U.S. patent application number 17/195548 was filed with the patent office on 2021-07-22 for methods and devices for detecting diabetic nephropathy and associated disorders.
This patent application is currently assigned to MYRIAD RBM, INC.. The applicant listed for this patent is MYRIAD RBM, INC.. Invention is credited to Karri L. Ballard, Dominic Eisinger, Samuel T. LaBrie, James P. Mapes, Ralph L. McDade, Michael D. SPAIN.
Application Number | 20210223267 17/195548 |
Document ID | / |
Family ID | 1000005489854 |
Filed Date | 2021-07-22 |
United States Patent
Application |
20210223267 |
Kind Code |
A1 |
LaBrie; Samuel T. ; et
al. |
July 22, 2021 |
METHODS AND DEVICES FOR DETECTING DIABETIC NEPHROPATHY AND
ASSOCIATED DISORDERS
Abstract
Methods and devices for diagnosing, monitoring, or determining
diabetic nephropathy or an associated disorder in a mammal are
described. In particular, methods and devices for diagnosing,
monitoring, or determining diabetic nephropathy or an associated
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) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MYRIAD RBM, INC. |
Salt Lake City |
UT |
US |
|
|
Assignee: |
MYRIAD RBM, INC.
Salt Lake City
UT
|
Family ID: |
1000005489854 |
Appl. No.: |
17/195548 |
Filed: |
March 8, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15675367 |
Aug 11, 2017 |
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17195548 |
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14643873 |
Mar 10, 2015 |
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15675367 |
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12852282 |
Aug 6, 2010 |
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14643873 |
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61327389 |
Apr 23, 2010 |
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61232091 |
Aug 7, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2333/70503
20130101; Y10T 436/147777 20150115; G01N 2333/475 20130101; G01N
2800/34 20130101; G01N 2333/52 20130101; G01N 2333/4727 20130101;
G01N 2333/765 20130101; G01N 2333/4706 20130101; G01N 2333/775
20130101; G01N 2800/56 20130101; G01N 2333/8139 20130101; G01N
2333/70539 20130101; G01N 2333/8146 20130101; G01N 2800/52
20130101; G01N 2333/47 20130101; G01N 2333/4703 20130101; G01N
2333/4725 20130101; G01N 2800/347 20130101; G01N 33/566 20130101;
G01N 33/5302 20130101; G01N 33/6893 20130101; G01N 2333/82
20130101; G01N 2800/60 20130101; G01N 2333/91177 20130101 |
International
Class: |
G01N 33/68 20060101
G01N033/68; G01N 33/53 20060101 G01N033/53; G01N 33/566 20060101
G01N033/566 |
Claims
1.-8. (canceled)
9. A method for generating a dataset for use in detecting diabetic
nephropathy or an associated disorder in a human, the method
comprising: a. performing a multiplexed immunoassay on analytes of
a sample of bodily fluid selected from blood, plasma, or serum
taken from a human, wherein the analytes are selected from alpha-1
microglobulin, beta-2 microglobulin, calbindin, clusterin,
Connective tissue growth factor (CTGF), creatinine, cystatin C,
Glutathione S-transferase alpha (GST-alpha), Kidney injury
molecule-1 (KIM-1), microalbumin, Neutrophil gelatinase-associated
lipocalin (NGAL), osteopontin, Tamm-Horsfall protein (THP), Tissue
inhibitor of metalloproteinase-1 (TIMP-1), Trefoil factor 3 (TFF3),
and Vascular endothelial growth factor (VEGF); b. determining the
concentration for each analyte in a combination of three or more
analytes in the sample to provide a sample combination dataset; c.
providing a diagnostic dataset comprising a combination of three or
more minimum diagnostic concentrations of the analytes indicative
of a particular renal disorder; d. comparing the entries of the
sample combination dataset to the entries of the diagnostic
dataset; and e. generating a dataset for use in detecting diabetic
nephropathy or an associated disorder in a human by selecting the
diagnostic dataset entries that are less than the corresponding
entries in the sample combination dataset thereby providing a
matched dataset.
10. The method of claim 9, wherein 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 .mu.g/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.
11. The method of claim 9, wherein a combination of sample
concentrations for six or more sample analytes in the test sample
are determined.
12. The method of claim 11, wherein sample concentrations are
determined for the analytes selected from the group consisting of
alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1,
THP, and TIMP-1.
13. The method of claim 9, wherein a combination of sample
concentrations for sixteen sample analytes in the test sample are
determined.
14. The method of claim 9 further comprising identifying a diabetic
nephropathy or an associated disorder based on the matched
dataset.
15. A method for generating a dataset for use in detecting diabetic
nephropathy or an associated disorder in a human, the method
comprising: a. performing a multiplexed immunoassay on analytes of
a sample of bodily fluid selected from blood, plasma, or serum
taken from a human, wherein the analytes are selected from alpha-1
microglobulin, beta-2 microglobulin, calbindin, clusterin,
Connective tissue growth factor (CTGF), creatinine, cystatin C,
Glutathione S-transferase alpha (GST-alpha), Kidney injury
molecule-1 (KIM-1), microalbumin, Neutrophil gelatinase-associated
lipocalin (NGAL), osteopontin, Tamm-Horsfall protein (THP), Tissue
inhibitor of metalloproteinase-1 (TIMP-1), Trefoil factor 3 (TFF3),
and Vascular endothelial growth factor (VEGF); b. determining the
concentration for each analyte in a combination of three or more
analytes in the sample to provide a sample combination dataset; c.
providing a diagnostic dataset comprising a combination of three or
more maximum diagnostic concentrations of the analytes indicative
of a particular renal disorder; d. comparing the entries of the
sample combination dataset to the entries of the diagnostic
dataset; and e. generating a dataset for use in detecting diabetic
nephropathy or an associated disorder in a human by selecting the
diagnostic dataset entries that are less than the corresponding
entries in the sample combination dataset thereby providing a
matched dataset.
16. A method for generating a dataset for use in detecting diabetic
nephropathy or an associated disorder in a human, the method
comprising: a. performing a multiplexed immunoassay on analytes of
a sample of bodily fluid selected from blood, plasma, or serum
taken from a human, wherein the analytes are selected from alpha-1
microglobulin, beta-2 microglobulin, calbindin, clusterin,
Connective tissue growth factor (CTGF), creatinine, cystatin C,
Glutathione S-transferase alpha (GST-alpha), Kidney injury
molecule-1 (KIM-1), microalbumin, Neutrophil gelatinase-associated
lipocalin (NGAL), osteopontin, Tamm-Horsfall protein (THP), Tissue
inhibitor of metalloproteinase-1 (TIMP-1), Trefoil factor 3 (TFF3),
and Vascular endothelial growth factor (VEGF); b. determining the
concentration for each analyte in a combination of three or more
analytes in the sample to provide a sample combination dataset; c.
providing a diagnostic dataset comprising a combination of three or
more diagnostic concentrations for each of the analytes indicative
of a particular renal disorder; d. comparing the entries of the
sample combination dataset to the entries of the diagnostic
dataset; and e. generating a dataset for use in detecting diabetic
nephropathy or an associated disorder in a human by selecting the
diagnostic dataset entries that are altered relative to the
corresponding entries in the sample combination dataset thereby
providing a matched dataset.
17. The method of claim 16, wherein 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.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of Ser. No. 14/643,873,
filed Mar. 10, 2015, which is a continuation of Ser. No.
