U.S. patent application number 13/891128 was filed with the patent office on 2014-02-20 for methods for diagnosing irritable bowel syndrome.
The applicant listed for this patent is NESTEC S.A.. Invention is credited to Augusto Lois.
Application Number | 20140051594 13/891128 |
Document ID | / |
Family ID | 39083087 |
Filed Date | 2014-02-20 |
United States Patent
Application |
20140051594 |
Kind Code |
A1 |
Lois; Augusto |
February 20, 2014 |
METHODS FOR DIAGNOSING IRRITABLE BOWEL SYNDROME
Abstract
The present invention provides methods, systems, and code for
accurately classifying whether a sample from an individual is
associated with irritable bowel syndrome (IBS). In particular, the
present invention is useful for classifying a sample from an
individual as an IBS sample using a statistical algorithm and/or
empirical data. The present invention is also useful for ruling out
one or more diseases or disorders that present with IBS-like
symptoms and ruling in IBS using a combination of statistical
algorithms and/or empirical data. Thus, the present invention
provides an accurate diagnostic prediction of IBS and prognostic
information useful for guiding treatment decisions.
Inventors: |
Lois; Augusto; (San Diego,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NESTEC S.A. |
Vevey |
|
CH |
|
|
Family ID: |
39083087 |
Appl. No.: |
13/891128 |
Filed: |
May 9, 2013 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
11841660 |
Aug 20, 2007 |
8463553 |
|
|
13891128 |
|
|
|
|
11838810 |
Aug 14, 2007 |
|
|
|
11841660 |
|
|
|
|
60895962 |
Mar 20, 2007 |
|
|
|
60884397 |
Jan 10, 2007 |
|
|
|
Current U.S.
Class: |
506/9 ; 435/6.11;
435/6.12; 435/7.4; 435/7.92; 702/19 |
Current CPC
Class: |
G01N 33/686 20130101;
G01N 33/74 20130101; G01N 2800/065 20130101; G01N 33/6893 20130101;
G01N 2800/52 20130101; G01N 33/564 20130101; G01N 33/6869
20130101 |
Class at
Publication: |
506/9 ; 435/7.92;
435/7.4; 435/6.12; 435/6.11; 702/19 |
International
Class: |
G01N 33/68 20060101
G01N033/68; G01N 33/74 20060101 G01N033/74 |
Claims
1-51. (canceled)
52. A method for classifying whether a sample from an individual is
associated with irritable bowel syndrome (IBS), said method
comprising: (a) determining a diagnostic marker profile by
detecting the presence or level of at least one diagnostic marker
selected from the group consisting a cytokine, epidermal growth
factor (EGF), anti-neutrophil antibody, anti-Saccharomyces
cerevisiae antibody (ASCA), antimicrobial antibody, lactoferrin,
lipocalin, matrix metalloproteinase-9 (MMP-9), Substance-P, and
combinations thereof in said sample; and (b) classifying said
sample as an IBS sample using an algorithm based upon comparing
said diagnostic marker profile to a training cohort comprising IBS,
inflammatory bowel disease (IBD) and normal samples.
53. The method of claim 52, wherein said cytokine is selected from
the group consisting of IL-8, IL-1.beta., TNF-related weak inducer
of apoptosis (TWEAK), leptin, osteoprotegerin (OPG), MIP-3.beta.,
GRO.alpha., CXCL4/PF-4, CXCL7/NAP-2, and combinations thereof.
54. The method of claim 52, wherein said at least one diagnostic
marker is epidermal growth factor (EGF).
55. The method of claim 52, wherein said anti-neutrophil antibody
is selected from the group consisting of an anti-neutrophil
cytoplasmic antibody (ANCA), perinuclear anti-neutrophil
cytoplasmic antibody (pANCA), and combinations thereof.
56. The method of claim 52, wherein said ASCA is selected from the
group consisting of ASCA-IgA, ASCA-IgG, and combinations
thereof.
57. The method of claim 52, wherein said antimicrobial antibody is
selected from the group consisting of an anti-outer membrane
protein C (anti-OmpC) antibody, anti-flagellin antibody, anti-I2
antibody, and combinations thereof.
58. The method of claim 52, wherein said lipocalin is selected from
the group consisting of neutrophil gelatinase-associated lipocalin
(NGAL), an NGAL/MMP-9 complex, and combinations thereof.
59. The method of claim 52, wherein said at least one diagnostic
marker is MMP-9.
60. The method of claim 52, wherein said at least one diagnostic
marker is lactoferrin.
61. The method of claim 52, wherein said at least one diagnostic
marker is lipocalin.
62. The method of claim 52, wherein said at least one diagnostic
marker is Substance P.
63. The method of claim 57, wherein said antimicrobial antibody is
an anti-outer membrane protein C (anti-OmpC) antibody.
64. The method of claim 57, wherein said anti-flagellin antibody is
an anti-CBir-1 flagellin antibody.
65. The method of claim 52, wherein said diagnostic marker profile
is determined by detecting the presence or level of at least two,
three, four, five, or six diagnostic markers.
66. The method of claim 52, wherein the presence or level of said
at least one diagnostic marker is detected using a hybridization
assay, amplification-based assay, immunoassay, or
immunohistochemical assay.
67. The method of claim 52, wherein said method comprises
determining said diagnostic marker profile in combination with a
symptom profile, wherein said symptom profile is determined by
identifying the presence or severity of at least one symptom in
said individual; and classifying said sample as an IBS sample using
an algorithm based upon said diagnostic marker profile and said
symptom profile.
68. The method of claim 67, wherein said at least one symptom is
selected from the group consisting of chest pain, chest discomfort,
heartburn, inability to finish a regular-sized meal, abdominal
pain, abdominal discomfort, constipation, diarrhea, bloating,
abdominal distension, and combinations thereof.
69. The method of claim 67, wherein the presence or severity of
said at least one symptom is identified using a questionnaire.
70. The method of claim 69, wherein said questionnaire is selected
from the group consisting of a set of questions asking said
individual about the presence or severity of said at least one
symptom.
71. The method of claim 67, wherein the presence or severity of
said at least one symptom is identified by asking said individual
whether said individual is currently experiencing any symptoms.
72. The method of claim 67, wherein said symptom profile is
determined by identifying the presence or severity of at least two,
three, four, five, or six symptoms.
73. The method of claim 52, wherein said sample is selected from
the group consisting of serum, plasma, whole blood, and stool.
74. The method of claim 52, wherein said algorithm comprises a
statistical algorithm.
75. The method of claim 74, wherein said statistical algorithm
comprises a learning statistical classifier system.
76. The method of claim 75, wherein said learning statistical
classifier system is selected from the group consisting of a random
forest, classification and regression tree, boosted tree, neural
network, support vector machine, general chi-squared automatic
interaction detector model, interactive tree, multiadaptive
regression spline, machine learning classifier, and combinations
thereof.
77. The method of claim 74, wherein said statistical algorithm
comprises a single learning statistical classifier system.
78. The method of claim 74, wherein said statistical algorithm
comprises a combination of at least two learning statistical
classifier systems.
79. The method of claim 52, wherein said method further comprises
sending the results from said classification to a clinician.
80. The method of claim 52, wherein said method further provides a
diagnosis in the form of a probability that said individual has
IBS.
81. The method of claim 52, wherein said method further comprises
classifying said IBS sample as an IBS-constipation (IBS-C),
IBS-diarrhea (IBS-D), IBS-mixed (IBS-M), IBS-alternating (IBS-A),
or post-infectious IBS (IBS-PI) sample.
82. The method of claim 52, wherein said method further comprises
ruling out intestinal inflammation.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S.
application Ser. No. 11/841,660, filed Aug. 20, 2007, which
application is a continuation of U.S. application Ser. No.
11/838,810, filed Aug. 14, 2007, which application claims priority
to U.S. Provisional Application Nos. 60/822,488, filed Aug. 15,
2006, 60/884,397, filed Jan. 10, 2007, and 60/895,962, filed Mar.
20, 2007, the disclosures of which are hereby incorporated by
reference in their entireties for all purposes.
BACKGROUND OF THE INVENTION
[0002] Irritable bowel syndrome (IBS) is the most common of all
gastrointestinal disorders, affecting 10-20% of the general
population and accounting for more than 50% of all patients with
digestive complaints. However, studies suggest that only about 10%
to 50% of those afflicted with IBS actually seek medical attention.
Patients with IBS present with disparate symptoms such as, for
example, abdominal pain predominantly related to defecation,
diarrhea, constipation or alternating diarrhea and constipation,
abdominal distention, gas, and excessive mucus in the stool. More
than 40% of IBS patients have symptoms so severe that they have to
take time off from work, curtail their social life, avoid sexual
intercourse, cancel appointments, stop traveling, take medication,
and even stay confined to their house for fear of embarrassment.
The estimated health care cost of IBS in the United States is $8
billion per year (Talley et al., Gastroenterol., 109:1736-1741
(1995)).
[0003] The precise pathophysiology of IBS is not well understood.
Nevertheless, there is a heightened sensitivity to visceral pain
perception, known as peripheral sensitization. This sensitization
involves a reduction in the threshold and an increase in the gain
of the transduction processes of primary afferent neurons,
attributable to a variety of mediators including monoamines (e.g.,
catecholamines and indoleamines), substance P, and a variety of
cytokines and prostanoids such as E-type prostaglandins (see, e.g.,
Mayer et al., Gastroenterol., 107:271-293 (1994)). Also implicated
in the etiopathology of IBS is intestinal motor dysfunction, which
leads to abnormal handling of intraluminal contents and/or gas
(see, e.g., Kellow et al., Gastroenterol., 92:1885-1893 (1987);
Levitt et al., Ann. Int. Med., 124:422-424 (1996)). Psychological
factors may also contribute to IBS symptoms appearing in
conjunction with, if not triggered by, disturbances including
depression and anxiety (see, e.g., Drossman et al., Gastroenterol.
Int., 8:47-90 (1995)).
[0004] The causes of IBS are not well understood. The walls of the
intestines are lined with layers of muscle that contract and relax
as they move food from the stomach through the intestinal tract to
the rectum. Normally, these muscles contract and relax in a
coordinated rhythm. In IBS patients, these contractions are
typically stronger and last longer than normal. As a result, food
is forced through the intestines more quickly in some cases causing
gas, bloating, and diarrhea. In other cases, the opposite occurs:
food passage slows and stools become hard and dry causing
constipation.
[0005] The precise pathophysiology of IBS remains to be elucidated.
While gut dysmotility and altered visceral perception are
considered important contributors to symptom pathogenesis (Quigley,
Scand. J. Gastroenterol., 38(Suppl. 237):1-8 (2003); Mayer et al.,
Gastroenterol., 122:2032-2048 (2002)), this condition is now
generally viewed as a disorder of the brain-gut axis. Recently,
roles for enteric infection and intestinal inflammation have also
been proposed. Studies have documented the onset of IBS following
bacteriologically confirmed gastroenteritis, while others have
provided evidence of low-grade mucosal inflammation (Spiller et
al., Gut, 47:804-811 (2000); Dunlop et al., Gastroenterol.,
125:1651-1659 (2003); Cumberland et al., Epidemiol. Infect.,
130:453-460 (2003)) and immune activation (Gwee et al., Gut,
52:523-526 (2003); Pimentel et al., Am. J. Gastroenterol.,
95:3503-3506 (2000)) in IBS. The enteric flora has also been
implicated, and a recent study demonstrated the efficacy of the
probiotic organism Bifidobacterium in treating the disorder through
modulation of immune activity (O'Mahony et al., Gastroenterol.,
128:541-551 (2005)).
[0006] The hypothalamic-pituitary-adrenal axis (HPA) is the core
endocrine stress system in humans (De Wied et al., Front.
Neuroendocrinol., 14:251-302 (1993)) and provides an important link
between the brain and the gut immune system. Activation of the axis
takes place in response to both physical and psychological
stressors (Dinan, Br. J. Psychiatry, 164:365-371 (1994)), both of
which have been implicated in the pathophysiology of IBS
(Cumberland et al., Epidemiol. Infect., 130:453-460 (2003)).
Patients with IBS have been reported as having an increased rate of
sexual and physical abuse in childhood together with higher rates
of stressful life events in adulthood (Gaynes et al., Baillieres
Clin. Gastroenterol., 13:437-452 (1999)). Such psychosocial trauma
or poor cognitive coping strategy profoundly affects symptom
severity, daily functioning, and health outcome.
[0007] Although the etiology of IBS is not fully characterized, the
medical community has developed a consensus definition and
criteria, known as the Rome II criteria, to aid in the diagnosis of
IBS based upon patient history. The Rome II criteria requires three
months of continuous or recurrent abdominal pain or discomfort over
a one-year period that is relieved by defecation and/or associated
with a change in stool frequency or consistency as well as two or
more of the following: altered stool frequency, altered stool form,
altered stool passage, passage of mucus, or bloating and abdominal
distention. The absence of any structural or biochemical disorders
that could be causing the symptoms is also a necessary condition.
As a result, the Rome II criteria can be used only when there is a
substantial patient history and is reliable only when there is no
abnormal intestinal anatomy or metabolic process that would
otherwise explain the symptoms. Similarly, the Rome III criteria
recently developed by the medical community can be used only when
there is presentation of a specific set of symptoms, a detailed
patient history, and a physical examination.
[0008] It is well documented that diagnosing a patient as having
IBS can be challenging due to the similarity in symptoms between
IBS and other diseases or disorders. In fact, because the symptoms
of IBS are similar or identical to the symptoms of so many other
intestinal illnesses, it can take years before a correct diagnosis
is made. For example, patients who have inflammatory bowel disease
(IBD), but who exhibit mild signs and symptoms such as bloating,
diarrhea, constipation, and abdominal pain, may be difficult to
distinguish from patients with IBS. As a result, the similarity in
symptoms between IBS and IBD renders rapid and accurate diagnosis
difficult. The difficulty in differentially diagnosing IBS and IBD
hampers early and effective treatment of these diseases.
Unfortunately, rapid and accurate diagnostic methods for
definitively distinguishing IBS from other intestinal diseases or
disorders presenting with similar symptoms are currently not
available. The present invention satisfies this need and provides
related advantages as well.
SUMMARY OF THE INVENTION
[0009] The present invention provides methods, systems, and code
for accurately classifying whether a sample from an individual is
associated with irritable bowel syndrome (IBS). As a non-limiting
example, the present invention is useful for classifying a sample
from an individual as an IBS sample using a statistical algorithm
and/or empirical data. The present invention is also useful for
ruling out one or more diseases or disorders that present with
IBS-like symptoms and ruling in IBS using a combination of
statistical algorithms and/or empirical data. Thus, the present
invention provides an accurate diagnostic prediction of IBS and
prognostic information useful for guiding treatment decisions.
[0010] In one aspect, the present invention provides a method for
classifying whether a sample from an individual is associated with
IBS, the method comprising: [0011] (a) determining a diagnostic
marker profile by detecting the presence or level of at least one
diagnostic marker in the sample; and [0012] (b) classifying the
sample as an IBS sample or non-IBS sample using an algorithm based
upon the diagnostic marker profile.
[0013] In some embodiments, the diagnostic marker profile is
determined by detecting the presence or level of at least one
diagnostic marker selected from the group consisting of a cytokine,
growth factor, anti-neutrophil antibody, anti-Saccharomyces
cerevisiae antibody (ASCA), antimicrobial antibody, lactoferrin,
anti-tissue transglutaminase (tTG) antibody, lipocalin, matrix
metalloproteinase (MMP), tissue inhibitor of metalloproteinase
(TIMP), alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, calcitonin gene-related peptide (CGRP), tachykinin,
ghrelin, neurotensin, corticotropin-releasing hormone, and
combinations thereof.
[0014] In a preferred aspect, the present invention provides a
method for classifying whether a sample from an individual is
associated with IBS, the method comprising: [0015] (a) determining
a diagnostic marker profile by detecting the presence or level of
at least one diagnostic marker selected from the group consisting
of a cytokine, growth factor, anti-neutrophil antibody, ASCA,
antimicrobial antibody, lactoferrin, anti-tTG antibody, lipocalin,
MMP, TIMP, alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereofin the
sample; and [0016] (b) classifying the sample as an IBS sample or
non-IBS sample using an algorithm based upon the diagnostic marker
profile.
[0017] In preferred embodiments, the presence or level of 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, or more of the biomarkers shown in Table 1 is
detected to generate a diagnostic marker profile that is useful for
predicting IBS. In certain instances, the biomarkers described
herein are analyzed using an immunoassay such as an enzyme-linked
immunosorbent assay (ELISA) or an immunohistochemical assay.
TABLE-US-00001 TABLE 1 Exemplary diagnostic markers suitable for
use in IBS classification. Family Biomarker Cytokine CXCL8/IL-8
IL-1.beta. TNF-related weak inducer of apoptosis (TWEAK) Leptin
Osteoprotegerin (OPG) CCL19/MIP-3.beta. CXCL1/GRO1/GRO.alpha.
CXCL4/PF-4 CXCL7/NAP-2 Growth Factor Epidermal growth factor (EGF)
Vascular endothelial growth factor (VEGF) Pigment
epithelium-derived factor (PEDF) Brain-derived neurotrophic factor
(BDNF) Schwannoma-derived growth factor (SDGF)/amphiregulin
Anti-neutrophil Anti-neutrophil cytoplasmic antibody (ANCA)
antibody Perinuclear anti-neutrophil cytoplasmic antibody (pANCA)
ASCA ASCA-IgA ASCA-IgG Antimicrobial antibody Anti-outer membrane
protein C (OmpC) antibody Anti-Cbir-1 flagellin antibody Lipocalin
Neutrophil gelatinase-associated lipocalin (NGAL) MMP MMP-9 TIMP
TIMP-1 Alpha-globulin Alpha-2-macroglobulin (.alpha.2-MG)
Haptoglobin precursor alpha-2 (Hp.alpha.2) Orosomucoid
Actin-severing protein Gelsolin S100 protein Calgranulin
A/S100A8/MRP-8 Fibrinopeptide Fibrinopeptide A (FIBA) Others
Lactoferrin Anti-tissue transglutaminase (tTG) antibody Calcitonin
gene-related peptide (CGRP)
[0018] In other embodiments, the method of ruling in IBS comprises
determining a diagnostic marker profile optionally in combination
with a symptom profile, wherein the symptom profile is determined
by identifying the presence or severity of at least one symptom in
the individual; and classifying the sample as an IBS sample or
non-IBS sample using an algorithm based upon the diagnostic marker
profile and the symptom profile.
[0019] The symptom profile is typically determined by identifying
the presence or severity of at least one symptom selected from the
group consisting of chest pain, chest discomfort, heartburn,
uncomfortable fullness after having a regular-sized meal, inability
to finish a regular-sized meal, abdominal pain, abdominal
discomfort, constipation, diarrhea, bloating, abdominal distension,
negative thoughts or feelings associated with having pain or
discomfort, and combinations thereof.
[0020] In preferred embodiments, the presence or severity of 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, or more of the symptoms described herein
is identified to generate a symptom profile that is useful for
predicting IBS. In certain instances, a questionnaire or other form
of written, verbal, or telephone survey is used to produce the
symptom profile. The questionnaire or survey typically comprises a
standardized set of questions and answers for the purpose of
gathering information from respondents regarding their current
and/or recent IBS-related symptoms.
[0021] In some embodiments, the symptom profile is produced by
compiling and/or analyzing all or a subset of the answers to the
questions set forth in the questionnaire or survey. In other
embodiments, the symptom profile is produced based upon the
individual's response to the following question: "Are you currently
experiencing any symptoms?" The symptom profile generated in
accordance with either of these embodiments can be used in
combination with a diagnostic marker profile in the
algorithmic-based methods described herein to improve the accuracy
of predicting IBS.
[0022] In another aspect, the present invention provides a method
for classifying whether a sample from an individual is associated
with IBS, the method comprising: [0023] (a) determining a
diagnostic marker profile by detecting the presence or level of at
least one diagnostic marker in the sample; [0024] (b) classifying
the sample as an IBD sample or non-IBD sample using a first
statistical algorithm based upon the diagnostic marker profile; and
[0025] if the sample is classified as a non-IBD sample, [0026] (c)
classifying the non-IBD sample as an IBS sample or non-IBS sample
using a second statistical algorithm based upon the same diagnostic
marker profile as determined in step (a) or a different diagnostic
marker profile.
[0027] In some embodiments, the diagnostic marker profile is
determined by detecting the presence or level of at least one
diagnostic marker selected from the group consisting of a cytokine,
growth factor, anti-neutrophil antibody, ASCA, antimicrobial
antibody, lactoferrin, anti-tTG antibody, lipocalin, MMP, TIMP,
alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof.
[0028] In other embodiments, the method of first ruling out IBD and
then ruling in IBS comprises determining a diagnostic marker
profile in combination with a symptom profile, wherein the symptom
profile is determined by identifying the presence or severity of at
least one symptom in the individual; classifying the sample as an
IBD sample or non-IBD sample using a first statistical algorithm
based upon the diagnostic marker profile and the symptom profile;
and if the sample is classified as a non-IBD sample, classifying
the non-IBD sample as an IBS sample or non-IBS sample using a
second statistical algorithm based upon the same profiles as
determined in step (a) or different profiles.
[0029] In yet another aspect, the present invention provides a
method for monitoring the progression or regression of IBS in an
individual, the method comprising: [0030] (a) determining a
diagnostic marker profile by detecting the presence or level of at
least one diagnostic marker in the sample; and [0031] (b)
determining the presence or severity of IBS in the individual using
an algorithm based upon the diagnostic marker profile.
[0032] In some embodiments, the diagnostic marker profile is
determined by detecting the presence or level of at least one
diagnostic marker selected from the group consisting of a cytokine,
growth factor, anti-neutrophil antibody, ASCA, antimicrobial
antibody, lactoferrin, anti-tTG antibody, lipocalin, MMP, TIMP,
alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof.
[0033] In other embodiments, the method of monitoring the
progression or regression of IBS comprises determining a diagnostic
marker profile optionally in combination with a symptom profile,
wherein the symptom profile is determined by identifying the
presence or severity of at least one symptom in the individual; and
determining the presence or severity of IBS in the individual using
an algorithm based upon the diagnostic marker profile and the
symptom profile.
[0034] In a related aspect, the present invention provides a method
for monitoring drug efficacy in an individual receiving a drug
useful for treating IBS, the method comprising: [0035] (a)
determining a diagnostic marker profile by detecting the presence
or level of at least one diagnostic marker in the sample; and
[0036] (b) determining the effectiveness of the drug using an
algorithm based upon the diagnostic marker profile.
[0037] In some embodiments, the diagnostic marker profile is
determined by detecting the presence or level of at least one
diagnostic marker selected from the group consisting of a cytokine,
growth factor, anti-neutrophil antibody, ASCA, antimicrobial
antibody, lactoferrin, anti-tTG antibody, lipocalin, MMP, TIMP,
alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof.
[0038] In other embodiments, the method of monitoring IBS drug
efficacy comprises determining a diagnostic marker profile
optionally in combination with a symptom profile, wherein the
symptom profile is determined by identifying the presence or
severity of at least one symptom in the individual; and determining
the effectiveness of the drug using an algorithm based upon the
diagnostic marker profile and the symptom profile.
[0039] In a further aspect, the present invention provides a
computer-readable medium including code for controlling one or more
processors to classify whether a sample from an individual is
associated with IBS, the code comprising: [0040] instructions to
apply a statistical process to a data set comprising a diagnostic
marker profile to produce a statistically derived decision
classifying the sample as an IBS sample or non-IBS sample based
upon the diagnostic marker profile, [0041] wherein the diagnostic
marker profile indicates the presence or level of at least one
diagnostic marker in the sample.
[0042] In some embodiments, the diagnostic marker profile indicates
the presence or level of at least one diagnostic marker selected
from the group consisting of a cytokine, growth factor,
anti-neutrophil antibody, ASCA, antimicrobial antibody,
lactoferrin, anti-tTG antibody, lipocalin, MMP, TIMP,
alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof.
[0043] In other embodiments, the computer-readable medium for
ruling in IBS comprises instructions to apply a statistical process
to a data set comprising a diagnostic marker profile optionally in
combination with a symptom profile which indicates the presence or
severity of at least one symptom in the individual to produce a
statistically derived decision classifying the sample as an IBS
sample or non-IBS sample based upon the diagnostic marker profile
and the symptom profile.
[0044] In a related aspect, the present invention provides a
computer-readable medium including code for controlling one or more
processors to classify whether a sample from an individual is
associated with IBS, the code comprising: [0045] (a) instructions
to apply a first statistical process to a data set comprising a
diagnostic marker profile to produce a statistically derived
decision classifying the sample as an IBD sample or non-IBD sample
based upon the diagnostic marker profile, wherein the diagnostic
marker profile indicates the presence or level of at least one
diagnostic marker in the sample; and [0046] if the sample is
classified as a non-IBD sample, [0047] (b) instructions to apply a
second statistical process to the same or different data set to
produce a second statistically derived decision classifying the
non-IBD sample as an IBS sample or non-IBS sample.
[0048] In some embodiments, the diagnostic marker profile indicates
the presence or level of at least one diagnostic marker selected
from the group consisting of a cytokine, growth factor,
anti-neutrophil antibody, ASCA, antimicrobial antibody,
lactoferrin, anti-tTG antibody, lipocalin, MMP, TIMP,
alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof.
[0049] In other embodiments, the computer-readable medium for first
ruling out IBD and then ruling in IBS comprises instructions to
apply a first statistical process to a data set comprising a
diagnostic marker profile optionally in combination with a symptom
profile which indicates the presence or severity of at least one
symptom in the individual to produce a statistically derived
decision classifying the sample as an IBD sample or non-IBD sample
based upon the diagnostic marker profile and the symptom profile;
and if the sample is classified as a non-IBD sample, instructions
to apply a second statistical process to the same or different data
set to produce a second statistically derived decision classifying
the non-IBD sample as an IBS sample or non-IBS sample.
[0050] In an additional aspect, the present invention provides a
system for classifying whether a sample from an individual is
associated with IBS, the system comprising: [0051] (a) a data
acquisition module configured to produce a data set comprising a
diagnostic marker profile, wherein the diagnostic marker profile
indicates the presence or level of at least one diagnostic marker
in the sample; [0052] (b) a data processing module configured to
process the data set by applying a statistical process to the data
set to produce a statistically derived decision classifying the
sample as an IBS sample or non-IBS sample based upon the diagnostic
marker profile; and [0053] (c) a display module configured to
display the statistically derived decision.
[0054] In some embodiments, the diagnostic marker profile indicates
the presence or level of at least one diagnostic marker selected
from the group consisting of a cytokine, growth factor,
anti-neutrophil antibody, ASCA, antimicrobial antibody,
lactoferrin, anti-tTG antibody, lipocalin, MMP, TIMP,
alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof.
[0055] In other embodiments, the system for ruling in IBS comprises
a data acquisition module configured to produce a data set
comprising a diagnostic marker profile optionally in combination
with a symptom profile which indicates the presence or severity of
at least one symptom in the individual; a data processing module
configured to process the data set by applying a statistical
process to the data set to produce a statistically derived decision
classifying the sample as an IBS sample or non-IBS sample based
upon the diagnostic marker profile and the symptom profile; and a
display module configured to display the statistically derived
decision.
[0056] In a related aspect, the present invention provides a system
for classifying whether a sample from an individual is associated
with IBS, the system comprising: [0057] (a) a data acquisition
module configured to produce a data set comprising a diagnostic
marker profile, wherein the diagnostic marker profile indicates the
presence or level of at least one diagnostic marker in the sample;
[0058] (b) a data processing module configured to process the data
set by applying a first statistical process to the data set to
produce a first statistically derived decision classifying the
sample as an IBD sample or non-IBD sample based upon the diagnostic
marker profile; [0059] if the sample is classified as a non-IBD
sample, a data processing module configured to apply a second
statistical process to the same or different data set to produce a
second statistically derived decision classifying the non-IBD
sample as an IBS sample or non-IBS sample; and [0060] (c) a display
module configured to display the first and/or the second
statistically derived decision.
[0061] In some embodiments, the diagnostic marker profile indicates
the presence or level of at least one diagnostic marker selected
from the group consisting of a cytokine, growth factor,
anti-neutrophil antibody, ASCA, antimicrobial antibody,
lactoferrin, anti-tTG antibody, lipocalin, MMP, TIMP,
alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof.
