U.S. patent application number 12/852202 was filed with the patent office on 2011-03-17 for computer methods and devices for detecting kidney damage.
This patent application is currently assigned to Rules-Based Medicine, Inc.. Invention is credited to Karri L. Ballard, Dominic Eisinger, Samuel T. Labrie, James P. Mapes, Ralph L. McDade, Michael D. Spain.
Application Number | 20110065593 12/852202 |
Document ID | / |
Family ID | 43544701 |
Filed Date | 2011-03-17 |
United States Patent
Application |
20110065593 |
Kind Code |
A1 |
Labrie; Samuel T. ; et
al. |
March 17, 2011 |
Computer Methods and Devices for Detecting Kidney Damage
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.
Inventors: |
Labrie; Samuel T.; (Austin,
TX) ; Mapes; James P.; (Lakeway, TX) ; McDade;
Ralph L.; (Austin, TX) ; Eisinger; Dominic;
(Keene, NY) ; Ballard; Karri L.; (Austin, TX)
; Spain; Michael D.; (Austin, TX) |
Assignee: |
Rules-Based Medicine, Inc.
Austin
TX
|
Family ID: |
43544701 |
Appl. No.: |
12/852202 |
Filed: |
August 6, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61327389 |
Apr 23, 2010 |
|
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61232091 |
Aug 7, 2009 |
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Current U.S.
Class: |
506/8 ; 506/13;
506/39 |
Current CPC
Class: |
G01N 2333/47 20130101;
G01N 2333/475 20130101; G01N 2333/82 20130101; G01N 2333/4703
20130101; G01N 33/6893 20130101; G01N 2333/4706 20130101; G01N
2333/70539 20130101; G01N 2333/775 20130101; G01N 33/5302 20130101;
G01N 2333/765 20130101; G01N 2333/4727 20130101; G01N 2333/70503
20130101; G01N 2800/34 20130101; G01N 2333/4725 20130101; G01N
2333/91177 20130101; G01N 2333/52 20130101; G01N 2333/8146
20130101; G01N 2800/347 20130101; Y10T 436/147777 20150115; G01N
2800/52 20130101; G01N 2800/56 20130101; G01N 2333/8139 20130101;
G01N 2800/60 20130101; G01N 33/566 20130101 |
Class at
Publication: |
506/8 ; 506/39;
506/13 |
International
Class: |
C40B 30/02 20060101
C40B030/02; C40B 60/12 20060101 C40B060/12; C40B 40/00 20060101
C40B040/00 |
Claims
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.
Description
RELATED APPLICATIONS
[0001] 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
[0002] 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
[0003] 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.
[0004] 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.
[0005] 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.
[0006] 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
[0007] 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.
[0008] 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.
[0009] 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.
[0010] 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.
[0011] 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.
[0012] 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.
[0013] 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.
[0014] 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.
[0015] 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.
[0016] 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.
[0017] 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.
[0018] 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.
[0019] Other aspects and iterations of the invention are described
in more detail below.
DESCRIPTION OF FIGURES
[0020] 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.
[0021] 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.
[0022] 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.
[0023] 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.
[0024] 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.
[0025] 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).
[0026] 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.
[0027] 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).
[0028] 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.
[0029] 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).
[0030] 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.
[0031] 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).
[0032] 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.
[0033] 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).
[0034] FIG. 15 is a block diagram of an emplary computing
environment for implementing a renal disorder diagnostic
system.
[0035] FIG. 16 is a block diagram that depicts an exemplary renal
disorder diagnostic system.
[0036] 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
[0037] 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.
[0038] 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.RTM.), Carboplatin (Paraplatin.RTM.), Cytarabine
(Cytosar-U.RTM.), Gemtuzumab ozogamicin (Mylotarg.RTM.),
Gemcitabine (Gemzar.RTM.), Melphalan (Alkeran.RTM.), Ifosfamide
(Ifex.RTM.), Methotrexate (Rheumatrex.RTM.), Interleukin-2
(Proleukin.RTM.), Oxaliplatin (Eloxatin.RTM.), Streptozocin
(Zanosar.RTM.), Pemetrexed (Alimta.RTM.), Plicamycin
(Mithracin.RTM.), and Trimetrexate (Neutrexin.RTM.). 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.