12/852,282, filed Aug. 6, 2010, pending, which claims the priority
of U.S. provisional application Ser. No. 61/327,389, filed Apr. 23,
2010, and U.S. provisional application Ser. No. 61/232,091, filed
Aug. 7, 2009, each of which is hereby incorporated by reference in
its entirety and is related to U.S. patent application Ser. Nos.
12/852,152; 12/852,202; 12/852,236; 12/852,295; 12/852,312; and
Ser. No. 12/852,322, 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 diabetic nephropathy or an
associated disorder in a mammal. In particular, the present
invention provides methods and devices for diagnosing, monitoring,
or determining diabetic nephropathy or an associated 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] Diabetic nephropathy is the most common cause of chronic
kidney failure and end-stage kidney disease in the United States.
People with both type 1 and type 2 diabetes are at risk. 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.
[0005] A need exists in the art for a fast, simple, reliable, and
sensitive method of detecting diabetic nephropathy or an associated
disorder. 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
[0006] The present invention provides 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.
[0007] One aspect of the invention encompasses a method for
diagnosing, monitoring, or determining diabetic nephropathy or an
associated disorder in a mammal. The method typically comprises
providing a test sample comprising a sample of bodily fluid taken
from the mammal. Then, the method comprises determining a
combination of sample concentrations for three or more sample
analytes in the test sample, wherein the sample analytes are
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. The combination of sample concentrations may be
compared to a data set comprising at least one entry, wherein each
entry of the data set comprises a list comprising three or more
minimum diagnostic concentrations indicative of diabetic
nephropathy or an associated disorder. Each minimum diagnostic
concentration comprises a maximum of a range of analyte
concentrations for a healthy mammal. Next, the method comprises
determining a matching entry of the dataset in which all minimum
diagnostic concentrations are less than the corresponding sample
concentrations and identifying an indicated disorder comprising the
particular disorder of the matching entry.
[0008] Another aspect of the invention encompasses a method for
diagnosing, monitoring, or determining diabetic nephropathy or an
associated disorder in a mammal. The method generally comprises
providing a test sample comprising a sample of bodily fluid taken
from the mammal. Then the method comprises determining the
concentrations of three or more sample analytes in a panel of
biomarkers in the test sample, wherein the sample analytes are
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 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 diabetic nephropathy or an associated disorder. The
combination of diagnostic analytes is 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 diabetic nephropathy or an associated disorder. The
particular disorder having the combination of diagnostic analytes
that essentially match the combination of sample analytes is then
identified.
[0009] An additional aspect of the invention encompasses a method
for diagnosing, monitoring, or determining diabetic nephropathy or
an associated disorder in a mammal. The method usually comprises
providing an analyte concentration measurement device comprising
three or more detection antibodies. Each detection antibody
comprises an antibody coupled to an indicator, wherein the
antigenic determinants of the antibodies are sample analytes
associated with diabetic nephropathy or an associated disorder. The
sample analytes are generally 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. The method next
comprises providing a test sample comprising three or more sample
analytes and a bodily fluid taken from the mammal. The test sample
is contacted with the detection antibodies and 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 correspond to a corresponding minimum diagnostic
concentration reflective of diabetic nephropathy or an associated
disorder.
[0010] Other aspects and iterations of the invention are described
in more detail below.
DESCRIPTION OF FIGURES
[0011] FIG. 1 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.
[0012] FIG. 2A 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,
[0013] FIG. 2B 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.
(C) B2M in urine, (D) cystatin C in urine.
[0014] FIG. 3 depicts the multivariate analysis of the disease
groups and their respective matched controls using plasma results.
Relative importance shown using the random forest model.
[0015] FIG. 4A depicts a graph showing the mean AUROC and its
standard deviation for plasma samples, and mean error rates
[0016] FIG. 4B depicts a graph showing the mean AUROC and its
standard deviation and mean AUROC
[0017] FIG. 4C depicts a graph showing the mean AUROC and its
standard deviation 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.
[0018] FIG. 5A depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to
distinguish disease (AA+GN+ON+DN) samples vs. normal samples from
plasma (FIG. 5A) and urine (FIG. 5B and FIG. 5C).
[0019] FIG. 5B depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to
distinguish disease (AA+GN+ON+DN) samples vs. normal samples from
plasma (FIG. 5A) and urine (FIG. 5B and FIG. 5C).
[0020] FIG. 5C depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to
distinguish disease (AA+GN+ON+DN) samples vs. normal samples from
plasma (FIG. 5A) and urine (FIG. 5B and FIG. 5C).
[0021] FIG. 6A depicts a graph showing the mean AUROC and its
standard deviation for plasma samples, and mean error rates
[0022] FIG. 6B depicts a graph showing the mean AUROC and its
standard deviation and mean AUROC
[0023] FIG. 6C depicts a graph showing the mean AUROC and its
standard deviation from urine samples for each classification
method used to distinguish diabetic nephropathy samples vs. normal
samples. Abbreviations as in FIG. 4.
[0024] FIG. 7A depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to
distinguish diabetic nephropathy samples vs. normal samples from
plasma (FIG. 7A) and urine (FIG. 7B and FIG. 7C).
[0025] FIG. 7B depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to
distinguish diabetic nephropathy samples vs. normal samples from
plasma (FIG. 7A) and urine (FIG. 7B and FIG. 7C).
[0026] FIG. 7C depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to
distinguish diabetic nephropathy samples vs. normal samples from
plasma (FIG. 7A) and urine (FIG. 7B and FIG. 7C).
[0027] FIG. 8A depicts a graph showing the mean AUROC and its
standard deviation for plasma samples, and mean error rates
[0028] FIG. 8B depicts a graph showing the mean AUROC and its
standard deviation and mean AUROC
[0029] FIG. 8C depicts a graph showing the mean AUROC and its
standard deviation from urine samples for each classification
method used to distinguish analgesic abuse samples vs. diabetic
nephropathy samples. Abbreviations as in FIG. 4.
[0030] FIG. 9A depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to
distinguish analgesic abuse samples vs. diabetic nephropathy
samples from plasma (FIG. 9A) and urine (FIG. 9B and FIG. 9C).
[0031] FIG. 9B depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to
distinguish analgesic abuse samples vs. diabetic nephropathy
samples from plasma (FIG. 9A) and urine (FIG. 9B and FIG. 9C).
[0032] FIG. 9C depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to
distinguish analgesic abuse samples vs. diabetic nephropathy
samples from plasma (FIG. 9A) and urine (FIG. 9B and FIG. 9C).
[0033] FIG. 10A depicts a graph showing the mean AUROC and its
standard deviation for plasma samples, and mean error rates
[0034] FIG. 10B depicts a graph showing the mean AUROC and its
standard deviation and mean AUROC
[0035] FIG. 10C depicts a graph showing the mean AUROC and its
standard deviation from urine samples for each classification
method used to distinguish obstructive uropathy samples vs.
diabetic nephropathy samples. Abbreviations as in FIG. 4.
[0036] FIG. 11A depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to
distinguish obstructive uropathy samples vs. diabetic nephropathy
samples from plasma (FIG. 11A) and urine (FIG. 11B and FIG.
11C).
[0037] FIG. 11B depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to
distinguish obstructive uropathy samples vs. diabetic nephropathy
samples from plasma (FIG. 11A) and urine (FIG. 11B and FIG.
11C).