[0062] In other embodiments, the system for first ruling out IBD
and then ruling in IBS comprises a data acquisition module
configured to produce a data set comprising a diagnostic marker
profile optionally in combination with a symptom profile which
indicates the presence or severity of at least one symptom in the
individual; a data processing module configured to process the data
set by applying a first statistical process to the data set to
produce a first statistically derived decision classifying the
sample as an IBD sample or non-IBD sample based upon the diagnostic
marker profile and the symptom profile; if the sample is classified
as a non-IBD sample, a data processing module configured to apply a
second statistical process to the same or different data set to
produce a second statistically derived decision classifying the
non-IBD sample as an IBS sample or non-IBS sample; and a display
module configured to display the first and/or the second
statistically derived decision.
[0063] Other objects, features, and advantages of the present
invention will be apparent to one of skill in the art from the
following detailed description and figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0064] FIG. 1 illustrates one embodiment of a molecular pathway
derived from the IBS markers identified and disclosed herein.
[0065] FIG. 2 illustrates a disease classification system (DCS)
according to one embodiment of the present invention.
[0066] FIG. 3 illustrates a quartile analysis of leptin levels in
IBS and non-IBS patient samples.
[0067] FIG. 4, Panel A illustrates the results of an ELISA assay
where leptin levels were measured in IBS-A, IBS-C, and IBS-D
patient samples as well as non-IBS patient samples; Panel B
illustrates gender differences in leptin levels for male IBS
patients compared to female IBS patients.
[0068] FIG. 5 illustrates a quartile analysis of TWEAK levels in
IBS and non-IBS patient samples.
[0069] FIG. 6 illustrates a quartile analysis (FIG. 6A) and
cumulative percent histogram analysis (FIG. 6B) of IL-8 levels in
IBS and non-IBS patient samples. Dot plot distribution with
bars=median.+-.interquartile range displaying 25%, 50%, and 75%
distributions of each patient population.
[0070] FIG. 7 illustrates a second cumulative percent histogram
analysis of IL-8 levels in IBS and non-IBS patient samples.
[0071] FIG. 8 illustrates the results of an ELISA assay where IL-8
levels were measured in IBS-A, IBS-C, and IBS-D patient samples as
well as healthy control patient samples.
[0072] FIG. 9 illustrates a quartile analysis (FIG. 9A) and
cumulative percent histogram analysis (FIG. 9B) of EGF levels in
IBS and non-IBS patient samples. Dot plot distribution with
bars=median.+-.interquartile range displaying 25%, 50%, and 75%
distributions of each patient population.
[0073] FIG. 10 illustrates a quartile analysis of NGAL levels in
IBS and non-IBS patient samples.
[0074] FIG. 11 illustrates a quartile analysis of MMP-9 levels in
IBS and non-IBS patient samples.
[0075] FIG. 12 illustrates a quartile analysis of NGAL/MMP-9
complex levels in IBS and non-IBS patient samples.
[0076] FIG. 13 illustrates a quartile analysis of Substance P
levels in IBS and non-IBS patient samples.
[0077] FIG. 14 illustrates a cumulative percent histogram analysis
using lactoferrin as a non-limiting example.
[0078] FIG. 15 illustrates a flow diagram for a sample model
algorithm used for classifying IBS.
[0079] FIG. 16 illustrates the data set obtained using the model of
FIG. 15.
[0080] FIG. 17 illustrates one embodiment of a neural network.
[0081] FIG. 18 illustrates the distribution of IBS and non-IBS
samples before and after modeling with a random forest algorithm.
0=Non-IBS; 1=IBS.
[0082] FIG. 19 illustrates one embodiment of a classification
tree.
DETAILED DESCRIPTION OF THE INVENTION
I. Introduction
[0083] Diagnosing a patient as having irritable bowel syndrome
(IBS) can be challenging due to the similarity in symptoms between
IBS and other diseases or disorders. For example, patients who have
inflammatory bowel disease (IBD), but who exhibit mild signs and
symptoms such as bloating, diarrhea, constipation, and abdominal
pain can be difficult to distinguish from patients with IBS. As a
result, the similarity in symptoms between IBS and IBD renders
rapid and accurate diagnosis difficult and hampers early and
effective treatment of the disease.
[0084] The present invention is based, in part, upon the surprising
discovery that the accuracy of classifying a biological sample from
an individual as an IBS sample can be substantially improved by
detecting the presence or level of certain diagnostic markers
(e.g., cytokines, growth factors, anti-neutrophil antibodies,
anti-Saccharomyces cerevisiae antibodies, antimicrobial antibodies,
lactoferrin, etc.), alone or in combination with identifying the
presence or severity of IBS-related symptoms based upon the
individual's response to one or more questions (e.g., "Are you
currently experiencing any symptoms?"). FIG. 1 shows a non-limiting
example of a molecular pathway derived from the IBS markers
identified and disclosed herein. In some aspects, the present
invention uses statistical algorithms to aid in the classification
of a sample as an IBS sample or non-IBS sample. In other aspects,
the present invention uses statistical algorithms for ruling out
other intestinal disorders (e.g., IBD), and then classifying the
non-IBD sample to aid in the classification of IBS.
II. Definitions
[0085] As used herein, the following terms have the meanings
ascribed to them unless specified otherwise.
[0086] The term "classifying" includes "to associate" or "to
categorize" a sample with a disease state. In certain instances,
"classifying" is based on statistical evidence, empirical evidence,
or both. In certain embodiments, the methods and systems of
classifying use a so-called training set of samples having known
disease states. Once established, the training data set serves as a
basis, model, or template against which the features of an unknown
sample are compared, in order to classify the unknown disease state
of the sample. In certain instances, classifying the sample is akin
to diagnosing the disease state of the sample. In certain other
instances, classifying the sample is akin to differentiating the
disease state of the sample from another disease state.
[0087] The term "irritable bowel syndrome" or "IBS" includes a
group of functional bowel disorders characterized by one or more
symptoms including, but not limited to, abdominal pain, abdominal
discomfort, change in bowel pattern, loose or more frequent bowel
movements, diarrhea, and constipation, typically in the absence of
any apparent structural abnormality. There are at least three forms
of IBS, depending on which symptom predominates: (1)
diarrhea-predominant (IBS-D); (2) constipation-predominant (IBS-C);
and (3) IBS with alternating stool pattern (IBS-A). IBS can also
occur in the form of a mixture of symptoms (IBS-M). There are also
various clinical subtypes of IBS, such as post-infectious IBS
(IBS-PI).
[0088] The term "sample" includes any biological specimen obtained
from an individual. Suitable samples for use in the present
invention include, without limitation, whole blood, plasma, serum,
saliva, urine, stool, sputum, tears, any other bodily fluid, tissue
samples (e.g., biopsy), and cellular extracts thereof (e.g., red
blood cellular extract). In a preferred embodiment, the sample is a
serum sample. The use of samples such as serum, saliva, and urine
is well known in the art (see, e.g., Hashida et al., J. Clin. Lab.
Anal., 11:267-86 (1997)). One skilled in the art will appreciate
that samples such as serum samples can be diluted prior to the
analysis of marker levels.
[0089] The term "biomarker" or "marker" includes any diagnostic
marker such as a biochemical marker, serological marker, genetic
marker, or other clinical or echographic characteristic that can be
used to classify a sample from an individual as an IBS sample or to
rule out one or more diseases or disorders associated with IBS-like
symptoms in a sample from an individual. The term "biomarker" or
"marker" also encompasses any classification marker such as a
biochemical marker, serological marker, genetic marker, or other
clinical or echographic characteristic that can be used to classify
IBS into one of its various forms or clinical subtypes.
Non-limiting examples of diagnostic markers suitable for use in the
present invention are described below and include cytokines, growth
factors, anti-neutrophil antibodies, anti-Saccharomyces cerevisiae
antibodies, antimicrobial antibodies, anti-tissue transglutaminase
(tTG) antibodies, lipocalins, matrix metalloproteinases (MMPs),
tissue inhibitor of metalloproteinases (TIMPs), alpha-globulins,
actin-severing proteins, 5100 proteins, fibrinopeptides, calcitonin
gene-related peptide (CGRP), tachykinins, ghrelin, neurotensin,
corticotropin-releasing hormone (CRH), elastase, C-reactive protein
(CRP), lactoferrin, anti-lactoferrin antibodies, calprotectin,
hemoglobin, NOD2/CARD15, serotonin reuptake transporter (SERT),
tryptophan hydroxylase-1,5-hydroxytryptamine (5-HT), lactulose, and
the like. Examples of classification markers include, without
limitation, leptin, SERT, tryptophan hydroxylase-1,5-HT, antrum
mucosal protein 8, keratin-8, claudin-8, zonulin, corticotropin
releasing hormone receptor-1 (CRHR1), corticotropin releasing
hormone receptor-2 (CRHR2), and the like. In some embodiments,
diagnostic markers can be used to classify IBS into one of its
various forms or clinical subtypes. In other embodiments,
classification markers can be used to classify a sample as an IBS
sample or to rule out one or more diseases or disorders associated
with IBS-like symptoms. One skilled in the art will know of
additional diagnostic and classification markers suitable for use
in the present invention.
[0090] As used herein, the term "profile" includes any set of data
that represents the distinctive features or characteristics
associated with a disease or disorder such as IBS or IBD. The term
encompasses a "diagnostic marker profile" that analyzes one or more
diagnostic markers in a sample, a "symptom profile" that identifies
one or more IBS-related clinical factors (i.e., symptoms) an
individual is experiencing or has experienced, and combinations
thereof. For example, a "diagnostic marker profile" can include a
set of data that represents the presence or level of one or more
diagnostic markers associated with IBS and/or IBD. Likewise, a
"symptom profile" can include a set of data that represents the
presence, severity, frequency, and/or duration of one or more
symptoms associated with IBS and/or IBD.
[0091] The term "individual," "subject," or "patient" typically
refers to humans, but also to other animals including, e.g., other
primates, rodents, canines, felines, equines, ovines, porcines, and
the like.
[0092] As used herein, the term "substantially the same amino acid
sequence" includes an amino acid sequence that is similar, but not
identical to, the naturally-occurring amino acid sequence. For
example, an amino acid sequence that has substantially the same
amino acid sequence as a naturally-occurring peptide, polypeptide,
or protein can have one or more modifications such as amino acid
additions, deletions, or substitutions relative to the amino acid
sequence of the naturally-occurring peptide, polypeptide, or
protein, provided that the modified sequence retains substantially
at least one biological activity of the naturally-occurring
peptide, polypeptide, or protein such as immunoreactivity.
Comparison for substantial similarity between amino acid sequences
is usually performed with sequences between about 6 and 100
residues, preferably between about 10 and 100 residues, and more
preferably between about 25 and 35 residues. A particularly useful
modification of a peptide, polypeptide, or protein of the present
invention, or a fragment thereof, is a modification that confers,
for example, increased stability. Incorporation of one or more
D-amino acids is a modification useful in increasing stability of a
polypeptide or polypeptide fragment. Similarly, deletion or
substitution of lysine residues can increase stability by
protecting the polypeptide or polypeptide fragment against
degradation.
[0093] The term "monitoring the progression or regression of IBS"
includes the use of the methods, systems, and code of the present
invention to determine the disease state (e.g., presence or
severity of IBS) of an individual. In certain instances, the
results of an algorithm (e.g., a learning statistical classifier
system) are compared to those results obtained for the same
individual at an earlier time. In some embodiments, the methods,
systems, and code of the present invention can be used to predict
the progression of IBS, e.g., by determining a likelihood for IBS
to progress either rapidly or slowly in an individual based on an
analysis of diagnostic markers and/or the identification or
IBS-related symptoms. In other embodiments, the methods, systems,
and code of the present invention can be used to predict the
regression of IBS, e.g., by determining a likelihood for IBS to
regress either rapidly or slowly in an individual based on an
analysis of diagnostic markers and/or the identification or
IBS-related symptoms.
[0094] The term "monitoring drug efficacy in an individual
receiving a drug useful for treating IBS" includes the use of the
methods, systems, and code of the present invention to determine
the effectiveness of a therapeutic agent for treating IBS after it
has been administered. In certain instances, the results of an
algorithm (e.g., a learning statistical classifier system) are
compared to those results obtained for the same individual before
initiation of use of the therapeutic agent or at an earlier time in
therapy. As used herein, a drug useful for treating IBS is any
compound or drug used to improve the health of the individual and
includes, without limitation, IBS drugs such as serotonergic
agents, antidepressants, chloride channel activators, chloride
channel blockers, guanylate cyclase agonists, antibiotics, opioids,
neurokinin antagonists, antispasmodic or anticholinergic agents,
belladonna alkaloids, barbiturates, glucagon-like peptide-1 (GLP-1)
analogs, corticotropin releasing factor (CRF) antagonists,
probiotics, free bases thereof, pharmaceutically acceptable salts
thereof, derivatives thereof, analogs thereof, and combinations
thereof.
[0095] The term "therapeutically effective amount or dose" includes
a dose of a drug that is capable of achieving a therapeutic effect
in a subject in need thereof. For example, a therapeutically
effective amount of a drug useful for treating IBS can be the
amount that is capable of preventing or relieving one or more
symptoms associated with IBS. The exact amount can be ascertainable
by one skilled in the art using known techniques (see, e.g.,
Lieberman, Pharmaceutical Dosage Forms, Vols. 1-3 (1992); Lloyd,
The Art, Science and Technology of Pharmaceutical Compounding
(1999); Pickar, Dosage Calculations (1999); and Remington: The
Science and Practice of Pharmacy, 20th Edition, Gennaro, Ed.,
Lippincott, Williams & Wilkins (2003)).
III. Description of the Embodiments
[0096] The present invention provides methods, systems, and code
for accurately classifying whether a sample from an individual is
associated with irritable bowel syndrome (IBS). In some
embodiments, the present invention is useful for classifying a
sample from an individual as an IBS sample using a statistical
algorithm (e.g., a learning statistical classifier system) and/or
empirical data (e.g., the presence or level of an IBS marker). The
present invention is also useful for ruling out one or more
diseases or disorders that present with IBS-like symptoms and
ruling in IBS using a combination of statistical algorithms and/or
empirical data. Accordingly, the present invention provides an
accurate diagnostic prediction of IBS and prognostic information
useful for guiding treatment decisions.
[0097] In one aspect, the present invention provides a method for
classifying whether a sample from an individual is associated with
IBS, the method comprising: [0098] (a) determining a diagnostic
marker profile by detecting the presence or level of at least one
diagnostic marker in the sample; and [0099] (b) classifying the
sample as an IBS sample or non-IBS sample using an algorithm based
upon the diagnostic marker profile.
[0100] In some embodiments, the diagnostic marker profile is
determined by detecting the presence or level of at least one
diagnostic marker selected from the group consisting of a cytokine,
growth factor, anti-neutrophil antibody, anti-Saccharomyces
cerevisiae antibody (ASCA), antimicrobial antibody, lactoferrin,
anti-tissue transglutaminase (tTG) antibody, lipocalin, matrix
metalloproteinase (MMP), tissue inhibitor of metalloproteinase
(TIMP), alpha-globulin, actin-severing protein, S100 protein,
fibrinopeptide, calcitonin gene-related peptide (CGRP), tachykinin,
ghrelin, neurotensin, corticotropin-releasing hormone, and
combinations thereof.
[0101] In other embodiments, the presence or level of at least two,
three, four, five, six, seven, eight, nine, ten, or more diagnostic
markers are determined in the individual's sample. In certain
instances, the cytokine comprises one or more of the cytokines
described below. Preferably, the presence or level of IL-8,
IL-1.beta., TNF-related weak inducer of apoptosis (TWEAK), leptin,
osteoprotegerin (OPG), MIP-3.beta., GRO.alpha., CXCL4/PF-4, and/or
CXCL7/NAP-2 is determined in the individual's sample. In certain
other instances, the growth factor comprises one or more of the
growth factors described below. Preferably, the presence or level
of epidermal growth factor (EGF), vascular endothelial growth
factor (VEGF), pigment epithelium-derived factor (PEDF),
brain-derived neurotrophic factor (BDNF), and/or amphiregulin
(SDGF) is determined in the individual's sample.
[0102] In some instances, the anti-neutrophil antibody comprises
ANCA, pANCA, cANCA, NSNA, SAPPA, and combinations thereof. In other
instances, the ASCA comprises ASCA-IgA, ASCA-IgG, ASCA-IgM, and
combinations thereof. In further instances, the antimicrobial
antibody comprises an anti-OmpC antibody, anti-flagellin antibody,
anti-I2 antibody, and combinations thereof.
[0103] In certain instances, the lipocalin comprises one or more of
the lipocalins described below. Preferably, the presence or level
of neutrophil gelatinase-associated lipocalin (NGAL) and/or a
complex of NGAL and a matrix metalloproteinase (e.g., NGAL/MMP-9
complex) is determined in the individual's sample. In other
instances, the matrix metalloproteinase (MMP) comprises one or more
of the MMPs described below. Preferably, the presence or level of
MMP-9 is determined in the individual's sample. In further
instances, the tissue inhibitor of metalloproteinase (TIMP)
comprises one or more of the TIMPs described below. Preferably, the
presence or level of TIMP-1 is determined in the individual's
sample. In yet further instances, the alpha-globulin comprises one
or more of the alpha-globulins described below. Preferably, the
presence or level of alpha-2-macroglobulin, haptoglobin, and/or
orosomucoid is determined in the individual's sample.
[0104] In certain other instances, the actin-severing protein
comprises one or more of the actin-severing protein described
below. Preferably, the presence or level of gelsolin is determined
in the individual's sample. In additional instances, the S100
protein comprises one or more of the S100 proteins described below
including, for example, calgranulin. In yet other instances, the
fibrinopeptide comprises one or more of the fibrinopeptides
described below. Preferably, the presence or level of
fibrinopeptide A (FIBA) is determined in the individual's sample.
In further instances, the presence or level of a tachykinin such as
Substance P, neurokinin A, and/or neurokinin B is determined in the
individual's sample. The presence or level of other diagnostic
markers such as, for example, anti-lactoferrin antibody,
L-selectin/CD62L, elastase, C-reactive protein (CRP), calprotectin,
anti-U1-70 kDa autoantibody, zona occludens 1 (ZO-1), vasoactive
intestinal peptide (VIP), serum amyloid A, and/or gastrin can also
be determined.
[0105] The sample used for detecting or determining the presence or
level of at least one diagnostic marker is typically whole blood,
plasma, serum, saliva, urine, stool (i.e., feces), tears, and any
other bodily fluid, or a tissue sample (i.e., biopsy) such as a
small intestine or colon sample. Preferably, the sample is serum,
whole blood, plasma, stool, urine, or a tissue biopsy. In certain
instances, the methods of the present invention further comprise
obtaining the sample from the individual prior to detecting or
determining the presence or level of at least one diagnostic marker
in the sample.
[0106] In some embodiments, a panel for measuring one or more of
the diagnostic markers described above may be constructed and used
for classifying the sample as an IBS sample or non-IBS sample. One
skilled in the art will appreciate that the presence or level of a
plurality of diagnostic markers can be determined simultaneously or
sequentially, using, for example, an aliquot or dilution of the
individual's sample. In certain instances, the level of a
particular diagnostic marker in the individual's sample is
considered to be elevated when it is at least about 25%, 50%, 75%,
100%, 125%, 150%, 175%, 200%, 250%, 300%, 350%, 400%, 450%, 500%,
600%, 700%, 800%, 900%, or 1000% greater than the level of the same
marker in a comparative sample (e.g., a normal, GI control, IBD,
and/or Celiac disease sample) or population of samples (e.g.,
greater than a median level of the same marker in a comparative
population of normal, GI control, IBD, and/or Celiac disease
samples). In certain other instances, the level of a particular
diagnostic marker in the individual's sample is considered to be
lowered when it is at least about 5%,10%, 15%, 20%, 25%, 30%, 35%,
40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% less
than the level of the same marker in a comparative sample (e.g., a
normal, GI control, IBD, and/or Celiac disease sample) or
population of samples (e.g., less than a median level of the same
marker in a comparative population of normal, GI control, IBD,
and/or Celiac disease samples).
[0107] In certain embodiments, the presence or level of at least
one diagnostic marker is determined using an assay such as a
hybridization assay or an amplification-based assay. Examples of
hybridization assays suitable for use in the methods of the present
invention include, but are not limited to, Northern blotting, dot
blotting, RNase protection, and a combination thereof. A
non-limiting example of an amplification-based assay suitable for
use in the methods of the present invention includes a reverse
transcriptase-polymerase chain reaction (RT-PCR).
[0108] In certain other embodiments, the presence or level of at
least one diagnostic marker is determined using an immunoassay or
an immunohistochemical assay. A non-limiting example of an
immunoassay suitable for use in the methods of the present
invention includes an enzyme-linked immunosorbent assay (ELISA).
Examples of immunohistochemical assays suitable for use in the
methods of the present invention include, but are not limited to,
immunofluorescence assays such as direct fluorescent antibody
assays, indirect fluorescent antibody (IFA) assays, anticomplement
immunofluorescence assays, and avidin-biotin immunofluorescence
assays. Other types of immunohistochemical assays include
immunoperoxidase assays.
[0109] In some embodiments, the method of ruling in IBS comprises
determining a diagnostic marker profile optionally in combination
with a symptom profile, wherein the symptom profile is determined
by identifying the presence or severity of at least one symptom in
the individual; and classifying the sample as an IBS sample or
non-IBS sample using an algorithm based upon the diagnostic marker
profile and the symptom profile. One skilled in the art will
appreciate that the diagnostic marker profile and the symptom
profile can be determined simultaneously or sequentially in any
order.
[0110] The symptom profile is typically determined by identifying
the presence or severity of at least one symptom selected from the
group consisting of chest pain, chest discomfort, heartburn,
uncomfortable fullness after having a regular-sized meal, inability
to finish a regular-sized meal, abdominal pain, abdominal
discomfort, constipation, diarrhea, bloating, abdominal distension,
negative thoughts or feelings associated with having pain or
discomfort, and combinations thereof.
[0111] In preferred embodiments, the presence or severity of 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, or more of the symptoms described herein
is identified to generate a symptom profile that is useful for
predicting IBS. In certain instances, a questionnaire or other form
of written, verbal, or telephone survey is used to produce the
symptom profile. The questionnaire or survey typically comprises a
standardized set of questions and answers for the purpose of
gathering information from respondents regarding their current
and/or recent IBS-related symptoms. For instance, Example 13
provides exemplary questions that can be included in a
questionnaire for identifying the presence or severity of one or
more IBS-related symptoms in the individual.
[0112] In certain embodiments, the symptom profile is produced by
compiling and/or analyzing all or a subset of the answers to the
questions set forth in the questionnaire or survey. In certain
other embodiments, the symptom profile is produced based upon the
individual's response to the following question: "Are you currently
experiencing any symptoms?" The symptom profile generated in
accordance with either of these embodiments can be used in
combination with a diagnostic marker profile in the
algorithmic-based methods described herein to improve the accuracy
of predicting IBS.
[0113] In some embodiments, classifying a sample as an IBS sample
or non-IBS sample is based upon the diagnostic marker profile,
alone or in combination with a symptom profile, in conjunction with
a statistical algorithm. In certain instances, the statistical
algorithm is a learning statistical classifier system. The learning
statistical classifier system can be selected from the group
consisting of a random forest (RF), classification and regression
tree (C&RT), boosted tree, neural network (NN), support vector
machine (SVM), general chi-squared automatic interaction detector
model, interactive tree, multiadaptive regression spline, machine
learning classifier, and combinations thereof. Preferably, the
learning statistical classifier system is a tree-based statistical
algorithm (e.g., RF, C&RT, etc.) and/or a NN (e.g., artificial
NN, etc.). Additional examples of learning statistical classifier
systems suitable for use in the present invention are described in
U.S. patent application Ser. No. 11/368,285.
[0114] In certain instances, the statistical algorithm is a single
learning statistical classifier system. Preferably, the single
learning statistical classifier system comprises a tree-based
statistical algorithm such as a RF or C&RT. As a non-limiting
example, a single learning statistical classifier system can be
used to classify the sample as an IBS sample or non-IBS sample
based upon a prediction or probability value and the presence or
level of at least one diagnostic marker (i.e., diagnostic marker
profile), alone or in combination with the presence or severity of
at least one symptom (i.e., symptom profile). The use of a single
learning statistical classifier system typically classifies the
sample as an IBS sample with a sensitivity, specificity, positive
predictive value, negative predictive value, and/or overall
accuracy of at least about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%,
83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%,
96%, 97%, 98%, or 99%.
[0115] In certain other instances, the statistical algorithm is a
combination of at least two learning statistical classifier
systems. Preferably, the combination of learning statistical
classifier systems comprises a RF and a NN, e.g., used in tandem or
parallel. As a non-limiting example, a RF can first be used to
generate a prediction or probability value based upon the
diagnostic marker profile, alone or in combination with a symptom
profile, and a NN can then be used to classify the sample as an IBS
sample or non-IBS sample based upon the prediction or probability
value and the same or different diagnostic marker profile or
combination of profiles. Advantageously, the hybrid RF/NN learning
statistical classifier system of the present invention classifies
the sample as an IBS sample with a sensitivity, specificity,
positive predictive value, negative predictive value, and/or
overall accuracy of at least about 75%, 76%, 77%, 78%, 79%, 80%,
81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%,
94%, 95%, 96%, 97%, 98%, or 99%.
[0116] In some instances, the data obtained from using the learning
statistical classifier system or systems can be processed using a
processing algorithm. Such a processing algorithm can be selected,
for example, from the group consisting of a multilayer perceptron,
backpropagation network, and Levenberg-Marquardt algorithm. In
other instances, a combination of such processing algorithms can be
used, such as in a parallel or serial fashion.
[0117] In certain embodiments, the methods of the present invention
further comprise classifying the non-IBS sample as a normal,
inflammatory bowel disease (IBD), or non-IBD sample. Classification
of the non-IBS sample can be performed, for example, using at least
one of the diagnostic markers described above.
[0118] In certain other embodiments, the methods of the present
invention further comprise sending the IBS classification results
to a clinician, e.g., a gastroenterologist or a general
practitioner. In another embodiment, the methods of the present
invention provide a diagnosis in the form of a probability that the
individual has IBS. For example, the individual can have about a
0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%,
70%, 75%, 80%, 85%, 90%, 95%, or greater probability of having IBS.
In yet another embodiment, the methods of the present invention
further provide a prognosis of IBS in the individual. For example,
the prognosis can be surgery, development of a category or clinical
subtype of IBS, development of one or more symptoms, or recovery
from the disease.
[0119] In some embodiments, the diagnosis of an individual as
having IBS is followed by administering to the individual a
therapeutically effective amount of a drug useful for treating one
or more symptoms associated with IBS. Suitable IBS drugs include,
but are not limited to, serotonergic agents, antidepressants,
chloride channel activators, chloride channel blockers, guanylate
cyclase agonists, antibiotics, opioid agonists, neurokinin
antagonists, antispasmodic or anticholinergic agents, belladonna
alkaloids, barbiturates, GLP-1 analogs, CRF antagonists,
probiotics, free bases thereof, pharmaceutically acceptable salts
thereof, derivatives thereof, analogs thereof, and combinations
thereof. Other IBS drugs include bulking agents, dopamine
antagonists, carminatives, tranquilizers, dextofisopam, phenyloin,
timolol, and diltiazem. Additionally, amino acids like glutamine
and glutamic acid which regulate intestinal permeability by
affecting neuronal or glial cell signaling can be administered to
treat patients with IBS.
[0120] In other embodiments, the methods of the present invention
further comprise classifying the IBS sample as an IBS-constipation
(IBS-C), IBS-diarrhea (IBS-D), IBS-mixed (IBS-M), IBS-alternating
(IBS-A), or post-infectious IBS (IBS-PI) sample. In certain
instances, the classification of the IBS sample into a category,
form, or clinical subtype of IBS is based upon the presence or
level of at least one, two, three, four, five, six, seven, eight,
nine, ten, or more classification markers. Non-limiting examples of
classification markers are described below. Preferably, at least
one form of IBS is distinguished from at least one other form of
IBS based upon the presence or level of leptin. In certain
instances, the methods of the present invention can be used to
differentiate an IBS-C sample from an IBS-A and/or IBS-D sample in
an individual previously identified as having IBS. In certain other
instances, the methods of the present invention can be used to
classify a sample from an individual not previously diagnosed with
IBS as an IBS-A sample, IBS-C sample, IBS-D sample, or non-IBS
sample.
