capture-sandwich immunoassays.
    • (a) Multiplexed Immunoassay Device
  • In one embodiment, the concentration of a combination of sample analytes is measured using a multiplexed assay device capable of measuring the concentrations of up to 189 of the biomarker analytes. A multiplexed assay device, as defined herein, is an assay capable of simultaneously determining the concentration of three or more different sample analytes using a single device and/or method. Any known method of measuring the concentration of the biomarker analytes may be used for the multiplexed assay device. Non-limiting examples of measurement methods suitable for the multiplexed assay device include electrophoresis, mass spectrometry, protein microarrays, and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, vibrational detection using MicroElectroMagnetic Devices (MEMS), and particle-based capture-sandwich immunoassays.
    • (i) Capture Antibodies
  • In the same embodiment, the multiplexed immunoassay device includes three or more capture antibodies. Capture antibodies, as defined herein, are antibodies in which the antigenic determinant is one of the biomarker analytes. Each of the at least three capture antibodies has a unique antigenic determinant that is one of the biomarker analytes. When contacted with the test sample, the capture antibodies form antigen-antibody complexes in which the analytes serve as antigens.
  • In another embodiment, the capture antibodies may be attached to a substrate in order to immobilize any analytes captured by the capture antibodies. Non-limiting examples of suitable substrates include paper or cellulose strips, polystyrene or latex microspheres, and the inner surface of the well of a microtitration tray.
    • (ii) Indicators
  • In one embodiment of the multiplexed immunoassay device, an indicator is attached to each of the three or more capture antibodies. The indicator, as defined herein, is any compound that registers a measurable change to indicate the presence of one of the sample analytes when bound to one of the capture antibodies. Non-limiting examples of indicators include visual indicators and electrochemical indicators.
  • Visual indicators, as defined herein, are compounds that register a change by reflecting a limited subset of the wavelengths of light illuminating the indicator, by fluorescing light after being illuminated, or by emitting light via chemiluminescence. The change registered by visual indicators may be in the visible light spectrum, in the infrared spectrum, or in the ultraviolet spectrum. Non-limiting examples of visual indicators suitable for the multiplexed immunoassay device include nanoparticulate gold, organic particles such as polyurethane or latex microspheres loaded with dye compounds, carbon black, fluorophores, phycoerythrin, radioactive isotopes, nanoparticles, quantum dots, and enzymes such as horseradish peroxidase or alkaline phosphatase that react with a chemical substrate to form a colored or chemiluminescent product.
  • Electrochemical indicators, as defined herein, are compounds that register a change by altering an electrical property. The changes registered by electrochemical indicators may be an alteration in conductivity, resistance, capacitance, current conducted in response to an applied voltage, or voltage required to achieve a desired current. Non-limiting examples of electrochemical indicators include redox species such as ascorbate (vitamin C), vitamin E, glutathione, polyphenols, catechols, quercetin, phytoestrogens, penicillin, carbazole, murranes, phenols, carbonyls, benzoates, and trace metal ions such as nickel, copper, cadmium, iron and mercury.
  • In this same embodiment, the test sample containing a combination of three or more sample analytes is contacted with the capture antibodies and allowed to form antigen-antibody complexes in which the sample analytes serve as the antigens. After removing any uncomplexed capture antibodies, the concentrations of the three or more analytes are determined by measuring the change registered by the indicators attached to the capture antibodies.
  • In one exemplary embodiment, the indicators are polyurethane or latex microspheres loaded with dye compounds and phycoerythrin.
    • (b) Multiplexed Sandwich Immunoassay Device
  • In another embodiment, the multiplexed immunoassay device has a sandwich assay format. In this embodiment, the multiplexed sandwich immunoassay device includes three or more capture antibodies as previously described. However, in this embodiment, each of the capture antibodies is attached to a capture agent that includes an antigenic moiety. The antigenic moiety serves as the antigenic determinant of a detection antibody, also included in the multiplexed immunoassay device of this embodiment. In addition, an indicator is attached to the detection antibody.
  • In this same embodiment, the test sample is contacted with the capture antibodies and allowed to form antigen-antibody complexes in which the sample analytes serve as antigens. The detection antibodies are then contacted with the test sample and allowed to form antigen-antibody complexes in which the capture agent serves as the antigen for the detection antibody. After removing any uncomplexed detection antibodies the concentration of the analytes are determined by measuring the changes registered by the indicators attached to the detection antibodies.
    • (c) Multiplexing Approaches
  • In the various embodiments of the multiplexed immunoassay devices, the concentrations of each of the sample analytes may be determined using any approach known in the art. In one embodiment, a single indicator compound is attached to each of the three or more antibodies. In addition, each of the capture antibodies having one of the sample analytes as an antigenic determinant is physically separated into a distinct region so that the concentration of each of the sample analytes may be determined by measuring the changes registered by the indicators in each physically separate region corresponding to each of the sample analytes.
  • In another embodiment, each antibody having one of the sample analytes as an antigenic determinant is marked with a unique indicator. In this manner, a unique indicator is attached to each antibody having a single sample analyte as its antigenic determinant. In this embodiment, all antibodies may occupy the same physical space. The concentration of each sample analyte is determined by measuring the change registered by the unique indicator attached to the antibody having the sample analyte as an antigenic determinant.
    • (d) Microsphere-Based Capture-Sandwich Immunoassay Device
  • In an exemplary embodiment, the multiplexed immunoassay device is a microsphere-based capture-sandwich immunoassay device. In this embodiment, the device includes a mixture of three or more capture-antibody microspheres, in which each capture-antibody microsphere corresponds to one of the biomarker analytes. Each capture-antibody microsphere includes a plurality of capture antibodies attached to the outer surface of the microsphere. In this same embodiment, the antigenic determinant of all of the capture antibodies attached to one microsphere is the same biomarker analyte.
  • In this embodiment of the device, the microsphere is a small polystyrene or latex sphere that is loaded with an indicator that is a dye compound. The microsphere may be between about 3 μm and about 5 μm in diameter. Each capture-antibody microsphere corresponding to one of the biomarker analytes is loaded with the same indicator. In this manner, each capture-antibody microsphere corresponding to a biomarker analyte is uniquely color-coded.
  • In this same exemplary embodiment, the multiplexed immunoassay device further includes three or more biotinylated detection antibodies in which the antigenic determinant of each biotinylated detection antibody is one of the biomarker analytes. The device further includes a plurality of streptaviden proteins complexed with a reporter compound. A reporter compound, as defined herein, is an indicator selected to register a change that is distinguishable from the indicators used to mark the capture-antibody microspheres.
  • The concentrations of the sample analytes may be determined by contacting the test sample with a mixture of capture-antigen microspheres corresponding to each sample analyte to be measured. The sample analytes are allowed to form antigen-antibody complexes in which a sample analyte serves as an antigen and a capture antibody attached to the microsphere serves as an antibody. In this manner, the sample analytes are immobilized onto the capture-antigen microspheres. The biotinylated detection antibodies are then added to the test sample and allowed to form antigen-antibody complexes in which the analyte serves as the antigen and the biotinylated detection antibody serves as the antibody. The streptaviden-reporter complex is then added to the test sample and allowed to bind to the biotin moieties of the biotinylated detection antibodies. The antigen-capture microspheres may then be rinsed and filtered.
  • In this embodiment, the concentration of each analyte is determined by first measuring the change registered by the indicator compound embedded in the capture-antigen microsphere in order to identify the particular analyte. For each microsphere corresponding to one of the biomarker analytes, the quantity of analyte immobilized on the microsphere is determined by measuring the change registered by the reporter compound attached to the microsphere.
  • For example, the indicator embedded in the microspheres associated with one sample analyte may register an emission of orange light, and the reporter may register an emission of green light. In this example, a detector device may measure the intensity of orange light and green light separately. The measured intensity of the green light would determine the concentration of the analyte captured on the microsphere, and the intensity of the orange light would determine the specific analyte captured on the microsphere.
  • Any sensor device may be used to detect the changes registered by the indicators embedded in the microspheres and the changes registered by the reporter compound, so long as the sensor device is sufficiently sensitive to the changes registered by both indicator and reporter compound. Non-limiting examples of suitable sensor devices include spectrophotometers, photosensors, colorimeters, cyclic coulometry devices, and flow cytometers. In an exemplary embodiment, the sensor device is a flow cytometer.
    • (e) Vibrational Detection Device
  • In another exemplary embodiment, the multiplexed immunoassay device has a vibrational detection format using a MEMS. In this embodiment, the immunoassay device uses capture antibodies as previously described. However, in this embodiment, the capture antibodies are attached to a microscopic silicon microcantilever beam structure. The microcantilevers are micromechanical beams that are anchored at one end, such as diving spring boards that can be readily fabricated on silicon wafers and other materials. The microcantilever sensors are physical sensors that respond to surface stress changes due to chemical or biological processes. When fabricated with very small force constants, they can measure forces and stresses with extremely high sensitivity. The very small force constant of a cantilever allows detection not surface stress variation due to the binding of an analyte to the capture antibody on the microcantilever. Binding of the analyte results in a differential surface stress due to adsorption-induced forces, which manifests as a deflection which can be measured. The vibrational detection may be multiplexed. For more details, see Datar et al., MRS Bulletin (2009) 34:449-459 and Gaster et al., Nature Medicine (2009) 15:1327-1332, both of which are hereby incorporated by reference in their entireties.
    • V. Method for Diagnosing, Monitoring, or Determining a Renal Disorder
  • In one embodiment, a method is provided for diagnosing, monitoring, or determining a renal disorder that includes providing a test sample, determining the concentration of a combination of three or more a sample analytes, comparing the measured concentrations of the combination of sample analytes to the entries of a dataset, and identifying a particular renal disorder based on the comparison between the concentrations of the sample analytes and the minimum diagnostic concentrations contained within each entry of the dataset.