[0038] FIG. 11C depicts a graph showing the average importance of
analytes and clinical variables from 100 bootstrap runs measured by
random forest (FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to
distinguish obstructive uropathy samples vs. diabetic nephropathy
samples from plasma (FIG. 11A) and urine (FIG. 11B and FIG.
11C).
[0039] FIG. 12A depicts a graph showing the mean AUROC and its
standard deviation for plasma samples, and mean error rates
[0040] FIG. 12B depicts a graph showing the mean AUROC and its
standard deviation and mean AUROC
[0041] FIG. 12C depicts a graph showing the mean AUROC and its
standard deviation from urine samples for each classification
method used to distinguish diabetic nephropathy samples vs.
glomerulonephritis samples. Abbreviations as in FIG. 4.
[0042] FIG. 13A depicts a graph showing the average importance of
analytes and clinical variables from I 00 bootstrap runs measured
by random forest (FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to
distinguish diabetic nephropathy samples vs. glomerulonephritis
samples from plasma (FIG. 13A) and urine (FIG. 13B and FIG.
13C).
[0043] FIG. 13B depicts a graph showing the average importance of
analytes and clinical variables from I 00 bootstrap runs measured
by random forest (FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to
distinguish diabetic nephropathy samples vs. glomerulonephritis
samples from plasma (FIG. 13A) and urine (FIG. 13B and FIG.
13C).
[0044] FIG. 13C depicts a graph showing the average importance of
analytes and clinical variables from I 00 bootstrap runs measured
by random forest (FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to
distinguish diabetic nephropathy samples vs. glomerulonephritis
samples from plasma (FIG. 13A) and urine (FIG. 13B and FIG.
13C).
[0045] FIG. 14A depicts several graphs illustrating the linear
correlation between an analyte and years diagnosed with diabetes.
Red=cases; Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C)
calbindin, (D) clusteri; FIG. 14B: (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.; FIG. 14C: (I) KIM-I, (J) microalbumin,
(K) NGAL, (L) osteopontin; FIG. 14D (M) THP, (N) TIMP-1, (O) TFF-3,
and (P) VEGF.
[0046] FIG. 14B depicts several graphs illustrating the linear
correlation between an analyte and years diagnosed with diabetes.
Red=cases; Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C)
calbindin, (D) clusteri; FIG. 14B: (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.; FIG. 14C: (I) KIM-I, (J) microalbumin,
(K) NGAL, (L) osteopontin; FIG. 14D (M) THP, (N) TIMP-1, (O) TFF-3,
and (P) VEGF.
[0047] FIG. 14C depicts several graphs illustrating the linear
correlation between an analyte and years diagnosed with diabetes.
Red=cases; Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C)
calbindin, (D) clusteri; FIG. 14B: (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.; FIG. 14C: (I) KIM-I, (J) microalbumin,
(K) NGAL, (L) osteopontin; FIG. 14D (M) THP, (N) TIMP-1, (O) TFF-3,
and (P) VEGF.
[0048] FIG. 14D depicts several graphs illustrating the linear
correlation between an analyte and years diagnosed with diabetes.
Red=cases; Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C)
calbindin, (D) clusteri; FIG. 14B: (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.; FIG. 14C: (I) KIM-I, (J) microalbumin,
(K) NGAL, (L) osteopontin; FIG. 14D (M) THP, (N) TIMP-1, (O) TFF-3,
and (P) VEGF.
[0049] FIG. 15A depicts several graphs illustrating the log
correlation between an analyte and years diagnosed with diabetes.
Red=cases; Black=controls. FIG. 15A: (A) A1M, (B) B2M, (C)
calbindin, (D) clusterin; FIG. 15B: (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.; FIG. 15C: (I) KIM-I, (J) microalbumin,
(K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N) TIMP-1, (O)
TFF-3, and (P) VEGF.
[0050] FIG. 15B depicts several graphs illustrating the log
correlation between an analyte and years diagnosed with diabetes.
Red=cases; Black=controls. FIG. 15A: (A) A1M, (B) B2M, (C)
calbindin, (D) clusterin; FIG. 15B: (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.; FIG. 15C: (I) KIM-I, (J) microalbumin,
(K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N) TIMP-1, (O)
TFF-3, and (P) VEGF.
[0051] FIG. 15C depicts several graphs illustrating the log
correlation between an analyte and years diagnosed with diabetes.
Red=cases; Black=controls. FIG. 15A: (A) A1M, (B) B2M, (C)
calbindin, (D) clusterin; FIG. 15B: (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.; FIG. 15C: (I) KIM-I, (J) microalbumin,
(K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N) TIMP-1, (O)
TFF-3, and (P) VEGF.
[0052] FIG. 15D depicts several graphs illustrating the log
correlation between an analyte and years diagnosed with diabetes.
Red=cases; Black=controls. FIG. 15A: (A) A1M, (B) B2M, (C)
calbindin, (D) clusterin; FIG. 15B: (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.; FIG. 15C: (I) KIM-I, (J) microalbumin,
(K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N) TIMP-1, (O)
TFF-3, and (P) VEGF.
[0053] FIG. 16A depicts several graphs illustrating the log
correlation between an analyte and clinical 24 hr microalbumin (A)
A1M, (B) B2M, (C) calbindin, (D) clusterin;
[0054] FIG. 16B depicts several graphs illustrating the log
correlation between an analyte and clinical 24 hr microalbumin (E)
CTGF, (F) creatinine, (G) cystatin C, (H) GST .alpha.;
[0055] FIG. 16C depicts several graphs illustrating the log
correlation between an analyte and clinical 24 hr microalbumin (I)
KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;
[0056] FIG. 16D depicts several graphs illustrating the log
correlation between an analyte and clinical 24 hr microalbumin (M)
THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.
[0057] FIG. 17 A depicts several graphs illustrating the linear
correlation between an analyte and clinical 24 hr microalbumin. (A)
A1M, (B) B2M, (C) calbindin, (D) clusterin;
[0058] FIG. 17B depicts several graphs illustrating the linear
correlation between an analyte and clinical 24 hr microalbumin. (E)
CTGF, (F) creatinine, (G) cystatin C, (H) GST .alpha.;
[0059] FIG. 17C depicts several graphs illustrating the linear
correlation between an analyte and clinical 24 hr microalbumin. (I)
KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin
[0060] FIG. 17D depicts several graphs illustrating the linear
correlation between an analyte and clinical 24 hr microalbumin. (M)
THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.
[0061] FIG. 18A depicts several graphs illustrating linear cdplots
of urine analytes compared to diabetic disease. Levels were
normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin,
(D) clusterin;
[0062] FIG. 18B depicts several graphs illustrating linear cdplots
of urine analytes compared to diabetic disease. Levels were
normalized to urine creatinine. (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.;
[0063] FIG. 18C depicts several graphs illustrating linear cdplots
of urine analytes compared to diabetic disease. Levels were
normalized to urine creatinine. (I) KIM-I, (J) microalbumin, (K)
NGAL, (L) osteopontin;
[0064] FIG. 18D depicts several graphs illustrating linear cdplots
of urine analytes compared to diabetic disease. Levels were
normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and
(P) VEGF.
