U.S. patent application number 12/972936 was filed with the patent office on 2011-04-14 for urine and serum biomarkers associated with diabetic nephropathy.
This patent application is currently assigned to Industrial Technology Research Institute(ITRI). Invention is credited to Jin-Shuen Chen, Yi-Ting Chen, Ping-Fu Cheng, Tsai-Wei Hsu, Hung-Yi Li, Yen-Peng Li, Wei-Ya Lin, Yuh-Feng Ling, Tzu-Ling Tseng, Mary Ya-Ping Yeh.
Application Number | 20110086371 12/972936 |
Document ID | / |
Family ID | 42395076 |
Filed Date | 2011-04-14 |
United States Patent
Application |
20110086371 |
Kind Code |
A1 |
Lin; Wei-Ya ; et
al. |
April 14, 2011 |
URINE AND SERUM BIOMARKERS ASSOCIATED WITH DIABETIC NEPHROPATHY
Abstract
Use of urine and serum biomarkers in diagnosing diabetic
nephropathy, staging diabetic nephropathy, monitoring diabetic
nephropathy progress, and assessing efficacy of diabetic
nephropathy treatments. These biomarkers include urine precursor
alpha-2-HS-glycoprotein, urine alpha-1 antitrypsin, urine alpha-1
acid glycoprotein, urine osteopontin, serum osteopontin, their
fragments, and combinations thereof.
Inventors: |
Lin; Wei-Ya; (Dali City,
TW) ; Yeh; Mary Ya-Ping; (Taipei City, TW) ;
Tseng; Tzu-Ling; (Xinzhuang City, TW) ; Cheng;
Ping-Fu; (Changhua County, TW) ; Hsu; Tsai-Wei;
(Miaoli County, TW) ; Li; Hung-Yi; (Xinying City,
TW) ; Chen; Yi-Ting; (Taoyuan County, TW) ;
Ling; Yuh-Feng; (Taipei City, TW) ; Chen;
Jin-Shuen; (Taipei City, TW) ; Li; Yen-Peng;
(Taipei City, TW) |
Assignee: |
Industrial Technology Research
Institute(ITRI)
Hsin Chu
TW
|
Family ID: |
42395076 |
Appl. No.: |
12/972936 |
Filed: |
December 20, 2010 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
12694575 |
Jan 27, 2010 |
|
|
|
12972936 |
|
|
|
|
61147778 |
Jan 28, 2009 |
|
|
|
Current U.S.
Class: |
435/7.92 ;
436/501; 530/387.9 |
Current CPC
Class: |
G01N 2333/8125 20130101;
G01N 2800/347 20130101; G01N 2800/56 20130101; G01N 2333/4728
20130101; G01N 2800/52 20130101; G01N 2333/52 20130101; G01N
2333/71 20130101; G01N 33/6893 20130101 |
Class at
Publication: |
435/7.92 ;
530/387.9; 436/501 |
International
Class: |
G01N 33/53 20060101
G01N033/53; C07K 16/18 20060101 C07K016/18; G01N 33/566 20060101
G01N033/566 |
Claims
1. An isolated antibody specifically binding to a peptide selected
from the group consisting of: TABLE-US-00043 (SEQ ID NO: 2)
MGVVSLGSPSGEVSHPRKT, (SEQ ID NO: 3) KGKWERPFEVKDTEEEDF, (SEQ ID NO:
4) MIEQNTKSPLFMGKVVNPTQK, (SEQ ID NO: 6)
EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA, (SEQ ID NO: 7)
GQEHFAHLLILRDTKTYMLADVNDEKNWGLS, (SEQ ID NO: 8) YPDAVATWLNPDPSQKQ
NLLAPQNAVSSEETNDFKQETLPSK, and (SEQ ID NO: 9)
KYPDAVATWLNPDPSQKQNLLAPQTLPSK.
2. The antibody of claim 1, specifically binding to
MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2).
3. The antibody of claim 1, specifically binding to
KGKWERPFEVKDTEEEDF (SEQ ID NO:3).
4. The antibody of claim 1, specifically binding to
MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4).
5. The antibody of claim 1, specifically binding to
EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6).
6. The antibody of claim 1, specifically binding to
GQEHFAHLLILRDTKTYMLADVNDEKNWGLS (SEQ ID NO:7).
7. The antibody of claim 1, specifically binding to
YPDAVATWLNPDPSQKQ NLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8).
8. The antibody of claim 1, specifically binding to
KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
9. The antibody of claim 1, wherein the antibody is a monoclonal
antibody.
10. The antibody of claim 1, wherein the antibody is a whole
immunoglobulin molecule.
11. A kit for diagnosing diabetic nephropathy, comprising two,
three, or four antibodies, each of which is capable of binding to
(i) alpha-2-HS-glycoprotein, (ii) alpha-1 antitrypsin, (iii)
alpha-1 acid glycoprotein, or (iv) osteopontin, wherein the two,
three, or four antibodies have different antigen specificities.
12. The kit of claim 11, wherein the antibodies are whole
immunoglobulin molecules.
13. The kit of claim 11, wherein the antibodies are monoclonal
antibodies.
14. The kit of claim 11, wherein the antibodies are selected from
the group consisting of: an antibody specifically binding to
MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2); an antibody specifically binding
to KGKWERPFEVKDTEEEDF (SEQ ID NO:3); an antibody specifically
binding to MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4); an antibody
specifically binding to EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID
NO:6); an antibody specifically binding to
GQEHFAHLLILRDTKTYMLADVNDEKNWGLS (SEQ ID NO:7); an antibody
specifically binding to YPDAVATWLNPDPSQKQ NLLAPQNAVSSEETNDFKQETLPSK
(SEQ ID NO:8); and an antibody specifically binding to
KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
15. The kit of claim 11, consisting essentially of two, three, or
four antibodies, each of which is capable of binding to (i)
alpha-2-HS-glycoprotein, (ii) alpha-1 antitrypsin, (iii) alpha-1
acid glycoprotein, or (iv) osteopontin, wherein the two, three, or
four antibodies have different antigen specificities.
16. The kit of claim 11, further comprising reagents and materials
for an immunoassay, the immunoassay being ELISA, Westernblot, RIA,
FIA or LIA.
Description
RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/694,575, filed Jan. 27, 2010, which claims
priority to U.S. Provisional Application No. 61/147,778, filed on
Jan. 28, 2009. The contents of both prior applications are hereby
incorporated by reference in their entirety.
BACKGROUND OF THE INVENTION
[0002] Diabetic nephropathy (DN) is a progressive kidney disease
associated with longstanding diabetes mellitus. It causes abnormal
fluid filtration and increased urinary albumin excretion,
eventually leading to kidney failure.
[0003] DN displays no symptoms in its early course. As such, it is
difficult to detect the incipiency of this disease. In fact,
present diagnosis of DN depends on development of microalbuminuria,
which occurs when kidney damage is already in place. The lack of an
early diagnostic test prevents effective treatment of early stage
DN.
[0004] It is of great importance to identify reliable biomarkers
useful in diagnosing early stage DN.
SUMMARY OF THE INVENTION
[0005] The present invention is based on unexpected discoveries
that a number of urine and serum proteins and their fragments,
either alone or in combination, are differentially presented in DN
patients as compared to DN-free subjects. These protein molecules
are therefore useful markers for diagnosing early stage DN.
[0006] Accordingly, one aspect of this invention features a method
of diagnosing DN in a subject. This method includes at least two
steps: (i) determining in a subject suspected of having DN a level
of a biomarker, and (ii) assessing whether the subject has DN based
on the level of the biomarker. An increase in the level of the
biomarker, as compared to that in a DN-free subject, indicates that
the subject has DN.