    • (a) Diagnostic Dataset
  • In an embodiment, the concentrations of the sample analytes are compared to the entries of a dataset. In this embodiment, each entry of the dataset includes a combination of three or more minimum diagnostic concentrations indicative of a particular renal disorder. A minimum diagnostic concentration, as defined herein, is the concentration of an analyte that defines the limit between the concentration range corresponding to normal, healthy renal function and the concentration reflective of a particular renal disorder. In one embodiment, each minimum diagnostic concentration is the maximum concentration of the range of analyte concentrations for a healthy, normal individual. The minimum diagnostic concentration of an analyte depends on a number of factors including but not limited to the particular analyte and the type of bodily fluid contained in the test sample. As an illustrative example, Table 1 lists the expected normal ranges of the biomarker analytes in human plasma, serum, and urine.
  • TABLE 1
    Normal Concentration Ranges In Human Plasma, Serum, and Urine Samples
    Plasma Sera Urine
    Analyte Units low high low high low high
    Calbindin ng/ml — <5.0 — <2.6 4.2 233
    Clusterin μg/ml 86 134 37 152 — <0.089
    CTGF ng/ml 2.8 7.5 — <8.2 — <0.90
    GST-alpha ng/ml 6.7 62 1.2 52 — <26
    KIM-1 ng/ml 0.053 0.57 — <0.35 0.023 0.67
    VEGF pg/ml 222 855 219 1630 69 517
    B2M μg/ml 0.68 2.2 1.00 2.6 <0.17
    Cyst C ng/ml 608 1170 476 1250 3.9 79
    NGAL ng/ml 89 375 102 822 2.9 81
    OPN ng/ml 4.1 25 0.49 12 291 6130
    TIMP-1 ng/ml 50 131 100 246 — <3.9
    A1M μg/ml 6.2 16 5.7 17 — <4.2
    THP μg/ml 0.0084 0.052 0.0079 0.053 0.39 2.6
    TFF3 μg/ml 0.040 0.49 0.021 0.17 — <21
    Creatinine mg/dL — — — — 13 212
    Microalbumin μg/ml — — — — — >16
  • In one embodiment, the high values shown for each of the biomarker analytes in Table 1 for the analytic concentrations in human plasma, sera and urine are the minimum diagnostics values for the analytes in human plasma, sera, and urine, respectively. In one exemplary embodiment, the minimum diagnostic concentration in human plasma of alpha-1 microglobulin is about 16 μg/ml, beta-2 microglobulin is about 2.2 μg/ml, calbindin is greater than about 5 ng/ml, clusterin is about 134 μg/ml, CTGF is about 16 ng/ml, cystatin C is about 1170 ng/ml, GST-alpha is about 62 ng/ml, KIM-1 is about 0.57 ng/ml, NGAL is about 375 ng/ml, osteopontin is about 25 ng/ml, THP is about 0.052 μg/ml, TIMP-1 is about 131 ng/ml, TFF-3 is about 0.49 μg/ml, and VEGF is about 855 μg/ml.
  • In another exemplary embodiment, the minimum diagnostic concentration in human sera of alpha-1 microglobulin is about 17 μg/ml, beta-2 microglobulin is about 2.6 μg/ml, calbindin is greater than about 2.6 ng/ml, clusterin is about 152 μg/ml, CTGF is greater than about 8.2 ng/ml, cystatin C is about 1250 ng/ml, GST-alpha is about 52 ng/ml, KIM-1 is greater than about 0.35 ng/ml, NGAL is about 822 ng/ml, osteopontin is about 12 ng/ml, THP is about 0.053 μg/ml, TIMP-1 is about 246 ng/ml, TFF-3 is about 0.17 μg/ml, and VEGF is about 1630 μg/ml.
  • In yet another exemplary embodiment, the minimum diagnostic concentration in human urine of alpha-1 microglobulin is about 233 μg/ml, beta-2 microglobulin is greater than about 0.17 μg/ml, calbindin is about 233 ng/ml, clusterin is greater than about 0.089 μg/ml, CTGF is greater than about 0.90 ng/ml, cystatin C is about 1170 ng/ml, GST-alpha is greater than about 26 ng/ml, KIM-1 is about 0.67 ng/ml, NGAL is about 81 ng/ml, osteopontin is about 6130 ng/ml, THP is about 2.6 μg/ml, TIMP-1 is greater than about 3.9 ng/ml, TFF-3 is greater than about 21 μg/ml, and VEGF is about 517 μg/ml.
  • In one embodiment, the minimum diagnostic concentrations represent the maximum level of analyte concentrations falling within an expected normal range. A renal disorder may be indicated if the concentration of an analyte is higher than the minimum diagnostic concentration for the analyte.
  • If diminished concentrations of a particular analyte are known to be associated with a particular renal disorder, the minimum diagnostic concentration may not be an appropriate diagnostic criterion for identifying the particular renal disorder indicated by the sample analyte concentrations. In these cases, a maximum diagnostic concentration may define the limit between the expected normal concentration range for the analyte and a sample concentration reflective of a renal disorder. In those cases in which a maximum diagnostic concentration is the appropriate diagnostic criterion, sample concentrations that fall below a maximum diagnostic concentration may indicate a particular renal disorder.
  • A critical feature of the method of the multiplexed analyte panel is that a combination of sample analyte concentrations may be used to diagnose a renal disorder. In addition to comparing subsets of the biomarker analyte concentrations to diagnostic criteria, the analytes may be algebraically combined and compared to corresponding diagnostic criteria. In one embodiment, two or more sample analyte concentrations may be added and/or subtracted to determine a combined analyte concentration. In another embodiment, two or more sample analyte concentrations may be multiplied and/or divided to determine a combined analyte concentration. To identify a particular renal disorder, the combined analyte concentration may be compared to a diagnostic criterion in which the corresponding minimum or maximum diagnostic concentrations are combined using the same algebraic operations used to determine the combined analyte concentration.
  • In yet another embodiment, the analyte concentration measured from a test sample containing one type of body fluid may be algebraically combined with an analyte concentration measured from a second test sample containing a second type of body fluid to determine a combined analyte concentration. For example, the ratio of urine calbindin to plasma calbindin may be determined and compared to a corresponding minimum diagnostic urine:plasma calbindin ratio to identify a particular renal disorder.
  • A variety of methods known in the art may be used to define the diagnostic criteria used to identify a particular renal condition. In one embodiment, any sample concentration falling outside the expected normal range indicates a renal disorder. In another embodiment, the multiplexed analyte panel may be used to evaluate the analyte concentrations in test samples taken from a population of patients having a particular renal disorder and compared to the normal expected analyte concentration ranges. In this same embodiment, any sample analyte concentrations that are significantly higher or lower than the expected normal concentration range may be used to define a minimum or maximum diagnostic concentration, respectively. A number of studies comparing the biomarker concentration ranges of a population of patients having a renal disorder to the corresponding analyte concentrations from a population of normal healthy subjects are described in the examples section below.
  • In another embodiment, the sample analyte concentrations of a population of patients exposed to varying dosages of a potentially drug may be compared to each other and to the expected normal analyte concentrations. Any sample analyte concentrations falling significantly outside the expected normal analyte concentration range may be used to define diagnostic criteria. In addition, the sample analyte concentrations may be correlated to the dosage of the potentially toxic drug in order to define a diagnostic criteria used to determine the severity of a particular renal disorder based on the sample analyte concentration.
  • (b) Renal Disorders Associated with Minimum Diagnostic Concentrations in Diagnostic Dataset
  • A variety of renal disorders and locations of damage within the kidney may be identified using a comparison of the sample analyte concentrations with a set of diagnostic criteria. In one embodiment, the types of kidney damage identified by the multiplexed analyte panel include, but are not limited to glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, and acute tubular necrosis. In another embodiment, the multiplexed analyte panel identifies secondary kidney damaged caused by exposure to agents including but not limited to therapeutic drugs, recreational drugs, medical imaging contrast agents, toxins, kidney stones, ischemia, liver transplantation, heart transplantation, lung transplantation, and hypovolemia. In yet another embodiment, the multiplexed analyte panel identifies kidney damage caused by disease including but not limited to diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to any infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate disease.
  • The following examples are included to demonstrate preferred embodiments of the invention.
    • EXAMPLES
  • The following examples illustrate various iterations of the invention.
    • Example 1 Least Detectable Dose and Lower Limit of Quantitation of Assay for Analytes Associated with Renal Disorders
  • To assess the least detectable doses (LDD) and lower limits of quantitation (LLOQ) of a variety of analytes associated with renal disorders, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • The concentrations of the analytes were measured using a capture-sandwich assay using antigen-specific antibodies. For each analyte, a range of standard sample dilutions ranging over about four orders of magnitude of analyte concentration were measured using the assay in order to obtain data used to construct a standard dose response curve. The dynamic range for each of the analytes, defined herein as the range of analyte concentrations measured to determine its dose response curve, is presented below.
  • To perform the assay, 5 μL of a diluted mixture of capture-antibody microspheres were mixed with 5 μL of blocker and 10 μL of pre-diluted standard sample in each of the wells of a hard-bottom microtiter plate. After incubating the hard-bottom plate for 1 hour, 10 μL of biotinylated detection antibody was added to each well, and then the hard-bottom plate was incubated for an additional hour. 10 μL of diluted streptavidin-phycoerythrin was added to each well and then the hard-bottom plate was incubated for another 60 minutes.
  • A filter-membrane microtiter plate was pre-wetted by adding 100 μL wash buffer, and then aspirated using a vacuum manifold device. The contents of the wells of the hard-bottom plate were then transferred to the corresponding wells of the filter-membrane plate. All wells of the hard-bottom plate were vacuum-aspirated and the contents were washed twice with 100 μL of wash buffer. After the second wash, 100 μL of wash buffer was added to each well, and then the washed microspheres were resuspended with thorough mixing. The plate was then analyzed using a Luminex 100 Analyzer (Luminex Corporation, Austin, Tex., USA). Dose response curves were constructed for each analyte by curve-fitting the median fluorescence intensity (MFI) measured from the assays of diluted standard samples containing a range of analyte concentrations.
  • The least detectable dose (LDD) was determined by adding three standard deviations to the average of the MFI signal measured for 20 replicate samples of blank standard solution (i.e. standard solution containing no analyte). The MFI signal was converted to an LDD concentration using the dose response curve and multiplied by a dilution factor of 2.