[0065] FIG. 19A depicts several graphs illustrating log cdplots of
urine analytes compared to diabetic disease. Levels were normalized
to urine creatinine. (A) A1M, (B) B2M, (C) calbindin, (D)
clusterin;
[0066] FIG. 19B depicts several graphs illustrating log cdplots of
urine analytes compared to diabetic disease. Levels were normalized
to urine creatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H)
GST .alpha.;
[0067] FIG. 19C depicts several graphs illustrating log cdplots of
urine analytes compared to diabetic disease. Levels were normalized
to urine creatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L)
osteopontin;
[0068] FIG. 19D depicts several graphs illustrating log cdplots of
urine analytes compared to diabetic disease. Levels were normalized
to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P)
VEGF.
[0069] FIG. 20A depicts several graphs illustrating linear qqplots
of urine analytes compared to diabetic disease. Levels were
normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin,
(D) clusterin;
[0070] FIG. 20B depicts several graphs illustrating linear qqplots
of urine analytes compared to diabetic disease. Levels were
normalized to urine creatinine. (E) CTGF, (F) creatinine, (G)
cystatin C, (H) GST .alpha.;
[0071] FIG. 20C depicts several graphs illustrating linear qqplots
of urine analytes compared to diabetic disease. Levels were
normalized to urine creatinine. (I) KIM-I, (J) microalbumin, (K)
NGAL, (L) osteopontin;
[0072] FIG. 20D depicts several graphs illustrating linear qqplots
of urine analytes compared to diabetic disease. Levels were
normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and
(P) VEGF.
[0073] FIG. 21A depicts several graphs illustrating log qqplots of
urine analytes compared to diabetic disease. Levels were normalized
to urine creatinine. (A) A1M, (B) B2M, (C) calbindin, (D)
clusterin;
[0074] FIG. 21B depicts several graphs illustrating log qqplots of
urine analytes compared to diabetic disease. Levels were normalized
to urine creatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H)
GST .alpha.;
[0075] FIG. 21C depicts several graphs illustrating log qqplots of
urine analytes compared to diabetic disease. Levels were normalized
to urine creatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L)
osteopontin;
[0076] FIG. 21D depicts several graphs illustrating log qqplots of
urine analytes compared to diabetic disease. Levels were normalized
to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P)
VEGF.
[0077] FIG. 22A depicts several graphs illustrating linear
stripcharts of urine analytes compared to diabetic kidney disease
(KD) or diabetic patients without kidney disease controls (NC).
Levels were normalized to urine creatinine. (A) A1M, (B) B2M, (C)
calbindin, (D) clusterin, (E) CTGF, (F) creatinine;
[0078] FIG. 22B depicts several graphs illustrating linear
stripcharts of urine analytes compared to diabetic kidney disease
(KD) or diabetic patients without kidney disease controls (NC).
Levels were normalized to urine creatinine. (G) cystatin C, (H) GST
.alpha., (I) KIM-I, (J) microalbumin, (K) NGAL, (L)
osteopontin;
[0079] FIG. 22C depicts several graphs illustrating linear
stripcharts of urine analytes compared to diabetic kidney disease
(KD) or diabetic patients without kidney disease controls (NC).
Levels were normalized to urine creatinine. (M) THP, (N) TIMP-1,
(O) TFF-3, and (P) VEGF.
[0080] FIG. 23A depicts several graphs illustrating log stripcharts
of urine analytes compared to diabetic kidney disease (KD) or
diabetic patients without kidney disease controls (NC). Levels were
normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin,
(D) clusterin, (E) CTGF, (F) creatinine;
[0081] FIG. 23B depicts several graphs illustrating log stripcharts
of urine analytes compared to diabetic kidney disease (KD) or
diabetic patients without kidney disease controls (NC). Levels were
normalized to urine creatinine. (G) cystatin C, (H) GST .alpha.,
(I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;
[0082] FIG. 23C depicts several graphs illustrating log stripcharts
of urine analytes compared to diabetic kidney disease (KD) or
diabetic patients without kidney disease controls (NC). Levels were
normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and
(P) VEGF.
[0083] FIG. 24 depicts a graph illustrating years diagnosed v.
disease.
[0084] FIG. 25A depicts several graphs illustrating linear
stripcharts of serum analytes compared to diabetic kidney disease
(KD) or diabetic patients without kidney disease controls (NC). (A)
A1M, (B) B2M, (C) clusterin, (D) CTGF, (E) cystatin C, (F) GST
.alpha.;
[0085] FIG. 25B depicts several graphs illustrating linear
stripcharts of serum analytes compared to diabetic kidney disease
(KD) or diabetic patients without kidney disease controls (NC). (G)
KIM-I, (H) NGAL, (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1;
and
[0086] FIG. 25C depicts a graph illustrating linear stripcharts of
serum analytes compared to diabetic kidney disease (KD) or diabetic
patients without kidney disease controls (NC). (M) VEGF.
[0087] FIG. 26A depicts several graphs illustrating log stripcharts
of serum analytes compared to diabetic kidney disease. (A) A1M, (B)
B2M;
[0088] FIG. 26B depicts several graphs illustrating log stripcharts
of serum analytes compared to diabetic kidney disease. (C)
clusterin, (D) CTGF, (E) cystatin C, (F) GST .alpha., (G) KIM-I,
(H) NGAL;
[0089] FIG. 26C depicts several graphs illustrating log stripcharts
of serum analytes compared to diabetic kidney disease. (I)
osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1, and (M) VEGF.
[0090] FIG. 27A depicts several graphs illustrating linear qqplots
of serum analytes compared to diabetic kidney disease. (A) A1M, (B)
B2M, (C) clusterin, (D) CTGF;
[0091] FIG. 27B depicts several graphs illustrating linear qqplots
of serum analytes compared to diabetic kidney disease. (E) cystatin
C, (F) GST .alpha., (G) KIM-I, (H) NGAL;
[0092] FIG. 27C depicts several graphs illustrating linear qqplots
of serum analytes compared to diabetic kidney disease. (I)
osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1; and
[0093] FIG. 27D depicts a graph illustrating linear qqplots of
serum analytes compared to diabetic kidney disease. (M) VEGF.
[0094] FIG. 28A depicts several graphs illustrating log qqplots of
serum analytes compared to diabetic kidney disease. (A) A1M, (B)
B2M;
[0095] FIG. 28B depicts several graphs illustrating log qqplots of
serum analytes compared to diabetic kidney disease. (C) clusterin,
(D) CTGF, (E) cystatin C, (F) GST .alpha.;
[0096] FIG. 28C depicts several graphs illustrating log qqplots of
serum analytes compared to diabetic kidney disease. (G) KIM-I, (H)
NGAL, (I) osteopontin, (J) TFF-3:
[0097] FIG. 28D depicts several graphs illustrating log qqplots of
serum analytes compared to diabetic kidney disease. (K) THP, (L)
TIMP-1, and (M) VEGF.
[0098] FIG. 29A depicts several graphs illustrating a linear
comparison of analytes v. years diagnosed. Red=cases;
Black=controls. (A) A1M, (B) B2M, (C) clusterin, (D) CTGF;
[0099] FIG. 29B depicts several graphs illustrating a linear
comparison of analytes v. years diagnosed. Red=cases;
Black=controls. (E) cystatinC, (F) GST .alpha., (G) KIM-I, (H)
NGAL;
[0100] FIG. 29C depicts several graphs illustrating a linear
comparison of analytes v. years diagnosed. Red=cases;
Black=controls. (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1,
and (M) VEGF.