[0007] The biomarker used in this diagnostic method is one of the
four protein molecules listed below:
[0008] (i) a first urine protein molecule that is precursor
alpha-2-HS-glycoprotein or a fragment thereof having at least ten
amino acid residues, such as, mature alpha-2-HS-glycoprotein,
VVSLGSPSGEVSHPRKT (SEQ ID NO:1), or MGVVSLGSPSGEVSHPRKT (SEQ ID
NO:2);
[0009] (ii) a second urine protein molecule that is alpha-1
antitrypsin or a fragment thereof having at least ten amino acid
residues, such as KGKWERPFEVKDTEEEDF (SEQ ID NO:3);
MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4),
EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5), or
EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6);
[0010] (iii) a third urine protein molecule that is a fragment of
alpha-1 acid glycoprotein having at least ten amino acid residues,
such as GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7); and
[0011] (iv) a serum protein molecule that is osteopontin or a
fragment thereof having at least ten amino acid residues, such as
YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) or
KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
[0012] The diagnostic method described above can further include,
after the assessing step, a step of correlating the biomarker level
with the DN status (i.e., whether it is at early or late stage).
When the biomarker is protein molecules (i) or (iv), an increase in
its level relative to that in a DN-free subject is indicative of
late stage DN. For a biomarker that is protein molecules (ii) or
(iii), its level indicates the DN status when compared with
pre-determined reference biomarker levels representing early and
late stage DN.
[0013] In another aspect, the present invention features a method
for assessing efficacy of a DN treatment in a subject (e.g., a
human patient or a laboratory animal). This method includes
determining in the subject pre-treatment and post-treatment levels
of protein molecules (i), (ii), (iii), or (iv), and assessing
efficacy of the treatment based on a change in the level of the
biomarker after the treatment. If the post-treatment level of the
biomarker remains the same or decreases as compared to the
pre-treatment level of the biomarker, it indicates that the
treatment is effective.
[0014] In yet another aspect, this invention features a method for
determining a DN stage, including at least four steps: (i)
obtaining a urine sample and optionally, a serum sample from a
subject suspected of having diabetic nephropathy, (ii) determining
in the sample(s) a level of one of the biomarkers listed in the
preceding paragraph, (iii) calculating a disease score based on the
level of the biomarker, and (iv) assessing the subject's diabetic
nephropathy stage based on the disease score as compared to
pre-determined cutoff values indicating different diabetic
nephropathy stages. In this method, the calculating step can be
performed by ridge regression analysis, factor analysis,
discriminant function analysis, and logistic regression
analysis.
[0015] The biomarker used in the just-described DN staging method
is composed of at least two of the following five protein
molecules: protein molecules (i)-(iv) listed above and protein
molecule (v) that is urine osteopontin or its fragment described
above. In one example, the biomarker is composed of all of the five
protein molecules. In another example, it is composed of at least
two of protein molecules (i)-(iii) and (v).
[0016] Alternatively, the biomarker is composed of at least two of
the five protein molecules listed above and additionally, one or
more clinical factors, e.g., age, gender, HbA1c, albuminlcreatinine
ratio (ACR), and glomerular filtration rate (GFR).
[0017] In still another aspect, the present invention provides a
method for monitoring DN progress based on the level of any of the
above-mentioned biomarkers. This method includes obtaining two
urine samples and optionally, two serum samples, within a time span
of 2 weeks to 12 months (e.g., 2-24 weeks or 3-12 months) from a
subject suspected of having DN, determining in the samples a level
of one of the biomarkers, calculating disease scores based on the
biomarker levels, and assessing DN progress in the subject based on
the disease scores. The disease score for the later-obtained
samples being greater than that for the earlier-obtained samples is
indicative of DN exacerbation.
[0018] The biomarkers mentioned above can also be used to assess
efficacy of a DN treatment. The treatment is effective if the
post-treatment level of one of the biomarkers remains unchanged or
decreases as compared to the pre-treatment level of the same
biomarker.
[0019] The present invention further provides a kit for use in any
of the methods described above. This kit includes two, three, or
four antibodies with different antigen specificities. Each of these
antibodies is capable of binding to one of (i)
alpha-2-HS-glycoprotein, (ii) alpha-1 antitrypsin, (iii) alpha-1
acid glycoprotein, and (iv) osteopontin. In one example, this kit
contains only antibodies specific to antigens to be detected (e.g.,
biomarkers associated with DN) for practice one of the methods
disclosed herein. Namely, it consists essentially of such
antibodies.
[0020] Also within the scope of this invention is an isolated
antibody specifically binding one of the following peptide:
TABLE-US-00001 (SEQ ID NO: 2) MGVVSLGSPSGEVSHPRKT, (SEQ ID NO: 3)
KGKWERPFEVKDTEEEDF, (SEQ ID NO: 4) MIEQNTKSPLFMGKVVNPTQK, (SEQ ID
NO: 6) EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA, (SEQ ID NO: 7)
GQEHFAHLLILRDTKTYMLADVNDEKNWGLS, (SEQ ID NO: 8) YPDAVATWLNPDPSQKQ
NLLAPQNAVSSEETNDFKQETLPSK, and (SEQ ID NO: 9)
KYPDAVATWLNPDPSQKQNLLAPQTLPSK.
[0021] The terms "an isolated antibody" used herein refers to an
antibody substantially free from naturally associated molecules.
More specifically, a preparation containing the antibody is deemed
as "an isolated antibody" when the naturally associated molecules
in the preparation constitute at most 20% by dry weight. Purity can
be measured by any appropriate method, e.g., column chromatography,
polyacrylamide gel electrophoresis, and HPLC.
[0022] Any of the antibodies described above can be used in
manufacturing a kit useful in practicing any of the methods of this
invention.
[0023] The details of one or more embodiments of the invention are
set forth in the description below. Other features or advantages of
the present invention will be apparent from the following drawings
and detailed description of several embodiments, and also from the
appended claims.
BRIEF DESCRIPTION OF THE DRAWING
[0024] The drawing is first described.
[0025] FIG. 1 is a diagram showing boxplots for urine
alpha-2-HS-glycoprotein (uDN2; see panel A), urine alpha-1
antitrypsin (uDN5; see panel B), urine alpha-1 acid glycoprotein
(uGR3; see panel C), and serum osteopontin (sDNO; see panel D) in
various groups of DN patients. The upper and lower limits of the
boxes mark the 25% and 75% values with the medians as the lines
across the boxes. The upper whisker marks the largest value below
the upper fence, which is the 75% value plus 1.5 interquartile
range and the lower whisker marks the smallest value above the
lower fence, which is the 25% value minus 1.5 interquartile
range.
DETAILED DESCRIPTION OF THE INVENTION
[0026] DN is a kidney disorder associated with diabetes. It has
five progression phases:
[0027] Stage 1: characterized by diabetic mellitus with normal GFR
and normal albuminuria (ACR<30 mg/g);
[0028] Stage 2: characterized by glomerular hyperfiltration
(greater than 120 mL/minute/1.73 m.sup.2) and renal enlargement
accompanying with normal GFR and normal albuminuria(ACR<30
mg/g);
[0029] Stage 3: characterized by microalbuminuria;
[0030] Stage 4: characterized by overt albuminuria and a
progressive decline in GFR; and
[0031] Stage 5: characterized by a GFR of less than 15
mL/minute/1.73 m.sup.2.
Commonly, stages 1-3 are deemed as early stage and stages 4 and 5
are deemed as late stage.
[0032] We have identified a number of biomarkers associated with
DN, especially DN in different stages. These biomarkers are
composed of one or more of the following four proteins and their
fragments, either in urine or in serum: (a) alpha-2-HS-glycoprotein
(GenBank accession no. NP.sub.--001613; 10 Jan. 2010); (b)
alpha-1-antitrypsin (GenBank accession no. AAB59495; 10 Jan. 2010);
(c) alpha-1 acid glycoprotein (GenBank accession no. EAW87416; 10
Jan. 2010); and
(d) Osteopontin, which includes two isoforms known as secreted
phosphoprotein 1a (GenBank accession no. NP.sub.--001035147; 17
Jan. 2010) and secreted phosphoprotein 1b (GenBank accession no.