  • The lower limit of quantification (LLOQ), defined herein as the point at which the coefficient of variation (CV) for the analyte measured in the standard samples was 30%, was determined by the analysis of the measurements of increasingly diluted standard samples. For each analyte, the standard solution was diluted by 2 fold for 8 dilutions. At each stage of dilution, samples were assayed in triplicate, and the CV of the analyte concentration at each dilution was calculated and plotted as a function of analyte concentration. The LLOQ was interpolated from this plot and multiplied by a dilution factor of 2.
  • The LDD and LLOQ results for each analyte are summarized in Table 2:
  • TABLE 2
    LDD, LLOQ, and Dynamic Range of Analyte Assay
    Dynamic Range
    Analyte Units LDD LLOQ minimum maximum
    Calbindin ng/mL 1.1 3.1 0.516 2580
    Clusterin ng/mL 2.4 2.3 0.676 3378
    CTGF ng/mL 1.3 3.8 0.0794 400
    GST-alpha ng/mL 1.4 3.6 0.24 1,200
    KIM-1 ng/mL 0.016 0.028 0.00478 24
    VEGF pg/mL 4.4 20 8.76 44,000
    β-2 M μg/mL 0.012 0.018 0.0030 15
    Cystatin C ng/mL 2.8 3.7 0.60 3,000
    NGAL ng/mL 4.1 7.8 1.2 6,000
    Osteopontin ng/mL 29 52 3.9 19,500
    TIMP-1 ng/mL 0.71 1.1 0.073 365
    A-1 M μg/mL 0.059 0.29 0.042 210
    THP μg/mL 0.46 0.30 0.16 800
    TFF-3 μg/mL 0.06 0.097 0.060 300
  • The results of this experiment characterized the least detectible dose and the lower limit of quantification for fourteen analytes associated with various renal disorders using a capture-sandwich assay.
    • Example 2 Precision of Assay for Analytes Associated with Renal Disorders
  • To assess the precision of an assay used to measure the concentration of analytes associated with renal disorders, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For each analyte, three concentration levels of standard solution were measured in triplicate during three runs using the methods described in Example 1. The percent errors for each run at each concentration are presented in Table 3 for all of the analytes tested:
  • TABLE 3
    Precision of Analyte Assay
    Average Run 1 Run 2 Run 2 Interrun
    concentration Error Error Error Error
    Analyte (ng/mL) (%) (%) (%) (%)
    Calbindin 4.0 6 2 6 13
    36 5 3 2 7
    281 1 6 0 3
    Clusterin 4.4 4 9 2 6
    39 5 1 6 8
    229 1 3 0 2
    CTGF 1.2 10 17 4 14
    2.5 19 19 14 14
    18 7 5 13 9
    GST-alpha 3.9 14 7 5 10
    16 13 7 10 11
    42 1 16 6 8
    KIM-1 0.035 2 0 5 13
    0.32 4 5 2 8
    2.9 0 5 7 4
    VEGF 65 10 1 6 14
    534 9 2 12 7
    5,397 1 13 14 9
    β-2 M 0.040 6 1 8 5
    0.43 2 2 0 10
    6.7 6 5 11 6
    Cystatin C 10.5 4 1 7 13
    49 0 0 3 9
    424 2 6 2 5
    NGAL 18.1 11 3 6 13
    147 0 0 6 5
    1,070 5 1 2 5
    Osteopontin 44 1 10 2 11
    523 9 9 9 7
    8,930 4 10 1 10
    TIMP-1 2.2 13 6 3 13
    26 1 1 4 14
    130 1 3 1 4
    A-1 M 1.7 11 7 7 14
    19 4 1 8 9
    45 3 5 2 4
    THP 9.4 3 10 11 11
    15 3 7 8 6
    37 4 5 0 5
    TFF-3 0.3 13 3 11 12
    4.2 5 8 5 7
    1.2 3 7 0 13
  • The results of this experiment characterized the precision of a capture-sandwich assay for fourteen analytes associated with various renal disorders over a wide range of analyte concentrations. The precision of the assay varied between about 1% and about 15% error within a given run, and between about 5% and about 15% error between different runs. The percent errors summarized in Table 2 provide information concerning random error to be expected in an assay measurement caused by variations in technicians, measuring instruments, and times of measurement.
    • Example 3 Linearity of Assay for Analytes Associated with Renal Disorders
  • To assess the linearity of an assay used to measure the concentration of analytes associated with renal disorders, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For each analyte, three concentration levels of standard solution were measured in triplicate during three runs using the methods described in Example 1. Linearity of the assay used to measure each analyte was determined by measuring the concentrations of standard samples that were serially-diluted throughout the assay range. The % recovery was calculated as observed vs. expected concentration based on the dose-response curve. The results of the linearity analysis are summarized in Table 4.
  • TABLE 4
    Linearity of Analyte Assay
    Expected Observed Recovery
    Analyte Dilution concentration concentration (%)
    Calbindin 1:2 61 61 100
    (ng/mL) 1:4 30 32 106
    1:8 15 17 110
    Clusterin 1:2 41 41 100
    (ng/mL) 1:4 21 24 116
    1:8 10 11 111
    CTGF 1:2 1.7 1.7 100
    (ng/mL) 1:4 0.84 1.0 124
    1:8 0.42 0.51 122
    GST-alpha 1:2 25 25 100
    (ng/mL) 1:4 12 14 115
    1:8 6.2 8.0 129
    KIM-1 1:2 0.87 0.87 100
    (ng/mL) 1:4 0.41 0.41 101
    1:8 0.21 0.19 93
    VEGF 1:2 2,525 2,525 100
    (pg/mL) 1:4 1,263 1,340 106
    1:8 631 686 109
    β-2M 1:100 0.63 0.63 100
    (μg/mL) 1:200 0.31 0.34 106
    1:400 0.16 0.17 107
    Cystatin C 1:100 249 249 100
    (ng/mL) 1:200 125 122 102
    1:400 62 56 110
    NGAL 1:100 1,435 1,435 100
    (ng/mL) 1:200 718 775 108
    1:400 359 369 103
    Osteopontin 1:100 6,415 6,415 100
    (ng/mL) 1:200 3,208 3,275 102
    1:400 1,604 1,525 95
    TIMP-1 1:100 35 35 100
    (ng/mL) 1:200 18 18 100
    1:400 8.8 8.8 100
    A-1M 1:2000 37 37 100
    (μg/mL) 1:4000 18 18 99
    1:8000 9.1 9.2 99
    THP 1:2000 28 28 100
    (μg/mL) 1:4000 14 14 96
    1:8000 6.7 7.1 94
    TFF-3 1:2000 8.8 8.8 100
    (μg/mL) 1:4000 3.8 4.4 86
    1:8000 1.9 2.2 86
  • The results of this experiment demonstrated reasonably linear responses of the sandwich-capture assay to variations in the concentrations of the analytes in the tested samples.
    • Example 4 Spike Recovery of Analytes Associated with Renal Disorders
  • To assess the recovery of analytes spiked into urine, serum, and plasma samples by an assay used to measure the concentration of analytes associated with renal disorders, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For each analyte, three concentration levels of standard solution were spiked into known urine, serum, and plasma samples. Prior to analysis, all urine samples were diluted 1:2000 (sample: diluent), all plasma samples were diluted 1:5 (sample: diluent), and all serum samples were diluted 1:2000 (sample: diluent).
  • The concentrations of the analytes in the samples were measured using the methods described in Example 1. The average % recovery was calculated as the proportion of the measurement of analyte spiked into the urine, serum, or plasma sample (observed) to the measurement of analyte spiked into the standard solution (expected). The results of the spike recovery analysis are summarized in Table 5.
  • TABLE 5
    Spike Recovery of Analyte Assay in Urine,
    Serum, and Plasma Samples
    Recovery in Recovery in Recovery in
    Spike Urine Serum Plasma
    Analyte Concentration Sample (%) Sample (%) Sample (%)
    Calbindin 66 76 82 83
    (ng/mL) 35 91 77 71
    18 80 82 73
    average 82 80 76
    Clusterin 80 72 73 75
    (ng/mL) 37 70 66 72
    20 90 73 70
    average 77 70 72
    CTGF 8.4 91 80 79
    (ng/mL) 4.6 114 69 78
    2.4 76 80 69
    average 94 77 75
    GST-alpha 27 75 84 80
    (ng/mL) 15 90 75 81
    7.1 82 84 72
    average 83 81 78
    KIM-1 0.63 87 80 83
    (ng/mL) .029 119 74 80
    0.14 117 80 78
    average 107 78 80
    VEGF 584 88 84 82
    (pg/mL) 287 101 77 86
    123 107 84 77
    average 99 82 82
    β-2M 0.97 117 98 98
    (μg/mL) 0.50 124 119 119
    0.24 104 107 107
    average 115 108 105
    Cystatin C 183 138 80 103
    (ng/mL) 90 136 97 103
    40 120 97 118
    average 131 91 108
    NGAL 426 120 105 111
    (ng/mL) 213 124 114 112
    103 90 99 113
    average 111 106 112
    Osteopontin 1,245 204 124 68
    (ng/mL) 636 153 112 69
    302 66 103 67
    average 108 113 68
    TIMP-1 25 98 97 113
    (ng/mL) 12 114 89 103
    5.7 94 99 113
    average 102 95 110
    A-1M 0.0028 100 101 79
    (μg/mL) 0.0012 125 80 81
    0.00060 118 101 82
    Average 114 94 81
    THP 0.0096 126 108 90
    (μg/mL) 0.0047 131 93 91
    0.0026 112 114 83
    average 123 105 88
    TFF-3 0.0038 105 114 97
    (μg/mL) 0.0019 109 104 95
    0.0010 102 118 93
    average 105 112 95
  • The results of this experiment demonstrated that the sandwich-type assay is reasonably sensitive to the presence of all analytes measured, whether the analytes were measured in standard samples, urine samples, plasma samples, or serum samples.