[0121] In certain embodiments, the methods further comprise sending
the results from the classification to a clinician. In certain
other embodiments, the methods further provide a diagnosis in the
form of a probability that the individual has IBS-A, IBS-C, IBS-D,
IBS-M, or IBS-PI. The methods of the present invention can further
comprise administering to the individual a therapeutically
effective amount of a drug useful for treating IBS-A, IBS-C, IBS-D,
IBS-M, or IBS-PI. Suitable drugs include, but are not limited to,
tegaserod (Zelnorm.TM.), alosetron (Lotronex.RTM.), lubiprostone
(Amitiza.TM.), rifamixin (Xifaxan.TM.), MD-1100, probiotics, and a
combination thereof. In instances where the sample is classified as
an IBS-A or IBS-C sample and/or the individual is diagnosed with
IBS-A or IBS-C, a therapeutically effective dose of tegaserod or
other 5-HT.sub.4 agonist (e.g., mosapride, renzapride, AG1-001,
etc.) can be administered to the individual. In some instances,
when the sample is classified as IBS-C and/or the individual is
diagnosed with IBS-C, a therapeutically effective amount of
lubiprostone or other chloride channel activator, rifamixin or
other antibiotic capable of controlling intestinal bacterial
overgrowth, MD-1100 or other guanylate cyclase agonist, asimadoline
or other opioid agonist, or talnetant or other neurokinin
antagonist can be administered to the individual. In other
instances, when the sample is classified as IBS-D and/or the
individual is diagnosed with IBS-D, a therapeutically effective
amount of alosetron or other 5-HT.sub.3 antagonist (e.g.,
ramosetron, DDP-225, etc.), crofelemer or other chloride channel
blocker, talnetant or other neurokinin antagonist (e.g.,
saredutant, etc.), or an antidepressant such as a tricyclic
antidepressant can be administered to the individual.
[0122] In additional embodiments, the methods of the present
invention further comprise ruling out intestinal inflammation.
Non-limiting examples of intestinal inflammation include acute
inflammation, diverticulitis, ileal pouch-anal anastomosis,
microscopic colitis, infectious diarrhea, and combinations thereof.
In some instances, the intestinal inflammation is ruled out based
upon the presence or level of C-reactive protein (CRP),
lactoferrin, calprotectin, or combinations thereof.
[0123] In another aspect, the present invention provides a method
for classifying whether a sample from an individual is associated
with IBS, the method comprising: [0124] (a) determining a
diagnostic marker profile by detecting the presence or level of at
least one diagnostic marker in the sample; [0125] (b) classifying
the sample as an IBD sample or non-IBD sample using a first
statistical algorithm based upon the diagnostic marker profile; and
[0126] if the sample is classified as a non-IBD sample, [0127] (c)
classifying the non-IBD sample as an IBS sample or non-IBS sample
using a second statistical algorithm based upon the same diagnostic
marker profile as determined in step (a) or a different diagnostic
marker profile.
[0128] In some embodiments, the diagnostic marker profile is
determined by detecting the presence or level of at least one, two,
three, four, five, six, seven, eight, nine, ten, or more diagnostic
markers selected from the group consisting of a cytokine (e.g.,
IL-8, IL-113, TWEAK, leptin, OPG, MIP-3.beta., GRO.alpha.,
CXCL4/PF-4, and/or CXCL7/NAP-2), growth factor (e.g., EGF, VEGF,
PEDF, BDNF, and/or SDGF), anti-neutrophil antibody (e.g., ANCA,
pANCA, cANCA, NSNA, and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG,
and/or ASCA-IgM), antimicrobial antibody (e.g., anti-OmpC antibody,
anti-flagellin antibody, and/or anti-I2 antibody), lactoferrin,
anti-tTG antibody, lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP
(e.g., MMP-9), TIMP (e.g., TIMP-1), alpha-globulin (e.g.,
alpha-2-macroglobulin, haptoglobin, and/or orosomucoid),
actin-severing protein (e.g., gelsolin), S100 protein (e.g.,
calgranulin), fibrinopeptide (e.g., FIBA), CGRP, tachykinin (e.g.,
Substance P), ghrelin, neurotensin, corticotropin-releasing
hormone, and combinations thereof. The presence or level of other
diagnostic markers such as, for example, anti-lactoferrin antibody,
L-selectin/CD62L, elastase, C-reactive protein (CRP), calprotectin,
anti-U1-70 kDa autoantibody, zona occludens 1 (ZO-1), vasoactive
intestinal peptide (VIP), serum amyloid A, and/or gastrin can also
be determined.
[0129] The diagnostic markers used for ruling out IBD can be the
same as the diagnostic markers used for ruling in IBS.
Alternatively, the diagnostic markers used for ruling out IBD can
be different than the diagnostic markers used for ruling in
IBS.
[0130] The sample used for detecting or determining the presence or
level of at least one diagnostic marker is typically whole blood,
plasma, serum, saliva, urine, stool (i.e., feces), tears, and any
other bodily fluid, or a tissue sample (i.e., biopsy) such as a
small intestine or colon sample. Preferably, the sample is serum,
whole blood, plasma, stool, urine, or a tissue biopsy. In certain
instances, the methods of the present invention further comprise
obtaining the sample from the individual prior to detecting or
determining the presence or level of at least one diagnostic marker
in the sample.
[0131] In some embodiments, a panel for measuring one or more of
the diagnostic markers described above may be constructed and used
for ruling out IBD and/or ruling in IBS. One skilled in the art
will appreciate that the presence or level of a plurality of
diagnostic markers can be determined simultaneously or
sequentially, using, for example, an aliquot or dilution of the
individual's sample. As described above, the level of a particular
diagnostic marker in the individual's sample is generally
considered to be elevated when it is at least about 25%, 50%, 75%,
100%, 125%, 150%, 175%, 200%, 250%, 300%, 350%, 400%, 450%, 500%,
600%, 700%, 800%, 900%, or 1000% greater than the level of the same
marker in a comparative sample or population of samples (e.g.,
greater than a median level). Similarly, the level of a particular
diagnostic marker in the individual's sample is typically
considered to be lowered when it is at least about 5%,10%, 15%,
20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%,
85%, 90%, or 95% less than the level of the same marker in a
comparative sample or population of samples (e.g., less than a
median level).
[0132] In certain instances, the presence or level of at least one
diagnostic marker is determined using an assay such as a
hybridization assay or an amplification-based assay. Examples of
hybridization assays and amplification-based assays suitable for
use in the methods of the present invention are described above. In
certain other instances, the presence or level of at least one
diagnostic marker is determined using an immunoassay or an
immunohistochemical assay. Non-limiting examples of immunoassays
and immunohistochemical assays suitable for use in the methods of
the present invention are described above.
[0133] In some embodiments, the method of first ruling out IBD
(i.e., classifying the sample as an IBD sample or non-IBD sample)
and then ruling in IBS (i.e., classifying the non-IBD sample as an
IBS sample or non-IBS sample) comprises determining a diagnostic
marker profile optionally in combination with a symptom profile,
wherein the symptom profile is determined by identifying the
presence or severity of at least one symptom in the individual;
classifying the sample as an IBD sample or non-IBD sample using a
first statistical algorithm based upon the diagnostic marker
profile and the symptom profile; and if the sample is classified as
a non-IBD sample, classifying the non-IBD sample as an IBS sample
or non-IBS sample using a second statistical algorithm based upon
the same profiles as determined in step (a) or different profiles.
One skilled in the art will appreciate that the diagnostic marker
profile and the symptom profile can be determined simultaneously or
sequentially in any order.
[0134] In other embodiments, the first statistical algorithm is a
learning statistical classifier system selected from the group
consisting of a random forest (RF), classification and regression
tree (C&RT), boosted tree, neural network (NN), support vector
machine (SVM), general chi-squared automatic interaction detector
model, interactive tree, multiadaptive regression spline, machine
learning classifier, and combinations thereof. In certain
instances, the first statistical algorithm is a single learning
statistical classifier system. Preferably, the single learning
statistical classifier system comprises a tree-based statistical
algorithm such as a RF or C&RT. In certain other instances, the
first statistical algorithm is a combination of at least two
learning statistical classifier systems, e.g., used in tandem or
parallel. As a non-limiting example, a RF can first be used to
generate a prediction or probability value based upon the
diagnostic marker profile, alone or in combination with a symptom
profile, and a NN (e.g., artificial NN) can then be used to
classify the sample as a non-IBD sample or IBD sample based upon
the prediction or probability value and the same or different
diagnostic marker profile or combination of profiles. The hybrid
RF/NN learning statistical classifier system of the present
invention typically classifies the sample as a non-IBD sample with
a sensitivity, specificity, positive predictive value, negative
predictive value, and/or overall accuracy of at least about 75%,
76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,
89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0135] In yet other embodiments, the second statistical algorithm
comprises any of the learning statistical classifier systems
described above. In certain instances, the second statistical
algorithm is a single learning statistical classifier system such
as, for example, a tree-based statistical algorithm (e.g., RF or
C&RT). In certain other instances, the second statistical
algorithm is a combination of at least two learning statistical
classifier systems, e.g., used in tandem or parallel. As a
non-limiting example, a RF can first be used to generate a
prediction or probability value based upon the diagnostic marker
profile, alone or in combination with a symptom profile, and a NN
(e.g., artificial NN) or SVM can then be used to classify the
non-IBD sample as a non-IBS sample or IBS sample based upon the
prediction or probability value and the same or different
diagnostic marker profile or combination of profiles. The hybrid
RF/NN or RF/SVM learning statistical classifier system described
herein typically classifies the sample as an IBS sample with a
sensitivity, specificity, positive predictive value, negative
predictive value, and/or overall accuracy of at least about 75%,
76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,
89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0136] In some instances, the data obtained from using the learning
statistical classifier system or systems can be processed using a
processing algorithm. Such a processing algorithm can be selected,
for example, from the group consisting of a multilayer perceptron,
backpropagation network, and Levenberg-Marquardt algorithm. In
other instances, a combination of such processing algorithms can be
used, such as in a parallel or serial fashion.
[0137] As described above, the methods of the present invention can
further comprise sending the IBS classification results to a
clinician, e.g., a gastroenterologist or a general practitioner.
The methods can also provide a diagnosis in the form of a
probability that the individual has IBS. For example, the
individual can have about a 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%,
40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or
greater probability of having IBS. In some instances, the methods
of the present invention further provide a prognosis of IBS in the
individual. For example, the prognosis can be surgery, development
of a category or clinical subtype of IBS, development of one or
more symptoms, or recovery from the disease.
[0138] In some embodiments, the diagnosis of an individual as
having IBS is followed by administering to the individual a
therapeutically effective amount of a drug useful for treating one
or more symptoms associated with IBS. Suitable IBS drugs are
described above.
[0139] In other embodiments, the methods of the present invention
further comprise classifying the IBS sample as an IBS-A, IBS-C,
IBS-D, IBS-M, or IBS-PI sample. In certain instances, the
classification of the IBS sample into a category, form, or clinical
subtype of IBS is based upon the presence or level of at least one
classification marker. Non-limiting examples of classification
markers are described below. Preferably, at least one form of IBS
is distinguished from at least one other form of IBS based upon the
presence or level of leptin. The results from the classification
can be sent to a clinician. In some instances, the methods can
further provide a diagnosis in the form of a probability that the
individual has IBS-A, IBS-C, IBS-D, IBS-M, or IBS-PI. In other
instances, the methods can further comprise administering to the
individual a therapeutically effective amount of a drug useful for
treating IBS-A, IBS-C, IBS-D, IBS-M, or IBS-PI such as, for
example, tegaserod (Zelnorm.TM.), alosetron (Lotronex.RTM.),
lubiprostone (Amitiza.TM.), rifamixin (Xifaxan.TM.), MD-1100,
probiotics, and combinations thereof.
[0140] In additional embodiments, the methods of the present
invention further comprise ruling out intestinal inflammation.
Non-limiting examples of intestinal inflammation are described
above. In certain instances, the intestinal inflammation is ruled
out based upon the presence or level of CRP, lactoferrin, and/or
calprotectin.
[0141] In yet another aspect, the present invention provides a
method for monitoring the progression or regression of IBS in an
individual, the method comprising: [0142] (a) determining a
diagnostic marker profile by detecting the presence or level of at
least one diagnostic marker in the sample; and [0143] (b)
determining the presence or severity of IBS in the individual using
an algorithm based upon the diagnostic marker profile.
[0144] In a related aspect, the present invention provides a method
for monitoring drug efficacy in an individual receiving a drug
useful for treating IBS, the method comprising: [0145] (a)
determining a diagnostic marker profile by detecting the presence
or level of at least one diagnostic marker in the sample; and
[0146] (b) determining the effectiveness of the drug using an
algorithm based upon the diagnostic marker profile.
[0147] In some embodiments, the diagnostic marker profile is
determined by detecting the presence or level of at least one, two,
three, four, five, six, seven, eight, nine, ten, or more diagnostic
markers selected from the group consisting of a cytokine (e.g.,
IL-8, IL-1.beta., TWEAK, leptin, OPG, MIP-3.beta., GRO.alpha.,
CXCL4/PF-4, and/or CXCL7/NAP-2), growth factor (e.g., EGF, VEGF,
PEDF, BDNF, and/or SDGF), anti-neutrophil antibody (e.g., ANCA,
pANCA, cANCA, NSNA, and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG,
and/or ASCA-IgM), antimicrobial antibody (e.g., anti-OmpC antibody,
anti-flagellin antibody, and/or anti-I2 antibody), lactoferrin,
anti-tTG antibody, lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP
(e.g., MMP-9), TIMP (e.g., TIMP-1), alpha-globulin (e.g.,
alpha-2-macroglobulin, haptoglobin, and/or orosomucoid),
actin-severing protein (e.g., gelsolin), S100 protein (e.g.,
calgranulin), fibrinopeptide (e.g., FIBA), CGRP, tachykinin (e.g.,
Substance P), ghrelin, neurotensin, corticotropin-releasing
hormone, and combinations thereof. The presence or level of other
diagnostic markers such as, for example, anti-lactoferrin antibody,
L-selectin/CD62L, elastase, C-reactive protein (CRP), calprotectin,
anti-U1-70 kDa autoantibody, zona occludens 1 (ZO-1), vasoactive
intestinal peptide (VIP), serum amyloid A, and/or gastrin can also
be determined.
[0148] The sample used for detecting or determining the presence or
level of at least one diagnostic marker is typically whole blood,
plasma, serum, saliva, urine, stool (i.e., feces), tears, and any
other bodily fluid, or a tissue sample (i.e., biopsy) such as a
small intestine or colon sample. Preferably, the sample is serum,
whole blood, plasma, stool, urine, or a tissue biopsy. In certain
instances, the methods of the present invention further comprise
obtaining the sample from the individual prior to detecting or
determining the presence or level of at least one diagnostic marker
in the sample.
[0149] In some embodiments, a panel for measuring one or more of
the diagnostic markers described above may be constructed and used
for determining the presence or severity of IBS or for determining
the effectiveness of an IBS drug. One skilled in the art will
appreciate that the presence or level of a plurality of diagnostic
markers can be determined simultaneously or sequentially, using,
for example, an aliquot or dilution of the individual's sample. As
described above, the level of a particular diagnostic marker in the
individual's sample is generally considered to be elevated when it
is at least about 25%, 50%, 75%, 100%, 125%, 150%, 175%, 200%,
250%, 300%, 350%, 400%, 450%, 500%, 600%, 700%, 800%, 900%, or
1000% greater than the level of the same marker in a comparative
sample or population of samples (e.g., greater than a median
level). Similarly, the level of a particular diagnostic marker in
the individual's sample is typically considered to be lowered when
it is at least about 5%,10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%,
50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% less than the
level of the same marker in a comparative sample or population of
samples (e.g., less than a median level).
[0150] In certain instances, the presence or level of at least one
diagnostic marker is determined using an assay such as a
hybridization assay or an amplification-based assay. Examples of
hybridization assays and amplification-based assays suitable for
use in the methods of the present invention are described above.
Alternatively, the presence or level of at least one diagnostic
marker is determined using an immunoassay or an immunohistochemical
assay. Non-limiting examples of immunoassays and
immunohistochemical assays suitable for use in the methods of the
present invention are described above.
[0151] In certain embodiments, the method of monitoring the
progression or regression of IBS comprises determining a diagnostic
marker profile optionally in combination with a symptom profile,
wherein the symptom profile is determined by identifying the
presence or severity of at least one symptom in the individual; and
determining the presence or severity of IBS in the individual using
an algorithm based upon the diagnostic marker profile and the
symptom profile. In certain other embodiments, the method of
monitoring IBS drug efficacy comprises determining a diagnostic
marker profile optionally in combination with a symptom profile,
wherein the symptom profile is determined by identifying the
presence or severity of at least one symptom in the individual; and
determining the effectiveness of the drug using an algorithm based
upon the diagnostic marker profile and the symptom profile. One
skilled in the art will appreciate that the diagnostic marker
profile and the symptom profile can be determined simultaneously or
sequentially in any order.
[0152] In some embodiments, determining the presence or severity of
IBS or the effectiveness of an IBS drug is based upon the
diagnostic marker profile, alone or in combination with a symptom
profile, in conjunction with a statistical algorithm. In certain
instances, the statistical algorithm is a learning statistical
classifier system. The learning statistical classifier system
comprises any of the learning statistical classifier systems
described above.
[0153] In certain instances, the statistical algorithm is a single
learning statistical classifier system. Preferably, the single
learning statistical classifier system is a tree-based statistical
algorithm (e.g., RF, C&RT, etc.). In certain other instances,
the statistical algorithm is a combination of at least two learning
statistical classifier systems. Preferably, the combination of
learning statistical classifier systems comprises a RF and NN
(e.g., artificial NN, etc.), e.g., used in tandem or parallel. As a
non-limiting example, a RF can first be used to generate a
prediction or probability value based upon the diagnostic marker
profile, alone or in combination with a symptom profile, and a NN
can then be used to determine the presence or severity of IBS in
the individual or IBS drug efficacy based upon the prediction or
probability value and the same or different diagnostic marker
profile or combination of profiles.
[0154] In some instances, the data obtained from using the learning
statistical classifier system or systems can be processed using a
processing algorithm. Such a processing algorithm can be selected,
for example, from the group consisting of a multilayer perceptron,
backpropagation network, and Levenberg-Marquardt algorithm. In
other instances, a combination of such processing algorithms can be
used, such as in a parallel or serial fashion.
[0155] In certain embodiments, the methods of the present invention
can further comprise comparing the presence or severity of IBS in
the individual determined in step (b) to the presence or severity
of IBS in the individual at an earlier time. As a non-limiting
example, the presence or severity of IBS determined for an
individual receiving an IBS drug can be compared to the presence or
severity of IBS determined for the same individual before
initiation of use of the IBS drug or at an earlier time in therapy.
In certain other embodiments, the methods of the present invention
can comprise determining the effectiveness of the IBS drug by
comparing the effectiveness of the IBS drug determined in step (b)
to the effectiveness of the IBS drug in the individual at an
earlier time in therapy. In additional embodiments, the methods can
further comprise sending the IBS monitoring results to a clinician,
e.g., a gastroenterologist or a general practitioner.
[0156] In a further aspect, the present invention provides a
computer-readable medium including code for controlling one or more
processors to classify whether a sample from an individual is
associated with IBS, the code comprising: [0157] instructions to
apply a statistical process to a data set comprising a diagnostic
marker profile to produce a statistically derived decision
classifying the sample as an IBS sample or non-IBS sample based
upon the diagnostic marker profile, [0158] wherein the diagnostic
marker profile indicates the presence or level of at least one
diagnostic marker in the sample.
[0159] In some embodiments, the diagnostic marker profile indicates
the presence or level of at least one, two, three, four, five, six,
seven, eight, nine, ten, or more diagnostic markers selected from
the group consisting of a cytokine (e.g., IL-8, IL-1.beta., TWEAK,
leptin, OPG, MIP-3.beta., GRO.alpha., CXCL4/PF-4, and/or
CXCL7/NAP-2), growth factor (e.g., EGF, VEGF, PEDF, BDNF, and/or
SDGF), anti-neutrophil antibody (e.g., ANCA, pANCA, cANCA, NSNA,
and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG, and/or ASCA-IgM),
antimicrobial antibody (e.g., anti-OmpC antibody, anti-flagellin
antibody, and/or anti-I2 antibody), lactoferrin, anti-tTG antibody,
lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9), TIMP
(e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,
haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,
gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,
FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof. The
presence or level of other diagnostic markers such as, for example,
anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactive
protein (CRP), calprotectin, anti-U 1-70 kDa autoantibody, zona
occludens 1 (ZO-1), vasoactive intestinal peptide (VIP), serum
amyloid A, and/or gastrin can also be indicative of the diagnostic
marker profile.
[0160] In other embodiments, the computer-readable medium for
ruling in IBS comprises instructions to apply a statistical process
to a data set comprising a diagnostic marker profile optionally in
combination with a symptom profile which indicates the presence or
severity of at least one symptom in the individual to produce a
statistically derived decision classifying the sample as an IBS
sample or non-IBS sample based upon the diagnostic marker profile
and the symptom profile. One skilled in the art will appreciate
that the statistical process can be applied to the diagnostic
marker profile and the symptom profile simultaneously or
sequentially in any order.
[0161] In one embodiment, the statistical process is a learning
statistical classifier system. Examples of learning statistical
classifier systems suitable for use in the present invention are
described above. In certain instances, the statistical process is a
single learning statistical classifier system such as, for example,
a RF or C&RT. In certain other instances, the statistical
process is a combination of at least two learning statistical
classifier systems. As a non-limiting example, the combination of
learning statistical classifier systems comprises a RF and a NN,
e.g., used in tandem. In some instances, the data obtained from
using the learning statistical classifier system or systems can be
processed using a processing algorithm.
[0162] In a related aspect, the present invention provides a
computer-readable medium including code for controlling one or more
processors to classify whether a sample from an individual is
associated with IBS, the code comprising: [0163] (a) instructions
to apply a first statistical process to a data set comprising a
diagnostic marker profile to produce a statistically derived
decision classifying the sample as an IBD sample or non-IBD sample
based upon the diagnostic marker profile, wherein the diagnostic
marker profile indicates the presence or level of at least one
diagnostic marker in the sample; and [0164] if the sample is
classified as a non-IBD sample, [0165] (b) instructions to apply a
second statistical process to the same or different data set to
produce a second statistically derived decision classifying the
non-IBD sample as an IBS sample or non-IBS sample.
[0166] In some embodiments, the diagnostic marker profile indicates
the presence or level of at least one, two, three, four, five, six,
seven, eight, nine, ten, or more diagnostic markers selected from
the group consisting of a cytokine (e.g., IL-8, IL-1.beta., TWEAK,
leptin, OPG, MIP-3.beta., GRO.alpha., CXCL4/PF-4, and/or
CXCL7/NAP-2), growth factor (e.g., EGF, VEGF, PEDF, BDNF, and/or
SDGF), anti-neutrophil antibody (e.g., ANCA, pANCA, cANCA, NSNA,
and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG, and/or ASCA-IgM),
antimicrobial antibody (e.g., anti-OmpC antibody, anti-flagellin
antibody, and/or anti-I2 antibody), lactoferrin, anti-tTG antibody,
lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9), TIMP
(e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,
haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,
gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,
FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof. The
presence or level of other diagnostic markers such as, for example,
anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactive
protein (CRP), calprotectin, anti-U1-70 kDa autoantibody, zona
occludens 1 (ZO-1), vasoactive intestinal peptide (VIP), serum
amyloid A, and/or gastrin can also be indicative of the diagnostic
marker profile.
[0167] In other embodiments, the computer-readable medium for first
ruling out IBD and then ruling in IBS comprises instructions to
apply a first statistical process to a data set comprising a
diagnostic marker profile optionally in combination with a symptom
profile which indicates the presence or severity of at least one
symptom in the individual to produce a statistically derived
decision classifying the sample as an IBD sample or non-IBD sample
based upon the diagnostic marker profile and the symptom profile;
and if the sample is classified as a non-IBD sample, instructions
to apply a second statistical process to the same or different data
set to produce a second statistically derived decision classifying
the non-IBD sample as an IBS sample or non-IBS sample. One skilled
in the art will appreciate that the first and/or second statistical
process can be applied to the diagnostic marker profile and the
symptom profile simultaneously or sequentially in any order.
[0168] In one embodiment, the first and second statistical
processes are implemented in different processors. Alternatively,
the first and second statistical processes are implemented in a
single processor. In another embodiment, the first statistical
process is a learning statistical classifier system. Examples of
learning statistical classifier systems suitable for use in the
present invention are described above. In certain instances, the
first and/or second statistical process is a single learning
statistical classifier system such as, for example, a RF or
C&RT. In certain other instances, the first and/or second
statistical process is a combination of at least two learning
statistical classifier systems. As a non-limiting example, the
combination of learning statistical classifier systems comprises a
RF and a NN or SVM, e.g., used in tandem. In some instances, the
data obtained from using the learning statistical classifier system
or systems can be processed using a processing algorithm.
[0169] In an additional aspect, the present invention provides a
system for classifying whether a sample from an individual is
associated with IBS, the system comprising: [0170] (a) a data
acquisition module configured to produce a data set comprising a
diagnostic marker profile, wherein the diagnostic marker profile
indicates the presence or level of at least one diagnostic marker
in the sample; [0171] (b) a data processing module configured to
process the data set by applying a statistical process to the data
set to produce a statistically derived decision classifying the
sample as an IBS sample or non-IBS sample based upon the diagnostic
marker profile; and [0172] (c) a display module configured to
display the statistically derived decision.
[0173] In some embodiments, the diagnostic marker profile indicates
the presence or level of at least one, two, three, four, five, six,
seven, eight, nine, ten, or more diagnostic markers selected from
the group consisting of a cytokine (e.g., IL-8, IL-1.beta., TWEAK,
leptin, OPG, MIP-3.beta., GRO.alpha., CXCL4/PF-4, and/or
CXCL7/NAP-2), growth factor (e.g., EGF, VEGF, PEDF, BDNF, and/or
SDGF), anti-neutrophil antibody (e.g., ANCA, pANCA, cANCA, NSNA,
and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG, and/or ASCA-IgM),
antimicrobial antibody (e.g., anti-OmpC antibody, anti-flagellin
antibody, and/or anti-I2 antibody), lactoferrin, anti-tTG antibody,
lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9), TIMP
(e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,
haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,
gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,
FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof. The
presence or level of other diagnostic markers such as, for example,
anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactive
protein (CRP), calprotectin, anti-U1-70 kDa autoantibody, zona
occludens 1 (ZO-1), vasoactive intestinal peptide (VIP), serum
amyloid A, and/or gastrin can also be indicative of the diagnostic
marker profile.
[0174] In other embodiments, the system for ruling in IBS comprises
a data acquisition module configured to produce a data set
comprising a diagnostic marker profile optionally in combination
with a symptom profile which indicates the presence or severity of
at least one symptom in the individual; a data processing module
configured to process the data set by applying a statistical
process to the data set to produce a statistically derived decision
classifying the sample as an IBS sample or non-IBS sample based
upon the diagnostic marker profile and the symptom profile; and a
display module configured to display the statistically derived
decision.
[0175] In one embodiment, the statistical process is a learning
statistical classifier system. Examples of learning statistical
classifier systems suitable for use in the present invention are
described above. In certain instances, the statistical process is a
single learning statistical classifier system such as, for example,
a RF or C&RT. In certain other instances, the statistical
process is a combination of at least two learning statistical
classifier systems, e.g., used in tandem or parallel. In some
embodiments, the data obtained from using the learning statistical
classifier system or systems can be processed using a processing
algorithm.
[0176] In a related aspect, the present invention provides a system
for classifying whether a sample from an individual is associated
with IBS, the system comprising: [0177] (a) a data acquisition
module configured to produce a data set comprising a diagnostic
marker profile, wherein the diagnostic marker profile indicates the
presence or level of at least one diagnostic marker in the sample;
[0178] (b) a data processing module configured to process the data
set by applying a first statistical process to the data set to
produce a first statistically derived decision classifying the
sample as an IBD sample or non-IBD sample based upon the diagnostic
marker profile; [0179] if the sample is classified as a non-IBD
sample, a data processing module configured to apply a second
statistical process to the same or different data set to produce a
second statistically derived decision classifying the non-IBD
sample as an IBS sample or non-IBS sample; and [0180] (c) a display
module configured to display the first and/or the second
statistically derived decision.