NP.sub.--000573; 10 Jan. 2010,).
[0033] The fragments of these four proteins have a minimum length
of ten amino acids and preferably, a maximum length of 190 to 410
amino acids. For example, fragments of proteins (a), (b), (c), and
(d) can contain up to 357, 408, 191, and 290 amino acid residues,
respectively.
[0034] We have also found that biomarkers composed of one or more
of the above mentioned proteins/fragments, and one or more clinical
factors (e.g., age, gender, HbA1c, ACR, and GFR) are also
associated with DN in different stages.
[0035] Accordingly, one aspect of the present invention relates to
a DN diagnostic method using any of the biomarkers described above.
To practice this method, a urine sample and, when necessary, a
serum sample, is collected from a subject suspected of having DN
and the urine and serum levels of one or more of the four proteins
listed above or their fragments can be determined via routine
methods, e.g., mass spectrometry and immune analysis. If
applicable, the clinical factors are determined by route
methods.
[0036] When a biomarker contains a single protein molecule, its
level in a subject can be compared with a reference point to
determine whether that subject has DN. The reference point,
representing the level of the same biomarker in a DN-free subject,
can be determined based on the representative levels of the
biomarker in groups of DN patients and DN-free subjects. For
example, it can be the middle point between the mean levels of
these two groups. A biomarker level higher than the reference point
is indicative of DN.
[0037] When a biomarker contains at least two protein molecules and
optionally, at least one clinical factor, the levels of the protein
molecules and the value(s) of the clinical factor(s) can be
subjected to a suitable analysis to generate a disease score (e.g.,
represented by a numeric number) that characterizes the level of
the biomarkers. Examples of the analysis include, but are not
limited to, discriminate function analysis, logistic regression
analysis, ridge regression analysis, principal component analysis,
factor analysis, and generalized linear model. The disease score is
then compared with a reference point representing the level of the
same biomarker in DN-free subjects. The reference point can be
determined by conventional methods. For example, it can be a score
obtained by analyzing the mean levels of the protein molecules and
when necessary, the mean value(s) of the clinical factor(s) in
DN-free subjects with the same analysis. The disease score being
higher than the reference point is indicative of DN presence.
[0038] Another aspect of this invention relates to a method for
determining a DN stage based on any of the biomarkers described
above. To practice this method, a biomarker level of a DN patient,
preferably represented by a disease score, is compared with a set
of pre-determined cutoff values that distinguish different DN
stages to determine the subject's DN stage. The cutoff values can
be determined by analyzing the representative levels of the same
biomarker in different-staged DN patients via the same
analysis.
[0039] Described below is an exemplary procedure for determining
the aforementioned cutoff values based on a biomarker associated
with DN in different stages:
[0040] (1) assigning DN patients to different groups according to
their disease conditions (e.g., DN stages and risk factors);
[0041] (2) determining in each patient group the levels/values of
the protein molecules and clinical factors constituting the
biomarker;
[0042] (4) subjecting the protein levels and clinical factor values
to a suitable analysis to establish a model (e.g., formula) for
calculating a disease score, and
[0043] (6) determining a cutoff value for each disease stage based
on a disease score (e.g., mean value) representing each patient
group, as well as other relevant factors, such as sensitivity,
specificity, positive predictive value (PPV) and negative
predictive value (NPV).
[0044] Any of the models thus generated can be assessed for its
diagnosis value by a receiver-operating characteristic (ROC)
analysis to create a ROC curve. An optimal multivariable model
provides a large Area under Curve (AUC) in the ROC analysis. See
the models described in Examples 1-3 below.
[0045] In still another aspect, this invention relates to a method
of monitoring nephropathy progress in a subject based on any of the
biomarkers described above. More specifically, two urine samples
and/or serum samples from a subject can be obtained within a
suitable time span (e.g., 2 weeks to 12 months) and examined to
determine the levels of one of the biomarkers. Disease scores are
then determined as described above. If the disease score
representing the biomarker level in the later obtained sample(s) is
lower than that in the earlier-obtained sample(s), it indicates DN
exacerbation in the subject.
[0046] The monitoring method can be applied to a human subject
suffering from or at risk for DN. When the human subject is at risk
for or in early stage DN, the level of the biomarker can be
examined once every 6 to 12 months to monitor DN progress. When the
human subject is already in late stage DN, it is preferred that the
biomarker level be examined once every 3 to 6 months.
[0047] The monitoring method described above is also applicable to
laboratory animals, following routine procedures, to study DN. The
term "a laboratory animal" used herein refers to a vertebrate
animal commonly used in animal testing, e.g., mouse, rat, rabbit,
cat, dog, pig, and non-human primate. Preferably, a laboratory
animal is examined to determine the biomarker level once every 2 to
24 weeks.
[0048] Any of the biomarkers can also be used to assess efficacy of
a DN treatment in a subject in need (i.e., a human DN patient or a
laboratory animal bearing DN). In this method, disease scores
representing levels of one of the biomarkers described above are
determined before, during, and after the treatment. If the disease
scores remain the same or decline over the course of the treatment,
it indicates that the treatment is effective.
[0049] Also disclosed herein is a kit useful in practicing any of
the above-described methods. This kit contains two, three, or four
antibodies with different antigen specificities. Each of these
antibodies is capable of binding to one of (i)
alpha-2-HS-glycoprotein, (ii) alpha-1 antitrypsin, (iii) alpha-1
acid glycoprotein, or (iv) osteopontin. The antibodies specific to
proteins (i), (ii), (iii), and (iv) can bind to their fragments
MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2), KGKWERPFEVKDTEEEDF (SEQ ID
NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4),
EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6),
GQEHFAHLLILRDTKTYMLADVNDEKNWGLS (SEQ ID NO:7),
YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8), and
KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9), i.e., specific to any
antibody epitopes contained in these fragments. In one example,
this kit contains only antibodies specific to antigens to be
detected (e.g., protein molecules associated with DN) for practice
one of the methods disclosed herein. Namely, the kit consists
essentially of such antibodies.
[0050] The kit described above can include two different antibodies
(i.e., a coating antibody and a detecting antibody) that bind to
the same antigen. Typically, the detecting antibody is conjugated
with a molecule which emits a detectable signal either on its own
or via binding to another agent. The term "antibody" used herein
refers to a whole immunoglobulin or a fragment thereof, such as Fab
or F(ab').sub.2 that retains antigen-binding activity. It can be
naturally occurring or genetically engineered (e.g., single-chain
antibody, chimeric antibody, or humanized antibody).
[0051] The antibodies included in the kit of this invention can be
obtained from commercial vendors. Alternatively, they can be
prepared by conventional methods. See, for example, Harlow and
Lane, (1988) Antibodies: A Laboratory Manual, Cold Spring Harbor
Laboratory, New York. To produce antibodies against a particular
biomarker as listed above, the marker, optionally coupled to a
carrier protein (e.g., KLH), can be mixed with an adjuvant, and
injected into a host animal. Antibodies produced in the animal can
then be purified by affinity chromatography. Commonly employed host
animals include rabbits, mice, guinea pigs, and rats. Various
adjuvants that can be used to increase the immunological response
depend on the host species and include Freund's adjuvant (complete
and incomplete), mineral gels such as aluminum hydroxide, CpG,
surface-active substances such as lysolecithin, pluronic polyols,
polyanions, peptides, oil emulsions, keyhole limpet hemocyanin, and
dinitrophenol. Useful human adjuvants include BCG (bacille
Calmette-Guerin) and Corynebacterium parvum. Polyclonal antibodies,
i.e., heterogeneous populations of antibody molecules, are present
in the sera of the immunized animal.