[0101] FIG. 30A depicts several graphs illustrating a log
comparison of analytes v. years diagnosed. Red=cases;
Black=controls. (A) A1M, (B) B2M, (C) clusterin, (D) CTGF;
[0102] FIG. 30B depicts several graphs illustrating a log
comparison of analytes v. years diagnosed. Red=cases;
Black=controls. (E) cystatin C, (F) GST .alpha., (G) KIM-I, (H)
NGAL;
[0103] FIG. 30C depicts several graphs illustrating a log
comparison of analytes v. years diagnosed. Red=cases;
Black=controls. (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1,
and (M) VEGF.
[0104] FIG. 31A depicts several graphs illustrating a linear
comparison of serum analytes v. clinical microalbumin. (A) A1M, (B)
B2M, (C) clusterin, (D) CTGF;
[0105] FIG. 31B depicts several graphs illustrating a linear
comparison of serum analytes v. clinical microalbumin. (E) cystatin
C, (F) GST .alpha., (G) KIM-I, (H) NGAL
[0106] FIG. 31C depicts several graphs illustrating a linear
comparison of serum analytes v. clinical microalbumin. (I)
osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1; and
[0107] FIG. 31D depicts a graph illustrating a linear comparison of
serum analytes v. clinical microalbumin. (M) VEGF.
[0108] FIG. 32A depicts several graphs illustrating a log
comparison of serum analytes v. clinical microalbumin. (A) A1M, (B)
B2M;
[0109] FIG. 32B depicts several graphs illustrating a log
comparison of serum analytes v. clinical microalbumin. (C)
clusterin, (D) CTGF, (E) cystatin C, (F) GST .alpha.;
[0110] FIG. 32C depicts several graphs illustrating a log
comparison of serum analytes v. clinical microalbumin. (G) KIM-I,
(H) NGAL, (I) osteopontin, (J) TFF-3;
[0111] FIG. 32D depicts several graphs illustrating a log
comparison of serum analytes v. clinical microalbumin. (K) THP, (L)
TIMP-1, and (M) VEGF.
DETAILED DESCRIPTION OF THE INVENTION
[0112] It has been discovered that a multiplexed panel of at least
three, six, or preferably 16 biomarkers may be used to detect
diabetic nephropathy and associated disorders. As used herein, the
term "diabetic nephropathy" refers to a disorder characterized by
angiopathy of capillaries in the kidney glomeruli. The term
encompasses Kimmelstiel-Wilson syndrome, or nodular diabetic
glomerulosclerosis and intercapillary glomerulonephritis.
Additionally, the present invention encompasses biomarkers that may
be used to detect a disorder associated with diabetic nephropathy.
As used herein, the phrase "a disorder associated with diabetic
nephropathy" refers to a disorder that stems from angiopathy of
capillaries in the kidney glomeruli. For instance, non-limiting
examples of associated disorders may include nephritic syndrome,
chronic kidney failure, and end-stage kidney disease.
[0113] The biomarkers included in a multiplexed panel of the
invention 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 diabetic nephropathy or an associated
disorder to determine whether diabetic nephropathy or an associated
disorder is indicated in the mammal.
[0114] One embodiment of the present invention provides a method
for diagnosing, monitoring, or determining diabetic nephropathy or
an associated 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 diabetic
nephropathy or an associated 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 diabetic nephropathy or an associated
disorder in a mammal.
[0115] 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
[0116] One embodiment of the invention measures the concentrations
of three, six, or preferable 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 diabetic nephropathy or an associated
disorder 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 renal damage in
humans. As defined herein, the biomarker analytes 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. A description of each biomarker analyte is given below.
(a) Alpha-1 Microglobulin (A1M)
[0117] 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)
[0118] 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
[0119] 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
[0120] 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)
[0121] Connective tissue growth factor (CTGF, Swiss-Prot Accession
Number P29279) is a 349-amino acid cysteine-rich polypeptide
belonging to the KCN 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
[0122] 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)
[0123] Cystatin C (Cyst C, Swiss-Prot Accession Number P01034) is a
13 kDa protein that is a potent inhibitor of the Cl 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)
[0124] 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)
[0125] 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
[0126] 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)
[0127] 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.
(l) Osteopontin (OPN)
[0128] 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)
[0129] 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)
[0130] 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)
[0131] 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)
[0132] 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
[0133] The method for diagnosing, monitoring, or determining a
renal disorder 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 diabetic
nephropathy or an associated disorder.
[0134] 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 be 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
[0135] 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 KIM-1, THP, and B2M. In still another exemplary embodiment,
the combination of sample analytes may include microalbumin, A1M,
and creatinine. In an alternative exemplary embodiment, the sample
is a urine sample, and the combination of sample analytes may
include microalbumin, alpha-1 microglobulin, NGAL, KIM-1, THP, and
clusterin. In another alternative exemplary embodiment, the sample
is a plasma sample, and the combination of sample analytes may
include alpha-1 microglobulin, cystatin C, THP, beta-2
microglobulin, TIMP-1, and KIM-1.
III. Test Sample
[0136] 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
[0137] 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 may 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
[0138] 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.
[0139] 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.
[0140] 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.
[0141] 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
[0142] 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 may include electrophoresis, mass
spectrometry, protein microarrays, surface plasmon resonance 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
[0143] In one embodiment, the concentrations of the analytes in the
test sample are measured using a multiplexed immunoassay device
that utilizes capture antibodies marked with indicators to
determine the concentration of the sample analytes.
(i) Capture Antibodies
[0144] 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.
[0145] The term "antibody," as used herein, encompasses a
monoclonal ab, an antibody fragment, a chimeric antibody, and a
single-chain antibody.
[0146] In some embodiments, 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, cellulose, glass, or plastic strips, beads, or
surfaces, such as the inner surface of the well of a microtitration
tray. Suitable beads may include polystyrene or latex
microspheres.
(ii) Indicators
[0147] 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.
[0148] 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.
[0149] 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.
[0150] 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.
[0151] In one exemplary embodiment, the indicators are polyurethane
or latex microspheres loaded with dye compounds and
phycoerythrin.
(b) Multiplexed Sandwich Immunoassay Device
[0152] 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.
[0153] 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
[0154] 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.
[0155] 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
[0156] 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.
[0157] 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.
[0158] 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.
[0159] 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.
[0160] 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.
[0161] 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.
[0162] 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.
V. Method for Diagnosing, Monitoring, or Determining a Renal
Disorder
[0163] In one embodiment, a method is provided for diagnosing,
monitoring, or determining diabetic nephropathy or an associated
disorder that includes providing a test sample, determining the
concentration of a combination of three or more sample analytes,
comparing the measured concentrations of the combination of sample
analytes to the entries of a dataset, and identifying diabetic
nephropathy or an associated 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
[0164] 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
[0165] 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.
[0166] 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.
[0167] 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.
[0168] In one embodiment, the minimum diagnostic concentrations
represent the maximum level of analyte concentrations falling
within an expected normal range. Diabetic nephropathy or an
associated disorder may be indicated if the concentration of an
analyte is higher than the minimum diagnostic concentration for the
analyte.
[0169] If diminished concentrations of a particular analyte are
known to be associated with diabetic nephropathy or an associated
disorder, the minimum diagnostic concentration may not be an
appropriate diagnostic criterion for identifying diabetic
nephropathy or an associated 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 diabetic nephropathy or an associated 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 diabetic
nephropathy or an associated disorder.