[0181] In some embodiments, the diagnostic marker profile indicates
the presence or level of at least one, two, three, four, five, six,
seven, eight, nine, ten, or more diagnostic markers selected from
the group consisting of a cytokine (e.g., IL-8, IL-1.beta., TWEAK,
leptin, OPG, MIP-3.beta., GRO.alpha., CXCL4/PF-4, and/or
CXCL7/NAP-2), growth factor (e.g., EGF, VEGF, PEDF, BDNF, and/or
SDGF), anti-neutrophil antibody (e.g., ANCA, pANCA, cANCA, NSNA,
and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG, and/or ASCA-IgM),
antimicrobial antibody (e.g., anti-OmpC antibody, anti-flagellin
antibody, and/or anti-I2 antibody), lactoferrin, anti-tTG antibody,
lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9), TIMP
(e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,
haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,
gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,
FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,
corticotropin-releasing hormone, and combinations thereof. The
presence or level of other diagnostic markers such as, for example,
anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactive
protein (CRP), calprotectin, anti-U 1-70 kDa autoantibody, zona
occludens 1 (ZO-1), vasoactive intestinal peptide (VIP), serum
amyloid A, and/or gastrin can also be indicative of the diagnostic
marker profile.
[0182] In other embodiments, the system for first ruling out IBD
and then ruling in IBS comprises a data acquisition module
configured to produce a data set comprising a diagnostic marker
profile optionally in combination with a symptom profile which
indicates the presence or severity of at least one symptom in the
individual; a data processing module configured to process the data
set by applying a first statistical process to the data set to
produce a first statistically derived decision classifying the
sample as an IBD sample or non-IBD sample based upon the diagnostic
marker profile and the symptom profile; if the sample is classified
as a non-IBD sample, a data processing module configured to apply a
second statistical process to the same or different data set to
produce a second statistically derived decision classifying the
non-IBD sample as an IBS sample or non-IBS sample; and a display
module configured to display the first and/or the second
statistically derived decision.
[0183] In one embodiment, the first and/or second statistical
process is a learning statistical classifier system. Examples of
learning statistical classifier systems suitable for use in the
present invention are described above. In certain instances, the
first and/or second statistical process is a single learning
statistical classifier system such as, for example, a RF or
C&RT. In certain other instances, the first and/or second
statistical process is a combination of at least two learning
statistical classifier systems, e.g., used in tandem or parallel.
In some instances, the data obtained from using the learning
statistical classifier system or systems can be processed using a
processing algorithm. In another embodiment, the first and second
statistical processes are implemented in different processors.
Alternatively, the first and second statistical processes are
implemented in a single processor.
IV. Diseases and Disorders with IBS-like Symptoms
[0184] A variety of structural or metabolic diseases and disorders
can cause signs or symptoms that are similar to IBS. As
non-limiting examples, patients with diseases and disorders such as
inflammatory bowel disease (IBD), Celiac disease (CD), acute
inflammation, diverticulitis, ileal pouch-anal anastomosis,
microscopic colitis, chronic infectious diarrhea, lactase
deficiency, cancer (e.g., colorectal cancer), a mechanical
obstruction of the small intestine or colon, an enteric infection,
ischemia, maldigestion, malabsorption, endometriosis, and
unidentified inflammatory disorders of the intestinal tract can
present with abdominal discomfort associated with mild to moderate
pain and a change in the consistency and/or frequency of stools
that are similar to IBS. Additional IBS-like symptoms can include
chronic diarrhea or constipation or an alternating form of each,
weight loss, abdominal distention or bloating, and mucus in the
stool.
[0185] Most IBD patients can be classified into one of two distinct
clinical subtypes, Crohn's disease and ulcerative colitis. Crohn's
disease is an inflammatory disease affecting the lower part of the
ileum and often involving the colon and other regions of the
intestinal tract. Ulcerative colitis is characterized by an
inflammation localized mostly in the mucosa and submucosa of the
large intestine. Patients suffering from these clinical subtypes of
IBD typically have IBS-like symptoms such as, for example,
abdominal pain, chronic diarrhea, weight loss, and cramping.
[0186] The clinical presentation of Celiac disease is also
characterized by IBS-like symptoms such as abdominal discomfort
associated with chronic diarrhea, weight loss, and abdominal
distension. Celiac disease is an immune-mediated disorder of the
intestinal mucosa that is typically associated with villous
atrophy, crypt hyperplasia, and/or inflammation of the mucosal
lining of the small intestine. In addition to the malabsorption of
nutrients, individuals with Celiac disease are at risk for mineral
deficiency, vitamin deficiency, osteoporosis, autoimmune diseases,
and intestinal malignancies (e.g., lymphoma and carcinoma). It is
thought that exposure to proteins such as gluten (e.g., glutenin
and prolamine proteins which are present in wheat, rye, barley,
oats, millet, triticale, spelt, and kamut), in the appropriate
genetic and environmental context, is responsible for causing
Celiac disease.
[0187] Other diseases and disorders characterized by intestinal
inflammation that present with IBS-like symptoms include, for
example, acute inflammation, diverticulitis, ileal pouch-anal
anastomosis, microscopic colitis, and chronic infectious diarrhea,
as well as unidentified inflammatory disorders of the intestinal
tract. Patients experiencing episodes of acute inflammation
typically have elevated C-reactive protein (CRP) levels in addition
to IBS-like symptoms. CRP is produced by the liver during the acute
phase of the inflammatory process and is usually released about 24
hours post-commencement of the inflammatory process. Patients
suffering from diverticulitis, ileal pouch-anal anastomosis,
microscopic colitis, and chronic infectious diarrhea typically have
elevated fecal lactoferrin and/or calprotectin levels in addition
to IBS-like symptoms. Lactoferrin is a glycoprotein secreted by
mucosal membranes and is the major protein in the secondary
granules of leukocytes. Leukocytes are commonly recruited to
inflammatory sites where they are activated, releasing granule
content to the surrounding area. This process increases the
concentration of lactoferrin in the stool.
[0188] Increased lactoferrin levels are observed in patients with
ileal pouch-anal anastomosis (i.e., a pouch is created following
complete resection of colon in severe cases of Crohn's disease)
when compared to other non-inflammatory conditions of the pouch,
like irritable pouch syndrome. Elevated levels of lactoferrin are
also observed in patients with diverticulitis, a condition in which
bulging pouches (i.e., diverticula) in the digestive tract become
inflamed and/or infected, causing severe abdominal pain, fever,
nausea, and a marked change in bowel habits. Microscopic colitis is
a chronic inflammatory disorder that is also associated with
increased fecal lactoferrin levels. Microscopic colitis is
characterized by persistent watery diarrhea (non-bloody), abdominal
pain usually associated with weight loss, a normal mucosa during
colonoscopy and radiological examination, and very specific
histopathological changes. Microscopic colitis consists of two
diseases, collagenous colitis and lymphocytic colitis. Collagenous
colitis is of unknown etiology and is found in patients with
long-term watery diarrhea and a normal colonoscopy examination.
Both collagenous colitis and lymphocytic colitis are characterized
by increased lymphocytes in the lining of the colon. Collagenous
colitis is further characterized by a thickening of the
sub-epithelial collagen layer of the colon. Chronic infectious
diarrhea is an illness that is also associated with increased fecal
lactoferrin levels. Chronic infectious diarrhea is usually caused
by a bacterial, viral, or protozoan infection, with patients
presenting with IBS-like symptoms such as diarrhea and abdominal
pain. Increased lactoferrin levels are also observed in patients
with IBD.
[0189] In addition to determining CRP and/or lactoferrin and/or
calprotectin levels, diseases and disorders associated with
intestinal inflammation can also be ruled out by detecting the
presence of blood in the stool, such as fecal hemoglobin.
Intestinal bleeding that occurs without the patient's knowledge is
called occult or hidden bleeding. The presence of occult bleeding
(e.g., fecal hemoglobin) is typically observed in a stool sample
from the patient. Other conditions such as ulcers (e.g., gastric,
duodenal), cancer (e.g., stomach cancer, colorectal cancer), and
hemorrhoids can also present with IBS-like symptoms including
abdominal pain and a change in the consistency and/or frequency of
stools.
[0190] In addition, fecal calprotectin levels can also be assessed.
Calprotectin is a calcium binding protein with antimicrobial
activity derived predominantly from neutrophils and monocytes.
Calprotectin has been found to have clinical relevance in cystic
fibrosis, rheumatoid arthritis, IBD, colorectal cancer, HIV, and
other inflammatory diseases. Its level has been measured in serum,
plasma, oral, cerebrospinal and synovial fluids, urine, and feces.
Advantages of fecal calprotectin in GI disorders have been
recognized: stable for 3-7 days at room temperature enabling sample
shipping through regular mail; correlated to fecal alpha
1-antitrypsin in patients with Crohn's disease; and elevated in a
great majority of patients with gastrointestinal carcinomas and
IBD. It was found that fecal calprotectin correlates well with
endoscopic and histological gradings of disease activity in
ulcerative colitis, and with fecal excretion of indium-111-labelled
neutrophilic granulocytes, which is a standard of disease activity
in IBD.
[0191] In view of the foregoing, it is clear that a wide array of
diseases and disorders can cause IBS-like symptoms, thereby
creating a substantial obstacle for definitively classifying a
sample as an IBS sample. However, the present invention overcomes
this limitation by classifying a sample from an individual as an
IBS sample using, for example, a statistical algorithm, or by
excluding (i.e., ruling out) those diseases and disorders that
share a similar clinical presentation as IBS and identifying (i.e.,
ruling in) IBS in a sample using, for example, a combination of
statistical algorithms.
V. Diagnostic Markers
[0192] A variety of diagnostic markers are suitable for use in the
methods, systems, and code of the present invention for classifying
a sample from an individual as an IBS sample or for ruling out one
or more diseases or disorders associated with IBS-like symptoms in
a sample from an individual. Examples of diagnostic markers
include, without limitation, cytokines, growth factors,
anti-neutrophil antibodies, anti-Saccharomyces cerevisiae
antibodies, antimicrobial antibodies, anti-tissue transglutaminase
(tTG) antibodies, lipocalins, matrix metalloproteinases (MMPs),
complexes of lipocalin and MMP, tissue inhibitor of
metalloproteinases (TIMPs), globulins (e.g., alpha-globulins),
actin-severing proteins, S100 proteins, fibrinopeptides, calcitonin
gene-related peptide (CGRP), tachykinins, ghrelin, neurotensin,
corticotropin-releasing hormone (CRH), elastase, C-reactive protein
(CRP), lactoferrin, anti-lactoferrin antibodies, calprotectin,
hemoglobin, NOD2/CARD15, serotonin reuptake transporter (SERT),
tryptophan hydroxylase-1,5-hydroxytryptamine (5-HT), lactulose, and
combinations thereof. Additional diagnostic markers for predicting
IBS in accordance with the present invention can be selected using
the techniques described in Example 14. One skilled in the art will
also know of other diagnostic markers suitable for use in the
present invention.
[0193] A. Cytokines
[0194] The determination of the presence or level of at least one
cytokine in a sample is particularly useful in the present
invention. As used herein, the term "cytokine" includes any of a
variety of polypeptides or proteins secreted by immune cells that
regulate a range of immune system functions and encompasses small
cytokines such as chemokines. The term "cytokine" also includes
adipocytokines, which comprise a group of cytokines secreted by
adipocytes that function, for example, in the regulation of body
weight, hematopoiesis, angiogenesis, wound healing, insulin
resistance, the immune response, and the inflammatory response.
[0195] In certain aspects, the presence or level of at least one
cytokine including, but not limited to, TNF-.alpha., TNF-related
weak inducer of apoptosis (TWEAK), osteoprotegerin (OPG),
IFN-.alpha., IFN-.beta., IFN.gamma., IL-1.alpha., IL-1.beta., IL-1
receptor antagonist (IL-1ra), IL-2, IL-4, IL-5, IL-6, soluble IL-6
receptor (sIL-6R), IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15,
IL-17, IL-23, and IL-27 is determined in a sample. In certain other
aspects, the presence or level of at least one chemokine such as,
for example, CXCL1/GRO1/GRO.alpha., CXCL2/GRO2, CXCL3/GRO3,
CXCL4/PF-4, CXCL5/ENA-78, CXCL6/GCP-2, CXCL7/NAP-2, CXCL9/MIG,
CXCL10/IP-10, CXCL11/I-TAC, CXCL12/SDF-1, CXCL13/BCA-1,
CXCL14/BRAK, CXCL15, CXCL16, CXCL17/DMC, CCL1, CCL2/MCP-1,
CCL3/MIP-1.alpha., CCL4/MIP-1.beta., CCL5/RANTES, CCL6/C10,
CCL7/MCP-3, CCL8/MCP-2, CCL9/CCL10, CCL11/Eotaxin, CCL12/MCP-5,
CCL13/MCP-4, CCL14/HCC-1, CCL15/MIP-5, CCL16/LEC, CCL17/TARC,
CCL18/MIP-4, CCL19/MIP-3.beta., CCL20/MIP-3.alpha., CCL21/SLC,
CCL22/MDC, CCL23/MPIF1, CCL24/Eotaxin-2, CCL25/TECK,
CCL26/Eotaxin-3, CCL27/CTACK, CCL28/MEC, CL1, CL2, and CX.sub.3CL1
is determined in a sample. In certain further aspects, the presence
or level of at least one adipocytokine including, but not limited
to, leptin, adiponectin, resistin, active or total plasminogen
activator inhibitor-1 (PAI-1), visfatin, and retinol binding
protein 4 (RBP4) is determined in a sample. Preferably, the
presence or level of IL-8, IL-1.beta., TWEAK, leptin, OPG,
MIP-3.beta., GRO.alpha., CXCL4/PF-4, and/or CXCL7/NAP-2 is
determined.
[0196] In certain instances, the presence or level of a particular
cytokine is detected at the level of mRNA expression with an assay
such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular cytokine is detected at the level of
protein expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. Suitable ELISA kits for
determining the presence or level of a cytokine such as IL-8,
IL-1.beta., MIP-3.beta., GRO.alpha., CXCL4/PF-4, or CXCL7/NAP-2 in
a serum, plasma, saliva, or urine sample are available from, e.g.,
R&D Systems, Inc. (Minneapolis, Minn.), Neogen Corp.
(Lexington, Ky.), Alpco Diagnostics (Salem, N.H.), Assay Designs,
Inc. (Ann Arbor, Mich.), BD Biosciences Pharmingen (San Diego,
Calif.), Invitrogen (Camarillo, Calif.), Calbiochem (San Diego,
Calif.), CHEMICON International, Inc. (Temecula, Calif.), Antigenix
America Inc. (Huntington Station, N.Y.), QIAGEN Inc. (Valencia,
Calif.), Bio-Rad Laboratories, Inc. (Hercules, Calif.), and/or
Bender MedSystems Inc. (Burlingame, Calif.).
[0197] 1. TWEAK
[0198] TWEAK is a member of the TNF superfamily of structurally
related cytokines. Full-length, membrane-anchored TWEAK can be
found on the surface of many cell types and a smaller, biologically
active form, generated via proteolytic processing, has also been
detected in the extracellular milieu (see, e.g., Chicheportiche et
al., J. Biol. Chem., 272:32401-32410 (1997)). TWEAK acts via
binding to a TNF receptor superfamily member named fibroblast
growth factor-inducible 14 (Fn14; also known as tumor necrosis
factor receptor superfamily member 12A or TNFRSF12A). TWEAK has
multiple biological activities, including stimulation of cell
growth and angiogenesis, induction of inflammatory cytokines, and
stimulation of apoptosis (see, e.g., Wiley et al., Cytokine Growth
Factor Rev., 14:241-249 (2003)). In particular, TWEAK has been
shown to induce the expression of PGE2, MMP-1, IL-6, IL-8, RANTES,
and IP-10 in fibroblasts and synoviocytes, and to upregulate
ICAM-1, E-selectin, IL-8, and MCP-1 expression in endothelial cells
(see, e.g., Campbell et al., Front. Biosci., 9:2273-2284 (2004)).
It has also been demonstrated that TWEAK binding to the Fn14
receptor, or constitutive Fn14 overexpression, activates the
NF-.kappa.B signaling pathway, which plays an important role in
immune and inflammatory processes, oncogenesis, cancer therapy
resistance, and tumorigenesis (see, e.g., Winkles et al., Cancer
Lett., 235:11-17 (2006); and Winkles et al., Front. Biosci.,
12:2761-2771 (2007)). One skilled in the art will appreciate that
TWEAK is also known as tumor necrosis factor ligand superfamily
member 12 (TNFSF12), APO3 ligand (APO3L), CD255, DR3 ligand, FN14,
and UNQ181/PRO207.
[0199] Suitable ELISA kits for determining the presence or level of
TWEAK in a biological sample such as a serum, plasma, saliva, or
urine sample are available from, e.g., Antigenix America Inc.
(Huntington Station, N.Y.), Bender MedSystems Inc. (Burlingame,
Calif.), Agdia Inc. (Elkhart, Ind.), American Research Products
Inc. (Belmont, Mass.), Biomeda Corp. (Foster City, Calif.),
BioVision, Inc. (Mountain View, Calif.), and Kamiya Biomedical Co.
(Seattle, Wash.).
[0200] 2. Osteoprotegerin (OPG)
[0201] OPG is a 401-amino acid member of the TNF superfamily of
structurally related cytokines. OPG, which is homologous to the
receptor activator of NF.kappa.B (RANK), inhibits the
differentiation of macrophages into osteoclasts and regulates the
resorption of osteoclasts by acting as a soluble decoy receptor for
RANK ligand (RANKL; also known as OPG ligand (OPGL)). As a result,
the OPG-RANK-RANKL system plays a direct and essential role in the
formation, function, and survival of osteoclasts. The
OPG-RANK-RANKL system has also been shown to modulate cancer cell
migration, thus controlling the development of bone metastases. One
skilled in the art will appreciate that OPG is also known as
osteoprotegrin and osteoclastogenesis inhibitory factor (OCIF).
[0202] Suitable ELISA kits for determining the presence or level of
OPG in a serum, plasma, saliva, or urine sample are available from,
e.g., Antigenix America Inc. (Huntington Station, N.Y.),
Immunodiagnostic Systems Ltd. (Boldon, United Kingdom), and
BioVendor, LLC (Candler, N.C.).
[0203] 3. Leptin
[0204] Leptin, a member of the adipocytokine family of cytokines,
is a 16-kD peptide hormone that plays a critical role in the
regulation of body weight by inhibiting food intake and stimulating
energy expenditure. It is predominantly synthesized by adipocytes
and circulates in the plasma in amounts proportional to body fat
content (see, e.g., Maffei et al., Nat. Med., 1:1155-1161 (1995);
Considine et al., Diabetes, 45:992-994 (1996)). Leptin displays a
high degree of homology among different species and it is also
analogous in structure to other cytokines (see, e.g., Madej et al.,
FEBS Lett., 373:13-18 (1995)). Leptin acts through the leptin
receptor, a single-transmembrane-domain receptor of the class I
cytokine superfamily of receptors, which are characterized by
extracellular motifs of four cysteine residues and a number of
fibronectin type III domains (see, e.g., Heim, Eur. J. Clin.
Invest., 26:1-12 (1996)). The leptin receptor is known to exist as
a homodimer and is activated by conformational changes that occur
following ligand binding to the receptor (see, e.g., Devos et al.,
J. Biol. Chem., 272:18304-18310 (1997)). Six leptin receptor
isoforms, generated by alternate slicing, have been identified to
date (see, e.g., Wang et al., Nature, 393:684-688 (1998); Lee et
al., Nature, 379:632-635 (1996)).
[0205] Suitable ELISA kits for determining the presence or level of
leptin in a biological sample such as a serum, plasma, saliva, or
urine sample are available from, e.g., R&D Systems, Inc.
(Minneapolis, Minn.), B-Bridge International (Mountain View,
Calif.), Neogen Corp. (Lexington, Ky.), Assay Designs, Inc. (Ann
Arbor, Mich.), Invitrogen (Camarillo, Calif.), CHEMICON
International, Inc. (Temecula, Calif.), Antigenix America Inc.
(Huntington Station, N.Y.), LINCOResearch, Inc. (St. Charles, Mo.),
Diagnostic Systems Laboratories, Inc. (Webster, Tex.),
Immuno-Biological Laboratories, Inc. (Minneapolis, Minn.), and
Cayman Chemical Co. (Ann Arbor, Mich.).
[0206] B. Growth Factors
[0207] The determination of the presence or level of one or more
growth factors in a sample is also useful in the present invention.
As used herein, the term "growth factor" includes any of a variety
of peptides, polypeptides, or proteins that are capable of
stimulating cellular proliferation and/or cellular
differentiation.
[0208] In certain aspects, the presence or level of at least one
growth factor including, but not limited to, epidermal growth
factor (EGF), heparin-binding epidermal growth factor (HB-EGF),
vascular endothelial growth factor (VEGF), pigment
epithelium-derived factor (PEDF; also known as SERPINF1),
amphiregulin (AREG; also known as schwannoma-derived growth factor
(SDGF)), basic fibroblast growth factor (bFGF), hepatocyte growth
factor (HGF), transforming growth factor-.alpha. (TGF-.alpha.),
transforming growth factor-.beta. (TGF-.beta.), bone morphogenetic
proteins (e.g., BMP1-BMP15), platelet-derived growth factor (PDGF),
nerve growth factor (NGF), .beta.-nerve growth factor (.beta.-NGF),
neurotrophic factors (e.g., brain-derived neurotrophic factor
(BDNF), neurotrophin 3 (NT3), neurotrophin 4 (NT4), etc.), growth
differentiation factor-9 (GDF-9), granulocyte-colony stimulating
factor (G-CSF), granulocyte-macrophage colony stimulating factor
(GM-CSF), myostatin (GDF-8), erythropoietin (EPO), and
thrombopoietin (TPO) is determined in a sample. Preferably, the
presence or level of EGF, VEGF, PEDF, amphiregulin (SDGF), and/or
BDNF is determined.
[0209] In certain instances, the presence or level of a particular
growth factor is detected at the level of mRNA expression with an
assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular growth factor is detected at the level of
protein expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. Suitable ELISA kits for
determining the presence or level of a growth factor such as EGF,
VEGF, PEDF, SDGF, or BDNF in a serum, plasma, saliva, or urine
sample are available from, e.g., Antigenix America Inc. (Huntington
Station, N.Y.), Promega (Madison, Wis.), R&D Systems, Inc.
(Minneapolis, Minn.), Invitrogen (Camarillo, Calif.), CHEMICON
International, Inc. (Temecula, Calif.), Neogen Corp. (Lexington,
Ky.), PeproTech (Rocky Hill, N.J.), Alpco Diagnostics (Salem,
N.H.), Pierce Biotechnology, Inc. (Rockford, Ill.), and/or Abazyme
(Needham, Mass.).
[0210] C. Lipocalins
[0211] The determination of the presence or level of one or more
lipocalins in a sample is also useful in the present invention. As
used herein, the term "lipocalin" includes any of a variety of
small extracellular proteins that are characterized by several
common molecular recognition properties: the ability to bind a
range of small hydrophobic molecules; binding to specific
cell-surface receptors; and the formation of complexes with soluble
macromolecules (see, e.g., Flowers, Biochem. J., 318:1-14 (1996)).
The varied biological functions of lipocalins are mediated by one
or more of these properties. The lipocalin protein family exhibits
great functional diversity, with roles in retinol transport,
invertebrate cryptic coloration, olfaction and pheromone transport,
and prostaglandin synthesis. Lipocalins have also been implicated
in the regulation of cell homoeostasis and the modulation of the
immune response, and, as carrier proteins, to act in the general
clearance of endogenous and exogenous compounds. Although
lipocalins have great diversity at the sequence level, their
three-dimensional structure is a unifying characteristic. Lipocalin
crystal structures are highly conserved and comprise a single
eight-stranded continuously hydrogen-bonded antiparallel
beta-barrel, which encloses an internal ligand-binding site.
[0212] In certain aspects, the presence or level of at least one
lipocalin including, but not limited to, neutrophil
gelatinase-associated lipocalin (NGAL; also known as human
neutrophil lipocalin (HNL) or lipocalin-2), von Ebner's gland
protein (VEGP; also known as lipocalin-1), retinol-binding protein
(RBP), purpurin (PURP), retinoic acid-binding protein (RABP),
.alpha..sub.2u-globulin (A2U), major urinary protein (MUP),
bilin-binding protein (BBP), .alpha.-crustacyanin, pregnancy
protein 14 (PP14), .beta.-lactoglobulin (Blg),
.alpha..sub.1-microglobulin (A1M), the gamma chain of C8
(C8.gamma.), Apolipoprotein D (ApoD), lazarillo (LAZ),
prostaglandin D2 synthase (PGDS), quiescence-specific protein
(QSP), choroid plexus protein, odorant-binding protein (OBP),
.alpha..sub.1-acid glycoprotein (AGP), probasin (PBAS), aphrodisin,
orosomucoid, and progestagen-associated endometrial protein (PAEP)
is determined in a sample. In certain other aspects, the presence
or level of at least one lipocalin complex including, for example,
a complex of NGAL and a matrix metalloproteinase (e.g., NGAL/MMP-9
complex) is determined. Preferably, the presence or level of NGAL
or a complex thereof with MMP-9 is determined.
[0213] In certain instances, the presence or level of a particular
lipocalin is detected at the level of mRNA expression with an assay
such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular lipocalin is detected at the level of
protein expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. Suitable ELISA kits for
determining the presence or level of a lipocalin such as NGAL in a
serum, plasma, or urine sample are available from, e.g.,
AntibodyShop A/S (Gentofte, Denmark), LabClinics SA (Barcelona,
Spain), Lucerna-Chem AG (Luzern, Switzerland), R&D Systems,
Inc. (Minneapolis, Minn.), and Assay Designs, Inc. (Ann Arbor,
Mich.). Suitable ELISA kits for determining the presence or level
of the NGAL/MMP-9 complex are available from, e.g., R&D
Systems, Inc. (Minneapolis, Minn.). Additional NGAL and NGAL/MMP-9
complex ELISA techniques are described in, e.g., Kjeldsen et al.,
Blood, 83:799-807 (1994); and Kjeldsen et al., J. Immunol. Methods,
198:155-164 (1996).
[0214] D. Matrix Metalloproteinases
[0215] The determination of the presence or level of at least one
matrix metalloproteinase (MMP) in a sample is also useful in the
present invention. As used herein, the term "matrix
metalloproteinase" or "MMP" includes zinc-dependent endopeptidases
capable of degrading a variety of extracellular matrix proteins,
cleaving cell surface receptors, releasing apoptotic ligands,
and/or regulating chemokines. MMPs are also thought to play a major
role in cell behaviors such as cell proliferation, migration
(adhesion/dispersion), differentiation, angiogenesis, and host
defense.
[0216] In certain aspects, the presence or level of at least one at
least one MMP including, but not limited to, MMP-1 (interstitial
collagenase), MMP-2 (gelatinase-A), MMP-3 (stromelysin-1), MMP-7
(matrilysin), MMP-8 (neutrophil collagenase), MMP-9 (gelatinase-B),
MMP-10 (stromelysin-2), MMP-11 (stromelysin-3), MMP-12 (macrophage
metalloelastase), MMP-13 (collagenase-3), MMP-14, MMP-15, MMP-16,
MMP-17, MMP-18 (collagenase-4), MMP-19, MMP-20 (enamelysin),
MMP-21, MMP-23, MMP-24, MMP-25, MMP-26 (matrilysin-2), MMP-27, and
MMP-28 (epilysin) is determined in a sample. Preferably, the
presence or level of MMP-9 is determined.
[0217] In certain instances, the presence or level of a particular
MMP is detected at the level of mRNA expression with an assay such
as, for example, a hybridization assay or an amplification-based
assay. In certain other instances, the presence or level of a
particular MMP is detected at the level of protein expression
using, for example, an immunoassay (e.g., ELISA) or an
immunohistochemical assay. Suitable ELISA kits for determining the
presence or level of an MMP such as MMP-9 in a serum or plasma
sample are available from, e.g., Calbiochem (San Diego, Calif.),
CHEMICON International, Inc. (Temecula, Calif.), and R&D
Systems, Inc. (Minneapolis, Minn.).
[0218] E. Tissue Inhibitor of Metalloproteinases
[0219] The determination of the presence or level of at least one
tissue inhibitor of metalloproteinase (TIMP) in a sample is also
useful in the present invention. As used herein, the term "tissue
inhibitor of metalloproteinase" or "TIMP" includes proteins capable
of inhibiting MMPs.
[0220] In certain aspects, the presence or level of at least one at
least one TIMP including, but not limited to, TIMP-1, TIMP-2,
TIMP-3, and TIMP-4 is determined in a sample. Preferably, the
presence or level of TIMP-1 is determined.