[0052] Monoclonal antibodies, i.e., homogeneous populations of
antibody molecules, can be prepared using standard hybridoma
technology (see, for example, Kohler et al. (1975) Nature 256, 495;
Kohler et al. (1976) Eur. J. Immunol. 6, 511; Kohler et al. (1976)
Eur J Immunol 6, 292; and Hammerling et al. (1981) Monoclonal
Antibodies and T Cell Hybridomas, Elsevier, N.Y.). In particular,
monoclonal antibodies can be obtained by any technique that
provides for the production of antibody molecules by continuous
cell lines in culture such as described in Kohler et al. (1975)
Nature 256, 495 and U.S. Pat. No. 4,376,110; the human B-cell
hybridoma technique (Kosbor et al. (1983) Immunol Today 4, 72; Cole
et al. (1983) Proc. Natl. Acad. Sci. USA 80, 2026, and the
EBV-hybridoma technique (Cole et al. (1983) Monoclonal Antibodies
and Cancer Therapy, Alan R. Liss, Inc., pp. 77-96). Such antibodies
can be of any immunoglobulin class including IgG, IgM, IgE, IgA,
IgD, and any subclass thereof. The hybridoma producing the
monoclonal antibodies of the invention may be cultivated in vitro
or in vivo. The ability to produce high titers of monoclonal
antibodies in vivo makes it a particularly useful method of
production.
[0053] Moreover, antibody fragments can be generated by known
techniques. For example, such fragments include, but are not
limited to, F(ab').sub.2 fragments that can be produced by pepsin
digestion of an antibody molecule, and Fab fragments that can be
generated by reducing the disulfide bridges of F(ab').sub.2
fragments.
[0054] Without further elaboration, it is believed that one skilled
in the art can, based on the above description, utilize the present
invention to its fullest extent. The following specific embodiments
are, therefore, to be construed as merely illustrative, and not
limitative of the remainder of the disclosure in any way
whatsoever. All publications cited herein are incorporated by
reference.
EXAMPLE 1
Diagnosing DN Based on Urine Alpha-2-HS-Glycoprotein, Urine Alpha-1
Antitrypsin, Urine Alpha-1 Acid Glycoprotein, or Serum
Osteopontin
Material and Methods
(i) Subjects
[0055] 83 diabetic mellitus patients (designated "DM subjects"),
and 82 DN patients (designated "DN subjects") were recruited at the
Tri-General Military Hospital in Taipei, Taiwan, following the
standards set forth by the American Diabetic Association and also
described below:
[0056] DM: suffering from diabetic mellitus but free of DN (see the
standards described below);
[0057] DN: suffering from diabetic mellitus and secreting urinary
protein at a level greater than 1 g per day, having DN as proven by
biopsy, or having uremia.
[0058] All of the subjects were assigned into a training group and
a testing group at a ratio of 7:3.
(ii) Sample Collection and Processing
[0059] First-morning-void urinary samples and serum samples were
collected from each of the subjects mentioned above. Peptides
contained in the urine samples were examined by urinary
matrix-assisted laser desorption/ionization time-of-flight mass
spectrometry (MALDI-TOF-MS) and by isobaric tags for relative and
absolute quantification (iTRAQ).
[0060] Protein molecules, including alpha-2-HS-glycoprotein (DN2),
alpha-1-anthtrypsin (DN5), osteopontin (DNO), and alpha-1 acid
glycoprotein (GR3), were examined to determine their concentrations
in both the urine and serum samples by ELISA. Briefly, urine
samples were mixed with protease inhibitors and diluted at 1:100
with a dilution buffer and the serum samples were diluted at 1:10.
The diluted samples were placed in ELISA plates in triplicates. The
levels of DNO, DN2, DN5 and GR3 concentrations were measured via
the standard sandwich ELISA method.
[0061] A 5-parameter standard curve was used for concentration
calculation. Only standards and samples with % CV of less than 15
were included, those not meeting criteria were repeated. The
protein levels in the urine samples were normalized against the
creatinine levels in the same urine samples, which were measured
with the Quantichrom Creatinine Assay (BioAssay Systems, (Hayward)
Calif., USA).
(iii) Statistical Analysis
[0062] The data indicating the urine and serum protein
concentrations of each examined protein was statistically analyzed
and performed as represented by auROC from 0.44-0.87 in their
independent ability to distinguish DN subjects from DM
subjects.
[0063] For each subject, correlation between values was determined
by Spearman or Pearson analysis depending on results of test for
normality. Group mean or median comparisons were made with the
Student T-test or the Nonparametric Mann-Whitney Test as
appropriate. Statistical significance was obtained when p<0.05.
Statistics were presented either as mean .+-.standard error of mean
(SEM) or as median with [25%, 75%].
[0064] Results
(i) Patient Characteristics
[0065] Tables 1 and 2 below show the characteristics of patients in
the training group and testing group and those in DM, and DN
groups.
TABLE-US-00002 TABLE 1 Training Testing (n = 118) (n = 47) P value
Characteristics of Patients in Training and Testing Groups Age,
mean (SD) 59.94 (9.37) 60.28 (9.48) 0.8362 Female, n (%) 83 (70) 27
(57) 0.16 MDRD_S_GFR, mean (SD) 86.56 (33.11) 83.05 (43.96) 0.5785
ACR(ug/mg), mean (SD) 737.82 (1465.47) 1084.18 (2030.98) 0.2239
Urine TP/Cr (mg/mg), mean (SD) 1.01 (2.01) 1 (1.78) 0.9963 Serum
Creatinine (mg/dL), mean (SD) 1.02 (0.87) 1.34 (1.44) 0.0903 HbA1c
(%), mean (SD) 8.49 (1.5) 8.29 (2.19) 0.5356 Markers
(creatinine-adjusted), mean (SD) uDNO (ng/mg) 1452.71 (1416.7)
1488.77 (1222.2) 0.8687 sDNO (ng/ml) 40.65 (34.52) 38.35 (34.13)
0.6926 uDN2 (ng/mg) 4225.77 (9279.63) 5999.64 (10305.95) 0.2983
uDN5 (ng/mg) 15951.12 (94956.78) 45479.82 (199827.84) 0.3228 uGR3
(ng/mg) 32823.47 (62290.96) 42709.23 (103787.54) 0.5333
[0066] Statistically significant differences in GFR, ACR, protein,
and serum creatinine levels were observed in the DN subjects versus
in the DM subjects. There was no difference in gender distribution
among the groups.
TABLE-US-00003 TABLE 2 Characteristics of Patients in DM and DN
Groups Training (n = 118) Testing (n = 47) DM (n = 61) DN (n = 57)
P value DM (n = 22) DN (n = 25) P value Age, mean (SD) 57.11 (8.05)
62.96 (9.8) 0.0006 59.09 (8.82) 61.32 (10.09) 0.4230 Female, n (%)
43 (70) 40 (70) 1.00 12 (55) 15 (60) 0.93 MDRD_S_GFR, mean (SD)
111.21 (15.75) 60.18 (25.59) <.0001 115.6 (33.66) 54.41 (29.79)
<.0001 ACR (ug/mg), mean (SD) 11.35 (6.81) 1515.26 (1815.72)
<.0001 9.63 (5.61) 2029.78 (2432.31) 0.0004 Urine TP/Cr (mg/mg),
mean (SD) 0.17 (0.51) 1.9 (2.56) <.0001 0.17 (0.32) 1.7 (2.18)
0.0019 Serum Creatinine 0.66 (0.12) 1.42 (1.12) <.0001 0.67
(0.15) 1.92 (1.79) 0.0019 (mg/dL), mean (SD) HbA1c (%), mean (SD)
8.34 (1.48) 8.7 (1.53) 0.2311 8.37 (1.61) 8.22 (2.66) 0.8238
Markers (creatinine-adjusted), mean (SD) uDNO (ng/mg) 1422.18
(1105.46) 1366.77 (1347.92) 0.8083 1769.54 (1260.15) 1516.44
(1945.7) 0.5953 sDNO (ng/ml) 29.03 (19.32) 46.17 (37.32) 0.0026
26.2 (11.53) 64.52 (50.47) 0.0010 uDN2 (ng/mg) 1968.47 (4218.58)
8084.87 (13101.68) 0.0013 968.79 (1144.47) 7348.69 (10865.95)
0.0074 uDN5 (ng/mg) 390.24 (1327.63) 40036.86 (147186.58) 0.0467
336.21 (568.08) 71802.69 (264863.27) 0.1899 uGR3 (ng/mg) 3576.06
(13562.8) 67470.92 (105208.28) <.0001 2447.77 (2742.38) 71693.1
(82996.86) 0.0003
(ii) Protein Molecules Associated with DN
[0067] Via urine proteomic analysis, the peptides listed in Table 3
below were found to be differentially presented in urine samples
from the DM subjects and DN subjects.