[0039] 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.
[0040] 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
[0041] 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)
[0042] 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)
[0043] 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
[0044] 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
[0045] 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)
[0046] 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
[0047] 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)
[0048] 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)
[0049] 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)
[0050] 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
[0051] 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)
[0052] 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)
[0053] 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)
[0054] 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 .about.85 kDa with .about.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)
[0055] 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)
[0056] 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)
[0057] 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
[0058] 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.
[0059] 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-US-00001 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
[0060] 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
[0061] 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
[0062] 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
[0063] 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.
[0064] 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 1/2 of the original test sample volume
to about 50,000 times the original test sample volume.
[0065] 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.
[0066] 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
[0067] 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
[0068] 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
[0069] 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.
[0070] 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
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] In one exemplary embodiment, the indicators are polyurethane
or latex microspheres loaded with dye compounds and
phycoerythrin.
(b) Multiplexed Sandwich Immunoassay Device
[0076] 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.
[0077] 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
[0078] 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.
[0079] 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
[0080] 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.
[0081] 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 .mu.m and
about 5 .mu.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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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
[0087] 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
[0088] 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
[0089] 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-US-00002 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 .mu.g/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 .mu.g/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
.mu.g/ml 6.2 16 5.7 17 -- <4.2 THP .mu.g/ml 0.0084 0.052 0.0079
0.053 0.39 2.6 TFF3 .mu.g/ml 0.040 0.49 0.021 0.17 -- <21
Creatinine mg/dL -- -- -- -- 13 212 Microalbumin .mu.g/ml -- -- --
-- -- >16
[0090] 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 .mu.g/ml, beta-2
microglobulin is about 2.2 .mu.g/ml, calbindin is greater than
about 5 ng/ml, clusterin is about 134 .mu.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 .mu.g/ml, TIMP-1 is about 131
ng/ml, TFF-3 is about 0.49 .mu.g/ml, and VEGF is about 855
.mu.g/ml.
[0091] In another exemplary embodiment, the minimum diagnostic
concentration in human sera of alpha-1 microglobulin is about 17
.mu.g/ml, beta-2 microglobulin is about 2.6 .mu.g/ml, calbindin is
greater than about 2.6 ng/ml, clusterin is about 152 .mu.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 .mu.g/ml, TIMP-1 is about 246 ng/ml, TFF-3 is about
0.17 .mu.g/ml, and VEGF is about 1630 .mu.g/ml.
[0092] In yet another exemplary embodiment, the minimum diagnostic
concentration in human urine of alpha-1 microglobulin is about 233
.mu.g/ml, beta-2 microglobulin is greater than about 0.17 .mu.g/ml,
calbindin is about 233 ng/ml, clusterin is greater than about 0.089
.mu.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 .mu.g/ml, TIMP-1 is greater than about
3.9 ng/ml, TFF-3 is greater than about 21 .mu.g/ml, and VEGF is
about 517 .mu.g/ml.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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
[0099] 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.
[0100] The following examples are included to demonstrate preferred
embodiments of the invention.
EXAMPLES
[0101] 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
[0102] 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.
[0103] 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.
[0104] To perform the assay, 5 .mu.L of a diluted mixture of
capture-antibody microspheres were mixed with 5 .mu.L of blocker
and 10 .mu.L 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 .mu.L of biotinylated detection antibody was
added to each well, and then the hard-bottom plate was incubated
for an additional hour. 10 .mu.L of diluted
streptavidin-phycoerythrin was added to each well and then the
hard-bottom plate was incubated for another 60 minutes.
[0105] A filter-membrane microtiter plate was pre-wetted by adding
100 .mu.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 .mu.L of wash buffer. After
the second wash, 100 .mu.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.
[0106] 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.
[0107] 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.