    • Example 5 Matrix Interferences of Analytes Associated with Renal Disorders
  • To assess the matrix interference of hemoglobin, bilirubin, and triglycerides spiked into standard samples, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For each analyte, three concentration levels of standard solution were spiked into known urine, serum, and plasma samples. Matrix interference was assessed by spiking hemoglobin, bilirubin, and triglyceride into standard analyte samples and measuring analyte concentrations using the methods described in Example 1. A % recovery was determined by calculating the ratio of the analyte concentration measured from the spiked sample (observed) divided by the analyte concentration measured form the standard sample (expected). The results of the matrix interference analysis are summarized in Table 6.
  • TABLE 6
    Matrix Interference of Hemoglobin, Bilirubin, and Triglyceride on the
    Measurement of Analytes
    Matrix
    Compound Maximum
    Spiked into Spike Overall
    Analyte Sample Concentration Recovery (%)
    Calbindin Hemoglobin 500 110
    (mg/mL) Bilirubin 20 98
    Triglyceride 500 117
    Clusterin Hemoglobin 500 125
    (mg/mL) Bilirubin 20 110
    Triglyceride 500 85
    CTGF Hemoglobin 500 91
    (mg/mL) Bilirubin 20 88
    Triglyceride 500 84
    GST-alpha Hemoglobin 500 100
    (mg/mL) Bilirubin 20 96
    Triglyceride 500 96
    KIM-1 Hemoglobin 500 108
    (mg/mL) Bilirubin 20 117
    Triglyceride 500 84
    VEGF Hemoglobin 500 112
    (mg/mL) Bilirubin 20 85
    Triglyceride 500 114
    β-2M Hemoglobin 500 84
    (μg/mL) Bilirubin 20 75
    Triglyceride 500 104
    Cystatin C Hemoglobin 500 91
    (ng/mL) Bilirubin 20 102
    Triglyceride 500 124
    NGAL Hemoglobin 500 99
    (ng/mL) Bilirubin 20 92
    Triglyceride 500 106
    Osteopontin Hemoglobin 500 83
    (ng/mL) Bilirubin 20 86
    Triglyceride 500 106
    TIMP-1 Hemoglobin 500 87
    (ng/mL) Bilirubin 20 86
    Triglyceride 500 93
    A-1M Hemoglobin 500 103
    (μg/mL) Bilirubin 20 110
    Triglyceride 500 112
    THP Hemoglobin 500 108
    (μg/mL) Bilirubin 20 101
    Triglyceride 500 121
    TFF-3 Hemoglobin 500 101
    (μg/mL) Bilirubin 20 101
    Triglyceride 500 110
  • The results of this experiment demonstrated that hemoglobin, bilirubin, and triglycerides, three common compounds found in urine, plasma, and serum samples, did not significantly degrade the ability of the sandwich-capture assay to detect any of the analytes tested.
    • Example 6 Sample Stability of Analytes Associated with Renal Disorders
  • To assess the ability of analytes spiked into urine, serum, and plasma samples to tolerate freeze-thaw cycles, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. Each analyte was spiked into known urine, serum, and plasma samples at a known analyte concentration. The concentrations of the analytes in the samples were measured using the methods described in Example 1 after the initial addition of the analyte, and after one, two and three cycles of freezing and thawing. In addition, analyte concentrations in urine, serum and plasma samples were measured immediately after the addition of the analyte to the samples as well as after storage at room temperature for two hours and four hours, and after storage at 4° C. for 2 hours, four hours, and 24 hours.
  • The results of the freeze-thaw stability analysis are summarized in Table 7. The % recovery of each analyte was calculated as a percentage of the analyte measured in the sample prior to any freeze-thaw cycles.
  • TABLE 7
    Freeze-Thaw Stability of the Analytes in Urine, Serum, and Plasma
    Period Urine Sample Serum Sample Plasma Sample
    and Concen- Recovery Concen- Recovery Concen- Recovery
    Analyte Temp tration (%) tration (%) tration (%)
    Calbindin Control 212 100 31 100 43 100
    (ng/mL) 1X 221 104 30 96 41 94
    2X 203 96 30 99 39 92
    3X 234 110 30 97 40 93
    Clusterin 0 315 100 232 100 187 100
    (ng/mL) 1X 329 104 227 98 177 95
    2X 341 108 240 103 175 94
    3X 379 120 248 107 183 98
    CTGF 0 6.7 100 1.5 100 1.2 100
    (ng/mL) 1X 7.5 112 1.3 82 1.2 94
    2X 6.8 101 1.4 90 1.2 100
    3X 7.7 115 1.2 73 1.3 107
    GST- 0 12 100 23 100 11 100
    alpha 1X 13 104 24 105 11 101
    (ng/mL) 2X 14 116 21 92 11 97
    3X 14 111 23 100 12 108
    KIM-1 0 1.7 100 0.24 100 0.24 100
    (ng/mL) 1X 1.7 99 0.24 102 0.22 91
    2X 1.7 99 0.22 94 0.19 78
    3X 1.8 107 0.23 97 0.22 93
    VEGF 0 1,530 100 1,245 100 674 100
    (pg/mL) 1X 1,575 103 1,205 97 652 97
    2X 1,570 103 1,140 92 612 91
    3X 1,700 111 1,185 95 670 99
    β-2 M 0 0.0070 100 1.2 100 15 100
    (μg/mL) 1X 0.0073 104 1.1 93 14 109
    2X 0.0076 108 1.2 103 15 104
    3X 0.0076 108 1.1 97 13 116
    Cystatin 0 1,240 100 1,330 100 519 100
    C 1X 1,280 103 1,470 111 584 113
    (ng/mL) 2X 1,410 114 1,370 103 730 141
    3X 1,420 115 1,380 104 589 113
    NGAL 0 45 100 245 100 84 100
    (ng/mL) 1X 46 102 179 114 94 112
    2X 47 104 276 113 91 108
    3X 47 104 278 113 91 109
    Osteo- 0 38 100 1.7 100 5.0 100
    pontin 1X 42 110 1.8 102 5.5 110
    (ng/mL) 2X 42 108 1.5 87 5.5 109
    3X 42 110 1.3 77 5.4 107
    TIMP-1 0 266 100 220 100 70 100
    (ng/mL) 1X 265 100 220 10 75 108
    2X 255 96 215 98 77 110
    3X 295 111 228 104 76 109
    A-1 M 0 14 100 26 100 4.5 100
    (μg/mL) 1X 13 92 25 96 4.2 94
    2X 15 107 25 96 4.3 97
    3X 16 116 23 88 4.0 90
    THP 0 4.6 100 31 100 9.2 100
    (μg/mL) 1X 4.4 96 31 98 8.8 95
    2X 5.0 110 31 100 9.2 100
    3X 5.2 114 27 85 9.1 99
    TFF-3 0 4.6 100 24 100 22 100
    (μg/mL) 1X 4.4 96 23 98 22 103
    2X 5.0 110 24 103 22 101
    3X 5.2 114 19 82 22 102
  • The results of the short-term stability assessment are summarized in Table 8. The % recovery of each analyte was calculated as a percentage of the analyte measured in the sample prior to any short-term storage.
  • TABLE 8
    Short-Term Stability of Analytes in Urine, Serum, and Plasma
    Storage Urine Sample Serum Sample Plasma Sample
    Time/ Sample Recovery Sample Recovery Sample Recovery
    Analyte Temp Conc. (%) Conc. (%) Conc. (%)
    Cal- Control 226 100 33 100 7 100
    bindin 2 hr/room 242 107 30 90 6.3 90
    (ng/mL) temp
    2 hr. @ 228 101 29 89 6.5 93
    4° C.
    4 hr @ 240 106 28 84 5.6 79
    room temp
    4 hr. @ 202 89 29 86 5.5 79
    4° C.
    24 hr. @ 199 88 26 78 7.1 101
    4° C.
    Clus- Control 185 100 224 100 171 100
    terin 2 hr @ 173 94 237 106 180 105
    (ng/mL) room temp
    2 hr. @ 146 79 225 100 171 100
    4° C.
    4 hr @ 166 89 214 96 160 94
    room temp
    4 hr. @ 157 85 198 88 143 84
    4° C.
    24 hr. @ 185 100 207 92 162 94
    4° C.
    CTGF Control 1.9 100 8.8 100 1.2 100
    (ng/mL) 2 hr @ 1.9 99 6.7 76 1 83
    room temp
    2 hr. @ 1.8 96 8.1 92 1.1 89
    4° C.
    4 hr @ 2.1 113 5.6 64 1 84
    room temp
    4 hr. @ 1.7 91 6.4 74 0.9 78
    4° C.
    24 hr. @ 2.2 116 5.9 68 1.1 89
    4° C.
    GST- Control 14 100 21 100 11 100
    alpha 2 hr @ 11 75 23 107 11 103
    (ng/mL) room temp
    2 hr. @ 13 93 22 104 9.4 90
    4° C.
    4 hr @ 11 79 21 100 11 109
    room temp
    4 hr. @ 12 89 21 98 11 100
    4° C.
    24 hr. @ 13 90 22 103 14 129
    4° C.
    KIM-1 Control 1.5 100 0.23 100 0.24 100
    (ng/mL) 2 hr @ 1.2 78 0.2 86 0.22 90
    room temp
    2 hr. @ 1.6 106 0.23 98 0.21 85
    4° C.
    4 hr @ 1.3 84 0.19 82 0.2 81
    room temp
    4 hr. @ 1.4 90 0.22 93 0.19 80
    4° C.
    24 hr. @ 1.1 76 0.18 76 0.23 94
    4° C.
    VEGF Control 851 100 1215 100 670 100
    (pg/mL) 2 hr @ 793 93 1055 87 622 93
    room temp
    2 hr. @ 700 82 1065 88 629 94
    4° C.
    4 hr @ 704 83 1007 83 566 84
    room temp
    4 hr. @ 618 73 1135 93 544 81
    4° C.
    24 hr. @ 653 77 1130 93 589 88
    4° C.