TABLE-US-00004 TABLE 3 Differentially Presented Urine/Serum
Peptides and Proteins in Which They are Located Corresponding
Peptide Sequences Proteins VVSLGSPSGEVSHPRKT Alpha-2-HS (SEQ ID NO:
1) glycoprotein MGVVSLGSPSGEVSHPRKT (DN2) (SEQ ID NO: 2)
KGKWERPFEVKDTEEEDF Alpha-1- (SEQ ID NO: 3) antitrypsin
MIEQNTKSPLFMGKVVNPTQK (DN5) (SEQ ID NO: 4)
EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO: 5)
EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6) YPDAVATWLNPDPSQKQ
Osteopontin NLLAPQNAVSSEETNDFKQETLPSK (DNO) (SEQ ID NO: 8)
GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS Alpha-1 acid (SEQ ID NO: 7)
glycoprotein (GR3)
[0068] Via ELISA analysis, three urine protein molecules, i.e.,
uDN2, uGR3, and uDN5, and one serum protein molecule, i.e., sDNO,
were found to be associated with DN. See FIG. 1, panels A-D and
Table 2 above. More specifically, the levels of uDN2, uDN5, uGR3,
and sDNO were found to be elevated in DN subjects as compared with
DMs (free of DN), indicating that they are reliable markers for DN.
Further, the levels of uDN5 and uGR3 in DN subjects exhibiting
macroalbumiuria (ACR>300 mg/g) were higher than those in DN
subjects exhibiting microalbumiuria (ACR 30 mg/g to 300 mg/g).
Macroalbumiuria is an indicator of late stage DN and
microalbumiuria indicates early stage DN.
EXAMPLE 2
Staging DN Based on a Combination of uDN2, uDN5, uGR3, uDNO, and
sDNO
Two Protein Model
[0069] The combined levels of two of uDN2, uDN5, uGR3, uDNO, and
sDNO in DM subjects, and DN subjects were subjected to discriminant
function analysis, logistic regression analysis, and ridge
regression analysis. The results from this study indicate that any
combination of two of the five proteins or their fragments can be
used as reliable markers for determining DN stages.
[0070] Shown below is an exemplary two-protein model, i.e., uDN5
and uGR3, including equations for calculating disease scores based
on the combined levels of these two protein molecules. Also shown
below are tables (i.e., Tables 4- 9) listing cutoff values,
sensitivities, specificities, positive predictive values (PPV) and
negative predictive values (NPV), and area under the ROC curve
(AUROC) for this two-protein model.
Discriminant Function Analysis:
[0071] Disease
Score=0.3303.times.log.sub.2[uDN5](ng/mg)+0.2732.times.log.sub.2[uGR3](ng-
/mg)+5
TABLE-US-00005 TABLE 4 Cutoff Values Representing DN Early and Late
Stages Indicated by Urine Albumin Levels Training set (n = 118)
Testing set (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 11.227 11.691 11.227 11.691 Sensitivity (%) 93 93 96 100
Specificity (%) 90 90 77 83 PPV (%) 90 83 83 78 NPV (%) 93 96 94
100 AUROC 0.95 0.96 0.98 0.96
TABLE-US-00006 TABLE 5 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 11.066 11.227 11.691 14.017 11.066 11.227 11.691
14.017 Sensitivity (%) 75 93 93 75 84 96 100 100 Specificity (%) 89
90 90 90 75 77 83 80 PPV (%) 92 90 83 21 87 83 78 18 NPV (%) 69 93
96 99 71 94 100 100 AUROC 0.86 0.95 0.96 0.95 0.9 0.98 0.96
0.91
Logistic Regression Analysis:
[0072] Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in
which
Logit_value=-12.5332+0.7197.times.log.sub.2[uDN5](ng/mg)+0.4941.times.lo-
g.sub.2[uGR3](ng/mg)
TABLE-US-00007 TABLE 6 Cutoff Values Representing DN Early and Late
Stages Indicated by Urine Albumin Levels Training set (n = 118)
Testing set (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 0.445 0.676 0.445 0.676 Sensitivity (%) 93 93 100 100
Specificity (%) 90 90 82 83 PPV (%) 90 83 86 78 NPV (%) 93 96 100
100 AUROC 0.95 0.96 0.98 0.97
TABLE-US-00008 TABLE 7 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 0.383 0.445 0.676 0.996 0.383 0.445 0.676 0.996
Sensitivity (%) 75 93 93 75 84 100 100 50 Specificity (%) 89 90 90
90 75 82 83 80 PPV (%) 92 90 83 21 87 86 78 10 NPV (%) 69 93 96 99
71 100 100 97 AUROC 0.86 0.95 0.96 0.95 0.9 0.98 0.97 0.88
Ridge Regression Analysis:
[0073] Disease
Score=-1.7697+0.1520.times.log.sub.2[uDN5](ng/mg)+0.2254.times.log.sub.2[-
uGR3](ng/mg)
TABLE-US-00009 TABLE 8 Cutoff Values Representing DN Early and Late
Stages Indicated by Urine Albumin Levels Training set (n = 118)
Testing set (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 2.254 2.606 2.254 2.606 Sensitivity (%) 93 93 100 94
Specificity (%) 90 90 77 79 PPV (%) 90 83 83 74 NPV (%) 93 96 100
96 AUROC 0.94 0.96 0.98 0.96
TABLE-US-00010 TABLE 9 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 2.185 2.254 2.606 4.016 2.185 2.254 2.606 4.016
Sensitivity (%) 75 93 93 75 84 100 94 100 Specificity (%) 89 90 90
90 75 77 79 84 PPV (%) 92 90 83 21 87 83 74 22 NPV (%) 69 93 96 99
71 100 96 100 AUROC 0.86 0.94 0.96 0.95 0.89 0.98 0.96 0.91
Three Protein Model
[0074] The combined levels of three of uDN2, uDN5, uGR3, uDNO, and
sDNO in DM subjects and DN subjects were subjected to discriminant
function analysis, logistic regression analysis, factor analysis,
and ridge regression analysis. The results indicate that any
three-protein combination can be used as a reliable marker for DN
staging.
[0075] Shown below is an exemplary three-protein model, i.e., uDN2,
uDN5 and uGR3, including equations for calculating disease scores
based on the combined levels of these three protein molecules. Also
shown below are tables (i.e., Tables 10- 17) listing cutoff values,
sensitivities, specificities, PPV, NPV, and AUROC for this
three-protein model.