[0108] The LDD and LLOQ results for each analyte are summarized in
Table 2:
TABLE-US-00003 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 .beta.-2 M .mu.g/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 .mu.g/mL
0.059 0.29 0.042 210 THP .mu.g/mL 0.46 0.30 0.16 800 TFF-3 .mu.g/mL
0.06 0.097 0.060 300
[0109] 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
[0110] 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-US-00004 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 .beta.-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
[0111] 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
[0112] 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-US-00005 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 .beta.-2M 1:100 0.63
0.63 100 (.mu.g/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 (.mu.g/mL) 1:4000 18 18
99 1:8000 9.1 9.2 99 THP 1:2000 28 28 100 (.mu.g/mL) 1:4000 14 14
96 1:8000 6.7 7.1 94 TFF-3 1:2000 8.8 8.8 100 (.mu.g/mL) 1:4000 3.8
4.4 86 1:8000 1.9 2.2 86
[0113] 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
[0114] 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).
[0115] 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-US-00006 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 .beta.-2M 0.97 117 98
98 (.mu.g/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 (.mu.g/mL) 0.0012 125 80 81 0.00060 118 101 82 Average
114 94 81 THP 0.0096 126 108 90 (.mu.g/mL) 0.0047 131 93 91 0.0026
112 114 83 average 123 105 88 TFF-3 0.0038 105 114 97 (.mu.g/mL)
0.0019 109 104 95 0.0010 102 118 93 average 105 112 95
[0116] 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
[0117] 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-US-00007 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 .beta.-2M Hemoglobin 500 84 (.mu.g/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 (.mu.g/mL) Bilirubin 20 110 Triglyceride 500 112 THP Hemoglobin
500 108 (.mu.g/mL) Bilirubin 20 101 Triglyceride 500 121 TFF-3
Hemoglobin 500 101 (.mu.g/mL) Bilirubin 20 101 Triglyceride 500
110
[0118] 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
[0119] 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.degree. C. for 2 hours, four hours, and 24 hours.
[0120] 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-US-00008 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 .beta.-2 M 0 0.0070 100 1.2
100 15 100 (.mu.g/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 (.mu.g/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 (.mu.g/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 (.mu.g/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
[0121] 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-US-00009 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.degree. C. 4 hr @ 240 106 28 84 5.6 79 room temp 4
hr. @ 202 89 29 86 5.5 79 4.degree. C. 24 hr. @ 199 88 26 78 7.1
101 4.degree. 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.degree. C. 4 hr @ 166 89 214 96 160 94 room temp 4 hr. @ 157
85 198 88 143 84 4.degree. C. 24 hr. @ 185 100 207 92 162 94
4.degree. 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.degree.
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.degree. C. 24 hr. @ 2.2 116 5.9 68 1.1 89 4.degree. 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.degree. C. 4 hr @
11 79 21 100 11 109 room temp 4 hr. @ 12 89 21 98 11 100 4.degree.
C. 24 hr. @ 13 90 22 103 14 129 4.degree. 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.degree. 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.degree. C. 24 hr.
@ 1.1 76 0.18 76 0.23 94 4.degree. 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.degree. C. 4 hr @ 704 83 1007 83 566 84 room
temp 4 hr. @ 618 73 1135 93 544 81 4.degree. C. 24 hr. @ 653 77
1130 93 589 88 4.degree. C. .beta.-2 M Control 0.064 100 2.6 100
1.2 100 (.mu.g/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.degree. 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.degree. C. 24 hr. @ 0.06
94 2.5 97 1.3 103 4.degree. 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.degree. C. 4 hr @ 49 93 829 101 498 105 room temp
4 hr. @ 46 88 883 108 513 108 4.degree. C. 24 hr. @ 51 97 767 94
471 99 4.degree. 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.degree. C. 4 hr @ 861 101 269 89 88 95 room temp 4 hr. @ 842 98
283 94 94 101 4.degree. C. 24 hr. @ 960 112 245 81 88 95 4.degree.