[0221] In certain instances, the presence or level of a particular
TIMP is detected at the level of mRNA expression with an assay such
as, for example, a hybridization assay or an amplification-based
assay. In certain other instances, the presence or level of a
particular TIMP is detected at the level of protein expression
using, for example, an immunoassay (e.g., ELISA) or an
immunohistochemical assay. Suitable ELISA kits for determining the
presence or level of a TIMP such as TIMP-1 in a serum or plasma
sample are available from, e.g., Alpco Diagnostics (Salem, N.H.),
Calbiochem (San Diego, Calif.), Invitrogen (Camarillo, Calif.),
CHEMICON International, Inc. (Temecula, Calif.), and R&D
Systems, Inc. (Minneapolis, Minn.).
[0222] F. Globulins
[0223] The determination of the presence or level of at least one
globulin in a sample is also useful in the present invention. As
used herein, the term "globulin" includes any member of a
heterogeneous series of families of serum proteins which migrate
less than albumin during serum electrophoresis. Protein
electrophoresis is typically used to categorize globulins into the
following three categories: alpha-globulins (i.e.,
alpha-1-globulins or alpha-2-globulins); beta-globulins; and
gamma-globulins.
[0224] Alpha-globulins comprise a group of globular proteins in
plasma which are highly mobile in alkaline or electrically-charged
solutions. They generally function to inhibit certain blood
protease and inhibitor activity. Examples of alpha-globulins
include, but are not limited to, alpha-2-macroglobulin
(.alpha.2-MG), haptoglobin (Hp), orosomucoid, alpha-1-antitrypsin,
alpha-1-antichymotrypsin, alpha-2-antiplasmin, antithrombin,
ceruloplasmin, heparin cofactor II, retinol binding protein, and
transcortin. Preferably, the presence or level of .alpha.2-MG,
haptoglobin, and/or orosomucoid is determined. In certain
instances, one or more haptoglobin allotypes such as, for example,
Hp precursor, Hb.beta., Hp.alpha.1, and Hp.alpha.2, are
determined.
[0225] In certain instances, the presence or level of a particular
globulin is detected at the level of mRNA expression with an assay
such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular globulin is detected at the level of
protein expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. Suitable ELISA kits for
determining the presence or level of a globulin such as
.alpha.2-MG, haptoglobin, or orosomucoid in a serum, plasma, or
urine sample are available from, e.g., GenWay Biotech, Inc. (San
Diego, Calif.) and/or Immundiagnostik AG (Bensheim, Germany)
[0226] G. Actin-Severing Proteins
[0227] The determination of the presence or level of at least one
actin-severing protein in a sample is also useful in the present
invention. As used herein, the term "actin-severing protein"
includes any member of a family of proteins involved in actin
remodeling and regulation of cell motility. Non-limiting examples
of actin-severing proteins include gelsolin (also known as brevin
or actin-depolymerizing factor), villin, fragmin, and adseverin.
For example, gelsolin is a protein of leukocytes, platelets, and
other cells which severs actin filaments in the presence of
submicromolar calcium, thereby solating cytoplasmic actin gels.
[0228] In certain instances, the presence or level of a particular
actin-severing protein is detected at the level of mRNA expression
with an assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular actin-severing protein is detected at the
level of protein expression using, for example, an immunoassay
(e.g., ELISA) or an immunohistochemical assay. Suitable ELISA
techniques for determining the presence or level of an
actin-severing protein such as gelsolin in a plasma sample are
described in, e.g., Smith et al., J. Lab. Clin. Med., 110:189-195
(1987); and Hiyoshi et al., Biochem. Mol. Biol. Int, 32:755-762
(1994).
[0229] H. S100 Proteins
[0230] The determination of the presence or level of at least one
S100 protein in a sample is also useful in the present invention.
As used herein, the term "S100 protein" includes any member of a
family of low molecular mass acidic proteins characterized by
cell-type-specific expression and the presence of 2 EF-hand
calcium-binding domains. There are at least 21 different types of
S100 proteins in humans. The name is derived from the fact that
S100 proteins are 100% soluble in ammonium sulfate at neutral pH.
Most S100 proteins are homodimeric, consisting of two identical
polypeptides held together by non-covalent bonds. Although S100
proteins are structurally similar to calmodulin, they differ in
that they are cell-specific, expressed in particular cells at
different levels depending on environmental factors. S-100 proteins
are normally present in cells derived from the neural crest (e.g.,
Schwann cells, melanocytes, glial cells), chondrocytes, adipocytes,
myoepithelial cells, macrophages, Langerhans cells, dendritic
cells, and keratinocytes. S100 proteins have been implicated in a
variety of intracellular and extracellular functions such as the
regulation of protein phosphorylation, transcription factors,
Ca.sup.2+ homeostasis, the dynamics of cytoskeleton constituents,
enzyme activities, cell growth and differentiation, and the
inflammatory response.
[0231] Calgranulin is an S100 protein that is expressed in multiple
cell types, including renal epithelial cells and neutrophils, and
are abundant in infiltrating monocytes and granulocytes under
conditions of chronic inflammation. Examples of calgranulins
include, without limitation, calgranulin A (also known as S100A8 or
MRP-8), calgranulin B (also known as S100A9 or MRP-14), and
calgranulin C (also known as S100A12).
[0232] In certain instances, the presence or level of a particular
S100 protein is detected at the level of mRNA expression with an
assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular S100 protein is detected at the level of
protein expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. Suitable ELISA kits for
determining the presence or level of an S100 protein such as
calgranulin A (S100A8) or calgranulin B (S100A9) in a serum,
plasma, or urine sample are available from, e.g., Peninsula
Laboratories Inc. (San Carlos, Calif.) and Hycult biotechnology
b.v. (Uden, The Netherlands).
[0233] Calprotectin, the complex of S100A8 and S100A9, is a
calcium- and zinc-binding protein in the cytosol of neutrophils,
monocytes, and keratinocytes. Calprotectin is a major protein in
neutrophilic granulocytes and macrophages and accounts for as much
as 60% of the total protein in the cytosol fraction in these cells.
It is therefore a surrogate marker of neutrophil turnover. Its
concentration in stool correlates with the intensity of neutrophil
infiltration of the intestinal mucosa and with the severity of
inflammation. In some instances, calprotectin can be measured with
an ELISA using small (50-100 mg) fecal samples (see, e.g., Johne et
al., Scand J. Gastroenterol., 36:291-296 (2001)).
[0234] I. Tachykinins
[0235] The determination of the presence or level of at least one
tachykinin in a sample is also useful in the present invention. As
used herein, the term "tachykinin" includes amidated neuropeptides
that share the carboxy-terminal sequence
Phe-X-Gly-Leu-Met-NH.sub.2. Tachykinins typically bind to one or
more tachykinin receptors (e.g., TACR1, TACR2, and/or TACR3).
[0236] In certain aspects, the presence or level of at least one
tachykinin including, but not limited to, substance P, neurokinin
A, and neurokinin B is determined in a sample. Preferably, the
presence or level of substance P is determined. Substance P is a
peptide of 11 amino acids in length that is released by nerve
endings in both the central and peripheral nervous systems. Among
the numerous biological sites innervated by substance P-releasing
neurons are the skin, intestines, stomach, bladder, and
cardiovascular system.
[0237] In certain instances, the presence or level of a particular
tachykinin is detected at the level of mRNA expression with an
assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence
or level of a particular tachykinin is detected at the level of
protein expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. Suitable ELISA kits for
determining the presence or level of a tachykinin such as substance
P in a serum, plasma, saliva, or urine sample are available from,
e.g., MD Biosciences Inc. (St. Paul, Minn.), Assay Designs, Inc.
(Ann Arbor, Mich.), R&D Systems, Inc. (Minneapolis, Minn.),
Sigma-Aldrich Corp. (St. Louis, Mo.), and Cayman Chemical Co. (Ann
Arbor, Mich.).
[0238] J. Ghrelin
[0239] The determination of the presence or level of ghrelin in a
sample is also useful in the present invention. As used herein, the
term "ghrelin" includes a peptide of 28 amino acids that is an
endogenous ligand for the growth hormone secretagogue receptor
(GHSR) and is involved in regulating growth hormone release.
Ghrelin can be acylated, typically with an n-octanoyl group at
serine residue three, to form active ghrelin. Alternatively,
ghrelin can exist as an unacylated form (i.e., desacyl-ghrelin).
Ghrelin is primarily expressed in specialized enterochromaffin
cells located mainly in the mucosa of the fundus of the stomach and
has metabolic effects opposite to those of leptin. Ghrelin
stimulates food intake, enhances the use of carbohydrates and
reduces fat utilization, increases gastric motility and acid
secretion, and reduces locomotor activity.
[0240] In certain instances, the presence or level of ghrelin is
detected at the level of mRNA expression with an assay such as, for
example, a hybridization assay or an amplification-based assay. In
certain other instances, the presence or level of ghrelin is
detected at the level of protein expression using, for example, an
immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable
ELISA kits for determining the presence or level of active ghrelin
or desacyl-ghrelin in a serum, plasma, saliva, or urine sample are
available from, e.g., Alpco Diagnostics (Salem, N.H.), Cayman
Chemical Co. (Ann Arbor, Mich.), LINCO Research, Inc. (St. Charles,
Mo.), and Diagnostic Systems Laboratories, Inc. (Webster,
Tex.).
[0241] K. Neurotensin
[0242] The determination of the presence or level of neurotensin in
a sample is also useful in the present invention. As used herein,
the term "neurotensin" includes a tridecapeptide that is widely
distributed throughout the central nervous system and the
gastrointestinal tract. Neurotensin has been identified as an
important mediator in the development and progression of several
gastrointestinal functions and disease conditions, exerting its
effects by interacting with specific receptors that act directly or
indirectly on nerves, epithelial cells, and/or cells of the immune
and inflammatory systems (see, e.g., Zhao et al., Peptides,
27:2434-2444 (2006)).
[0243] In certain instances, the presence or level of neurotensin
is detected at the level of mRNA expression with an assay such as,
for example, a hybridization assay or an amplification-based assay.
In certain other instances, the presence or level of neurotensin is
detected at the level of protein expression using, for example, an
immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable
ELISA techniques for determining the presence or level of
neurotensin in a sample are described in, e.g., Davis et al., J.
Neurosci. Methods, 14:15-23 (1985); and Williams et al., J.
Histochem. Cytochem., 37:831-841 (1989).
[0244] L. Corticotropin-Releasing Hormone
[0245] The determination of the presence or level of
corticotropin-releasing hormone (CRH; also known as
corticotropin-releasing factor or CRF) in a sample is also useful
in the present invention. As used herein, the term
"corticotropin-releasing hormone," "CRH," "corticotropin-releasing
factor," or "CRF" includes a 41-amino acid peptide secreted by the
paraventricular nucleus of the hypothalamus that mediates the
proximal part of the response to stress in mammals such as humans.
CRH typically binds to one or more corticotropin-releasing hormone
receptors (e.g., CRHR1 and/or CRHR2). CRH is expressed by the
hypothalamus, spinal cord, stomach, spleen, duodenum, adrenal
gland, and placenta.
[0246] In certain instances, the presence or level of CRH is
detected at the level of mRNA expression with an assay such as, for
example, a hybridization assay or an amplification-based assay. In
certain other instances, the presence or level of CRH is detected
at the level of protein expression using, for example, an
immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable
ELISA kits for determining the presence or level of CRH in a serum,
plasma, saliva, or urine sample are available from, e.g., Alpco
Diagnostics (Salem, N.H.) and Cosmo Bio Co., Ltd. (Tokyo,
Japan).
[0247] M. Anti-Neutrophil Antibodies
[0248] The determination of ANCA levels and/or the presence or
absence of pANCA in a sample is also useful in the present
invention. As used herein, the term "anti-neutrophil cytoplasmic
antibody" or "ANCA" includes antibodies directed to cytoplasmic
and/or nuclear components of neutrophils. ANCA activity can be
divided into several broad categories based upon the ANCA staining
pattern in neutrophils: (1) cytoplasmic neutrophil staining without
perinuclear highlighting (cANCA); (2) perinuclear staining around
the outside edge of the nucleus (pANCA); (3) perinuclear staining
around the inside edge of the nucleus (NSNA); and (4) diffuse
staining with speckling across the entire neutrophil (SAPPA). In
certain instances, pANCA staining is sensitive to DNase treatment.
The term ANCA encompasses all varieties of anti-neutrophil
reactivity, including, but not limited to, cANCA, pANCA, NSNA, and
SAPPA. Similarly, the term ANCA encompasses all immunoglobulin
isotypes including, without limitation, immunoglobulin A and G.
[0249] ANCA levels in a sample from an individual can be
determined, for example, using an immunoassay such as an
enzyme-linked immunosorbent assay (ELISA) with alcohol-fixed
neutrophils. The presence or absence of a particular category of
ANCA such as pANCA can be determined, for example, using an
immunohistochemical assay such as an indirect fluorescent antibody
(IFA) assay. Preferably, the presence or absence of pANCA in a
sample is determined using an immunofluorescence assay with
DNase-treated, fixed neutrophils. In addition to fixed neutrophils,
antigens specific for ANCA that are suitable for determining ANCA
levels include, without limitation, unpurified or partially
purified neutrophil extracts; purified proteins, protein fragments,
or synthetic peptides such as histone H1 or ANCA-reactive fragments
thereof (see, e.g., U.S. Pat. No. 6,074,835); histone H1-like
antigens, porin antigens, Bacteroides antigens, or ANCA-reactive
fragments thereof (see, e.g., U.S. Pat. No. 6,033,864); secretory
vesicle antigens or ANCA-reactive fragments thereof (see, e.g.,
U.S. patent application Ser. No. 08/804,106); and anti-ANCA
idiotypic antibodies. One skilled in the art will appreciate that
the use of additional antigens specific for ANCA is within the
scope of the present invention.
[0250] N. Anti-Saccharomyces cerevisiae Antibodies
[0251] The determination of ASCA (e.g., ASCA-IgA and/or ASCA-IgG)
levels in a sample is also useful in the present invention. As used
herein, the term "anti-Saccharomyces cerevisiae immunoglobulin A"
or "ASCA-IgA" includes antibodies of the immunoglobulin A isotype
that react specifically with S. cerevisiae. Similarly, the term
"anti-Saccharomyces cerevisiae immunoglobulin G" or "ASCA-IgG"
includes antibodies of the immunoglobulin G isotype that react
specifically with S. cerevisiae.
[0252] The determination of whether a sample is positive for
ASCA-IgA or ASCA-IgG is made using an antigen specific for ASCA.
Such an antigen can be any antigen or mixture of antigens that is
bound specifically by ASCA-IgA and/or ASCA-IgG. Although ASCA
antibodies were initially characterized by their ability to bind S.
cerevisiae, those of skill in the art will understand that an
antigen that is bound specifically by ASCA can be obtained from S.
cerevisiae or from a variety of other sources so long as the
antigen is capable of binding specifically to ASCA antibodies.
Accordingly, exemplary sources of an antigen specific for ASCA,
which can be used to determine the levels of ASCA-IgA and/or
ASCA-IgG in a sample, include, without limitation, whole killed
yeast cells such as Saccharomyces or Candida cells; yeast cell wall
mannan such as phosphopeptidomannan (PPM); oligosachharides such as
oligomannosides; neoglycolipids; anti-ASCA idiotypic antibodies;
and the like. Different species and strains of yeast, such as S.
cerevisiae strain Su1, Su2, CBS 1315, or BM 156, or Candida
albicans strain VW32, are suitable for use as an antigen specific
for ASCA-IgA and/or ASCA-IgG. Purified and synthetic antigens
specific for ASCA are also suitable for use in determining the
levels of ASCA-IgA and/or ASCA-IgG in a sample. Examples of
purified antigens include, without limitation, purified
oligosaccharide antigens such as oligomannosides. Examples of
synthetic antigens include, without limitation, synthetic
oligomannosides such as those described in U.S. Patent Publication
No. 20030105060, e.g., D-Man .beta.(1-2) D-Man .beta.(1-2) D-Man
.beta.(1-2) D-Man-OR, D-Man .alpha.(1-2) D-Man .alpha.(1-2) D-Man
.alpha.(1-2) D-Man-OR, and D-Man .alpha.(1-3) D-Man .alpha.(1-2)
D-Man .alpha.(1-2) D-Man-OR, wherein R is a hydrogen atom, a
C.sub.1 to C.sub.20 alkyl, or an optionally labeled connector
group.
[0253] Preparations of yeast cell wall mannans, e.g., PPM, can be
used in determining the levels of ASCA-IgA and/or ASCA-IgG in a
sample. Such water-soluble surface antigens can be prepared by any
appropriate extraction technique known in the art, including, for
example, by autoclaving, or can be obtained commercially (see,
e.g., Lindberg et al., Gut, 33:909-913 (1992)). The acid-stable
fraction of PPM is also useful in the statistical algorithms of the
present invention (Sendid et al., Clin. Diag. Lab. Immunol.,
3:219-226 (1996)). An exemplary PPM that is useful in determining
ASCA levels in a sample is derived from S. uvarum strain ATCC
#38926.
[0254] Purified oligosaccharide antigens such as oligomannosides
can also be useful in determining the levels of ASCA-IgA and/or
ASCA-IgG in a sample. The purified oligomannoside antigens are
preferably converted into neoglycolipids as described in, for
example, Faille et al., Eur. J. Microbiol. Infect. Dis., 11:438-446
(1992). One skilled in the art understands that the reactivity of
such an oligomannoside antigen with ASCA can be optimized by
varying the mannosyl chain length (Frosh et al., Proc Natl. Acad.
Sci. USA, 82:1194-1198 (1985)); the anomeric configuration
(Fukazawa et al., In "Immunology of Fungal Disease," E. Kurstak
(ed.), Marcel Dekker Inc., New York, pp. 37-62 (1989); Nishikawa et
al., Microbiol. Immunol., 34:825-840 (1990); Poulain et al., Eur.
J. Clin. Microbiol., 23:46-52 (1993); Shibata et al., Arch.
Biochem. Biophys., 243:338-348 (1985); Trinel et al., Infect.
Immun., 60:3845-3851 (1992)); or the position of the linkage
(Kikuchi et al., Planta, 190:525-535 (1993)).
[0255] Suitable oligomannosides for use in the methods of the
present invention include, without limitation, an oligomannoside
having the mannotetraose Man(1-3) Man(1-2) Man(1-2) Man. Such an
oligomannoside can be purified from PPM as described in, e.g.,
Faille et al., supra. An exemplary neoglycolipid specific for ASCA
can be constructed by releasing the oligomannoside from its
respective PPM and subsequently coupling the released
oligomannoside to 4-hexadecylaniline or the like.
[0256] O. Anti-Microbial Antibodies
[0257] The determination of anti-OmpC antibody levels in a sample
is also useful in the present invention. As used herein, the term
"anti-outer membrane protein C antibody" or "anti-OmpC antibody"
includes antibodies directed to a bacterial outer membrane porin as
described in, e.g., PCT Patent Publication No. WO 01/89361. The
term "outer membrane protein C" or "OmpC" refers to a bacterial
porin that is immunoreactive with an anti-OmpC antibody.
[0258] The level of anti-OmpC antibody present in a sample from an
individual can be determined using an OmpC protein or a fragment
thereof such as an immunoreactive fragment thereof. Suitable OmpC
antigens useful in determining anti-OmpC antibody levels in a
sample include, without limitation, an OmpC protein, an OmpC
polypeptide having substantially the same amino acid sequence as
the OmpC protein, or a fragment thereof such as an immunoreactive
fragment thereof. As used herein, an OmpC polypeptide generally
describes polypeptides having an amino acid sequence with greater
than about 50% identity, preferably greater than about 60%
identity, more preferably greater than about 70% identity, still
more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,
98%, or 99% amino acid sequence identity with an OmpC protein, with
the amino acid identity determined using a sequence alignment
program such as CLUSTALW. Such antigens can be prepared, for
example, by purification from enteric bacteria such as E. coli, by
recombinant expression of a nucleic acid such as Genbank Accession
No. K00541, by synthetic means such as solution or solid phase
peptide synthesis, or by using phage display.
[0259] The determination of anti-I2 antibody levels in a sample is
also useful in the present invention. As used herein, the term
"anti-I2 antibody" includes antibodies directed to a microbial
antigen sharing homology to bacterial transcriptional regulators as
described in, e.g., U.S. Pat. No. 6,309,643. The term "I2" refers
to a microbial antigen that is immunoreactive with an anti-I2
antibody. The microbial I2 protein is a polypeptide of 100 amino
acids sharing some similarity weak homology with the predicted
protein 4 from C. pasteurianum, Rv3557c from Mycobacterium
tuberculosis, and a transcriptional regulator from Aquifex
aeolicus. The nucleic acid and protein sequences for the I2 protein
are described in, e.g., U.S. Pat. No. 6,309,643.
[0260] The level of anti-I2 antibody present in a sample from an
individual can be determined using an I2 protein or a fragment
thereof such as an immunoreactive fragment thereof. Suitable I2
antigens useful in determining anti-I2 antibody levels in a sample
include, without limitation, an I2 protein, an I2 polypeptide
having substantially the same amino acid sequence as the I2
protein, or a fragment thereof such as an immunoreactive fragment
thereof. Such I2 polypeptides exhibit greater sequence similarity
to the I2 protein than to the C. pasteurianum protein 4 and include
isotype variants and homologs thereof. As used herein, an I2
polypeptide generally describes polypeptides having an amino acid
sequence with greater than about 50% identity, preferably greater
than about 60% identity, more preferably greater than about 70%
identity, still more preferably greater than about 80%, 85%, 90%,
95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a
naturally-occurring I2 protein, with the amino acid identity
determined using a sequence alignment program such as CLUSTALW.
Such I2 antigens can be prepared, for example, by purification from
microbes, by recombinant expression of a nucleic acid encoding an
I2 antigen, by synthetic means such as solution or solid phase
peptide synthesis, or by using phage display.
[0261] The determination of anti-flagellin antibody levels in a
sample is also useful in the present invention. As used herein, the
term "anti-flagellin antibody" includes antibodies directed to a
protein component of bacterial flagella as described in, e.g., PCT
Patent Publication No. WO 03/053220 and U.S. Patent Publication No.
20040043931. The term "flagellin" refers to a bacterial flagellum
protein that is immunoreactive with an anti-flagellin antibody.
Microbial flagellins are proteins found in bacterial flagellum that
arrange themselves in a hollow cylinder to form the filament.
[0262] The level of anti-flagellin antibody present in a sample
from an individual can be determined using a flagellin protein or a
fragment thereof such as an immunoreactive fragment thereof.
Suitable flagellin antigens useful in determining anti-flagellin
antibody levels in a sample include, without limitation, a
flagellin protein such as Cbir-1 flagellin, flagellin X, flagellin
A, flagellin B, fragments thereof, and combinations thereof, a
flagellin polypeptide having substantially the same amino acid
sequence as the flagellin protein, or a fragment thereof such as an
immunoreactive fragment thereof. As used herein, a flagellin
polypeptide generally describes polypeptides having an amino acid
sequence with greater than about 50% identity, preferably greater
than about 60% identity, more preferably greater than about 70%
identity, still more preferably greater than about 80%, 85%, 90%,
95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a
naturally-occurring flagellin protein, with the amino acid identity
determined using a sequence alignment program such as CLUSTALW.
Such flagellin antigens can be prepared, e.g., by purification from
bacterium such as Helicobacter Bilis, Helicobacter mustelae,
Helicobacter pylori, Butyrivibrio fibrisolvens, and bacterium found
in the cecum, by recombinant expression of a nucleic acid encoding
a flagellin antigen, by synthetic means such as solution or solid
phase peptide synthesis, or by using phage display.
[0263] P. Other Diagnostic Markers
[0264] The determination of the presence or level of lactoferrin in
a sample is also useful in the present invention. In certain
instances, the presence or level of lactoferrin is detected at the
level of mRNA expression with an assay such as, for example, a
hybridization assay or an amplification-based assay. In certain
other instances, the presence or level of lactoferrin is detected
at the level of protein expression using, for example, an
immunoassay (e.g., ELISA) or an immunohistochemical assay. A
lactoferrin ELISA kit available from Calbiochem (San Diego, Calif.)
can be used to detect human lactoferrin in a plasma, urine,
bronchoalveolar lavage, or cerebrospinal fluid sample. Similarly,
an ELISA kit available from U.S. Biological (Swampscott, Mass.) can
be used to determine the level of lactoferrin in a plasma sample.
U.S. Patent Publication No. 20040137536 describes an ELISA assay
for determining the presence of elevated lactoferrin levels in a
stool sample. Likewise, U.S. Patent Publication No. 20040033537
describes an ELISA assay for determining the concentration of
endogenous lactoferrin in a stool, mucus, or bile sample. In some
embodiments, then presence or level of anti-lactoferrin antibodies
can be detected in a sample using, e.g., lactoferrin protein or a
fragment thereof.
[0265] Immunoassays such as ELISA are also particularly useful for
determining the presence or level of C-reactive protein (CRP) in a
sample. For example, a sandwich colorimetric ELISA assay available
from Alpco Diagnostics (Salem, N.H.) can be used to determine the
level of CRP in a serum, plasma, urine, or stool sample. Similarly,
an ELISA kit available from Biomeda Corporation (Foster City,
Calif.) can be used to detect CRP levels in a sample. Other methods
for determining CRP levels in a sample are described in, e.g., U.S.
Pat. Nos. 6,838,250 and 6,406,862; and U.S. Patent Publication Nos.
20060024682 and 20060019410.
[0266] In addition, hemoccult, fecal occult blood, is often
indicative of gastrointestinal illness and various kits have been
developed to monitor gastrointestinal bleeding. For example,
Hemoccult SENSA, a Beckman Coulter product, is a diagnostic aid for
gastrointestinal bleeding, iron deficiency, peptic ulcers,
ulcerative colitis, and, in some instances, in screening for
colorectal cancer. This particular assay is based on the oxidation
of guaiac by hydrogen peroxide to produce a blue color. A similar
colorimetric assay is commercially available from Helena
Laboratories (Beaumont, Tex.) for the detection of blood in stool
samples. Other methods for detecting occult blood in a stool sample
by determining the presence or level of hemoglobin or heme activity
are described in, e.g., U.S. Pat. Nos. 4,277,250, 4,920,045,
5,081,040, and 5,310,684.
[0267] The determination of the presence or level of fibrinogen or
a proteolytic product thereof such as a fibrinopeptide in a sample
is also useful in the present invention. Fibrinogen is a plasma
glycoprotein synthesized in the liver composed of 3 structurally
different subunits: alpha (FGA); beta (FGB); and gamma (FGG).
Thrombin causes a limited proteolysis of the fibrinogen molecule,
during which fibrinopeptides A and B are released from the
N-terminal regions of the alpha and beta chains, respectively.
Fibrinopeptides A and B, which have been sequenced in many species,
may have a physiological role as vasoconstrictors and may aid in
local hemostasis during blood clotting. In one embodiment, human
fibrinopeptide A comprises the sequence:
Ala-Asp-Ser-Gly-Glu-Gly-Asp-Phe-Leu-Ala-Glu-Gly-Gly-Gly-Val-Arg
(SEQ ID NO:1). In another embodiment, human fibrinopeptide B
comprises the sequence:
Glp-Gly-Val-Asn-Asp-Asn-Glu-Glu-Gly-Phe-Phe-Ser-Ala-Arg (SEQ ID
NO:2). An ELISA kit available from American Diagnostica Inc.
(Stamford, Conn.) can be used to detect the presence or level of
human fibrinopeptide A in plasma or other biological fluids.
[0268] In certain embodiments, the determination of the presence or
level of calcitonin gene-related peptide (CGRP) in a sample is
useful in the present invention. Calcitonin is a 32-amino acid
peptide hormone synthesized by the parafollicular cells of the
thyroid. It causes reduction in serum calcium, an effect opposite
to that of parathyroid hormone. CGRP is derived, with calcitonin,
from the CT/CGRP gene located on chromosome 11. CGRP is a 37-amino
acid peptide and is a potent endogenous vasodilator. CGRP is
primarily produced in nervous tissue; however, its receptors are
expressed throughout the body. An ELISA kit available from Cayman
Chemical Co. (Ann Arbor, Mich.) can be used to detect the presence
or level of human CGRP in a variety of samples including plasma,
serum, nervous tissue, CSF, and culture media.
[0269] In other embodiments, the determination of the presence or
level of an anti-tissue transglutaminase (tTG) antibody in a sample
is useful in the present invention. As used herein, the term
"anti-tTG antibody" includes any antibody that recognizes tissue
transglutaminase (tTG) or a fragment thereof. Transglutaminases are
a diverse family of Ca.sup.2+-dependent enzymes that are ubiquitous
and highly conserved across species. Of all the transglutaminases,
tTG is the most widely distributed. In certain instances, the
anti-tTG antibody is an anti-tTG IgA antibody, anti-tTG IgG
antibody, or mixtures thereof. An ELISA kit available from ScheBo
Biotech USA Inc. (Marietta, Ga.) can be used to detect the presence
or level of human anti-tTG IgA antibodies in a blood sample.