Discriminant Function Analysis
[0076] Disease
Score=0.3340.times.log.sub.2[uDN5](ng/mg)-0.0142.times.log.sub.2[uDN2](ng-
/mg)+0.2784.times.log.sub.2[uGR3](ng/mg)+5
TABLE-US-00011 TABLE 10 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set (n =
118) Testing set (n = 47) DM, Micro DM, Micro albuminuria
albuminuria vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN
albuminuria Cut-off 11.190 11.663 11.190 11.663 Sensitivity (%) 93
93 96 100 Specificity (%) 90 90 77 83 PPV (%) 90 83 83 78 NPV (%)
93 96 94 100 AUROC 0.95 0.96 0.98 0.96
TABLE-US-00012 TABLE 11 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 11.064 11.190 11.663 13.986 11.064 11.190 11.663
13.986 Sensitivity (%) 75 93 93 75 84 96 100 100 Specificity (%) 89
90 90 90 75 77 83 82 PPV (%) 92 90 83 21 87 83 78 20 NPV (%) 69 93
96 99 71 94 100 100 AUROC 0.87 0.95 0.96 0.95 0.9 0.98 0.96
0.91
Factor Analysis
[0077] Disease
Score=0.9190.times.log.sub.2[uDN5](ng/mg)+0.6997.times.log.sub.2[uDN2](ng-
/mg)+0.9003.times.log.sub.2[uGR3](ng/mg)
TABLE-US-00013 TABLE 12 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 26.356 28.057 26.356 28.057 Sensitivity (%) 84 93 88 100
Specificity (%) 90 90 91 86 PPV (%) 89 83 92 82 NPV (%) 86 96 87
100 AUROC 0.93 0.95 0.99 0.97
TABLE-US-00014 TABLE 13 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 25.669 26.356 28.057 36.464 25.669 26.356 28.057
36.464 Sensitivity (%) 68 84 93 75 84 88 100 50 Specificity (%) 89
90 90 90 88 91 86 84 PPV (%) 91 89 83 21 93 92 82 12 NPV (%) 63 86
96 99 74 87 100 97 AUROC 0.83 0.93 0.95 0.95 0.91 0.99 0.97
0.86
Logistic Regression Analysis:
[0078] Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in
which
Logit
value=-11.2820+0.8810.times.log.sub.2[uDN5](ng/mg)-0.3478.times.lo-
g.sub.2[uDN2](ng/mg)+0.5576.times.log.sub.2[uGR3](ng/mg)
TABLE-US-00015 TABLE 14 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 0.462 0.798 0.462 0.798 Sensitivity (%) 91 88 96 94
Specificity (%) 90 90 82 83 PPV (%) 90 82 86 77 NPV (%) 92 93 95 96
AUROC 0.95 0.96 0.97 0.95
TABLE-US-00016 TABLE 15 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 0.361 0.462 0.798 0.997 0.361 0.462 0.798 0.997
Sensitivity (%) 75 91 88 75 90 96 94 100 Specificity (%) 89 90 90
90 75 82 83 82 PPV (%) 92 90 82 21 88 86 77 20 NPV (%) 69 92 93 99
80 95 96 100 AUROC 0.88 0.95 0.96 0.95 0.89 0.97 0.95 0.93
Ridge Regression Analysis:
[0079] Disease
Score=-1.2900+0.1800.times.log.sub.2[uDN5](ng/mg)-0.1013.times.log.sub.2[-
uDN2](ng/mg)+0.2505.times.log.sub.2[uGR3](ng/mg)
TABLE-US-00017 TABLE 16 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 2.122 2.831 2.122 2.831 Sensitivity (%) 95 85 100 94
Specificity (%) 90 90 68 86 PPV (%) 90 81 78 81 NPV (%) 95 92 100
96 AUROC 0.95 0.95 0.97 0.95
TABLE-US-00018 TABLE 17 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 2.083 2.122 2.831 3.943 2.083 2.122 2.831 3.943
Sensitivity (%) 78 95 85 75 87 100 94 100 Specificity (%) 89 90 90
90 69 68 86 82 PPV (%) 92 90 81 21 84 78 81 20 NPV (%) 71 95 92 99
73 100 96 100 AUROC 0.88 0.95 0.95 0.95 0.89 0.97 0.95 0.93
Four-Protein Model
[0080] The combined levels of four of uDN2, uDN5, uGR3, uDNO, and
sDNO in DM subjects and DN subjects were subjected to discriminant
function analysis, logistic regression analysis, factor analysis,
and ridge regression analysis. The results indicate that any
combination of four of the five proteins or their fragments can be
used as a reliable marker for determining DN stages.
[0081] Shown below is an exemplary four-protein model, i.e., uDN2,
uDN5, uGR3, and sDNO, including equations for calculating disease
scores based on the combined levels of these four protein
molecules. Also shown below are tables (i.e., Tables 18-25) listing
cutoff values, sensitivities, specificities, PPVs, NPVs, and AUROC
for this four-protein model.
Discriminant Function Analysis:
[0082] Disease
Score=0.2972.times.log.sub.2[uDN5](ng/mg)+0.0159.times.log.sub.2[uDN2](ng-
/mg)+0.2014.times.log.sub.2[uGR3](ng/mg)+0.5688.times.log.sub.2[sDNO](ng/m-
l)+5
TABLE-US-00019 TABLE 18 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 12.945 13.520 12.945 13.520 Sensitivity (%) 88 95 96 100
Specificity (%) 90 90 82 86 PPV (%) 89 83 86 82 NPV (%) 89 97 95
100 AUROC 0.94 0.96 0.97 0.97
TABLE-US-00020 TABLE 19 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stages 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 12.887 12.945 13.520 15.560 12.887 12.945 13.520
15.560 Sensitivity (%) 73 88 95 100 81 96 100 100 Specificity (%)
89 90 90 90 81 82 86 82 PPV (%) 91 89 83 27 89 86 82 20 NPV (%) 67
89 97 100 68 95 100 100 AUROC 0.87 0.94 0.96 0.97 0.93 0.97 0.97
0.89
Factor Analysis:
[0083] Disease
Score=0.9132.times.log.sub.2[uDN5](ng/mg)+0.6950.times.log.sub.2[uDN2](ng-
/mg)+0.9080.times.log.sub.2[uGR3](ng/mg)+0.4549.times.log.sub.2[sDNO](ng/m-
l)
TABLE-US-00021 TABLE 20 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. Macro vs. Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 28.459 30.095 28.459 30.095 Sensitivity (%) 82 93 92 100
Specificity (%) 90 90 91 83 PPV (%) 89 83 92 78 NPV (%) 85 96 91
100 AUROC 0.93 0.96 0.99 0.98
TABLE-US-00022 TABLE 21 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 28.347 28.459 30.095 38.624 28.347 28.459 30.095
38.624 Sensitivity (%) 67 82 93 75 81 92 100 50 Specificity (%) 89
90 90 90 94 91 83 84 PPV (%) 91 89 83 21 96 92 78 12 NPV (%) 62 85
96 99 71 91 100 97 AUROC 0.84 0.93 0.96 0.95 0.92 0.99 0.98
0.86
Logistic Regression Analysis:
[0084] Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in
which
Logit_value=-13.7529+0.9460.times.log.sub.2[uDN5](ng/mg)-0.3110.times.lo-
g.sub.2[uDN2](ng/mg)+0.4957.times.log.sub.2[uGR3](ng/mg)+0.4787.times.log.-
sub.2[sDNO](ng/ml)
TABLE-US-00023 TABLE 22 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set (n =
118) Testing set (n = 47) DM, Micro DM, Micro albuminuria
albuminuria vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN
albuminuria Cut-off 0.423 0.804 0.423 0.804 Sensitivity (%) 91 88
96 100 Specificity (%) 90 90 77 86 PPV (%) 90 82 83 82 NPV (%) 92
93 94 100 AUROC 0.96 0.96 0.97 0.96
TABLE-US-00024 TABLE 23 Cutoff Values Representing DN States 1-5
Training set (n = 118) Testing set (n = 47) DN-Stages 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 0.341 0.423 0.804 0.998 0.341 0.423 0.804 0.998
Sensitivity (%) 75 91 88 75 90 96 100 100 Specificity (%) 89 90 90
90 75 77 86 82 PPV (%) 92 90 82 21 88 83 82 20 NPV (%) 69 92 93 99
80 94 100 100 AUROC 0.89 0.96 0.96 0.96 0.91 0.97 0.96 0.9
Ridge Regression Analysis:
[0085] Disease
Score=-1.7588+0.1729.times.log.sub.2[uDN5](ng/mg)-0.0971.times.log.sub.2[-
uDN2](ng/mg)+0.2381.times.log.sub.2[uGR3](ng/mg)+0.1312.times.log.sub.2[sD-
NO](ng/ml)
TABLE-US-00025 TABLE 24 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 2.261 2.854 2.261 2.854 Sensitivity (%) 91 85 96 94
Specificity (%) 90 90 77 90 PPV (%) 90 81 83 85 NPV (%) 92 92 94 96
AUROC 0.95 0.95 0.97 0.95
TABLE-US-00026 TABLE 25 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 2.079 2.261 2.854 3.950 2.079 2.261 2.854 3.950
Sensitivity (%) 77 91 85 75 87 96 94 100 Specificity (%) 89 90 90
90 69 77 90 82 PPV (%) 92 90 81 21 84 83 85 20 NPV (%) 70 92 92 99
73 94 96 100 AUROC 0.89 0.95 0.95 0.95 0.89 0.97 0.95 0.93
Five-Protein Model
[0086] The combined levels of uDN2, uDN5, uGR3, uDNO, and sDNO in
DM subjects and DN subjects were subjected to discriminant function
analysis, logistic regression analysis, factor analysis, and ridge
regression analysis. The results indicate that the combination of
these five proteins or their fragments can be used as a reliable
marker for determining DN stages.