    β-2 M Control 0.064 100 2.6 100 1.2 100
    (μg/mL) 2 hr @ 0.062 97 2.4 92 1.1 93
    room temp
    2 hr. @ 0.058 91 2.2 85 1.2 94
    4° C.
    4 hr @ 0.064 101 2.2 83 1.2 94
    room temp
    4 hr. @ 0.057 90 2.2 85 1.2 98
    4° C.
    24 hr. @ 0.06 94 2.5 97 1.3 103
    4° C.
    Cys- Control 52 100 819 100 476 100
    tatin 2 hr @ 50 96 837 102 466 98
    C room temp
    (ng/mL) 2 hr. @ 44 84 884 108 547 115
    4° C.
    4 hr @ 49 93 829 101 498 105
    room temp
    4 hr. @ 46 88 883 108 513 108
    4° C.
    24 hr. @ 51 97 767 94 471 99
    4° C.
    NGAL Control 857 100 302 100 93 100
    (ng/mL) 2 hr @ 888 104 287 95 96 104
    room temp
    2 hr. @ 923 108 275 91 92 100
    4° C.
    4 hr @ 861 101 269 89 88 95
    room temp
    4 hr. @ 842 98 283 94 94 101
    4° C.
    24 hr. @ 960 112 245 81 88 95
    4° C.
    Osteo- Control 2243 100 6.4 100 5.2 100
    pontin 2 hr @ 2240 100 6.8 107 5.9 114
    (ng/mL) room temp
    2 hr. @ 2140 95 6.4 101 6.2 120
    4° C.
    4 hr @ 2227 99 6.9 108 5.8 111
    room temp
    4 hr. @ 2120 95 7.7 120 5.2 101
    4° C.
    24 hr. @ 2253 100 6.5 101 6 116
    4° C.
    TIMP-1 Control 17 100 349 100 72 100
    (ng/mL) 2 hr @ 17 98 311 89 70 98
    room temp
    2 hr. @ 16 94 311 89 68 95
    4° C.
    4 hr @ 17 97 306 88 68 95
    room temp
    4 hr. @ 16 93 329 94 74 103
    4° C.
    24 hr. @ 18 105 349 100 72 100
    4° C.
    A-1 M Control 3.6 100 2.2 100 1 100
    (μg/mL) 2 hr @ 3.5 95 2 92 1 105
    room temp
    2 hr. @ 3.4 92 2.1 97 0.99 99
    4° C.
    4 hr @ 3.2 88 2.2 101 0.99 96
    room temp
    4 hr. @ 3 82 2.2 99 0.97 98
    4° C.
    24 hr. @ 3 83 2.2 100 1 101
    4° C.
    THP Control 1.2 100 34 100 2.1 100
    (μg/mL) 2 hr @ 1.2 99 34 99 2 99
    room temp
    2 hr. @ 1.1 90 34 100 2 98
    4° C.
    4 hr @ 1.1 88 27 80 2 99
    room temp
    4 hr. @ 0.95 79 33 97 2 95
    4° C.
    24 hr. @ 0.91 76 33 98 2.4 116
    4° C.
    TFF-3 Control 1230 100 188 100 2240 100
    (μg/mL) 2 hr @ 1215 99 179 95 2200 98
    room temp
    2 hr. @ 1200 98 195 104 2263 101
    4° C.
    4 hr @ 1160 94 224 119 2097 94
    room temp
    4 hr. @ 1020 83 199 106 2317 103
    4° C.
    24 hr. @ 1030 84 229 122 1940 87
    4° C.
  • The results of this experiment demonstrated that the analytes associated with renal disorders tested were suitably stable over several freeze/thaw cycles, and up to 24 hrs. of storage at a temperature of 4° C.
    • Example 8 Diagnosis of Renal Damage Using Detection of Analytes in Human Urine Samples
  • To assess the effectiveness of a human kidney toxicity panel to detect renal damage due to disease states, the following experiment was conducted. Urine samples were obtained from healthy control patients (n=5), renal cancer patients (n=4) and “other” cancer patients (n=8) afflicted with lung cancer, pancreatic cancer, liver cancer, or colon cancer. All urine samples were diluted as described in Example 4 and subjected to a sandwich-capture assay as described in Example 1. Urine concentrations of analytes included in a human kidney toxicity panel were measured by the assay, including alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.
  • FIG. 1 summarizes the urine concentrations of those analytes that differed significantly from control urine concentrations. The urine concentrations of A1M, NGAL, and THP were slightly elevated for the renal cancer patient group and more significantly elevated for the “other” cancer patient group. Urine B2M concentrations appeared to be elevated for both the renal cancer and “other” cancer patient groups, although the BRM concentrations exhibited more variability than the other analyte concentrations shown in FIG. 1.
  • The results of this experiment demonstrated that panels of analytes detected in urine samples were capable of identifying patients having renal damage resulting from renal cancer and other cancers.
    • Example 9 Analysis of Kidney Biomarkers in Plasma and Urine from Patients with Renal Injury
  • A screen for potential protein biomarkers in relation to kidney toxicity/damage was performed using a panel of biomarkers, in a set of urine and plasma samples from patients with documented renal damage. The investigated patient groups included diabetic nephropathy (DN), obstructive uropathy (OU), analgesic abuse (AA) and glomerulonephritis (GN) along with age, gender and BMI matched control groups. Multiplexed immunoassays were applied in order to quantify the following protein analytes: Alpha-1 Microglobulin (α1M), KIM-1, Microalbumin, Beta-2-Microglobulin (β2M), Calbindin, Clusterin, CystatinC, TreFoilFactor-3 (TFF-3), CTGF, GST-alpha, VEGF, Calbindin, Osteopontin, Tamm-HorsfallProtein (THP), TIMP-1 and NGAL.
  • Li-Heparin plasma and mid-stream spot urine samples were collected from four different patient groups. Samples were also collected from age, gender and BMI matched control subjects. 20 subjects were included in each group resulting in a total number of 160 urine and plasma samples. All samples were stored at −80° C. before use. Glomerular filtration rate for all samples was estimated using two different estimations (Modification of Diet in Renal Disease or MDRD, and the Chronic Kidney Disease Epidemiology Collaboration or CKD-EPI) to outline the eGFR (estimated glomerular filtration rate) distribution within each patient group (FIG. 2). Protein analytes were quantified in human plasma and urine using multiplexed immunoassays in the Luminex xMAP™ platform. The microsphere-based multiplex immunoassays consist of antigen-specific antibodies and optimized reagents in a capture-sandwich format. Output data was given as g/ml calculated from internal standard curves. Because urine creatinine (uCr) correlates with renal filtration rate, data analysis was performed without correction for uCr. Univariate and multivariate data analysis was performed comparing all case vs. control samples as well as cases vs. control samples for the various disease groups.
  • The majority of the measured proteins showed a correlation to eGFR. Measured variables were correlated to eGFR using Pearson's correlations coefficient, and samples from healthy controls and all disease groups were included in the analysis. 11 and 7 proteins displayed P-values below 0.05 for plasma and urine (Table 9) respectively.
  • TABLE 9
    Correlation analysis of eGFR and variables for all case samples
    URINE PLASMA
    Variable Pearson's r P-Value Variable Pearson's r P-Value
    Alpha-1- −0.08   0.3 Alpha-1- −0.33
    Figure US20110065593A1-20110317-P00001
    Microglobulin Microglobulin
    Beta-2- −0.23   0.003 Beta-2- −0.39
    Figure US20110065593A1-20110317-P00001
    Microglobulin Microglobulin
    Calbindin −0.16   0.04 Calbindin −0.18 <0.02
    Clusterin −0.07   0.4 Clusterin −0.51
    Figure US20110065593A1-20110317-P00001
    CTGF −0.08   0.3 CTGF −0.05   0.5
    Creatinine −0.32
    Figure US20110065593A1-20110317-P00001
    		 Cystatin-C −0.42 <0.0001
    Cystatin-C −0.24   0.002 GST-alpha −0.12   0.1
    GST-alpha −0.11   0.2 KIM-1 −0.17   0.03
    KIM-1 −0.08   0.3 NGAL −0.28 <0.001
    Microalbumin_UR −0.17   0.03 Osteopontin −0.33
    Figure US20110065593A1-20110317-P00001
    NGAL −0.15   0.07 THP −0.31
    Figure US20110065593A1-20110317-P00001
    Osteopontin −0.19   0.02 TIMP-1 −0.28 <0.001
    THP −0.05   0.6 TFF3 −0.38
    Figure US20110065593A1-20110317-P00001
    TIMP-1 −0.19   0.01 VEGF −0.14   0.08
    TFF2 −0.09   0.3
    VEGF −0.07   0.4
    P values <0.0001 are shown in bold italics
    P values <0.005 are shown in bold
    P values <0.05 are shown in italics
  • For the various disease groups, univariate statistical analysis revealed that in a direct comparison (T-test) between cases and controls, a number of proteins were differentially expressed in both urine and plasma (Table 10 and FIG. 3). In particular, clusterin showed a marked differential pattern in plasma.
  • TABLE 10
    Differentially regulated proteins by
    univariate statistical analysis
    Group Matrix Protein p-value
    AA Urine Calbindin 0.016
    AA Urine NGAL 0.04
    AA Urine Osteopontin 0.005
    AA Urine Creatinine 0.001
    AA Plasma Calbindin 0.05
    AA Plasma Clusterin 0.003
    AA Plasma KIM-1 0.03
    AA Plasma THP 0.001
    AA Plasma TIMP-1 0.02
    DN Urine Creatinine 0.04
    DN Plasma Clusterin 0.006
    DN Plasma KIM-1 0.01
    GN Urine Creatinine 0.004
    GN Urine Microalbumin 0.0003
    GN Urine NGAL 0.05
    GN Urine Osteopontin 0.05
    GN Urine TFF3 0.03
    GN Plasma Alpha 1 Microglobulin 0.002
    GN Plasma Beta 2 Microglobulin 0.03
    GN Plasma Clusterin 0.00
    GN Plasma Cystatin C 0.01
    GN Plasma KIM-1 0.003
    GN Plasma NGAL 0.03
    GN Plasma THP 0.001
    GN Plasma TIMP-1 0.003
    GN Plasma TFF3 0.01
    GN Plasma VEGF 0.02
    OU Urine Clusterin 0.02
    OU Urine Microalbumin 0.007
    OU Plasma Clusterin 0.00
  • Application of multivariate analysis yielded statistical models that predicted disease from control samples (plasma results are shown in FIG. 4).