[0270] The determination of the presence of polymorphisms in the
NOD2/CARD15 gene in a sample is also useful in the present
invention. For example, polymorphisms in the NOD2 gene such as a
C2107T nucleotide variant that results in a R703W protein variant
can be identified in a sample from an individual (see, e.g., U.S.
Patent Publication No. 20030190639). In an alternative embodiment,
NOD2 mRNA levels can be used as a diagnostic marker of the present
invention to aid in classifying IBS.
[0271] The determination of the presence of polymorphisms in the
serotonin reuptake transporter (SERT) gene in a sample is also
useful in the present invention. For example, polymorphisms in the
promoter region of the SERT gene have effects on transcriptional
activity, resulting in altered 5-HT reuptake efficiency. It has
been shown that a strong genotypic association was observed between
the SERT-P deletion/deletion genotype and the IBS phenotype (see,
e.g., Yeo Gut, 53:1396-1399 (2004)). In an alternative embodiment,
SERT mRNA levels can be used as a diagnostic marker of the present
invention to aid in classifying IBS (see, e.g., Gershon, J. Clin.
Gastroenterol., 39(5 Suppl.):S184-193 (2005)).
[0272] In certain aspects, the level of tryptophan hydroxylase-1
mRNA is a diagnostic marker. For example, tryptophan hydroxylase-1
mRNA has been shown to be significantly reduced in IBS (see, e.g.,
Coats, Gastroenterology, 126:1897-1899 (2004)). In certain other
aspects, a lactulose breath test to measure methane, which is
indicative of bacterial overgrowth, can be used as a diagnostic
marker for IBS.
[0273] Additional diagnostic markers include, but are not limited
to, L-selectin/CD62L, anti-U1-70 kDa autoantibodies, zona occludens
1 (ZO-1), vasoactive intestinal peptide (VIP), serum amyloid A,
gastrin, NB3 gene polymorphisms, NCI1 gene polymorphisms, fecal
leukocytes, .alpha.2A and .alpha.2C adrenoreceptor gene
polymorphisms, IL-10 gene polymorphisms, TNF-.alpha. gene
polymorphisms, TGF-.beta.1 gene polymorphisms, .alpha.-adrenergic
receptors, G-proteins, 5-HT.sub.2A gene polymorphisms, 5-HTT LPR
gene polymorphisms, 5-HT.sub.4 receptor gene polymorphisms,
zonulin, and the 33-mer peptide (Shan et al., Science,
297:2275-2279 (2002); PCT Patent Publication No. WO 03/068170).
VI. Classification Markers
[0274] A variety of classification markers are suitable for use in
the methods, systems, and code of the present invention for
classifying IBS into a category, form, or clinical subtype such as,
for example, IBS-constipation (IBS-C), IBS-diarrhea (IBS-D),
IBS-mixed (IBS-M), IBS-alternating (IBS-A), or post-infectious IBS
(IBS-PI). Examples of classification markers include, without
limitation, any of the diagnostic markers described above (e.g.,
leptin, serotonin reuptake transporter (SERT), tryptophan
hydroxylase-1,5-hydroxytryptamine (5-HT), and the like), as well as
antrum mucosal protein 8, keratin-8, claudin-8, zonulin,
corticotropin-releasing hormone receptor-1 (CRHR1),
corticotropin-releasing hormone receptor-2 (CRHR2), and the
like.
[0275] For instance, Example 1 illustrates that measuring leptin
levels is particularly useful for distinguishing IBS-C patient
samples from IBS-A and IBS-D patient samples. In addition, mucosal
SERT and tryptophan hydroxylase-1 expression have been shown to be
decreased in IBS-C and IBS-D (see, e.g., Gershon, J. Clin.
Gastroenterol., 39(5 Suppl):S184-193 (2005)). Furthermore, IBS-C
patients show impaired postprandial 5-HT release, whereas IBS-PI
patients have higher peak levels of 5-HT (see, e.g., Dunlop, Clin
Gastroenterol Hepatol., 3:349-357 (2005)).
VII. Assays
[0276] Any of a variety of assays, techniques, and kits known in
the art can be used to determine the presence or level of one or
more markers in a sample to classify whether the sample is
associated with IBS.
[0277] The present invention relies, in part, on determining the
presence or level of at least one marker in a sample obtained from
an individual. As used herein, the term "determining the presence
of at least one marker" includes determining the presence of each
marker of interest by using any quantitative or qualitative assay
known to one of skill in the art. In certain instances, qualitative
assays that determine the presence or absence of a particular
trait, variable, or biochemical or serological substance (e.g.,
protein or antibody) are suitable for detecting each marker of
interest. In certain other instances, quantitative assays that
determine the presence or absence of RNA, protein, antibody, or
activity are suitable for detecting each marker of interest. As
used herein, the term "determining the level of at least one
marker" includes determining the level of each marker of interest
by using any direct or indirect quantitative assay known to one of
skill in the art. In certain instances, quantitative assays that
determine, for example, the relative or absolute amount of RNA,
protein, antibody, or activity are suitable for determining the
level of each marker of interest. One skilled in the art will
appreciate that any assay useful for determining the level of a
marker is also useful for determining the presence or absence of
the marker.
[0278] As used herein, the term "antibody" includes a population of
immunoglobulin molecules, which can be polyclonal or monoclonal and
of any isotype, or an immunologically active fragment of an
immunoglobulin molecule. Such an immunologically active fragment
contains the heavy and light chain variable regions, which make up
the portion of the antibody molecule that specifically binds an
antigen. For example, an immunologically active fragment of an
immunoglobulin molecule known in the art as Fab, Fab' or
F(ab').sub.2 is included within the meaning of the term
antibody.
[0279] Flow cytometry can be used to determine the presence or
level of one or more markers in a sample. Such flow cytometric
assays, including bead based immunoassays, can be used to
determine, e.g., antibody marker levels in the same manner as
described for detecting serum antibodies to Candida albicans and
HIV proteins (see, e.g., Bishop and Davis, J. Immunol. Methods,
210:79-87 (1997); McHugh et al., J. Immunol. Methods, 116:213
(1989); Scillian et al., Blood, 73:2041 (1989)).
[0280] Phage display technology for expressing a recombinant
antigen specific for a marker can also be used to determine the
presence or level of one or more markers in a sample. Phage
particles expressing an antigen specific for, e.g., an antibody
marker can be anchored, if desired, to a multi-well plate using an
antibody such as an anti-phage monoclonal antibody (Felici et al.,
"Phage-Displayed Peptides as Tools for Characterization of Human
Sera" in Abelson (Ed.), Methods in Enzymol., 267, San Diego:
Academic Press, Inc. (1996)).
[0281] A variety of immunoassay techniques, including competitive
and non-competitive immunoassays, can be used to determine the
presence or level of one or more markers in a sample (see, e.g.,
Self and Cook, Curr. Opin. Biotechnol., 7:60-65 (1996)). The term
immunoassay encompasses techniques including, without limitation,
enzyme immunoassays (EIA) such as enzyme multiplied immunoassay
technique (EMIT), enzyme-linked immunosorbent assay (ELISA),
antigen capture ELISA, sandwich ELISA, IgM antibody capture ELISA
(MAC ELISA), and microparticle enzyme immunoassay (MEIA); capillary
electrophoresis immunoassays (CEIA); radioimmunoassays (RIA);
immunoradiometric assays (IRMA); fluorescence polarization
immunoassays (FPIA); and chemiluminescence assays (CL). If desired,
such immunoassays can be automated. Immunoassays can also be used
in conjunction with laser induced fluorescence (see, e.g.,
Schmalzing and Nashabeh, Electrophoresis, 18:2184-2193 (1997); Bao,
J. Chromatogr. B. Biomed. Sci, 699:463-480 (1997)). Liposome
immunoassays, such as flow-injection liposome immunoassays and
liposome immunosensors, are also suitable for use in the present
invention (see, e.g., Rongen et al., J. Immunol. Methods,
204:105-133 (1997)). In addition, nephelometry assays, in which the
formation of protein/antibody complexes results in increased light
scatter that is converted to a peak rate signal as a function of
the marker concentration, are suitable for use in the present
invention. Nephelometry assays are commercially available from
Beckman Coulter (Brea, Calif.; Kit #449430) and can be performed
using a Behring Nephelometer Analyzer (Fink et al., J. Clin. Chem.
Clin. Biol. Chem., 27:261-276 (1989)).
[0282] Antigen capture ELISA can be useful for determining the
presence or level of one or more markers in a sample. For example,
in an antigen capture ELISA, an antibody directed to a marker of
interest is bound to a solid phase and sample is added such that
the marker is bound by the antibody. After unbound proteins are
removed by washing, the amount of bound marker can be quantitated
using, e.g., a radioimmunoassay (see, e.g., Harlow and Lane,
Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, New
York, 1988)). Sandwich ELISA can also be suitable for use in the
present invention. For example, in a two-antibody sandwich assay, a
first antibody is bound to a solid support, and the marker of
interest is allowed to bind to the first antibody. The amount of
the marker is quantitated by measuring the amount of a second
antibody that binds the marker. The antibodies can be immobilized
onto a variety of solid supports, such as magnetic or
chromatographic matrix particles, the surface of an assay plate
(e.g., microtiter wells), pieces of a solid substrate material or
membrane (e.g., plastic, nylon, paper), and the like. An assay
strip can be prepared by coating the antibody or a plurality of
antibodies in an array on a solid support. This strip can then be
dipped into the test sample and processed quickly through washes
and detection steps to generate a measurable signal, such as a
colored spot.
[0283] A radioimmunoassay using, for example, an iodine-125
(.sup.125I) labeled secondary antibody (Harlow and Lane, supra) is
also suitable for determining the presence or level of one or more
markers in a sample. A secondary antibody labeled with a
chemiluminescent marker can also be suitable for use in the present
invention. A chemiluminescence assay using a chemiluminescent
secondary antibody is suitable for sensitive, non-radioactive
detection of marker levels. Such secondary antibodies can be
obtained commercially from various sources, e.g., Amersham
Lifesciences, Inc. (Arlington Heights, Ill.).
[0284] The immunoassays described above are particularly useful for
determining the presence or level of one or more markers in a
sample. As a non-limiting example, an ELISA using an IL-8-binding
molecule such as an anti-IL-8 antibody or an extracellular
IL-8-binding protein (e.g., IL-8 receptor) is useful for
determining whether a sample is positive for IL-8 protein or for
determining IL-8 protein levels in a sample. A fixed neutrophil
ELISA is useful for determining whether a sample is positive for
ANCA or for determining ANCA levels in a sample. Similarly, an
ELISA using yeast cell wall phosphopeptidomannan is useful for
determining whether a sample is positive for ASCA-IgA and/or
ASCA-IgG, or for determining ASCA-IgA and/or ASCA-IgG levels in a
sample. An ELISA using OmpC protein or a fragment thereof is useful
for determining whether a sample is positive for anti-OmpC
antibodies, or for determining anti-OmpC antibody levels in a
sample. An ELISA using I2 protein or a fragment thereof is useful
for determining whether a sample is positive for anti-I2
antibodies, or for determining anti-I2 antibody levels in a sample.
An ELISA using flagellin protein (e.g., Cbir-1 flagellin) or a
fragment thereof is useful for determining whether a sample is
positive for anti-flagellin antibodies, or for determining
anti-flagellin antibody levels in a sample. In addition, the
immunoassays described above are particularly useful for
determining the presence or level of other diagnostic markers in a
sample.
[0285] Specific immunological binding of the antibody to the marker
of interest can be detected directly or indirectly. Direct labels
include fluorescent or luminescent tags, metals, dyes,
radionuclides, and the like, attached to the antibody. An antibody
labeled with iodine-125 (.sup.125I) can be used for determining the
levels of one or more markers in a sample. A chemiluminescence
assay using a chemiluminescent antibody specific for the marker is
suitable for sensitive, non-radioactive detection of marker levels.
An antibody labeled with fluorochrome is also suitable for
determining the levels of one or more markers in a sample. Examples
of fluorochromes include, without limitation, DAPI, fluorescein,
Hoechst 33258, R-phycocyanin, B-phycoerythrin, R-phycoerythrin,
rhodamine, Texas red, and lissamine. Secondary antibodies linked to
fluorochromes can be obtained commercially, e.g., goat F(ab').sub.2
anti-human IgG-FITC is available from Tago Immunologicals
(Burlingame, Calif.).
[0286] Indirect labels include various enzymes well-known in the
art, such as horseradish peroxidase (HRP), alkaline phosphatase
(AP), .beta.-galactosidase, urease, and the like. A
horseradish-peroxidase detection system can be used, for example,
with the chromogenic substrate tetramethylbenzidine (TMB), which
yields a soluble product in the presence of hydrogen peroxide that
is detectable at 450 nm. An alkaline phosphatase detection system
can be used with the chromogenic substrate p-nitrophenyl phosphate,
for example, which yields a soluble product readily detectable at
405 nm. Similarly, a .beta.-galactosidase detection system can be
used with the chromogenic substrate
o-nitrophenyl-.beta.-D-galactopyranoside (ONPG), which yields a
soluble product detectable at 410 nm. An urease detection system
can be used with a substrate such as urea-bromocresol purple (Sigma
Immunochemicals; St. Louis, Mo.). A useful secondary antibody
linked to an enzyme can be obtained from a number of commercial
sources, e.g., goat F(ab').sub.2 anti-human IgG-alkaline
phosphatase can be purchased from Jackson ImmunoResearch (West
Grove, Pa.).
[0287] A signal from the direct or indirect label can be analyzed,
for example, using a spectrophotometer to detect color from a
chromogenic substrate; a radiation counter to detect radiation such
as a gamma counter for detection of .sup.125I; or a fluorometer to
detect fluorescence in the presence of light of a certain
wavelength. For detection of enzyme-linked antibodies, a
quantitative analysis of the amount of marker levels can be made
using a spectrophotometer such as an EMAX Microplate Reader
(Molecular Devices; Menlo Park, Calif.) in accordance with the
manufacturer's instructions. If desired, the assays of the present
invention can be automated or performed robotically, and the signal
from multiple samples can be detected simultaneously.
[0288] Quantitative western blotting can also be used to detect or
determine the presence or level of one or more markers in a sample.
Western blots can be quantitated by well-known methods such as
scanning densitometry or phosphorimaging. As a non-limiting
example, protein samples are electrophoresed on 10% SDS-PAGE
Laemmli gels. Primary murine monoclonal antibodies are reacted with
the blot, and antibody binding can be confirmed to be linear using
a preliminary slot blot experiment. Goat anti-mouse horseradish
peroxidase-coupled antibodies (BioRad) are used as the secondary
antibody, and signal detection performed using chemiluminescence,
for example, with the Renaissance chemiluminescence kit (New
England Nuclear; Boston, Mass.) according to the manufacturer's
instructions. Autoradiographs of the blots are analyzed using a
scanning densitometer (Molecular Dynamics; Sunnyvale, Calif.) and
normalized to a positive control. Values are reported, for example,
as a ratio between the actual value to the positive control
(densitometric index). Such methods are well known in the art as
described, for example, in Parra et al., J. Vasc. Surg., 28:669-675
(1998).
[0289] Alternatively, a variety of immunohistochemical assay
techniques can be used to determine the presence or level of one or
more markers in a sample. The term immunohistochemical assay
encompasses techniques that utilize the visual detection of
fluorescent dyes or enzymes coupled (i.e., conjugated) to
antibodies that react with the marker of interest using fluorescent
microscopy or light microscopy and includes, without limitation,
direct fluorescent antibody assay, indirect fluorescent antibody
(IFA) assay, anticomplement immunofluorescence, avidin-biotin
immunofluorescence, and immunoperoxidase assays. An IFA assay, for
example, is useful for determining whether a sample is positive for
ANCA, the level of ANCA in a sample, whether a sample is positive
for pANCA, the level of pANCA in a sample, and/or an ANCA staining
pattern (e.g., cANCA, pANCA, NSNA, and/or SAPPA staining pattern).
The concentration of ANCA in a sample can be quantitated, e.g.,
through endpoint titration or through measuring the visual
intensity of fluorescence compared to a known reference
standard.
[0290] Alternatively, the presence or level of a marker of interest
can be determined by detecting or quantifying the amount of the
purified marker. Purification of the marker can be achieved, for
example, by high pressure liquid chromatography (HPLC), alone or in
combination with mass spectrometry (e.g., MALDI/MS, MALDI-TOF/MS,
SELDI-TOF/MS, tandem MS, etc.). Qualitative or quantitative
detection of a marker of interest can also be determined by
well-known methods including, without limitation, Bradford assays,
Coomassie blue staining, silver staining, assays for radiolabeled
protein, and mass spectrometry.
[0291] The analysis of a plurality of markers may be carried out
separately or simultaneously with one test sample. For separate or
sequential assay of markers, suitable apparatuses include clinical
laboratory analyzers such as the ElecSys (Roche), the AxSym
(Abbott), the Access (Beckman), the ADVIA.RTM., the CENTAUR.RTM.
(Bayer), and the NICHOLS ADVANTAGE.RTM. (Nichols Institute)
immunoassay systems. Preferred apparatuses or protein chips perform
simultaneous assays of a plurality of markers on a single surface.
Particularly useful physical formats comprise surfaces having a
plurality of discrete, addressable locations for the detection of a
plurality of different markers. Such formats include protein
microarrays, or "protein chips" (see, e.g., Ng et J. Cell Mol.
Med., 6:329-340 (2002)) and certain capillary devices (see, e.g.,
U.S. Pat. No. 6,019,944). In these embodiments, each discrete
surface location may comprise antibodies to immobilize one or more
markers for detection at each location. Surfaces may alternatively
comprise one or more discrete particles (e.g., microparticles or
nanoparticles) immobilized at discrete locations of a surface,
where the microparticles comprise antibodies to immobilize one or
more markers for detection.
[0292] In addition to the above-described assays for determining
the presence or level of various markers of interest, analysis of
marker mRNA levels using routine techniques such as Northern
analysis, reverse-transcriptase polymerase chain reaction (RT-PCR),
or any other methods based on hybridization to a nucleic acid
sequence that is complementary to a portion of the marker coding
sequence (e.g., slot blot hybridization) are also within the scope
of the present invention. Applicable PCR amplification techniques
are described in, e.g., Ausubel et al., Current Protocols in
Molecular Biology, John Wiley & Sons, Inc. New York (1999),
Chapter 7 and Supplement 47; Theophilus et al., "PCR Mutation
Detection Protocols," Humana Press, (2002); and Innis et al., PCR
Protocols, San Diego, Academic Press, Inc. (1990). General nucleic
acid hybridization methods are described in Anderson, "Nucleic Acid
Hybridization," BIOS Scientific Publishers, 1999. Amplification or
hybridization of a plurality of transcribed nucleic acid sequences
(e.g., mRNA or cDNA) can also be performed from mRNA or cDNA
sequences arranged in a microarray. Microarray methods are
generally described in Hardiman, "Microarrays Methods and
Applications: Nuts & Bolts," DNA Press, 2003; and Baldi et al.,
"DNA Microarrays and Gene Expression: From Experiments to Data
Analysis and Modeling," Cambridge University Press, 2002.
[0293] Analysis of the genotype of a marker such as a genetic
marker can be performed using techniques known in the art
including, without limitation, polymerase chain reaction
(PCR)-based analysis, sequence analysis, and electrophoretic
analysis. A non-limiting example of a PCR-based analysis includes a
Taqman.RTM. allelic discrimination assay available from Applied
Biosystems. Non-limiting examples of sequence analysis include
Maxam-Gilbert sequencing, Sanger sequencing, capillary array DNA
sequencing, thermal cycle sequencing (Sears et al., Biotechniques,
13:626-633 (1992)), solid-phase sequencing (Zimmerman et al.,
Methods Mol. Cell Biol., 3:39-42 (1992)), sequencing with mass
spectrometry such as matrix-assisted laser desorption/ionization
time-of-flight mass spectrometry (MALDI-TOF/MS; Fu et al., Nature
Biotech., 16:381-384 (1998)), and sequencing by hybridization (Chee
et al., Science, 274:610-614 (1996); Drmanac et al., Science,
260:1649-1652 (1993); Drmanac et al., Nature Biotech., 16:54-58
(1998)). Non-limiting examples of electrophoretic analysis include
slab gel electrophoresis such as agarose or polyacrylamide gel
electrophoresis, capillary electrophoresis, and denaturing gradient
gel electrophoresis. Other methods for genotyping an individual at
a polymorphic site in a marker include, e.g., the INVADER.RTM.
assay from Third Wave Technologies, Inc., restriction fragment
length polymorphism (RFLP) analysis, allele-specific
oligonucleotide hybridization, a heteroduplex mobility assay, and
single strand conformational polymorphism (SSCP) analysis.
[0294] Several markers of interest may be combined into one test
for efficient processing of a multiple of samples. In addition, one
skilled in the art would recognize the value of testing multiple
samples (e.g., at successive time points, etc.) from the same
subject. Such testing of serial samples can allow the
identification of changes in marker levels over time. Increases or
decreases in marker levels, as well as the absence of change in
marker levels, can also provide useful information to classify IBS
or to rule out diseases and disorders associated with IBS-like
symptoms.
[0295] A panel for measuring one or more of the markers described
above may be constructed to provide relevant information related to
the approach of the present invention for classifying a sample as
being associated with IBS. Such a panel may be constructed to
determine the presence or level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, or more
individual markers. The analysis of a single marker or subsets of
markers can also be carried out by one skilled in the art in
various clinical settings. These include, but are not limited to,
ambulatory, urgent care, critical care, intensive care, monitoring
unit, inpatient, outpatient, physician office, medical clinic, and
health screening settings.
[0296] The analysis of markers could be carried out in a variety of
physical formats as well. For example, the use of microtiter plates
or automation could be used to facilitate the processing of large
numbers of test samples. Alternatively, single sample formats could
be developed to facilitate treatment and diagnosis in a timely
fashion.
VIII. Statistical Algorithms
[0297] In some aspects, the present invention provides methods,
systems, and code for classifying whether a sample is associated
with IBS using a statistical algorithm or process to classify the
sample as an IBS sample or non-IBS sample. In other aspects, the
present invention provides methods, systems, and code for
classifying whether a sample is associated with IBS using a first
statistical algorithm or process to classify the sample as a
non-IBD sample or IBD sample (i.e., IBD rule-out step), followed by
a second statistical algorithm or process to classify the non-IBD
sample as an IBS sample or non-IBS sample (i.e., IBS rule-in step).
Preferably, the statistical algorithms or processes independently
comprise one or more learning statistical classifier systems. As
described herein, a combination of learning statistical classifier
systems advantageously provides improved sensitivity, specificity,
negative predictive value, positive predictive value, and/or
overall accuracy for classifying whether a sample is associated
with IBS.
[0298] The term "statistical algorithm" or "statistical process"
includes any of a variety of statistical analyses used to determine
relationships between variables. In the present invention, the
variables are the presence or level of at least one marker of
interest and/or the presence or severity of at least one
IBS-related symptom. Any number of markers and/or symptoms can be
analyzed using a statistical algorithm described herein. For
example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more biomarkers
and/or symptoms can be included in a statistical algorithm. In one
embodiment, logistic regression is used. In another embodiment,
linear regression is used. In certain instances, the statistical
algorithms of the present invention can use a quantile measurement
of a particular marker within a given population as a variable.
Quantiles are a set of "cut points" that divide a sample of data
into groups containing (as far as possible) equal numbers of
observations. For example, quartiles are values that divide a
sample of data into four groups containing (as far as possible)
equal numbers of observations. The lower quartile is the data value
a quarter way up through the ordered data set; the upper quartile
is the data value a quarter way down through the ordered data set.
Quintiles are values that divide a sample of data into five groups
containing (as far as possible) equal numbers of observations. The
present invention can also include the use of percentile ranges of
marker levels (e.g., tertiles, quartile, quintiles, etc.), or their
cumulative indices (e.g., quartile sums of marker levels, etc.) as
variables in the algorithms (just as with continuous
variables).
[0299] Preferably, the statistical algorithms of the present
invention comprise one or more learning statistical classifier
systems. As used herein, the term "learning statistical classifier
system" includes a machine learning algorithmic technique capable
of adapting to complex data sets (e.g., panel of markers of
interest and/or list of IBS-related symptoms) and making decisions
based upon such data sets. In some embodiments, a single learning
statistical classifier system such as a classification tree (e.g.,
random forest) is used. In other embodiments, a combination of 2,
3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifier
systems are used, preferably in tandem. Examples of learning
statistical classifier systems include, but are not limited to,
those using inductive learning (e.g., decision/classification trees
such as random forests, classification and regression trees
(C&RT), boosted trees, etc.), Probably Approximately Correct
(PAC) learning, connectionist learning (e.g., neural networks (NN),
artificial neural networks (ANN), neuro fuzzy networks (NFN),
network structures, perceptrons such as multi-layer perceptrons,
multi-layer feed-forward networks, applications of neural networks,
Bayesian learning in belief networks, etc.), reinforcement learning
(e.g., passive learning in a known environment such as naive
learning, adaptive dynamic learning, and temporal difference
learning, passive learning in an unknown environment, active
learning in an unknown environment, learning action-value
functions, applications of reinforcement learning, etc.), and
genetic algorithms and evolutionary programming. Other learning
statistical classifier systems include support vector machines
(e.g., Kernel methods), multivariate adaptive regression splines
(MARS), Levenberg-Marquardt algorithms, Gauss-Newton algorithms,
mixtures of Gaussians, gradient descent algorithms, and learning
vector quantization (LVQ).
[0300] Random forests are learning statistical classifier systems
that are constructed using an algorithm developed by Leo Breiman
and Adele Cutler. Random forests use a large number of individual
decision trees and decide the class by choosing the mode (i.e.,
most frequently occurring) of the classes as determined by the
individual trees. Random forest analysis can be performed, e.g.,
using the RandomForests software available from Salford Systems
(San Diego, Calif.). See, e.g., Breiman, Machine Learning, 45:5-32
(2001); and
http://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm,
for a description of random forests.
[0301] Classification and regression trees represent a computer
intensive alternative to fitting classical regression models and
are typically used to determine the best possible model for a
categorical or continuous response of interest based upon one or
more predictors. Classification and regression tree analysis can be
performed, e.g., using the CART software available from Salford
Systems or the Statistica data analysis software available from
StatSoft, Inc. (Tulsa, Okla.). A description of classification and
regression trees is found, e.g., in Breiman et al. "Classification
and Regression Trees," Chapman and Hall, New York (1984); and
Steinberg et al., "CART: Tree-Structured Non-Parametric Data
Analysis," Salford Systems, San Diego, (1995).
[0302] Neural networks are interconnected groups of artificial
neurons that use a mathematical or computational model for
information processing based on a connectionist approach to
computation. Typically, neural networks are adaptive systems that
change their structure based on external or internal information
that flows through the network. Specific examples of neural
networks include feed-forward neural networks such as perceptrons,
single-layer perceptrons, multi-layer perceptrons, backpropagation
networks, ADALINE networks, MADALINE networks, Learnmatrix
networks, radial basis function (RBF) networks, and self-organizing
maps or Kohonen self-organizing networks; recurrent neural networks
such as simple recurrent networks and Hopfield networks; stochastic
neural networks such as Boltzmann machines; modular neural networks
such as committee of machines and associative neural networks; and
other types of networks such as instantaneously trained neural
networks, spiking neural networks, dynamic neural networks, and
cascading neural networks. Neural network analysis can be
performed, e.g., using the Statistica data analysis software
available from StatSoft, Inc. See, e.g., Freeman et al., In "Neural
Networks: Algorithms, Applications and Programming Techniques,"
Addison-Wesley Publishing Company (1991); Zadeh, Information and
Control, 8:338-353 (1965); Zadeh, "IEEE Trans. on Systems, Man and
Cybernetics," 3:28-44 (1973); Gersho et al., In "Vector
Quantization and Signal Compression," Kluywer Academic Publishers,
Boston, Dordrecht, London (1992); and Hassoun, "Fundamentals of
Artificial Neural Networks," MIT Press, Cambridge, Mass., London
(1995), for a description of neural networks.
[0303] Support vector machines are a set of related supervised
learning techniques used for classification and regression and are
described, e.g., in Cristianini et al., "An Introduction to Support
Vector Machines and Other Kernel-Based Learning Methods," Cambridge
University Press (2000). Support vector machine analysis can be
performed, e.g., using the SVM.sup.light software developed by
Thorsten Joachims (Cornell University) or using the LIBSVM software
developed by Chih-Chung Chang and Chih-Jen Lin (National Taiwan
University).