[0087] Shown below are equations for calculating disease scores
based on the combined levels of these five protein molecules, as
well as tables (i.e., Tables 26-33) listing cutoff values,
sensitivities, specificities, NPVs, PPVs, and AUROC for this
five-protein model.
Discriminant Function Analysis:
[0088] Disease
Score=0.2780.times.log.sub.2[uDN5](ng/mg)+0.0231.times.log.sub.2[uDN2](ng-
/mg)+0.2236.times.log.sub.2[uGR3](ng/mg)+0.6043.times.log2[sDNO](ng/ml)-0.-
1513.times.log2[uDNO](ng/mg)+5
TABLE-US-00027 TABLE 26 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set (n =
118) Testing set (n = 47) DM, Micro DM, Micro albuminuria
albuminuria vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN
albuminuria Cut-off 11.818 12.164 11.818 12.164 Sensitivity (%) 86
98 96 100 Specificity (%) 90 90 86 86 PPV (%) 89 83 89 82 NPV (%)
87 99 95 100 AUROC 0.94 0.97 0.98 0.98
TABLE-US-00028 TABLE 27 Cutoff Values Representing DN States 1-5
Training set (n = 118) Testing set (n = 47) DN Stages 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 11.766 11.818 12.164 14.432 11.766 11.818 12.164
14.432 Sensitivity (%) 73 86 98 100 81 96 100 100 Specificity (%)
89 90 90 90 88 86 86 82 PPV (%) 91 89 83 27 93 89 82 20 NPV (%) 67
87 99 100 70 95 100 100 AUROC 0.86 0.94 0.97 0.98 0.94 0.98 0.98
0.91
Factor Analysis:
[0089] Disease
Score=0.9117.times.log.sub.2[uDN5](ng/mg)+0.6949.times.log.sub.2[uDN2](ng-
/mg)+0.9095.times.log.sub.2[uGR3](ng/mg)+0.4554.times.log2[sDNO](ng/ml)+0.-
0384.times.log2[uDNO](ng/mg)
TABLE-US-00029 TABLE 28 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 29.475 30.541 29.475 30.541 Sensitivity (%) 81 93 88 100
Specificity (%) 90 90 91 83 PPV (%) 88 83 92 78 NPV (%) 83 96 87
100 AUROC 0.93 0.96 0.99 0.98
TABLE-US-00030 TABLE 29 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN Stages 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 28.740 29.475 30.541 39.042 28.740 29.475 30.541
39.042 Sensitivity (%) 67 81 93 75 81 88 100 50 Specificity (%) 89
90 90 90 94 91 83 84 PPV (%) 91 88 83 21 96 92 78 12 NPV (%) 62 83
96 99 71 87 100 97 AUROC 0.84 0.93 0.96 0.95 0.92 0.99 0.98
0.86
Logistic Regression Analysis:
[0090] Disease Score=exp(Logit_value)/(1+exp (Logit_value)), in
which
Logit_value=-11.4318+0.8188.times.log.sub.2[uDN5](ng/mg)-0.5376.times.lo-
g.sub.2[uDN2](ng/mg)+0.7561.times.log.sub.2[uGR3](ng/mg)+0.3940.times.log.-
sub.2[sDNO](ng/ml)-0.1741.times.log.sub.2[uDNO](ng/mg)
TABLE-US-00031 TABLE 30 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set (n =
118) Testing set (n = 47) DM, Micro DM, Micro albuminuria
albuminuria vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN
albuminuria Cut-off 0.436 0.780 0.436 0.780 Sensitivity (%) 91 93
96 100 Specificity (%) 90 90 77 86 PPV (%) 90 83 83 82 NPV (%) 92
96 94 100 AUROC 0.96 0.96 0.97 0.96
TABLE-US-00032 TABLE 31 Cutoff Values Representing DN States 1-5
Training set (n = 118) Testing set (n = 47) DN Stages 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 0.329 0.436 0.780 0.997 0.329 0.436 0.780 0.997
Sensitivity (%) 75 91 93 100 90 96 100 100 Specificity (%) 89 90 90
90 75 77 86 80 PPV (%) 92 90 83 27 88 83 82 18 NPV (%) 69 92 96 100
80 94 100 100 AUROC 0.89 0.96 0.96 0.96 0.91 0.97 0.96 0.91
Ridge Regression Analysis:
[0091] Disease
Score=-1.3112+0.1648.times.log.sub.2[uDN5](ng/mg)-0.0968.times.log.sub.2[-
uDN2](ng/mg)+0.2468.times.log.sub.2[uGR3](ng/mg)+0.1426.times.log2[sDNO](n-
g/ml)-0.0552.times.log2[uDNO](ng/mg)
TABLE-US-00033 TABLE 32 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 2.244 2.729 2.244 2.729 Sensitivity (%) 91 88 96 100
Specificity (%) 90 90 82 90 PPV (%) 90 82 86 86 NPV (%) 92 93 95
100 AUROC 0.95 0.95 0.98 0.97
TABLE-US-00034 TABLE 33 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN Stages 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 2.043 2.244 2.729 3.913 2.043 2.244 2.729 3.913
Sensitivity (%) 77 91 88 100 87 96 100 100 Specificity (%) 89 90 90
90 69 82 90 80 PPV (%) 92 90 82 27 84 86 86 18 NPV (%) 70 92 93 100
73 95 100 100 AUROC 0.89 0.95 0.95 0.96 0.9 0.98 0.97 0.93
EXAMPLE 3
Staging DN Based on a Combination of uDN2, uDN5, uGR3, and Age
[0092] Shown below are equations for calculating disease scores
determined by discriminant function analysis, factor analysis,
logistic regression analysis, and ridge regression analysis, based
on the level of a biomarker composed of three protein molecules,
i.e., uDN2, uDN5, and uGR3, and one clinical factor, i.e., age.
Also shown below are tables (i.e., Tables 34-41) listing cutoff
values, sensitivities, specificities, PPVs, NPVs, and AUROC for
this model.