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.degree. C. 4 hr @ 2227 99 6.9 108 5.8 111 room temp 4 hr. @ 2120
95 7.7 120 5.2 101 4.degree. C. 24 hr. @ 2253 100 6.5 101 6 116
4.degree. 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.degree. C. 4
hr @ 17 97 306 88 68 95 room temp 4 hr. @ 16 93 329 94 74 103
4.degree. C. 24 hr. @ 18 105 349 100 72 100 4.degree. C. A-1 M
Control 3.6 100 2.2 100 1 100 (.mu.g/mL) 2 hr @ 3.5 95 2 92 1 105
room temp 2 hr. @ 3.4 92 2.1 97 0.99 99 4.degree. 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.degree. C.
24 hr. @ 3 83 2.2 100 1 101 4.degree. C. THP Control 1.2 100 34 100
2.1 100 (.mu.g/mL) 2 hr @ 1.2 99 34 99 2 99 room temp 2 hr. @ 1.1
90 34 100 2 98 4.degree. C. 4 hr @ 1.1 88 27 80 2 99 room temp 4
hr. @ 0.95 79 33 97 2 95 4.degree. C. 24 hr. @ 0.91 76 33 98 2.4
116 4.degree. C. TFF-3 Control 1230 100 188 100 2240 100 (.mu.g/mL)
2 hr @ 1215 99 179 95 2200 98 room temp 2 hr. @ 1200 98 195 104
2263 101 4.degree. C. 4 hr @ 1160 94 224 119 2097 94 room temp 4
hr. @ 1020 83 199 106 2317 103 4.degree. C. 24 hr. @ 1030 84 229
122 1940 87 4.degree. C.
[0122] 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.degree. C.
Example 8
Diagnosis of Renal Damage Using Detection of Analytes in Human
Urine Samples
[0123] 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.
[0124] 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.
[0125] 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
[0126] 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
(.alpha.1M), KIM-1, Microalbumin, Beta-2-Microglobulin (.beta.2M),
Calbindin, Clusterin, CystatinC, TreFoilFactor-3 (TFF-3), CTGF,
GST-alpha, VEGF, Calbindin, Osteopontin, Tamm-HorsfallProtein
(THP), TIMP-1 and NGAL.
[0127] 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.degree. 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.TM. 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.
[0128] 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-US-00010 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
Microglobulin Microglobulin Beta-2- -0.23 0.003 Beta-2- -0.39
Microglobulin Microglobulin Calbindin -0.16 0.04 Calbindin -0.18
<0.02 Clusterin -0.07 0.4 Clusterin -0.51 CTGF -0.08 0.3 CTGF
-0.05 0.5 Creatinine -0.32 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 NGAL -0.15 0.07 THP -0.31 Osteopontin -0.19
0.02 TIMP-1 -0.28 <0.001 THP -0.05 0.6 TFF3 -0.38 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
[0129] 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-US-00011 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
[0130] Application of multivariate analysis yielded statistical
models that predicted disease from control samples (plasma results
are shown in FIG. 4).
[0131] 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
[0132] 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
(.alpha.1M), beta-2 microglobulin (.beta.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)).
[0133] 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.
[0134] 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).
[0135] 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-US-00012 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-US-00013 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.sub.--
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-US-00014 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-US-00015 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.sub.-- 5.736 Osteopontin 5.561 Vascular_Endoth 5.338
clusterin 4.892 Glutathione_S_T 4.675
TABLE-US-00016 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-US-00017 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.sub.-- 5.88 Kidney_Injury_M 5.49
Vascular_Endoth 5.38 Tamm_Horsfall_P 5.33 Osteopontin 4.86
Neutrophil_Gela 4.47
TABLE-US-00018 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-US-00019 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.sub.-- 6.184 Tamm_Horsfall_P
5.872 Tissue_Inhibito 5.690 Osteopontin 4.855
TABLE-US-00020 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-US-00021 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.sub.-- 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
[0136] 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.
[0137] 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.
[0138] 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.
[0139] 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.
[0140] 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.
[0141] 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.
[0142] 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.
[0143] 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.
[0144] 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.
[0145] 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.
[0146] 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.
[0147] 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.
[0148] 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.
[0149] 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.
[0150] 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.
[0151] 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.
[0152] 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.
[0153] 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.
[0154] 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.
[0155] 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.
[0156] 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.
[0157] 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.
[0158] 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.
[0159] 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.
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