[0304] The learning statistical classifier systems described herein
can be trained and tested using a cohort of samples (e.g.,
serological samples) from healthy individuals, IBS patients, IBD
patients, and/or Celiac disease patients. For example, samples from
patients diagnosed by a physician, and preferably by a
gastroenterologist as having IBD using a biopsy, colonoscopy, or an
immunoassay as described in, e.g., U.S. Pat. No. 6,218,129, are
suitable for use in training and testing the learning statistical
classifier systems of the present invention. Samples from patients
diagnosed with IBD can also be stratified into Crohn's disease or
ulcerative colitis using an immunoassay as described in, e.g., U.S.
Pat. Nos. 5,750,355 and 5,830,675. Samples from patients diagnosed
with IBS using a published criteria such as the Manning, Rome I,
Rome II, or Rome III diagnostic criteria are suitable for use in
training and testing the learning statistical classifier systems of
the present invention. Samples from healthy individuals can include
those that were not identified as IBD and/or IBS samples. One
skilled in the art will know of additional techniques and
diagnostic criteria for obtaining a cohort of patient samples that
can be used in training and testing the learning statistical
classifier systems of the present invention.
[0305] As used herein, the term "sensitivity" refers to the
probability that a diagnostic method, system, or code of the
present invention gives a positive result when the sample is
positive, e.g., having IBS. Sensitivity is calculated as the number
of true positive results divided by the sum of the true positives
and false negatives. Sensitivity essentially is a measure of how
well a method, system, or code of the present invention correctly
identifies those with IBS from those without the disease. The
statistical algorithms can be selected such that the sensitivity of
classifying IBS is at least about 60%, and can be, for example, at
least about 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%,
84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,
97%, 98%, or 99%. In preferred embodiments, the sensitivity of
classifying IBS is at least about 90% when a combination of
learning statistical classifier systems is used (see, Example 10)
or at least about 85% when a single learning statistical classifier
system is used (see, Example 11).
[0306] The term "specificity" refers to the probability that a
diagnostic method, system, or code of the present invention gives a
negative result when the sample is not positive, e.g., not having
IBS. Specificity is calculated as the number of true negative
results divided by the sum of the true negatives and false
positives. Specificity essentially is a measure of how well a
method, system, or code of the present invention excludes those who
do not have IBS from those who have the disease. The statistical
algorithms can be selected such that the specificity of classifying
IBS is at least about 70%, for example, at least about 75%, 80%,
85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,
98%, or 99%. In preferred embodiments, the specificity of
classifying IBS is at least about 86% when a combination of
learning statistical classifier systems is used (see, Example 10)
or at least about 84% when a single learning statistical classifier
system is used (see, Example 11).
[0307] As used herein, the term "negative predictive value" or
"NPV" refers to the probability that an individual identified as
not having IBS actually does not have the disease. Negative
predictive value can be calculated as the number of true negatives
divided by the sum of the true negatives and false negatives.
Negative predictive value is determined by the characteristics of
the diagnostic method, system, or code as well as the prevalence of
the disease in the population analyzed. The statistical algorithms
can be selected such that the negative predictive value in a
population having a disease prevalence is in the range of about 70%
to about 99% and can be, for example, at least about 70%, 75%, 76%,
77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,
90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. In preferred
embodiments, the negative predictive value of classifying IBS is at
least about 87% when a combination of learning statistical
classifier systems is used (see, Example 10).
[0308] The term "positive predictive value" or "PPV" refers to the
probability that an individual identified as having IBS actually
has the disease. Positive predictive value can be calculated as the
number of true positives divided by the sum of the true positives
and false positives. Positive predictive value is determined by the
characteristics of the diagnostic method, system, or code as well
as the prevalence of the disease in the population analyzed. The
statistical algorithms can be selected such that the positive
predictive value in a population having a disease prevalence is in
the range of about 80% to about 99% and can be, for example, at
least about 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%,
95%, 96%, 97%, 98%, or 99%. In preferred embodiments, the positive
predictive value of classifying IBS is at least about 90% when a
combination of learning statistical classifier systems is used
(see, Example 10).
[0309] Predictive values, including negative and positive
predictive values, are influenced by the prevalence of the disease
in the population analyzed. In the methods, systems, and code of
the present invention, the statistical algorithms can be selected
to produce a desired clinical parameter for a clinical population
with a particular IBS prevalence. For example, learning statistical
classifier systems can be selected for an IBS prevalence of up to
about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%,
35%, 40%, 45%, 50%, 55%, 60%, 65%, or 70%, which can be seen, e.g.,
in a clinician's office such as a gastroenterologist's office or a
general practitioner's office.
[0310] As used herein, the term "overall agreement" or "overall
accuracy" refers to the accuracy with which a method, system, or
code of the present invention classifies a disease state. Overall
accuracy is calculated as the sum of the true positives and true
negatives divided by the total number of sample results and is
affected by the prevalence of the disease in the population
analyzed. For example, the statistical algorithms can be selected
such that the overall accuracy in a patient population having a
disease prevalence is at least about 60%, and can be, for example,
at least about 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%,
83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%,
96%, 97%, 98%, or 99%. In preferred embodiments, the overall
accuracy of classifying IBS is at least about 80% when a
combination of learning statistical classifier systems is used
(see, Example 10).
IX. Disease Classification System
[0311] FIG. 2 illustrates a disease classification system (DCS)
(200) according to one embodiment of the present invention. As
shown therein, a DCS includes a DCS intelligence module (205), such
as a computer, having a processor (215) and memory module (210).
The intelligence module also includes communication modules (not
shown) for transmitting and receiving information over one or more
direct connections (e.g., USB, Firewire, or other interface) and
one or more network connections (e.g., including a modem or other
network interface device). The memory module may include internal
memory devices and one or more external memory devices. The
intelligence module also includes a display module (225), such as a
monitor or printer. In one aspect, the intelligence module receives
data such as patient test results from a data acquisition module
such as a test system (250), either through a direct connection or
over a network (240). For example, the test system may be
configured to run multianalyte tests on one or more patient samples
(255) and automatically provide the test results to the
intelligence module. The data may also be provided to the
intelligence module via direct input by a user or it may be
downloaded from a portable medium such as a compact disk (CD) or a
digital versatile disk (DVD). The test system may be integrated
with the intelligence module, directly coupled to the intelligence
module, or it may be remotely coupled with the intelligence module
over the network. The intelligence module may also communicate data
to and from one or more client systems (230) over the network as is
well known. For example, a requesting physician or healthcare
provider may obtain and view a report from the intelligence module,
which may be resident in a laboratory or hospital, using a client
system (230).
[0312] The network can be a LAN (local area network), WAN (wide
area network), wireless network, point-to-point network, star
network, token ring network, hub network, or other configuration.
As the most common type of network in current use is a TCP/IP
(Transfer Control Protocol and Internet Protocol) network such as
the global internetwork of networks often referred to as the
"Internet" with a capital "I," that will be used in many of the
examples herein, but it should be understood that the networks that
the present invention might use are not so limited, although TCP/IP
is the currently preferred protocol.
[0313] Several elements in the system shown in FIG. 2 may include
conventional, well-known elements that need not be explained in
detail here. For example, the intelligence module could be
implemented as a desktop personal computer, workstation, mainframe,
laptop, etc. Each client system could include a desktop personal
computer, workstation, laptop, PDA, cell phone, or any WAP-enabled
device or any other computing device capable of interfacing
directly or indirectly to the Internet or other network connection.
A client system typically runs an HTTP client, e.g., a browsing
program, such as Microsoft's Internet Explorer.TM. browser,
Netscape's Navigator.TM. browser, Opera's browser, or a WAP-enabled
browser in the case of a cell phone, PDA or other wireless device,
or the like, allowing a user of the client system to access,
process, and view information and pages available to it from the
intelligence module over the network. Each client system also
typically includes one or more user interface devices, such as a
keyboard, a mouse, touch screen, pen or the like, for interacting
with a graphical user interface (GUI) provided by the browser on a
display (e.g., monitor screen, LCD display, etc.) (235) in
conjunction with pages, forms, and other information provided by
the intelligence module. As discussed above, the present invention
is suitable for use with the Internet, which refers to a specific
global internetwork of networks. However, it should be understood
that other networks can be used instead of the Internet, such as an
intranet, an extranet, a virtual private network (VPN), a
non-TCP/IP based network, any LAN or WAN, or the like.
[0314] According to one embodiment, each client system and all of
its components are operator configurable using applications, such
as a browser, including computer code run using a central
processing unit such as an Intel.RTM. Pentium.RTM. processor or the
like. Similarly, the intelligence module and all of its components
might be operator configurable using application(s) including
computer code run using a central processing unit (215) such as an
Intel Pentium processor or the like, or multiple processor units.
Computer code for operating and configuring the intelligence module
to process data and test results as described herein is preferably
downloaded and stored on a hard disk, but the entire program code,
or portions thereof, may also be stored in any other volatile or
non-volatile memory medium or device as is well known, such as a
ROM or RAM, or provided on any other computer readable medium (260)
capable of storing program code, such as a compact disk (CD)
medium, digital versatile disk (DVD) medium, a floppy disk, ROM,
RAM, and the like.
[0315] The computer code for implementing various aspects and
embodiments of the present invention can be implemented in any
programming language that can be executed on a computer system such
as, for example, in C, C++, HTML, Java, JavaScript, or any other
scripting language, such as VBScript. Additionally, the entire
program code, or portions thereof, may be embodied as a carrier
signal, which may be transmitted and downloaded from a software
source (e.g., server) over the Internet, or over any other
conventional network connection as is well known (e.g., extranet,
VPN, LAN, etc.) using any communication medium and protocols (e.g.,
TCP/I P, HTTP, HTTPS, Ethernet, etc.) as are well known.
[0316] According to one embodiment, the intelligence module
implements a disease classification process for analyzing patient
test results and/or questionnaire responses to determine whether a
patient sample is associated with IBS. The data may be stored in
one or more data tables or other logical data structures in memory
(210) or in a separate storage or database system coupled with the
intelligence module. One or more statistical processes are
typically applied to a data set including test data for a
particular patient. For example, the test data might include a
diagnostic marker profile, which comprises data indicating the
presence or level of at least one marker in a sample from the
patient. The test data might also include a symptom profile, which
comprises data indicating the presence or severity of at least one
symptom associated with IBS that the patient is experiencing or has
recently experienced. In one aspect, a statistical process produces
a statistically derived decision classifying the patient sample as
an IBS sample or non-IBS sample based upon the diagnostic marker
profile and/or symptom profile. In another aspect, a first
statistical process produces a first statistically derived decision
classifying the patient sample as an IBD sample or non-IBD sample
based upon the diagnostic marker profile and/or symptom profile. If
the patient sample is classified as a non-IBD sample, a second
statistical process is applied to the same or a different data set
to produce a second statistically derived decision classifying the
non-IBD sample as an IBS sample or non-IBS sample. The first and/or
the second statistically derived decision may be displayed on a
display device associated with or coupled to the intelligence
module, or the decision(s) may be provided to and displayed at a
separate system, e.g., a client system (230). The displayed results
allow a physician to make a reasoned diagnosis or prognosis.
X. Therapy and Therapeutic Monitoring
[0317] Once a sample from an individual has been classified as an
IBS sample, the methods, systems, and code of the present invention
can further comprise administering to the individual a
therapeutically effective amount of a drug useful for treating one
or more symptoms associated with IBS (i.e., an IBS drug). For
therapeutic applications, the IBS drug can be administered alone or
co-administered in combination with one or more additional IBS
drugs and/or one or more drugs that reduce the side-effects
associated with the IBS drug.
[0318] IBS drugs can be administered with a suitable pharmaceutical
excipient as necessary and can be carried out via any of the
accepted modes of administration. Thus, administration can be, for
example, intravenous, topical, subcutaneous, transcutaneous,
transdermal, intramuscular, oral, buccal, sublingual, gingival,
palatal, intra joint, parenteral, intra-arteriole, intradermal,
intraventricular, intracranial, intraperitoneal, intralesional,
intranasal, rectal, vaginal, or by inhalation. By "co-administer"
it is meant that an IBS drug is administered at the same time, just
prior to, or just after the administration of a second drug (e.g.,
another IBS drug, a drug useful for reducing the side-effects of
the IBS drug, etc.).
[0319] A therapeutically effective amount of an IBS drug may be
administered repeatedly, e.g., at least 2, 3, 4, 5, 6, 7, 8, or
more times, or the dose may be administered by continuous infusion.
The dose may take the form of solid, semi-solid, lyophilized
powder, or liquid dosage forms, such as, for example, tablets,
pills, pellets, capsules, powders, solutions, suspensions,
emulsions, suppositories, retention enemas, creams, ointments,
lotions, gels, aerosols, foams, or the like, preferably in unit
dosage forms suitable for simple administration of precise
dosages.
[0320] As used herein, the term "unit dosage form" refers to
physically discrete units suitable as unitary dosages for human
subjects and other mammals, each unit containing a predetermined
quantity of an IBS drug calculated to produce the desired onset,
tolerability, and/or therapeutic effects, in association with a
suitable pharmaceutical excipient (e.g., an ampoule). In addition,
more concentrated dosage forms may be prepared, from which the more
dilute unit dosage forms may then be produced. The more
concentrated dosage forms thus will contain substantially more
than, e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more times
the amount of the IBS drug.
[0321] Methods for preparing such dosage forms are known to those
skilled in the art (see, e.g., REMINGTON'S PHARMACEUTICAL SCIENCES,
18TH ED., Mack Publishing Co., Easton, Pa. (1990)). The dosage
forms typically include a conventional pharmaceutical carrier or
excipient and may additionally include other medicinal agents,
carriers, adjuvants, diluents, tissue permeation enhancers,
solubilizers, and the like. Appropriate excipients can be tailored
to the particular dosage form and route of administration by
methods well known in the art (see, e.g., REMINGTON'S
PHARMACEUTICAL SCIENCES, supra).
[0322] Examples of suitable excipients include, but are not limited
to, lactose, dextrose, sucrose, sorbitol, mannitol, starches, gum
acacia, calcium phosphate, alginates, tragacanth, gelatin, calcium
silicate, microcrystalline cellulose, polyvinylpyrrolidone,
cellulose, water, saline, syrup, methylcellulose, ethylcellulose,
hydroxypropylmethylcellulose, and polyacrylic acids such as
Carbopols, e.g., Carbopol 941, Carbopol 980, Carbopol 981, etc. The
dosage forms can additionally include lubricating agents such as
talc, magnesium stearate, and mineral oil; wetting agents;
emulsifying agents; suspending agents; preserving agents such as
methyl-, ethyl-, and propyl-hydroxy-benzoates (i.e., the parabens);
pH adjusting agents such as inorganic and organic acids and bases;
sweetening agents; and flavoring agents. The dosage forms may also
comprise biodegradable polymer beads, dextran, and cyclodextrin
inclusion complexes.
[0323] For oral administration, the therapeutically effective dose
can be in the form of tablets, capsules, emulsions, suspensions,
solutions, syrups, sprays, lozenges, powders, and sustained-release
formulations. Suitable excipients for oral administration include
pharmaceutical grades of mannitol, lactose, starch, magnesium
stearate, sodium saccharine, talcum, cellulose, glucose, gelatin,
sucrose, magnesium carbonate, and the like.
[0324] In some embodiments, the therapeutically effective dose
takes the form of a pill, tablet, or capsule, and thus, the dosage
form can contain, along with an IBS drug, any of the following: a
diluent such as lactose, sucrose, dicalcium phosphate, and the
like; a disintegrant such as starch or derivatives thereof; a
lubricant such as magnesium stearate and the like; and a binder
such a starch, gum acacia, polyvinylpyrrolidone, gelatin, cellulose
and derivatives thereof. An IBS drug can also be formulated into a
suppository disposed, for example, in a polyethylene glycol (PEG)
carrier.
[0325] Liquid dosage forms can be prepared by dissolving or
dispersing an IBS drug and optionally one or more pharmaceutically
acceptable adjuvants in a carrier such as, for example, aqueous
saline (e.g., 0.9% w/v sodium chloride), aqueous dextrose,
glycerol, ethanol, and the like, to form a solution or suspension,
e.g., for oral, topical, or intravenous administration. An IBS drug
can also be formulated into a retention enema.
[0326] For topical administration, the therapeutically effective
dose can be in the form of emulsions, lotions, gels, foams, creams,
jellies, solutions, suspensions, ointments, and transdermal
patches. For administration by inhalation, an IBS drug can be
delivered as a dry powder or in liquid form via a nebulizer. For
parenteral administration, the therapeutically effective dose can
be in the form of sterile injectable solutions and sterile packaged
powders. Preferably, injectable solutions are formulated at a pH of
from about 4.5 to about 7.5.
[0327] The therapeutically effective dose can also be provided in a
lyophilized form. Such dosage forms may include a buffer, e.g.,
bicarbonate, for reconstitution prior to administration, or the
buffer may be included in the lyophilized dosage form for
reconstitution with, e.g., water. The lyophilized dosage form may
further comprise a suitable vasoconstrictor, e.g., epinephrine. The
lyophilized dosage form can be provided in a syringe, optionally
packaged in combination with the buffer for reconstitution, such
that the reconstituted dosage form can be immediately administered
to an individual.
[0328] In therapeutic use for the treatment of IBS, an IBS drug can
be administered at the initial dosage of from about 0.001 mg/kg to
about 1000 mg/kg daily. A daily dose range of from about 0.01 mg/kg
to about 500 mg/kg, from about 0.1 mg/kg to about 200 mg/kg, from
about 1 mg/kg to about 100 mg/kg, or from about 10 mg/kg to about
50 mg/kg, can be used. The dosages, however, may be varied
depending upon the requirements of the individual, the severity of
IBS symptoms, and the IBS drug being employed. For example, dosages
can be empirically determined considering the severity of IBS
symptoms in an individual classified as having IBS according to the
methods described herein. The dose administered to an individual,
in the context of the present invention, should be sufficient to
affect a beneficial therapeutic response in the individual over
time. The size of the dose can also be determined by the existence,
nature, and extent of any adverse side-effects that accompany the
administration of a particular IBS drug in an individual.
Determination of the proper dosage for a particular situation is
within the skill of the practitioner. Generally, treatment is
initiated with smaller dosages which are less than the optimum dose
of the IBS drug. Thereafter, the dosage is increased by small
increments until the optimum effect under circumstances is reached.
For convenience, the total daily dosage may be divided and
administered in portions during the day, if desired.
[0329] As used herein, the term "IBS drug" includes all
pharmaceutically acceptable forms of a drug that is useful for
treating one or more symptoms associated with IBS. For example, the
IBS drug can be in a racemic or isomeric mixture, a solid complex
bound to an ion exchange resin, or the like. In addition, the IBS
drug can be in a solvated form. The term "IBS drug" is also
intended to include all pharmaceutically acceptable salts,
derivatives, and analogs of the IBS drug being described, as well
as combinations thereof. For example, the pharmaceutically
acceptable salts of an IBS drug include, without limitation, the
tartrate, succinate, tartarate, bitartarate, dihydrochloride,
salicylate, hemisuccinate, citrate, maleate, hydrochloride,
carbamate, sulfate, nitrate, and benzoate salt forms thereof, as
well as combinations thereof and the like. Any form of an IBS drug
is suitable for use in the methods of the present invention, e.g.,
a pharmaceutically acceptable salt of an IBS drug, a free base of
an IBS drug, or a mixture thereof.
[0330] Suitable drugs that are useful for treating one or more
symptoms associated with IBS include, but are not limited to,
serotonergic agents, antidepressants, chloride channel activators,
chloride channel blockers, guanylate cyclase agonists, antibiotics,
opioids, neurokinin antagonists, antispasmodic or anticholinergic
agents, belladonna alkaloids, barbiturates, glucagon-like peptide-1
(GLP-1) analogs, corticotropin releasing factor (CRF) antagonists,
probiotics, free bases thereof; pharmaceutically acceptable salts
thereof, derivatives thereof, analogs thereof, and combinations
thereof. Other IBS drugs include bulking agents, dopamine
antagonists, carminatives, tranquilizers, dextofisopam, phenyloin,
timolol, and diltiazem.
[0331] Serotonergic agents are useful for the treatment of IBS
symptoms such as constipation, diarrhea, and/or alternating
constipation and diarrhea. Non-limiting examples of serotonergic
agents are described in Cash et al., Aliment. Pharmacol. Ther.,
22:1047-1060 (2005), and include 5-HT.sub.3 receptor agonists
(e.g., MKC-733, etc.), 5-HT.sub.4 receptor agonists (e.g.,
tegaserod (Zelnorm.TM.), prucalopride, AG1-001, etc.), 5-HT.sub.3
receptor antagonists (e.g., alosetron (Lotronex.RTM.), cilansetron,
ondansetron, granisetron, dolasetron, ramosetron, palonosetron,
E-3620, DDP-225, DDP-733, etc.), mixed 5-HT.sub.3 receptor
antagonists/5-HT.sub.4 receptor agonists (e.g., cisapride,
mosapride, renzapride, etc.), free bases thereof, pharmaceutically
acceptable salts thereof, derivatives thereof, analogs thereof, and
combinations thereof. Additionally, amino acids like glutamine and
glutamic acid which regulate intestinal permeability by affecting
neuronal or glial cell signaling can be administered to treat
patients with IBS.
[0332] Antidepressants such as selective serotonin reuptake
inhibitor (SSRI) or tricyclic antidepressants are particularly
useful for the treatment of IBS symptoms such as abdominal pain,
constipation, and/or diarrhea. Non-limiting examples of SSRI
antidepressants include citalopram, fluvoxamine, paroxetine,
fluoxetine, sertraline, free bases thereof; pharmaceutically
acceptable salts thereof; derivatives thereof; analogs thereof, and
combinations thereof. Examples of tricyclic antidepressants
include, but are not limited to, desipramine, nortriptyline,
protriptyline, amitriptyline, clomipramine, doxepin, imipramine,
trimipramine, maprotiline, amoxapine, clomipramine, free bases
thereof; pharmaceutically acceptable salts thereof; derivatives
thereof; analogs thereof; and combinations thereof.
[0333] Chloride channel activators are useful for the treatment of
IBS symptoms such as constipation. A non-limiting example of a
chloride channel activator is lubiprostone (Amitiza.TM.), a free
base thereof; a pharmaceutically acceptable salt thereof; a
derivative thereof, or an analog thereof. In addition, chloride
channel blockers such as crofelemer are useful for the treatment of
IBS symptoms such as diarrhea. Guanylate cyclase agonists such as
MD-1100 are useful for the treatment of constipation associated
with IBS (see, e.g., Bryant et al., Gastroenterol., 128:A-257
(2005)). Antibiotics such as neomycin can also be suitable for use
in treating constipation associated with IBS (see, e.g., Park et
al., Gastroenterol., 128:A-258 (2005)). Non-absorbable antibiotics
like rifaximin (Xifaxan.TM.) are suitable to treat small bowel
bacterial overgrowth and/or constipation associated with IBS (see,
e.g., Sharara et al., Am. J. Gastroenterol., 101:326-333
(2006)).
[0334] Opioids such as kappa opiods (e.g., asimadoline) may be
useful for treating pain and/or constipation associated with IBS.
Neurokinin (NK) antagonists such as talnetant, saredutant, and
other NK2 and/or NK3 antagonists may be useful for treating IBS
symptoms such as oversensitivity of the muscles in the colon,
constipation, and/or diarrhea. Antispasmodic or anticholinergic
agents such as dicyclomine may be useful for treating IBS symptoms
such as spasms in the muscles of the gut and bladder. Other
antispasmodic or anticholinergic agents such as belladonna
alkaloids (e.g., atropine, scopolamine, hyoscyamine, etc.) can be
used in combination with barbiturates such as phenobarbital to
reduce bowel spasms associated with IBS. GLP-1 analogs such as
GTP-010 may be useful for treating IBS symptoms such as
constipation. CRF antagonists such as astressin and probiotics such
as VSL#3.RTM. may be useful for treating one or more IBS symptoms.
One skilled in the art will know of additional IBS drugs currently
in use or in development that are suitable for treating one or more
symptoms associated with IBS.
[0335] An individual can also be monitored at periodic time
intervals to assess the efficacy of a certain therapeutic regimen
once a sample from the individual has been classified as an IBS
sample. For example, the levels of certain markers change based on
the therapeutic effect of a treatment such as a drug. The patient
is monitored to assess response and understand the effects of
certain drugs or treatments in an individualized approach.
Additionally, patients may not respond to a drug, but the markers
may change, suggesting that these patients belong to a special
population (not responsive) that can be identified by their marker
levels. These patients can be discontinued on their current therapy
and alternative treatments prescribed.
XI. Examples
[0336] The following examples are offered to illustrate, but not to
limit, the claimed invention.
Example 1
Leptin Discriminates Between IBS and Non-IBS Patient Samples
[0337] This example illustrates that determining the presence or
level of leptin is useful for classifying a patient sample as an
IBS sample, e.g., by ruling in IBS. The concentration of leptin was
measured in serum samples from normal, IBS, IBD (i.e., CD, UC), and
Celiac disease patients using an immunoassay (i.e., ELISA). As
shown in FIG. 3, quartile analysis revealed that leptin levels were
elevated in IBS samples relative to non-IBS (i.e., CD, UC, Celiac
disease, normal) samples. Thus, leptin can advantageously
discriminate between IBS and non-IBS samples.
[0338] Leptin is also useful for distinguishing between various
forms of IBS. FIG. 4A shows the results of an ELISA where leptin
levels were measured in normal, IBD (i.e., CD, UC), and Celiac
disease patient samples and samples from patients having IBS-A,
IBS-C, or IBS-D. Leptin levels were elevated in IBS-A and IBS-D
patient samples relative to IBS-C samples. FIG. 4B shows the
differences of leptin levels between samples from female IBS
patients compared to and male IBS patients.
Example 2
TWEAK Discriminates Between IBS and Non-IBS Patient Samples
[0339] This example illustrates that determining the presence or
level of TWEAK is useful for classifying a patient sample as an IBS
sample, e.g., by ruling in IBS. The concentration of TWEAK was
measured in samples from normal, GI control, IBS, and IBD (i.e.,
CD, UC) patients using an immunoassay (i.e., ELISA). As shown in
FIG. 5, quartile analysis revealed that TWEAK levels were elevated
in IBS samples relative to non-IBS (i.e., CD, UC, GI control,
normal) samples. Thus, TWEAK can advantageously discriminate
between IBS and non-IBS samples.
Example 3
IL-8 Discriminates Between IBS and Normal Patient Samples
[0340] This example illustrates that determining the presence or
level of IL-8 is useful for classifying a patient sample as an IBS
sample, e.g., by ruling in IBS. The concentration of IL-8 was
measured in samples from normal, GI control, IBS, IBD (i.e., CD,
UC), and Celiac disease patients using an immunoassay (i.e.,
ELISA). As shown in FIG. 6A, quartile analysis revealed that IL-8
levels were elevated in IBS samples relative to normal samples.
Thus, IL-8 can advantageously discriminate between IBS and normal
patient samples.
[0341] FIG. 6B shows a cumulative percent histogram analysis
demonstrating that IL-8 discriminates about 45% of IBS patient
samples from normal patient samples at a cutoff level of 40 pg/ml.
IL-8 can also discriminate about 55% of Celiac disease patient
samples from normal patient samples at the same cutoff level. FIG.
7 shows a cumulative percent histogram analysis demonstrating that
IL-8 discriminates about 80% of IBS patient samples from normal
patient samples at a cutoff level of 30 pg/ml. An exemplary method
for performing the cumulative percent histogram analysis is
provided below.
[0342] FIG. 8 shows the results of an ELISA where IL-8 levels were
measured in healthy control patient samples and samples from
patients having IBS-D, IBS-C, or IBS-A. IL-8 levels were elevated
in IBS-D, IBS-C, and IBS-A patient samples relative to control
samples.
Example 4
EGF Discriminates Between IBS and IBD Patient Samples
[0343] This example illustrates that determining the presence or
level of EGF is useful for classifying a patient sample as an IBS
sample, e.g., by ruling in IBS or ruling out IBD. The concentration
of EGF was measured in samples from normal, GI control, IBS, IBD
(i.e., CD, UC), and Celiac disease patients using an immunoassay
(i.e., ELISA). As shown in FIG. 9A, quartile analysis revealed that
EGF levels were lower in IBS samples relative to IBD samples. Thus,
EGF can advantageously discriminate between IBS and IBD patient
samples.