[0170] A critical feature of the method of the multiplexed analyte
panel is that a combination of sample analyte concentrations may be
used to diagnose diabetic nephropathy or an associated 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 diabetic
nephropathy or an associated 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.
[0171] 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.
[0172] A variety of methods known in the art may be used to define
the diagnostic criteria used to identify diabetic nephropathy or an
associated disorder. In one embodiment, any sample concentration
falling outside the expected normal range indicates diabetic
nephropathy or an associated 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 diabetic nephropathy or an associated 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.
[0173] In an exemplary embodiment, an analyte value in a test
sample higher than the minimum diagnostic value for the top 3
analytes of the particular sample type (e.g. plasma, urine, etc.),
wherein the top 3 are determined by the random forest
classification method may result in a diagnosis of diabetic
nephropathy.
VI. Automated Method for Diagnosing, Monitoring, or Determining a
Renal Disorder
[0174] In one embodiment, a system for diagnosing, monitoring, or
determining diabetic nephropathy or an associated disorder in a
mammal is provided that includes a database to store a plurality of
renal disorder database entries, and a processing device that
includes the modules of a renal disorder determining application.
In this embodiment, the modules are executable by the processing
device, and include an analyte input module, a comparison module,
and an analysis module.
[0175] The analyte input module 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 application. In another embodiment, the sample
analyte concentrations are transmitted directly to the analyte
input module by the sensor device used to measure the sample
analyte concentration via a data cable, infrared signal, wireless
connection or other methods of data transmission known in the
art.
[0176] The comparison module compares each sample analyte
concentration to an entry of a renal disorder database. Each entry
of the renal disorder database includes a list of minimum
diagnostic concentrations reflective of a particular renal
disorder. The entries of the renal disorder database may further
contain 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.
[0177] The 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. In
one embodiment, the most likely renal disorder is the particular
renal disorder from the database entry having the most minimum
diagnostic concentrations that are less than the corresponding
sample analyte concentrations. In another embodiment, the most
likely renal disorder is the particular renal disorder from the
database entry having minimum diagnostic concentrations that are
all less than the corresponding sample analyte concentrations. In
yet other embodiments, the analysis module 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. 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.
[0178] The system includes one or more processors and volatile
and/or nonvolatile memory and can be embodied by or in one or more
distributed or integrated components or systems. The system may
include computer readable media (CRM) on which one or more
algorithms, software, modules, data, and/or firmware is loaded
and/or operates and/or which operates on the one or more processors
to implement the systems and methods identified herein. The
computer readable media may include volatile media, nonvolatile
media, removable media, non-removable media, and/or other media or
mediums that can be accessed by a general purpose or special
purpose computing device. For example, computer readable media may
include computer storage media and communication media, including
but not limited to computer readable media. Computer storage media
further may include volatile, nonvolatile, removable, and/or
non-removable media implemented in a method or technology for
storage of information, such as computer readable instructions,
data structures, program modules, and/or other data. Communication
media may, for example, embody computer readable instructions, data
structures, program modules, algorithms, and/or other data,
including but not limited to as or in a modulated data signal. The
communication media may be embodied in a carrier wave or other
transport mechanism and may include an information delivery method.
The communication media may include wired and wireless connections
and technologies and may be used to transmit and/or receive wired
or wireless communications. Combinations and/or sub-combinations of
the above and systems, components, modules, and methods and
processes described herein may be made.
[0179] The following examples are included to demonstrate preferred
embodiments of the invention.
EXAMPLES
[0180] 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
[0181] 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.
[0182] 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.
[0183] 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.
[0184] 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.
[0185] 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.
[0186] 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.
[0187] 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.-2M .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-1M .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
[0188] 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
[0189] 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
concentration Run 1 Run 2 Run 2 Interrun Analyte (ng/mL) Error (%)
Error (%) Error (%) Error (%) 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.-2M 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-1M 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
[0190] 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
[0191] 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
[0192] 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
[0193] 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).
[0194] 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 Recovery Recovery in in 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
[0195] 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
[0196] 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 Overall Spiked into Spike Recovery Analyte Sample
Concentration (%) 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
[0197] 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
[0198] 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.
[0199] 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.-2M 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 Osteopontin 0 38 100 1.7 100 5.0 100
(ng/mL) 1X 42 110 1.8 102 5.5 110 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
[0200] 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. (%) Calbindin Control 226 100 33 100 7
100 (ng/mL) 2 hr/ 242 107 30 90 6.3 90 room 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. Clusterin Control 185 100 224 100 171 100 (ng/mL) 2 hr
@ 173 94 237 106 180 105 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 @ (ng/mL) room 11 75 23 107
11 103 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.-2M 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. Cystatin Control 52 100 819 100 476 100 C 2 hr
@ 50 96 837 102 466 98 (ng/mL) room temp 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.
[0201] 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: Analysis of Kidney Biomarkers in Plasma and Urine from
Patients with Renal Injury
[0202] 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
(a1M), 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.
[0203] 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. 1). 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.
[0204] 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
[0205] 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. 2). 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
[0206] Application of multivariate analysis yielded statistical
models that predicted disease from control samples (plasma results
are shown in FIG. 3)
[0207] 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 9: Statistical Analysis of Kidney Biomarkers in Plasma and
Urine from Patients with Renal Injury
[0208] 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 (a1M),
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
a diabetic nephropathy (DN) sample, and finally, an diabetic
nephropathy sample from the other disease samples (obstructive
uropathy (DN), analgesic abuse (AA), and glomerulonephritis
(GN)).
[0209] 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.
[0210] 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. 4, Table 11), DN vs. normal (FIG. 6, Table 13), DN
vs. AA (FIG. 8, Table 15), OU vs. DN (FIG. 10, Table 17), and GN
vs. DN (FIG. 12, Table 19).