  • In conclusion, these results form a valuable base for further studies on these biomarkers in urine and plasma both regarding baseline levels in normal populations and regarding the differential expression of the analytes in various disease groups. Using this panel of analytes, error rates from adaboosting and/or random forest were low enough (<10%) to allow a prediction model to differentiate between control and disease patient samples. Several of the analytes showed a greater correlation to eGFR in plasma than in urine.
    • Example 10 Statistical Analysis of Kidney Biomarkers in Plasma and Urine from Patients with Renal Injury
  • Urine and plasma samples were taken from 80 normal control group subjects and 20 subjects from each of four disorders: analgesic abuse, diabetic nephropathy, glomerulonephritis, and obstructive uropathy. The samples were analyzed for the quantity and presence of 16 different proteins (alpha-1 microglobulin (α1M), beta-2 microglobulin (β2M), calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF) as described in Example 1 above. The goal was to determine the analytes that distinguish between a normal sample and a diseased sample, a normal sample and an obstructive uropathy (OU) sample, and finally, an glomerulonephritis sample from the other disease samples (diabetic nephropathy (DN), analgesic abuse (AA), and glomerulonephritis (GN)).
  • From the above protein analysis data, bootstrap analysis was used to estimate the future performance of several classification algorithms. For each bootstrap run, training data and testing data was randomly generated. Then, the following algorithms were applied on the training data to generate models and then apply the models to the testing data to make predictions: automated Matthew's classification algorithm, classification and regression tree (CART), conditional inference tree, bagging, random forest, boosting, logistic regression, SVM, and Lasso. The accuracy rate and ROC areas were recorded for each method on the prediction of the testing data. The above was repeated 100 times. The mean and the standard deviation of the accuracy rates and of the ROC areas were calculated.
  • The mean error rates and AUROC were calculated from urine and AUROC was calculated from plasma for 100 runs of the above method for each of the following comparisons: disease (AA+GN+OU+DN) vs. normal (FIG. 5, Table 11), AA vs. normal (FIG. 7, Table 13), DN vs. AA (FIG. 9, Table 15, AA vs. GN (FIG. 11, Table 17), and AA vs. OU (FIG. 13, Table 19).
  • The average relative importance of 16 different analytes (alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF) and 4 different clinical variables (weight, BMI, age, and gender) from 100 runs were analyzed with two different statistical methods—random forest (plasma and urine samples) and boosting (urine samples)—for each of the following comparisons: disease (AA+GN+OU+DN) vs. normal (FIG. 6, Table 12), AA vs. normal (FIG. 8, Table 14), DN vs. AA (FIG. 10, Table 16), AA vs. GN (FIG. 12, Table 18), and AA vs. OU (FIG. 14, Table 20).
  • TABLE 11
    Disease v. Normal
    Standard
    Mean deviation
    method AUROC AUROC
    random 0.931 0.039
    forest
    bagging 0.919 0.045
    svm 0.915 0.032
    boosting 0.911 0.06
    lasso 0.897 0.044
    logistic 0.891 0.041
    regression
    ctree 0.847 0.046
    cart 0.842 0.032
    matt 0.83 0.023
  • TABLE 12
    Disease v. Normal
    relative
    analyte importance
    Creatinine 11.606
    Kidney_Injury_M 8.486
    Tamm_Horsfall_P 8.191
    Total_Protein 6.928
    Osteopontin 6.798
    Neutrophil_Gela 6.784
    Tissue_Inhibito 6.765
    Vascular_Endoth 6.716
    Trefoil_Factor— 6.703
    Cystatin_C 6.482
    Alpha_1_Microgl 6.418
    Beta_2_Microglo 6.228
    Glutathione_S_T 6.053
    clusterin 5.842
  • TABLE 13
    AA v. NL
    Standard
    deviation
    Mean of
    method AUROC AUROC
    cart
    1 0
    bagging 1 0
    boosting 1 0
    lasso 0.998 0.008
    ctree 0.998 0.015
    random 0.997 0.012
    forest
    svm 0.977 0.033
    logistic 0.933 0.092
    regression
    matt 0.873 0.112
  • TABLE 14
    AA v. NL
    Relative
    analyte importance
    Creatinine 17.800
    Tissue_Inhibito 9.953
    Total_Protein 8.837
    Tamm_Horsfall_P 7.379
    Cystatin_C 6.237
    Kidney_Injury_M 6.174
    Beta_2_Microglo 5.915
    Neutrophil_Gela 5.761
    Alpha_1_Microgl 5.742
    Trefoil_Factor— 5.736
    Osteopontin 5.561
    Vascular_Endoth 5.338
    clusterin 4.892
    Glutathione_S_T 4.675
  • TABLE 15
    AA v. DN
    Standard
    Mean deviation
    method AUROC AUROC
    lasso 0.999 0.008
    random 0.989 0.021
    forest
    svm 0.988 0.039
    boosting 0.988 0.022
    bagging 0.972 0.036
    logistic 0.969 0.057
    regression
    cart 0.93 0.055
    ctree 0.929 0.063
    matt 0.862 0.12
  • TABLE 16
    AA v. DN
    Relative
    analyte importance
    Creatinine 17.57
    Total_Protein 10.90
    Tissue_Inhibito 8.77
    clusterin 6.89
    Glutathione_S_T 6.24
    Alpha_1_Microgl 6.15
    Beta_2_Microglo 6.06
    Cystatin_C 5.99
    Trefoil_Factor— 5.88
    Kidney_Injury_M 5.49
    Vascular_Endoth 5.38
    Tamm_Horsfall_P 5.33
    Osteopontin 4.86
    Neutrophil_Gela 4.47
  • TABLE 17
    AA v. GN
    Standard
    deviation
    Mean of
    method AUROC AUROC
    svm 0.689 0.11
    boosting 0.675 0.102
    bagging 0.674 0.106
    random 0.66 0.096
    forest
    matt 0.631 0.085
    cart 0.626 0.089
    logistic 0.614 0.091
    regression
    lasso 0.606 0.102
    ctree 0.53 0.061
  • TABLE 18
    AA v. GN
    Relative
    analyte importance
    Creatinine 10.780
    Alpha_1_Microgl 8.847
    Kidney_Injury_M 8.604
    clusterin 8.109
    Total_Protein 7.679
    Glutathione_S_T 7.493
    Neutrophil_Gela 6.721
    Vascular_Endoth 6.461
    Cystatin_C 6.444
    Beta_2_Microglo 6.261
    Trefoil_Factor— 6.184
    Tamm_Horsfall_P 5.872
    Tissue_Inhibito 5.690
    Osteopontin 4.855
  • TABLE 19
    AA v. OU
    Standard
    deviation
    Mean of
    method AUROC AUROC
    random 0.814 0.11
    forest
    bagging 0.792 0.115
    svm 0.788 0.112
    lasso 0.786 0.118
    boosting 0.757 0.117
    matt 0.687 0.111
    logistic 0.683 0.116
    regression
    cart 0.665 0.097
    ctree 0.659 0.118
  • TABLE 20
    AA v. OU
    Relative
    analyte importance
    Total_Protein 11.502
    Tissue_Inhibito 9.736
    Cystatin_C 9.161
    Alpha_1_Microgl 8.637
    Trefoil_Factor— 7.329
    Osteopontin 7.326
    Beta_2_Microglo 6.978
    Neutrophil_Gela 6.577
    Glutathione_S_T 6.100
    Tamm_Horsfall_P 6.066
    Kidney_Injury_M 6.038
    Vascular_Endoth 5.946
    clusterin 4.751
    Creatinine 3.854
    • VI. Automated Method for Diagnosing, Monitoring, or Determining a Renal Disorder
  • FIG. 15 is a block diagram of an exemplary computing environment 1500 for diagnosing, monitoring, and/or determining a renal disorder in a mammal. The computing environment 1500 includes sample input device 1502, a renal disorder diagnostics system (RDSS) 1504, and a data source 1506.
  • According to one aspect, sample input device 1502 is a computer or processing device 1508, such as a personal computer, a server computer, or a mobile processing device. The computer 1508 may include a display such as a computer monitor, for viewing data, and an input device, such as a keyboard or a pointing device (e.g., a mouse, trackball, pen, touch pad, or other device), for entering data. The computer 1508 is used by a user to enter analyte concentrations of a test sample for processing by the RDSS 1504. For example, the user uses the keyboard to interact with an analyte concentration entry form (not shown) on the display to enter test sample analyte data that includes, for example, three or more analyte concentrations.
  • In another embodiment, the test sample analyte concentrations are collected and then transmitted to the RDSS 1504 via an analyte measurement/sensor device 1510 (e.g., multiplexed immunoassay device) that measures the sample analyte concentration. The analyte measurement/sensor device 1510 communicates the measured sample analyte concentrations data to the RDSS 1504 via a data cable, infrared signal, wireless connection, or other methods of data transmission known in the art.
  • The RDDS 1504 executes a renal disorder determining application 1512 in response to test sample analyte concentration data received from the received from the sample input device 102. The renal disorder determining application (RDDA) 1512 analyzes the analyte concentration data for the test sample and determines whether the received analyte concentration data is indicative of renal disorder and, if so, a type of renal disorder. The renal disorder determining application 1512 then displays whether the result of the analysis is positive or negative for a renal disorder and, if applicable, the type of renal disorder.
  • According to one aspect, the RDDS 1904 retrieves concentration threshold data and/or disorder threshold data from the data source 1506 to determine whether the received analyte concentration data is indicative of one or more renal disorders. The data source 1506 is, for example, a computer system, a database, or another data system that stores data, electronic documents, records, other documents, and/or other data. The data source 1506 may include memory and one or more processors or processing systems to receive, process, and transmit communications and store and retrieve data.