Discriminant Function Analysis:
[0093] Disease
Score=0.3342.times.log.sub.2[uDN5](ng/mg)-0.0201.times.log.sub.2[uDN2](ng-
/mg)+0.2826.times.log.sub.2[uGR3](ng/mg)+0.0059.times.Age(year)+5
TABLE-US-00035 TABLE 34 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 11.515 12.088 11.515 12.088 Sensitivity (%) 93 93 100 100
Specificity (%) 90 90 77 79 PPV (%) 90 83 83 75 NPV (%) 93 96 100
100 AUROC 0.95 0.96 0.98 0.97
TABLE-US-00036 TABLE 35 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 11.353 11.515 12.088 14.343 11.353 11.515 12.088
14.343 Sensitivity (%) 75 93 93 75 84 100 100 100 Specificity (%)
89 90 90 90 75 77 79 80 PPV (%) 92 90 83 21 87 83 75 18 NPV (%) 69
93 96 99 71 100 100 100 AUROC 0.87 0.95 0.96 0.95 0.9 0.98 0.97
0.9
Factor Analysis:
[0094] Disease
Score=0.9184.times.log.sub.2[uDN5](ng/mg)+0.7006.times.log.sub.2[uDN2](ng-
/mg)+0.9005.times.log.sub.2[uGR3](ng/mg)+0.1863.times.Age(year)
TABLE-US-00037 TABLE 36 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Micro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 38.341 40.075 38.341 40.075 Sensitivity (%) 82 85 96 100
Specificity (%) 90 90 86 83 PPV (%) 89 81 89 78 NPV (%) 85 92 95
100 AUROC 0.93 0.94 0.99 0.98
TABLE-US-00038 TABLE 37 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stages 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Number of patients (%) 73 (62) 57 (48) 41 (35) 4 (3) 31 (66)
25 (53) 18 (38) 2 (4) Cut-off 38.341 38.341 40.075 48.538 38.341
38.341 40.075 48.538 Sensitivity (%) 66 82 85 50 81 96 100 50
Specificity (%) 89 90 90 90 88 86 83 89 PPV (%) 91 89 81 15 93 89
78 17 NPV (%) 62 85 92 98 70 95 100 98 AUROC 0.82 0.93 0.94 0.91
0.9 0.99 0.98 0.77
Logistic Regression Analysis:
[0095] Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in
which
Logit_value=-15.9748+0.8688.times.log.sub.2[uDN5](ng/mg)-0.4966.times.lo-
g.sub.2[uDN2](ng/mg)+0.6436.times.log.sub.2[uGR3](ng/mg)+0.0879.times.Age(-
year)
TABLE-US-00039 TABLE 38 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 0.321 0.889 0.321 0.889 Sensitivity (%) 93 80 100 94
Specificity (%) 90 90 77 83 PPV (%) 90 80 83 77 NPV (%) 93 90 100
96 AUROC 0.96 0.95 0.97 0.95
TABLE-US-00040 TABLE 39 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stages 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 0.301 0.321 0.889 0.997 0.301 0.321 0.889 0.997
Sensitivity (%) 75 93 80 75 87 100 94 100 Specificity (%) 89 90 90
90 75 77 83 89 PPV (%) 92 90 80 21 87 83 77 29 NPV (%) 69 93 90 99
75 100 96 100 AUROC 0.89 0.96 0.95 0.92 0.88 0.97 0.95 0.91
Ridge Regression Analysis:
[0096] Disease
Score=-2.1690+0.1771.times.log.sub.2[uDN5](ng/mg)-0.1074.times.log.sub.2[-
uDN2](ng/mg)+0.2474.times.log.sub.2[uGR3](ng/mg)+0.0168.times.Age(year)
TABLE-US-00041 TABLE 40 Cutoff Values Representing DN Early and
Late Stages Indicated by Urine Albumin Levels Training set Testing
set (n = 118) (n = 47) DM, Micro DM, Micro albuminuria albuminuria
vs. vs. Macro Macro DM vs. DN albuminuria DM vs. DN albuminuria
Cut-off 2.139 2.880 2.139 2.880 Sensitivity (%) 93 85 100 89
Specificity (%) 90 90 73 83 PPV (%) 90 81 81 76 NPV (%) 93 92 100
92 AUROC 0.96 0.95 0.98 0.96
TABLE-US-00042 TABLE 41 Cutoff Values Representing DN Stages 1-5
Training set (n = 118) Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2
vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4
vs. 5 Cut-off 2.128 2.139 2.880 4.051 2.128 2.139 2.880 4.051
Sensitivity (%) 75 93 85 75 84 100 89 100 Specificity (%) 89 90 90
90 69 73 83 89 PPV (%) 92 90 81 21 84 81 76 29 NPV (%) 69 93 92 99
69 100 92 100 AUROC 0.89 0.96 0.95 0.92 0.89 0.98 0.96 0.92
Other Embodiments
[0097] All of the features disclosed in this specification may be
combined in any combination. Each feature disclosed in this
specification may be replaced by an alternative feature serving the
same, equivalent, or similar purpose. Thus, unless expressly stated
otherwise, each feature disclosed is only an example of a generic
series of equivalent or similar features.
[0098] From the above description, one skilled in the art can
easily ascertain the essential characteristics of the present
invention, and without departing from the spirit and scope thereof,
can make various changes and modifications of the invention to
adapt it to various usages and conditions. Thus, other embodiments
are also within the claims.
Sequence CWU 1
1
9117PRTArtificial SequenceFragment of alpha-2-HS-glycoprotein 1Val
Val Ser Leu Gly Ser Pro Ser Gly Glu Val Ser His Pro Arg Lys1 5 10
15Thr219PRTArtificial SequenceFragment of alpha-2-HS-glycoprotein
2Met Gly Val Val Ser Leu Gly Ser Pro Ser Gly Glu Val Ser His Pro1 5
10 15Arg Lys Thr318PRTArtificial SequenceFragment of alpha-1
antitrypsin 3Lys Gly Lys Trp Glu Arg Pro Phe Glu Val Lys Asp Thr
Glu Glu Glu1 5 10 15Asp Phe421PRTArtificial SequenceFragment of
alpha-1 antitrypsin 4Met Ile Glu Gln Asn Thr Lys Ser Pro Leu Phe
Met Gly Lys Val Val1 5 10 15Asn Pro Thr Gln Lys 20532PRTArtificial
SequenceFragment of alpha-1 antitrypsin 5Glu Asp Pro Gln Gly Asp
Ala Ala Gln Lys Thr Asp Thr Ser His His1 5 10 15Asp Gln Asp His Pro
Thr Phe Asn Lys Ile Thr Pro Asn Leu Ala Glu 20 25
30634PRTArtificial SequenceFragment of alpha-1 antitrypsin 6Glu Asp
Pro Gln Gly Asp Ala Ala Gln Lys Thr Asp Thr Ser His His1 5 10 15Asp
Gln Asp His Pro Thr Phe Asn Lys Ile Thr Pro Asn Leu Ala Glu 20 25
30Phe Ala732PRTArtificial SequenceFragment of alpha-1 acid
glycoprotein 7Gly Gln Glu His Phe Ala His Leu Leu Ile Leu Arg Asp
Thr Lys Thr1 5 10 15Tyr Met Leu Ala Phe Asp Val Asn Asp Glu Lys Asn
Trp Gly Leu Ser 20 25 30842PRTArtificial SequenceFragment of
osteopontin 8Tyr Pro Asp Ala Val Ala Thr Trp Leu Asn Pro Asp Pro
Ser Gln Lys1 5 10 15Gln Asn Leu Leu Ala Pro Gln Asn Ala Val Ser Ser
Glu Glu Thr Asn 20 25 30Asp Phe Lys Gln Glu Thr Leu Pro Ser Lys 35
40929PRTArtificial SequenceFragment of osteopontin 9Lys Tyr Pro Asp
Ala Val Ala Thr Trp Leu Asn Pro Asp Pro Ser Gln1 5 10 15Lys Gln Asn
Leu Leu Ala Pro Gln Thr Leu Pro Ser Lys 20 25
* * * * *