[0211] 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. 5, Table 12), DN vs. normal (FIG. 7, Table 14), DN vs. AA
(FIG. 9, Table 16), OU vs. DN (FIG. 11, Table 18), and GN vs. DN
(FIG. 13, 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 Tarnm_Horsfall_P
8.191 Total_Protein 6.928 Osteopontin 6.798 Neutrophil_Gela 6.784
Tissue_Inhibito 6.765 Vascular_Endoth 6.716 Trefoil_Factor_ 6.703
Cystatin_C 6.482 Alpha_1_Microgl 6.418 Beta_2_Microglo 6.228
Glutathione_S_T 6.053 clusterin 5.842
TABLE-US-00014 TABLE 13 ON v. NL Standard Mean deviation method
AUROC AUROC svm 0.672 0.102 logistic 0.668 0.114 regression random
0.668 0.1 forest boosting 0.661 0.107 lasso 0.66 0.117 bagging
0.654 0.103 matt 0.642 0.087 cart 0.606 0.088 ctree 0.569 0.091
TABLE-US-00015 TABLE 14 DN v. NL Relative analyte importance
Kidney_Injury_M 8.713 Tamm_Horsfall_P 8.448 Beta_2_Microglo 8.037
Trefoil_Factor_ 7.685 clusterin 7.394 Vascular_Endoth 7.298
Alpha_1_Microgl 6.987 Glutathione_S_T 6.959 Cystatin_C 6.920
Tissue_Inhibito 6.511 Creatinine 6.344 Neutrophil_Gela 6.305
Osteopontin 6.265 Total_Protein 6.133
TABLE-US-00016 TABLE 15 DN v. AA 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 DN v. AA Relative analyte importance
Creatinine 17.57 Total_Protein 10.90 Tissue_Inhibito 8.77 clusterin
6.89 Glutathione_S_T 6.24 Alpha_1_Microgl 6.15 Beta_2_Microglo 6.06
Cystatin_C 5.99 Trefoil_Factor_ 5.88 Kidney_Injury_M 5.49
Vascular_Endoth 5.38 Tamm_Horsfall_P 5.33 Osteopontin 4.86
Neutrophil_Gela 4.47
TABLE-US-00018 TABLE 17 OU v. DN method mean_AUROC std_AUROC lasso
0.993 0.019 random 0.986 0.027 forest boosting 0.986 0.027 bagging
0.977 0.04 cart 0.962 0.045 ctree 0.954 0.05 svm 0.95 0.059
logistic 0.868 0.122 regression matt 0.862 0.111
TABLE-US-00019 TABLE 18 OU v. DN Relative analyte importance
Creatinine 18.278 Alpha_1_Microgl 9.808 clusterin 9.002
Beta_2_Microglo 8.140 Cystatin_C 7.101 Osteopontin 6.775
Glutathione_S_T 5.731 Neutrophil_Gela 5.720 Trefoil_Factor_ 5.290
Kidney_Injury_M 5.031 Total_Protein 5.030 Vascular_Endoth 4.868
Tissue_Inhibito 4.615 Tamm_Horsfall_P 4.611
TABLE-US-00020 TABLE 19 GN v. DN Standard deviation Mean of method
AUROC AUROC lasso 0.955 0.077 random 0.912 0.076 forest bagging
0.906 0.087 boosting 0.904 0.087 svm 0.887 0.089 ctree 0.824 0.095
matt 0.793 0.114 logistic 0.788 0.134 regression cart 0.768 0.1
TABLE-US-00021 TABLE 20 GN v. DN Relative analyte importance
Total_Protein 13.122 Creatinine 11.028 Alpha_1_Microgl 8.291
Beta_2_Microglo 7.856 Tissue_Inhibito 7.799 Glutathione_S_T 6.532
Kidney_injury_M 6.489 Osteopontin 6.424 Vascular_Endoth 6.262
Neutrophil_Gela 5.418 Trefoil_Factor_ 5.382 Cystatin_C 5.339
Tamm_Horsfall_P 5.117 clusterin 4.940
Example 10: Diabetic Kidney Disease Urine Analyte Analyses
[0212] Collaborators from Texas Diabetes and Endocrinology (H1)
provided urine samples for 150 patients with diabetes, of which 75
patients had kidney disease and 75 did not. The samples were
analyzed using the sixteen analytes detailed in section I above.
The goals of the analyses were as follows: 1) Determine if there
are analytes (alone or in combination) that can separate patients
with kidney disease from patients without kidney disease
(controls); 2) Determine the relationships of analytes and kidney
disease category to years since diagnosis, age, gender, and
BMI.
[0213] Values of <LOW> were replaced by half of the minimum
value for each variable. Variables with more than 50% missing
values were not analyzed. Values given as `> nnn` were taken as
the "nnn" value following the ">" sign.
[0214] Analyte values were normalized to the urine creatinine value
in the panel for each patient. Normalized value=100*the original
analyte value divided by the creatinine value.
[0215] The distribution of values for most analytes was skewed, so
the original values were log transformed. Analyses were performed
using both the original values and the log transformed values.
[0216] In the graphs and statistical output, patients without
kidney disease are labeled "NC" (normal control). Patients with
kidney disease are labeled "KD" (kidney disease).
[0217] Graphs of the analyte values versus disease category (NC vs.
KD) on original scale and log scale are shown in FIG. 22 and FIG.
23. Normal distribution qqplots are shown in FIG. 20 and FIG. 21.
Scatterplots of each analyte versus the 24-hour microalbumin (from
the clinical data) are shown FIG. 16 and FIG. 17. A graph of the
kidney disease category versus years since diagnosis and of analyte
values versus years since diagnosis are in FIG. 14, FIG. 15, and
FIG. 24. In these graphs, red are patients with kidney disease,
black are controls. It is evident that the presence of kidney
disease is a function of years since diagnosis. Thus, models to
predict kidney disease may perform better if the number of years
since diagnosis is included as a covariate.
[0218] We performed t-tests of the values of each analyte versus
disease category (NC vs. KD). Linear models of analyte versus
disease category and covariates gave similar results.
TABLE-US-00022 TABLE 21 T-test p-values for each analyte versus
disease category (NC vs. KD) using log scale. t-test Analytes
p-value Microalbumin 2.68E-21 Alpha.1.Microglobulin 1.29E-05
Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.004
Kidney.Injury.Molecule.1 . . . KIM.1. 0.024 Clusterin 0.037
Tamm.Horsfall.Protein . . . THP. 0.041
Connective.Tissue.Growth.Factor . . . CTGF. 0.044
Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1. 0.180
Beta.2.Microglobulin 0.334 Cystatin.C 0.348 Osteopontin 0.352
Vascular.Endothelial.Growth.Factor . . . VEGF. 0.426 Creatinine
0.567 Calbindin 0.707 Glutathione.S.Transferase.alpha . . .
GST.alpha. 0.863 Trefoil.Factor.3 . . . TFF3. 0.878
TABLE-US-00023 TABLE 22 T-test p-values for each analyte versus
disease category (NC vs. KD) using original scale. t-test Analytes
p-value Microalbumin 1.11E-08 Alpha.1.Microglobulin 0.0007
Kidney.Injury.Molecule.1 . . . KIM.1. 0.0072
Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.0190
Osteopontin 0.1191 Glutathione.S.Transferase.alpha . . . GST.alpha.
0.1250 Beta.2.Microglobulin 0.1331 Tamm.Horsfall.Protein . . . THP.
0.1461 Cystatin.C 0.1489 Connective.Tissue.Growth.Factor . . .
CTGF. 0.2746 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.3114
Calbindin 0.6189 Tissue.Inhibitor.of.Metalloproteinase.1 . . .
TIMP.1. 0.6944 Clusterin 0.7901 Trefoil.Factor.3 . . . TFF3. 0.7918
Creatinine 0.9710
[0219] We calculated the area under the ROC curve (AUROC) for
classification of disease (NC vs. KD) for the following analytes
and covariates: AUROC for each analyte individually (Table 23) and
AUROC for individual analytes in logistic regression models that
included the covariates year diagnosed, age, gender, and BMI (Table
24).
TABLE-US-00024 TABLE 23 AUROC for each analyte individually for
classification of disease (NC vs. KD) using log scale Analytes
AUROC Microalbumin 0.90 Alpha.1.Microglobulin 0.71
Kidney.Injury.Molecule.1 . . . KIM.1. 0.63
Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.62
Clusterin 0.61 Tamm.Horsfall.Protein . . . THP. 0.60
Connective.Tissue.Growth.Factor . . . CTGF. 0.60
Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1. 0.58
Cystatin.C 0.56 Osteopontin 0.56 Beta.2.Microglobulin 0.56
Vascular.Endothelial.Growth.Factor . . . VEGF. 0.55 Creatinine 0.52
Calbindin 0.51 Trefoil.Factor.3 . . . TFF3. 0.51
Glutathione.S.Transferase.alpha . . . GST.alpha. 0.50
TABLE-US-00025 TABLE 24 AUROC for individual analytes in logistic
regression models that included the covariates year since
diagnosis, age, gender, and BMI. Analytes AUROC Microalbumin 0.90
Alpha.1.Microglobulin 0.74 Connective.Tissue.Growth.Factor . . .