  • According to one aspect, the data source 1506 includes a diagnostic analytic concentrations database 1514 that stores normal ranges of biomarker analytes for human plasma, serum, and urine, such described above in connection with Table 1. The entries of the diagnostic analytic concentrations database 1514 may also include additional minimum diagnostic concentrations to further define diagnostic criteria including but not limited to minimum diagnostic concentrations for additional types of bodily fluids, additional types of mammals, and severities of a particular disorder. As described above, if the measured concentration for a particular analyte of a sample of plasma exceeds the high value in Table 1, then the measured concentration of that particular may be indicative of a renal disorder or disease in the subject from with the test sample was collected.
  • According to one aspect, the disorder database 1516 includes various data tables index by disorder or disease type. Each data table corresponds to a specific disorder/disease type and identifies a list of minimum diagnostic concentrations that are indicative of that particular disease. For example, diabetic nephropathy data table indicates by sample type (i.e., plasma, urine, serum) the minimum concentration required, if any, for each of sixteen analyte biomarkers described above in connection with Table 1.
  • Although, the data source is illustrated in FIG. 15 as being integrated with the RDDS 1504, it is contemplated that in other aspects the data source 1506 may be separate and/or remote from the RDDS 1504. According to one such aspect, the RDDS 1504 communicates with the data source 1506 over a communication network, such as the Internet, an intranet, an Ethernet network, a wireline network, a wireless network, and/or another communication network, to identify relevant images, electronic documents, records, other documents, and/or other data to retrieve from the data source 1506. In another aspect, the sample input device 1502 communicates with the RDDS 1904 through the communication network. In still another aspect, the RDDS 1504 communicates with the data source 1506 through a direct connection.
  • FIG. 16 is a block diagram that depicts an exemplary RDDS 1504. According to one aspect, the RDDS 1504 includes a processing system 1602 that executes the RDDA 1512 to determine whether the received analyte concentration data is indicative of renal disorder and, if so, the type of renal disorder. The processing system 1602 includes memory and one or more processors, and the processing system 1602 can reside on a computer or other processing system. In this aspect, the data source 1506 is not shown and is, for example, located remotely from the RDDS 1504.
  • The RDDA 1512 includes instructions or modules that are executable by the processing system 1602 to manage the retrieval of renal disorder diagnostic data, including a record, from the data source 1506. The RDDS 1504 includes computer readable media 1604 configured with the RDDA 1512.
  • Computer readable medium (CRM) 1604 may include volatile media, nonvolatile media, removable media, non-removable media, and/or another available medium that can be accessed by the RDDS 1504. By way of example and not limitation, computer readable medium 1604 comprises computer storage media and communication media. Computer storage media includes memory, volatile media, nonvolatile media, removable media, and/or non-removable media implemented in a method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Communication media may embody computer readable instructions, data structures, program modules, or other data and include an information delivery media or system.
  • An analyte input module 1606 receives three or more sample analyte concentrations that include the biomarker analytes. In one embodiment, the sample analyte concentrations are entered as input by a user of the computer 1508. In another embodiment, the sample analyte concentrations are received directly from analyte measure/sensor device 1510, such as a multiplexed immunoassay device.
  • In another embodiment, the analyte input module 1606 receives sample analyte concentrations for at least six biomarker analytes. In one example, the at least six biomarker analytes include alpha 1 microglobulin, cystatin C, KIM-1, Tamm-Horsfall, Beta 2-microglobulin, and TIMP-1.
  • In another embodiment, the analyte input module 1606 receives sample analyte concentrations for sixteen biomarker analytes. In one example, the sixteen biomarker analytes include the analyte types shown in Table 1.
  • A comparison module 1608 compares each analyte concentration of a sample received from the analyte input module 1606 to a corresponding analyte entry in the diagnostic analyte database to determine if one or more concentrations for a particular analyte of the sample are exceed the minimum diagnostic value for that particular analyte. For example, referring briefly to Table 1, if the sample concentrations are obtained from plasma and the particular analyte is calbindin, the comparison module compares the measured calbindin analyte concentration to the sample to the corresponding high concentration value for plasma to determine if it is greater than about 5 ng/ml. A measured calbindin analyte concentration less than about 5 ng/ml indicates is not indicative of renal disorder. In contrast, a measured calbindin analyte concentration that is greater than about 5 ng/ml is indicative of a renal disorder.
  • An analysis module 1610 determines a most likely renal disorder as a function of the particular measured analyte concentrations identified as indicative of a renal disorder by the comparison module. For example, the analysis module 1610 compares the particular measured analyte concentrations to entries in the disorder tables stored in the renal disorder database 1516 to identify the most likely type renal disorder. Each disorder table includes, for example, the minimum concentrations or threshold concentrations for each of the sixteen analytes types shown in Table 1 that are associated with the diagnosis of a particular renal disorder or disease. It is also contemplated that the analyte types listed in a disorder table for particular renal disorder or disease may be different from the analyte types listed in another disorder table for a different renal disorder or disease.
  • In one embodiment, the most likely renal disorder is the particular renal disorder type in the disorder database 1516 having the most minimum diagnostic concentrations that are less than the corresponding sample analyte concentrations. In other words, the most likely disorder is identified from the disorder table that includes the most threshold concentrations that are exceeded by the sample analyte concentrations. For example, consider that five of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in the disorder table for a first renal disorder, such as analgesic abuse. Also, consider that four of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a second renal disorder, such as obstructive uropathy. In this example, the most likely renal disorder is analgesic abuse.
  • In one embodiment, the most likely renal disorder is the particular renal disorder type in the disorder database 1516 having the most minimum diagnostic concentrations that are less than the corresponding sample analyte concentrations. In other words, the most likely disorder is identified from the disorder table that includes the most threshold concentrations that are exceeded by the sample analyte concentrations. For example, consider that five of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a first renal disorder, such as analgesic abuse. Also, consider that four of the sample analyte concentrations exceed the minimum threshold concentrations for corresponding analytes in a disorder table for a second renal disorder, such as obstructive uropathy. In this example, the most likely renal disorder is analgesic abuse.
  • In another embodiment, the most likely renal disorder is the particular renal disorder from the database entry having minimum diagnostic concentrations that are all less than the corresponding sample analyte concentrations.
  • In yet other embodiments, the analysis module 1610 combines the sample analyte concentrations algebraically to calculate a combined sample analyte concentration that is compared to a combined minimum diagnostic concentration calculated from the corresponding minimum diagnostic criteria using the same algebraic operations. See Table A for example combinations. Other combinations of sample analyte concentrations from within the same test sample, or combinations of sample analyte concentrations from two or more different test samples containing two or more different bodily fluids may be used to determine a particular renal disorder in still other embodiments.
  • An output module 1612 generates a display of analyte types and corresponding concentrations for each of the measured analytes identified as indicative of a renal disorder by the comparison module. The output module 1612 also generates a display of the most likely renal disorder determined by the analysis module 1610.
  • FIG. 17 illustrates a method for diagnosing, monitoring, or determining a renal disorder in a mammal in accordance with an aspect of the RDDS 1504. At 1702, analyte concentrations read by an assay device or defined via user input at a computer are communicated to the renal disorder determining application 1512. At 1704, the sample analyte concentrations are transferred to the RDSS 1504 for processing. The concentration of each analyte type in the sample is compared to a corresponding threshold analyte concentration in a diagnostic analyte database at 1706. As described above, the threshold analyte concentrations in the diagnostic analyte database correspond to analyte concentration for various sample types that have been previous determined to be indicative of one or more renal disorders or diseases. If none of the analyte concentrations for the sample are determined to be greater than the corresponding threshold analyte concentrations at 1708. The one or more of the analyte concentrations and/or a message indicating the concentrations are within normal range is generated for display via the computer at 1710.
  • If one or more of the analyte concentrations for the sample are determined to be greater than the corresponding threshold analyte concentrations at 1708, the one or more analyte concentrations are then compared to disorder threshold analyte concentrations in a disorder database at 1712. The disorder threshold analyte concentrations correspond to minimum analyte concentrations associated with a particular renal disorder or disease. At 1714, the particular disorder that corresponds to the disorder table that has the most disorder threshold analyte concentrations exceeded by the sample analyte concentrations is identified as the most likely renal disorder. The one or more of the analyte concentrations for the sample and the most likely renal disorder type is generated for display via the computer at 1716.
  • It should be appreciated by those of skill in the art that the techniques disclosed in the examples above represent techniques discovered by the inventors to function well in the practice of the invention. Those of skill in the art should, however, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments that are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention, therefore all matter set forth or shown in the accompanying drawings is to be interpreted as illustrative and not in a limiting sense.

Claims (32)

1. A diagnostic system for determining a renal disorder in a mammal from an assay comprising at least three analyte biomarkers collected from a sample of a body fluid of the mammal, the diagnostic system comprising:
a data source comprising a first threshold concentration for each of the at least three biomarker analytes and a second threshold concentration for each of the at least three biomarker analytes for each of a plurality of disorders;
a renal disorder determining application comprising modules executable by a processor to determine the renal disorder, the renal disorder determining application comprising:
an analyte input module to receive analyte concentrations data for the sample from an input device, the analyte concentrations data comprising analyte concentrations for each of the at least three biomarker analytes;
a comparison module to:
compare each analyte concentration of the sample to a corresponding first threshold concentration; and
identify one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration;
an analysis module to:
compare each of the one or more analyte concentrations of the sample identified by the comparison module to a corresponding second threshold concentration associated with each of the at least three biomarker analytes for each of the plurality of disorders;
determine a number of corresponding second threshold concentrations exceeded by the one or more analyte concentrations of the sample for each of the plurality of disorders; and
identify a particular one of the plurality of disorders having a maximum number of corresponding second threshold concentrations that are exceeded by the one or more analyte concentrations of the sample as the most likely renal disorder; and
an output module to generate the one or more analyte concentrations and the particular one of the plurality of disorders for display.