CTGF. 0.71 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL.
0.69 Kidney.Injury.Molecule.1 . . . KIM.1. 0.69
Tamm.Horsfall.Protein . . . THP. 0.69 Creatinine 0.69
Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1. 0.68
Clusterin 0.68 Glutathione.S.Transferase.alpha . . . GST.alpha.
0.68 Osteopontin 0.68 Calbindin 0.68 Trefoil.Factor.3 . . . TFF3.
0.68 Cystatin.C 0.67 Vascular.Endothelial.Growth.Factor . . . VEGF.
0.67 Beta.2.Microglobulin 0.67
[0220] We calculated the area under the ROC curve (AUROC) for
classification of disease (NC vs. KD) for the following
combinations of analytes and covariates. For the combination of all
analytes in a logistic regression model (without covariates), the
AUROC=0.94. For the combination of all analytes in a logistic
regression model (including covariates), the AUROC=0.95. For the
combination of all analytes, excluding microalbumin, in a logistic
regression model (without covariates), the AUROC=0.85. For the
combination of all analytes, excluding microalbumin, in a logistic
regression model (including covariates), the AUROC=0.87. Finally,
we calculated the area under the ROC curve (AUROC) for
classification of disease (NC vs. KD) for 24-hour clinical
microalbumin from the patient record, which gave AUROC=0.97.
Example 11: Diabetic Kidney Disease Serum Analyte Analyses
[0221] This report presents the statistical analysis of the serum
data for the patients detailed in Example 10 above. The samples
were analyzed using fourteen of the analytes detailed in section I
above. The goals of the analyses were as follows: 1) Determine if
there are analytes (alone or in combination) that can separate
patients with kidney disease from patients without kidney disease
(controls); 2) Determine the relationships of analytes and kidney
disease category to years since diagnosis, age, gender, and
BMI.
[0222] Values of <LOW> were replaced by half of the minimum
value for each variable. Variables with more than 50% missing
values were not analyzed. The only such analyte in this data set
was Calbindin. Values given as nnn' were taken as the "nnn" value
following the ">" sign.
[0223] The distribution of values for most analytes was skewed, so
we log transformed the original values. We performed analyses using
both the original values and the log transformed values.
[0224] In the graphs and statistical output, patients without
kidney disease are labeled "NC" (normal control). Patients with
kidney disease are labeled "KD" (kidney disease).
[0225] Graphs of the analyte values versus disease category (NC vs.
KD) on original scale and log scale are shown in FIG. 25 and FIG.
26. Normal distribution qqplots are shown in FIG. 27 and FIG. 28.
Scatterplots of each analyte versus the 24-hour microalbumin (from
the clinical data) are shown in FIG. 31 and FIG. 32. Graphs of
analyte values versus years since diagnosis are shown in FIG. 29
and FIG. 30. In these graphs, red are patients with kidney disease,
black are controls. It is evident that analyte values and the
presence of kidney disease is a function of years since diagnosis.
Thus, models to predict kidney disease may perform better if the
number of years since diagnosis is included as a covariate.
[0226] We performed t-tests of the values of each analyte versus
disease category (NC vs. KD). Linear models of analyte versus
disease category and covariates gave similar results.
TABLE-US-00026 TABLE 25 T-test p-values for each analyte versus
disease category (NC vs. KD) using log scale. t-test Analytes
p-value Alpha.1.Microglobulin . . . A1Micro. 8.03E-08 Cystatin.C
4.51E-06 Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 5.35E-06
Beta.2.Microglobulin . . . B2M. 3.88E-05
Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 4.20E-05
Kidney.Injury.Molecule.1 . . . . . KIM.1. 0.00343048
Trefoil.Factor.3 . . . TFF3. 0.05044019
Connective.Tissue.Growth.Factor . . . CTGF. 0.06501133
Glutathione.S.Transferase.alpha . . . GST.alpha. 0.27177709
Osteopontin 0.2762483 Vascular.Endothelial.Growth.Factor . . .
VEGF. 0.33297341 Neutrophil.Gelatinase.Associated.Lipocalin . . .
NGAL. 0.5043943 Clusterin . . . CLU. 0.5730406
TABLE-US-00027 TABLE 26 T-test p-values for each analyte versus
disease category (NC vs. KD) using original scale. t-test Analytes
p-value Alpha.1.Microglobulin..A1Micro. 4.29E-07 Cystatin.C
5.52E-06 Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 3.19E-05
Beta.2.Microglobulin . . . B2M. 4.56E-05
Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 5.02E-05
Kidney.Injury.Molecule.1 . . . . . KIM.1. 0.000343
Vascular.Endothelial.Growth.Factor . . . VEGF. 0.044555
Glutathione.S.Transferase.alpha . . . GST.alpha. 0.052145
Osteopontin 0.146316 Neutrophil.Gelatinase.Associated.Lipocalin . .
. NGAL. 0.21544 Trefoil.Factor.3 . . . TFF3. 0.300221 Clusterin . .
. CLU. 0.756401 Connective.Tissue.Growth.Factor . . . CTGF.
0.985909
[0227] We calculated the area under the ROC curve (AUROC) for
classification of disease (NC vs. KD) for the following analytes
and covariates using log scale. AUROC for each analyte individually
(Table 27) and AUROC for individual analytes in logistic regression
models that included the covariates year diagnosed, age, gender,
and BMI (Table 28).
TABLE-US-00028 TABLE 27 AUROC for each analyte individually for
classification of disease (NC vs. KD) Analytes AUROC
Alpha.1.Microglobulin . . . A1Micro. 0.743154 Cystatin.C 0.705548
Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 0.695857
Beta.2.Microglobulin . . . B2M. 0.693901
Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 0.684566
Kidney.Injury.Molecule.1 . . . . . KIM.1. 0.654783 Trefoil.Factor.3
. . . TFF3. 0.617977 Connective.Tissue.Growth.Factor . . . CTGF.
0.60144 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.549698
Osteopontin 0.546497 Vascular.Endothelial.Growth.Factor . . . VEGF.
0.541874 Clusterin . . . CLU. 0.512002
Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.506312
TABLE-US-00029 TABLE 28 AUROC for individual analytes in logistic
regression models that included the covariates year since
diagnosis, age, gender, and BMI. Analytes AUROC
Alpha.1.Microglobulin . . . A1Micro. 0.760846 Cystatin.C 0.731863
Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 0.728841
Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 0.725818
Beta.2.Microglobulin . . . B2M. 0.718706 Kidney.Injury.Molecule.1 .
. . . . KIM.1. 0.697724 Trefoil.Factor.3 . . . TFF3. 0.689189
Connective.Tissue.Growth.Factor . . . CTGF. 0.682877
Glutathione.S.Transferase.alpha . . . GST.alpha. 0.678165 Clusterin
. . . CLU. 0.676565 Vascular.Endothelial.Growth.Factor . . . VEGF.
0.674431 Osteopontin 0.673898
Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.672653
[0228] We calculated the area under the ROC curve (AUROC) for
classification of disease (NC vs. KD) for the following
combinations of analytes and covariates. For the combination of all
analytes in a logistic regression model (without covariates), the
AUROC=0.85. For the combination of all analytes in a logistic
regression model (including covariates), the AUROC=0.86.
[0229] 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.
* * * * *