2. The system of claim 1 wherein the data source comprises:
a diagnostic analytic concentrations database to store each of the first threshold concentrations; and
a disorder database to store a plurality of tables, each of the plurality tables comprising the second threshold concentration data for each of the at least three biomarker analytes and corresponding to one of the plurality of disorders
3. The system of claim 2 wherein the plurality of disorders comprises at least one member from a group consisting of glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, acute renal failure, chronic renal failure, nephrosis, nephropathy, polycystic kidney disease, Bright's disease, renal transplant, chronic unilateral obstructive uropathy, chronic bilateral obstructive uropathy, acute unilateral obstructive uropathy, acute bilateral obstructive uropathy, renal damage secondary to a disease state including diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, and Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate diseases, and renal damage caused by exposure to secondary agents and conditions including therapeutic drugs, recreational drugs, contrast agents, toxins, nephrolithiasis, ischemia, liver transplantation, heart transplantation, lung transplantation, and hypovolemia.
4. The system of claim 1 wherein input device comprises a multiplexed immunoassay device.
5. The system of claim 4 wherein the multiplexed immunoassay device comprises at least one member of another group consisting of a multiplexed sandwich immunoassay device, a microsphere-based capture-sandwich immunoassay device, and a vibrational detection-based immunoassay device.
6. The system of claim 1 wherein input device comprises a computer.
7. The system of claim 1 wherein first threshold concentration corresponds to a maximum concentration of an analyte concentration range associated with a normal renal function.
8. The system of claim 1 wherein:
the data source further comprises a combination threshold concentration for each of a plurality of combinations of concentration of the sixteen biomarker analytes associated with each of the plurality of disorders; and
the analysis module is further configured to:
assign a weight to each of the one or more analyte concentrations identified by the comparison module;
calculate a combined sample analyte concentration based on the weight assigned to each analyte concentration; and
compare a corresponding combination threshold concentration to the combined sample analyte concentration to identify the particular renal disorder.
9. The system of claim 1 wherein the assay comprises at least six analyte biomarkers collected from the sample, the at least six analytes comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
10. The system of claim 1 wherein the assay comprises at least sixteen analyte biomarkers collected from the sample, the at least sixteen analytes comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
11. A diagnostic system for determining a renal disorder in a mammal from an assay comprising at least three analyte biomarkers collected from a sample of a body fluid of the mammal, the diagnostic system comprising:
a renal disorder determining application comprising modules executable by a processor to determine the renal disorder, the renal disorder determining application comprising:
an analyte input module to receive analyte concentrations data for the sample from an input device, the analyte concentrations data comprising analyte concentrations for each of the at least three analyte biomarkers;
a comparison module to:
compare each analyte concentration of the sample to a corresponding threshold concentration retrieved from an analyte concentration database; and
identify one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration;
an analysis module to:
retrieve a combination threshold concentration for each of a plurality of combinations of concentrations of the at least three analyte biomarkers associated with each of a plurality of disorders; and
assign a weight to each of the one or more analyte concentrations of the sample identified by the comparison module;
calculate a combined sample analyte concentration based on the weight assigned to each analyte concentration; and
compare a corresponding combination threshold concentration to the combined sample analyte concentration to identify the renal disorder; and
an output module to generate the one or more analyte concentrations and the renal disorder for display.
12. The system of claim 11, wherein the combined sample analyte concentration and the corresponding combination threshold concentration correspond to a combination of a same grouping of three analyte types.
13. The system of claim 11 wherein the assay comprises at least six analyte biomarkers collected from the sample, the at least six analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
14. The system of claim 11 wherein the assay comprises at least sixteen analyte biomarkers collected from the sample, the at least sixteen analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
15. The system of claim 11 wherein input device comprises a multiplexed immunoassay device, and wherein the multiplexed immunoassay device comprises at least one member of another group consisting of a multiplexed sandwich immunoassay device, a microsphere-based capture-sandwich immunoassay device, and a vibrational detection-based immunoassay device.
16. A computer-readable medium encoded with a renal disorder determining application comprising modules executable by a processor to determine a renal disorder from an assay comprising at least three biomarker analytes collected from a sample of a body fluid of a mammal, the renal disorder determining application comprising:
an analyte input module to receive analyte concentrations data for the sample from an input device, the analyte concentrations data comprising analyte concentrations for each of the at least three biomarker analytes;
a comparison module to:
compare each analyte concentration of the sample to a corresponding first threshold concentration retrieved from a data source; and
identify one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration;
an analysis module to:
compare each of the one or more analyte concentrations of the sample identified by the comparison module to a corresponding second threshold concentration retrieved from the data source, the corresponding second threshold concentration associated with each of the at least three biomarker analytes for each of a plurality of disorders;
determine a number of corresponding second threshold concentrations exceeded by the one or more analyte concentrations of the sample for each of the plurality of disorders; and
identify a particular one of the plurality of disorders having a maximum number of corresponding second threshold concentrations that are exceeded by the one or more analyte concentrations of the sample as the most likely renal disorder; and
an output module to generate the one or more analyte concentrations and the particular one of the plurality of disorders for display.
17. The computer-readable medium of claim 16 wherein the data source comprises:
a diagnostic analytic concentrations database to store each of the first threshold concentrations; and
a disorder database to store a plurality of tables, each of the plurality tables comprising the second threshold concentration data for each of the sixteen biomarker analytes and corresponding to one of the plurality of disorders
18. The computer-readable medium of claim 16 wherein the plurality of disorders is selected from a group consisting of glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, acute renal failure, chronic renal failure, nephrosis, nephropathy, polycystic kidney disease, Bright's disease, renal transplant, chronic unilateral obstructive uropathy, chronic bilateral obstructive uropathy, acute unilateral obstructive uropathy, acute bilateral obstructive uropathy, renal damage secondary to a disease state including diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, and Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate diseases, and renal damage caused by exposure to secondary agents and conditions including therapeutic drugs, recreational drugs, contrast agents, toxins, nephrolithiasis, ischemia, liver transplantation, heart transplantation, lung transplantation, and hypovolemia.
19. The computer-readable medium of claim 16 wherein first threshold concentration corresponds to a maximum concentration of an analyte concentration range associated with a normal renal function.
20. The computer-readable medium of claim 16 wherein:
the data source further comprises a combination threshold concentration for each of a plurality of combinations of concentration of the at least three biomarker analytes associated with each of the plurality of disorders; and
the analysis module is further configured to:
assign a weight to each of the one or more analyte concentrations identified by the comparison module;
calculate a combined sample analyte concentration based on the weight assigned to each analyte concentration; and
compare a corresponding combination threshold concentration to the combined sample analyte concentration to identify the renal disorder to display.
21. The computer-readable medium of claim 20, wherein the combined sample analyte concentration and the corresponding combination threshold concentration correspond to a combination of a same grouping of three analyte types.
22. The system of claim 16 wherein the assay comprises at least six analyte biomarkers collected from the sample, the at least six analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
23. The system of claim 16 wherein the assay comprises at least sixteen analyte biomarkers collected from the sample, the at least sixteen analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
24. A method for determining a renal disorder in a mammal, the method comprising:
receiving analyte concentrations data for a sample a body fluid of the mammal from an input device, the analyte concentrations data comprising analyte concentrations for each of at least three analyte biomarkers;
processing the analyte concentrations data at a processor, the processing comprising:
comparing each analyte concentration of the sample to a corresponding first threshold concentration retrieved from a data source;
identifying one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration;
comparing each of the one or more analyte concentrations of the sample identified by the comparison module to a corresponding second threshold concentration retrieved from the data source, the corresponding second threshold concentration associated with each of the at least three analyte biomarkers for each of the plurality of disorders;
determining a number of corresponding second threshold concentrations exceeded by the one or more analyte concentrations of the sample for each of the plurality of disorders; and
identifying a particular one of the plurality of disorders having a maximum number of corresponding second threshold concentrations that are exceeded by the one or more analyte concentrations of the sample as the most likely renal disorder; and
generating the one or more analyte concentrations and the particular one of the plurality of disorders for display.
25. The method of claim 24 further comprising:
retrieving, at the processor, a combination threshold concentration for each of a plurality of combinations of concentration of the sixteen biomarker analytes associated with each of a plurality of disorders; and
assigning a weight to each of the one or more analyte concentrations identified by the comparison module;
calculating a combined sample analyte concentration based on the weight assigned to each analyte concentration; and
comparing a corresponding combination threshold concentration to the combined sample analyte concentration to identify the renal disorder to display.
26. The method of claim 25 wherein the combined sample analyte concentration and the corresponding combination threshold concentration correspond to a combination of a same three analyte types.
27. The method of claim 24 wherein the analyte concentrations data comprises analyte concentrations for each of at least six analyte biomarkers, the at least six analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
28. The method of claim 24 wherein the analyte concentrations data comprises analyte concentrations for each of at least sixteen analyte biomarkers, the at least sixteen analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
29. A method for determining a renal disorder in a mammal, the method comprising:
receiving analyte concentrations data for a sample a body fluid of the mammal from an input device, the analyte concentrations data comprising analyte concentrations for each of at least three analyte biomarkers;
processing the analyte concentrations data at a processor, the processing comprising:
comparing each analyte concentration of the sample to a corresponding threshold concentration retrieved from an analyte concentration database; and
identifying one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration;
retrieving a combination threshold concentration for each of a plurality of combinations of concentrations of the plurality of biomarker analytes associated with each of a plurality of disorders; and
assigning a weight to each of the one or more analyte concentrations of the sample identified by the comparison module;
calculating a combined sample analyte concentration based on the weight assigned to each analyte concentration; and
comparing a corresponding combination threshold concentration to the combined sample analyte concentration to identify the renal disorder; and
generating the one or more analyte concentrations and the renal disorder for display.
30. The method of claim 29 wherein the combined sample analyte concentration and the corresponding combination threshold concentration correspond to a combination of a same grouping of analyte types.
31. The method of claim 29 wherein the analyte concentrations data comprises analyte concentrations for each of at least six analyte biomarkers, the at least six analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
32. The method of claim 29 wherein the analyte concentrations data comprises analyte concentrations for each of at least sixteen analyte biomarkers, the at least sixteen analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
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