[0344] FIG. 9B shows a cumulative percent histogram analysis
demonstrating that EGF discriminates about 60% of IBS patient
samples from IBD patient samples at a cutoff level of 300 pg/ml.
EGF can also discriminate about 45% of Celiac disease patient
samples from normal patient samples at the same cutoff level. An
exemplary method for performing the cumulative percent histogram
analysis is provided below.
Example 5
NGAL Discriminates Between IBS and Normal Patient Samples
[0345] This example illustrates that determining the presence or
level of NGAL is useful for classifying a patient sample as an IBS
sample, e.g., by ruling in IBS. The concentration of NGAL was
measured in samples from normal, IBS, IBD, and Celiac disease
patients using an immunoassay (i.e., ELISA). As shown in FIG. 10,
quartile analysis revealed that NGAL levels were elevated in IBS
samples relative to normal samples. Thus, NGAL can advantageously
discriminate between IBS and normal patient samples.
Example 6
MMP-9 Discriminates Between IBS and IBD Patient Samples
[0346] This example illustrates that determining the presence or
level of MMP-9 is useful for classifying a patient sample as an IBS
sample, e.g., by ruling in IBS or ruling out IBD. The concentration
of MMP-9 was measured in samples from normal, GI control, IBS, and
IBD patients using an immunoassay (i.e., ELISA). As shown in FIG.
11, quartile analysis revealed that MMP-9 levels were lower in IBS
samples relative to IBD samples. Thus, MMP-9 can advantageously
discriminate between IBS and IBD patient samples.
Example 7
NGAL/MMP-9 Complex Discriminates Between IBS and IBD Patient
Samples
[0347] This example illustrates that determining the presence or
level of a complex of NGAL and MMP-9 (i.e., NGAL/MMP-9 complex) is
useful for classifying a patient sample as an IBS sample, e.g., by
ruling in IBS or ruling out IBD. The concentration of NGAL/MMP-9
complex was measured in samples from normal, IBS, and IBD patients
using an immunoassay (i.e., ELISA). As shown in FIG. 12, quartile
analysis revealed that NGAL/MMP-9 complex levels were lower in IBS
samples relative to IBD samples. Thus, the NGAL/MMP-9 complex can
advantageously discriminate between IBS and IBD patient
samples.
Example 8
Substance P Discriminates Between IBS and Normal Patient
Samples
[0348] This example illustrates that determining the presence or
level of Substance P is useful for classifying a patient sample as
an IBS sample, e.g., by ruling in IBS. The concentration of
Substance P was measured in samples from normal, IBS, IBD (i.e.,
CD, UC), and Celiac disease patients using an immunoassay (i.e.,
ELISA). As shown in FIG. 13, quartile analysis revealed that
Substance P levels were elevated in IBS samples relative to normal
samples. Thus, Substance P can advantageously discriminate between
IBS and normal patient samples.
Example 9
Cumulative Percent Histogram Analysis
[0349] FIG. 14 shows a cumulative percent histogram analysis using
lactoferrin as a non-limiting example based on the frequency of
samples at a range of lactoferrin concentrations in serum. These
values can be plotted as a standard bar graph histogram (grey bars)
displaying frequency versus concentration. Each frequency divided
by the total number of samples provides the percent frequency for
that range, normalized for sampling population size. The percent
frequency for each successive range added to the sum of lower
ranges is the cumulative percent frequency, which is plotted to
generate a curve culminating at 100 percent at the maximum
lactoferrin concentration. The cumulative frequency curve for each
patient population is then combined in a single graph to allow more
intuitive visualization of the measured differences between the
different populations. The further a particular curve is from
another curve, the greater the likelihood that the patients can be
accurately assigned to one of the two populations.
Example 10
Combinatorial Statistical Algorithm for Predicting IBS
Samples
[0350] Serum samples from 2,357 patients were obtained
retrospectively from multiple centers (Table 2). Diagnoses were
provided for all samples by the Principal Investigator at each site
following biopsies and/or colonoscopy results. Approximately 1 ml
samples were drawn into SST or serum separators at the sites. The
tubes were spun and frozen at -70.degree. C. until shipment.
Samples were shipped with cold packs and upon receipt were spun
again and frozen at -70.degree. C. until testing.
TABLE-US-00002 TABLE 2 Centers used to obtain samples for study
cohort, N = 2,357. Location No. of patients CA 402 (HC + IBD)
Toronto, Canada 1,287 (HC + IBD) Herestraat, Belgium 319 (HC + IBD)
Bethesda, MD 163 (IBS) New York, NY 31 (IBS) Boston, MA 59 (IBS)
Chicago, IL 60 (IBS) Lebanon, NH 36 (IBS) IBS = Irritable Bowel
Syndrome, IBD = Inflammatory Bowel Disease, HC = Healthy Controls.
Not all IBD samples were used in the development of the test.
Assays
[0351] Serum levels of ANCA, ASCA-G, anti-Omp-C antibodies,
anti-Cbir1 antibodies, and IL-8 were carried out using an ELISA or
an immunofluorescence assay. The analytical performance of these
assays has previously been validated. IL-8 levels were measured
with a commercial ELISA kit (Invitrogen).
Statistical Analyses
[0352] In this study, a novel approach was developed that uses two
different learning statistical classifiers (e.g., random forests
(RF) and artificial neural networks (ANN)) to predict IBS based
upon the levels and/or presence of a panel of serological markers.
These learning statistical classifiers use multivariate statistical
methods like, for example, multilayer perceptrons with feed forward
Back Propagation, that can adapt to complex data and make decisions
based strictly on the data presented, without the constraints of
regular statistical classifiers. In particular, a combinatorial
approach that makes use of multiple discriminant functions by
analyzing marker levels with more than one learning statistical
classifier was created to further improve the sensitivity and
specificity of the diagnostic test. One preferred method is a
combination of RF and ANN applied in tandem. Overall accuracy was
used to determine the clinical performance of the test in the
validation population.
[0353] Marker values from more than 2,000 patient samples were
first split into training, testing, and validating cohorts (Table
3). Different patient samples were used for training, testing, and
for validation purposes.
TABLE-US-00003 TABLE 3 Sample sets used to create diagnostic
algorithms. Number of IBS Samples Prevalence Normal/IBS/IBD
Training Cohort 263 30% 108/79/76 Testing Cohort 100 35% 36/35/29
Total: Training & Testing 363 31% 144/114/105 Validating Cohort
200 28% 86/55/59 Normal and IBD patients were used as non-IBS
controls. IBS samples were a mix of D-IBS, C-IBS and A-IBS.
Random Forests
[0354] The antibody levels from each of the 4 ELISA assays
(predictors) and the diagnosis (0=Non-IBS, 1=IBS, 2=IBD, Dependent
Variable) from a cohort of 263 patient samples (30% IBS prevalence,
training set, illustrated in Table 2) were used as input for the RF
software module. Multiple RF models were created and analyzed for
accuracy of IBS prediction using the test cohort. The best
predictive RF models were selected and tested for accuracy of IBS
prediction using data from the validation cohort.
[0355] Several RF models were used to predict IBS, IBD, or non-IBS
from the training set. The output data were used as input for
training neural networks. The outputs from the RF software module
included a prediction value (i.e., 0 [non-IBS], 1 [IBS], or 2
[IBD]) and 3 probability or confidence values (one for each
prediction). The three probability values were used together with
the levels of the markers, as predictor values for further
statistical analysis using ANN. A schematic representation of data
processing is illustrated in FIG. 15. FIG. 16 illustrates the data
set obtained using the model of FIG. 15.
Artificial Neural Networks
[0356] The values of the markers and the probabilities of non-IBS,
IBS, and IBD predictions obtained from the RF model (Salford
Systems; San Diego, Calif.) were used as predictors and the
diagnosis as a dependent variable to create multiple ANN with the
use of the neural networks software. The Intelligent Problem Solver
module of the neural networks software package (Statistica;
StatSoft, Inc.; Tulsa, Okla.) was used to create ANN models in a
feed-forward, backpropagation, and classification mode with the
training cohort. More than 1,000 ANN were created using the input
from various RF models. The best models were selected based on the
lowest error of IBS prediction on the test dataset.
[0357] A diagram of an ANN is shown in FIG. 17. This model is
composed of a Multi-level Perceptron containing 1 hidden layer with
10 neurons. The relative activation of the neuron is identified by
its color.
Algorithm Validation and Accuracy of Prediction
[0358] The selected algorithm was then validated with a cohort of
samples that had not been used in the training and testing sets
(i.e., the validation set). The data obtained from this test was
used to calculate all accuracy parameters for the algorithm.
[0359] Additionally, final validation and calculation of accuracy
was performed on data from a sample cohort non-overlapping with the
training and testing sets. The 2.times.2 confusion matrix (Table 4)
shows the algorithm prediction results on the validation
cohort.
TABLE-US-00004 TABLE 4 2 .times. 2 confusion matrix. 2 .times. 2
Matrix of Algorithm Prediction on the Validation Cohort Non-IBS IBS
Non-IBS 91 8 IBS 7 125
[0360] The algorithm prediction accuracy for IBS is shown in Table
5.
TABLE-US-00005 TABLE 5 Clinical performance of algorithm in the
prediction of IBS. Accuracy of IBS Prediction of Hybrid Model
Tested in Validation Cohort TP 187 IBS Sensitivity 91.2% FN 18 IBS
Specificity 86.8% FP 19 IBS PPV 90.8% TN 125 IBS NPV 87.4% TP =
True positives, FN = False negatives, FP = False positives TN =
True negatives, PPV = Positive predictive value NPV = Negative
predictive value. Prediction accuracy was calculated using the
algorithm on the validation set.
[0361] The sensitivity and specificity of IBS prediction were about
91% and about 87%, respectively. IBS PPV and NPV were about 91% and
about 87%, respectively. Accurate identification of IBS was
revealed by sensitivities and specificities near or above 90%.
Overall accuracy of prediction was calculated as shown in Table 6.
The hybrid RF/ANN model predicted IBS with a high level of
accuracy.
TABLE-US-00006 TABLE 6 Overall prediction accuracy. Correctly
Predicted/ Total Number % Correct Hybrid Model Diagnosed Prediction
Overall Assay Accuracy 159/200 80% Percent correct prediction was
calculated as follows: Accuracy = IBS TP + IBD TP + TN/Total number
of samples tested.
Example 11
Random Forest Statistical Algorithm for Predicting IBS
Dataset
[0362] A total of 939 patient samples were analyzed using a random
forest (RF) statistical algorithm. The samples were split into
training, testing, and validating cohorts as follows: (1) 739
training and testing samples (Table 7); and (2) 200 validating
samples. Different patient samples were used for training, testing,
and for validation purposes.
TABLE-US-00007 TABLE 7 Composition of the training and testing
cohort. Composition of Train/Test Cohort Normal 257 35% IBS 152 21%
Celiac 34 5% CD 154 21% UC 142 19% Total 739
Assays
[0363] Serum levels of IL-8, lactoferrin, ANCA, ASCA-G, and
anti-Omp-C antibodies were carried out using an ELISA as described
above.
Study Approach
[0364] In this study, a novel approach was developed that uses a
single learning statistical classifier (i.e., random forests) to
predict IBS based upon the levels and/or presence of a panel of
serological markers. The antibody levels from each of the ELISA
assays (predictors; Table 8) and the diagnosis from the train/test
cohort of patient samples were used as input for the RF software
module (Salford Systems; San Diego, Calif.). Multiple RF models
were created and analyzed for accuracy of IBS prediction using the
train/test cohort. The best predictive RF models were selected and
tested for accuracy of IBS prediction using data from the
validation cohort.
TABLE-US-00008 TABLE 8 Predictive importance of each of the
diagnostic markers analyzed. Marker Score IL-8 100.0 Lactoferrin
34.14 ANCA 19.15 Anti-Omp-C Antibodies 7.18 ASCA-G 6.14 Values are
normalized to IL-8.
Algorithm Validation and Accuracy of Prediction
[0365] The selected RF algorithm was then validated with a cohort
of samples that had not been used in the training and testing sets
(i.e., the validation set). The data obtained from this test was
used to calculate all accuracy parameters for the algorithm.
[0366] The RF algorithm prediction accuracy for IBS is shown in
Table 9.
TABLE-US-00009 TABLE 9 Clinical performance of the RF algorithm in
the prediction of IBS. Non-IBS IBS Cases Total Cases Percent
Correct (N = 135) (N = 65) Non-IBS 151 84.7 (Specificity) 128 23
IBS 49 85.7 (Sensitivity) 7 42
[0367] The sensitivity and specificity of IBS prediction were 85.7%
and 84.7%, respectively. Accurate identification of IBS was
revealed by sensitivities and specificities near or above 85%. The
RF model predicted IBS with a high level of accuracy.
[0368] FIG. 18 illustrates the distribution of IBS and non-IBS
samples before and after modeling with a RF algorithm using serum
levels of IL-8, EGF, ANCA, and ASCA-G.
Example 12
Classification Tree Statistical Algorithm for Predicting IBS
Dataset
[0369] Approximately 430 cases are analyzed using a classification
tree statistical algorithm. These cases can have serological marker
information for IL-8, ANCA ELISA, anti-Omp-C antibodies, ASCA-A,
ASCA-G, anti-Cbir1 antibodies, pANCA, and/or lactoferrin.
Study Approach
[0370] In this study, a novel approach is developed that uses a
single learning statistical classifier (i.e., classification trees)
to predict IBS based upon the levels and/or presence of a panel of
serological markers. In order to generate robust estimates of the
efficacy of each classification method, a simulation with 500
iterations is performed. For each iteration, the data is divided
into a training set and a validation set. Each time, 80% of the
observations are randomly assigned to the training set and 20% of
the observations are randomly assigned to the validation set. Using
the training set, classification models are built using
classification trees.
Classification Trees
[0371] Classification trees are constructed by repeated binary
splits of subsets of the data, beginning with the complete dataset.
Each time a binary split is performed, there is an attempt to
create descendent subsets that are "purer," or more homogeneous,
than the parent subset. This is done by computationally finding a
split that achieves the largest decrease in the average impurity of
the descendent subsets. Impurity is usually defined in operational
terms by one of three metrics:
[0372] 1) Misclassification rate;
[0373] 2) Gini index; or
[0374] 3) Entropy (deviance).
[0375] Though minimizing the misclassification rate is the overall
goal, it is considered a poor criterion for the split search
because it produces only a one-step optimization. The Gini index
and entropy criterion produce similar results for two-class
problems (Hastie et al., The Elements of Statistical Learning, New
York; Springer (2001)). The nodes created by each binary split are
recursively split until one of the following three conditions
becomes true: [0376] 1) All cases in the node are of the same
observed class (i.e., the impurity is equal to zero); [0377] 2) The
node only contains observations that have identical measurements
(i.e., there is no way to split the remaining observations); or,
[0378] 3) The node is small, typically 1 to 5 observations.
[0379] Once a terminal point has been reached for every node, the
tree is pruned upward. This procedure creates a sequence of smaller
and smaller trees. The overall impurity of each of these trees can
be measured and the one with the smallest total impurity selected.
This may be regarded as the "best" classification tree (Breiman et
al., Classification and Regression Trees, Wadsworth; Belmont,
Calif. (1984)).
[0380] Once the "best" tree is selected, the predicted class of
each of the terminal nodes is determined by a simple majority
"vote" of each observation in the node. In order to classify a new
case, the new observation is simply sent down the tree. The
predicted class of the new observation is the predicted class of
the terminal node in which it is placed. Further discussion and
examples may be found, e.g., in Hastie et al., supra; and Venables
et al., Modern Applied Statistics with S-Plus, 4th edition; New
York; Springer (2002).
[0381] FIG. 19 shows a three node classification tree for
classifying a sample as an IBS sample or non-IBS sample based upon
the levels of IL-8, lactoferrin, and ANCA ELISA. This
classification tree provides an approximate overall correct
classification rate of 87.6%.
Example 13
Questionnaire for Identifying the Presence or Severity of Symptoms
Associated with IBS
[0382] This example illustrates a questionnaire that is useful for
identifying the presence or severity of one or more IBS-related
symptoms in an individual. The questionnaire can be completed by
the individual at the clinic or physician's office, or can be
brought home and submitted when the individual returns to the
clinic or physician's office, e.g., to have his or her blood
drawn.
[0383] In some embodiments, the questionnaire comprises a first
section containing a set of questions asking the individual to
provide answers regarding the presence or severity of one or more
symptoms associated with IBS. The questionnaire generally includes
questions directed to identifying the presence, severity,
frequency, and/or duration of IBS-related symptoms such as chest
pain, chest discomfort, heartburn, uncomfortable fullness after
having a regular-sized meal, inability to finish a regular-sized
meal, abdominal pain, abdominal discomfort, constipation, diarrhea,
bloating, and/or abdominal distension.
[0384] In certain instances, the first section of the questionnaire
includes all or a subset of the questions from a questionnaire
developed by the Rome Foundation Board based on the Rome III
criteria, available at romecriteria.org. For example, the
questionnaire can include all or a subset of the 93 questions set
forth on pages 920-936 of the Rome III Diagnostic Questionnaire for
the Adult Functional GI Disorders (Appendix C), available at
romecriteria.org. Preferably, the first section of the
questionnaire contains 16 of the 93 questions set forth in the Rome
III Diagnostic Questionnaire (see, Table 10). Alternatively, the
first section of the questionnaire can contain a subset (e.g., 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15) of the 16
questions shown in Table 10. As a non-limiting example, the
following 10 questions set forth in Table 10 can be included in the
questionnaire: Question Nos. 2, 3, 5, 6, 9, 10, 11, 13, 15, and 16.
One skilled in the art will appreciate that the first section of
the questionnaire can comprise questions similar to the questions
shown in Table 10 regarding pain, discomfort, and/or changes in
stool consistency.
TABLE-US-00010 TABLE 10 Exemplary first section of a questionnaire
for identifying the presence or severity of IBS-related symptoms.
1. In the last 3 months, {circle around (0)} Never how often did
you have {circle around (1)} Less than one day a month pain or
discomfort in the {circle around (2)} One day a month middle of
your chest {circle around (3)} Two to three days a month (not
related to heart {circle around (4)} One day a week problems)?
{circle around (5)} More than one day a week {circle around (6)}
Every day 2. In the last 3 months, {circle around (0)} Never how
often did you have {circle around (1)} Less than one day a month
heartburn (a burning {circle around (2)} One day a month discomfort
or burning {circle around (3)} Two to three days a month pain in
your chest)? {circle around (4)} One day a week {circle around (5)}
More than one day a week {circle around (6)} Every day 3. In the
last 3 months, {circle around (0)} Never .fwdarw. how often did you
feel {circle around (1)} Less than one day a month uncomfortably
full after {circle around (2)} One day a month a regular-sized
meal? {circle around (3)} Two to three days a month {circle around
(4)} One day a week {circle around (5)} More than one day a week
{circle around (6)} Every day 4. In the last 3 months, {circle
around (0)} Never .fwdarw. how often were you {circle around (1)}
Less than one day a month unable to finish a {circle around (2)}
One day a month regular size meal? {circle around (3)} Two to three
days a month {circle around (4)} One day a week {circle around (5)}
More than one day a week {circle around (6)} Every day 5. In the
last 3 months, {circle around (0)} Never .fwdarw. how often did you
have {circle around (1)} Less than one day a month pain or burning
in the {circle around (2)} One day a month middle of your {circle
around (3)} Two to three days a month abdomen, above your {circle
around (4)} One day a week belly button but not in {circle around
(5)} More than one day a week your chest? {circle around (6)} Every
day 6. In the last 3 months, {circle around (0)} Never .fwdarw. how
often did you have {circle around (1)} Less than one day a month
discomfort or pain {circle around (2)} One day a month anywhere in
your {circle around (3)} Two to three days a month abdomen? {circle
around (4)} One day a week {circle around (5)} More than one day a
week {circle around (6)} Every day 7. In the last 3 months, {circle
around (0)} Never or rarely how often did you have {circle around
(1)} Sometimes fewer than three bowel {circle around (2)} Often
movements (0-2) a {circle around (3)} Most of the time week?
{circle around (4)} Always 8. In the last 3 months, {circle around
(0)} Never or rarely how often did you have {circle around (1)}
Sometimes (25% of the time) hard or lumpy stools? {circle around
(2)} Often (50% of the time) {circle around (3)} Most of the time
(75% of the time) {circle around (4)} Always 9. In the last 3
months, {circle around (0)} Never or rarely how often did you
strain {circle around (1)} Sometimes during bowel {circle around
(2)} Often movements? {circle around (3)} Most of the time {circle
around (4)} Always 10. In the last 3 months, {circle around (0)}
Never or rarely how often did you have {circle around (1)}
Sometimes a feeling of incomplete {circle around (2)} Often
emptying after bowel {circle around (3)} Most of the time
movements? {circle around (4)} Always 11. In the last 3 months,
{circle around (0)} Never or rarely how often did you have {circle
around (1)} Sometimes a sensation that the stool {circle around
(2)} Often could not be passed, {circle around (3)} Most of the
time (i.e., blocked), when {circle around (4)} Always having a
bowel movement? 12. In the last 3 months, {circle around (0)} Never
or rarely how often did you press {circle around (1)} Sometimes on
or around your {circle around (2)} Often bottom or remove stool
{circle around (3)} Most of the time in order to complete a {circle
around (4)} Always bowel movement? 13. Did any of the {circle
around (0)} No symptoms of {circle around (1)} Yes constipation
listed in questions 27-32 above begin more than 6 months ago? 14.
In the last 3 months, {circle around (0)} Never or rarely .fwdarw.
how often did you have {circle around (1)} Sometimes (25% of the
time) loose, mushy or watery {circle around (2)} Often (50% of the
time) stools? {circle around (3)} Most of the time (75% of the
time) {circle around (4)} Always 15. In the last 3 months, {circle
around (0)} Never .fwdarw. how often did you have {circle around
(1)} Less than one day a month bloating or distension? {circle
around (2)} One day a month {circle around (3)} Two to three days a
month {circle around (4)} One day a week {circle around (5)} More
than one day a week {circle around (6)} Every day 16. Did your
symptoms of {circle around (0)} No bloating or distention {circle
around (1)} Yes begin more than 6 months ago?
[0385] In other embodiments, the questionnaire comprises a second
section containing a set of questions asking the individual to
provide answers regarding the presence or severity of negative
thoughts or feelings associated with having IBS-related pain or
discomfort. For example, the questionnaire can include questions
directed to identifying the presence, severity, frequency, and/or
duration of anxiety, fear, nervousness, concern, apprehension,
worry, stress, depression, hopelessness, despair, pessimism, doubt,
and/or negativity when the individual is experiencing pain or
discomfort associated with one or more symptoms of IBS.
[0386] In certain instances, the second section of the
questionnaire includes all or a subset of the questions from a
questionnaire described in Sullivan et al., The Pain
Catastrophizing Scale: Development and Validation, Psychol.
Assess., 7:524-532 (1995). For example, the questionnaire can
include a set of questions to be answered by an individual
according to a Pain Catastrophizing Scale (PCS), which indicates
the degree to which the individual has certain negative thoughts
and feelings when experiencing pain: 0=not at all; 1=to a slight
degree; 2=to a moderate degree; 3=to a great degree; 4=all the
time. The second section of the questionnaire can contain 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or more questions or
statements related to identifying the presence or severity of
negative thoughts or feelings associated with having IBS-related
pain or discomfort. As a non-limiting example, an individual can be
asked to rate the degree to which he or she has one or more of the
following thoughts and feelings when experiencing pain: "I worry
all the time about whether the pain will end"; "I feel I can't
stand it anymore"; "I become afraid that the pain will get worse";
"I anxiously want the pain to go away"; and "I keep thinking about
how much it hurts." One skilled in the art will understand that the
questionnaire can comprise similar questions regarding negative
thoughts or feelings associated with having IBS-related pain or
discomfort.
[0387] In some embodiments, the questionnaire includes only
questions from the first section of the questionnaire or a subset
thereof (see, e.g., Table 10). In other embodiments, the
questionnaire includes only questions from the second section of
the questionnaire or a subset thereof.
[0388] Upon completion of the questionnaire by the individual, the
numbers corresponding to the answers to each question can be summed
and the resulting value can be combined with the analysis of one or
more diagnostic markers in a sample from the individual and
processed using the statistical algorithms described herein to
increase the accuracy of predicting IBS.
[0389] Alternatively, a "Yes" or "No" answer from the individual to
the following question: "Are you currently experiencing any
symptoms?" can be combined with the analysis of one or more of the
biomarkers described herein and processed using a single
statistical algorithm or a combination of statistical algorithms to
increase the accuracy of predicting IBS.
Example 14
Selection of Diagnostic Markers and Symptoms for Predicting IBS
[0390] This example illustrates techniques for the selection of
features that can be included in the diagnostic marker and symptom
profiles of the present invention for predicting IBS.
1. Introduction
[0391] The goal of classification is to take an input vector X and
assign it to one or more of K distinct classes C.sub.j, where j is
in the range (1 . . . K). (Bishop, Pattern Recognition and Machine
Learning, Springer, p. 179 (2006)). In the context of a diagnostic
test algorithm, the input vector may consist of a combination of
quantitative measurements (e.g., biomarkers), nominal variables
(e.g., gender), and ordinal variables (e.g., symptom presence or
severity from survey responses). These components of the input
vector may collectively be termed features. The input vector
describes a patient for whom a diagnosis is desired. The output of
the model is the diagnosis, a categorical variable (e.g., a binary
variable, where 0=healthy and 1=disease).
[0392] A diagnostic test involves specifying the features of the
input vector, and the algorithm used to predict the
classifications. While it is possible to use a maximal model, in
which all input features and their interactions are included, this
is not preferred, for reasons of economy and parsimony (Crawley,
Statistical Computing: An Introduction to Data Analysis using
S-Plus, Wiley, p. 211 (2002)). Economy suggests that since
gathering inputs entails costs, the cost of obtaining an input must
be weighed against its benefit. Parsimony suggests that simpler
models are preferable, and that inputs and/or terms which are
insignificant should not be included, in order to optimize the
clarity and reliability of the test.
[0393] A number of techniques may be used to select the features of
the input vector which will be used in a diagnostic test. These
techniques are discussed in the following paragraphs. Some input
selection techniques are algorithm-independent, and may be used
with any classification algorithm. Others are algorithm-specific.
Examples of several algorithm-independent techniques, followed by
techniques which are specifically applicable to random forest,
logistic regression, or discriminant analysis algorithms are
provided.
2. Algorithm--Independent Techniques
[0394] In considering generally applicable techniques, two families
of approaches are available: statistical and stepwise-exploratory.
If the input data fits certain assumptions (regarding normality and
equality of variance), statistical techniques may be used, as
described below. Stepwise methods may be used whether or not those
assumptions are met by the data.
2.1 Statistical Techniques
[0395] A number of classic standard tests may be used on features,
both individually (univariate tests) and in groups (multivariate
tests). For example, for quantitative biomarkers, the diagnostic
classifications in the input data lead to group means which can be
compared using t-tests. This requires that two assumptions are
valid: the variable is normally distributed in each group; and the
variance of the two groups are the same (Petrie & Sabin,
Medical Statistics at a Glance, 2nd ed., Blackwell Publishing, p.
52 (2005)). This test has a multivariate analog: in a multivariate
comparison, Hotelling's T.sup.2 test may be used (Flury, A First
Course in Multivariate Statistics, Springer-Verlag, p. 402
(1997)).
[0396] If the required assumptions are not met, a number of
nonparametric tests are available, such as the Mann-Whitney
Rank-Sum test, the Wilcoxon rank sum test, and the Kruskal-Wallis
statistic for three or more groups (Glantz, Primer of
Biostatistics, 4th ed., McGraw-Hill, Chapter 10 (1997)).
[0397] For both the parametric and nonparametric tests, the results
may be used to suggest which biomarkers (or groups of features) do
or do not have significantly different mean scores for the
diagnostic groups.
2.2 Stepwise Methods
[0398] The following stepwise methods assume that an algorithm has
been chosen (e.g., random forest, logistic regression), but these
methods may be used with any algorithm, and they are in that sense
algorithm-independent. In the context of the selected algorithm, it
is desirable to choose a set of features from those available in
the input vector. In order to use an exploratory technique, a
scoring metric and a search method must be defined.
2.2.1 Scoring Metric
[0399] The first step is to choose a metric by which competing
feature sets may be scored. One possible metric is accuracy, the
percentage of correct predictions made by the classifier (both true
positive and true negative). Alternatively, the scoring metric may
be defined in terms of sensitivity (the percentage of individuals
with disease who are classified as having the disease) and/or
specificity (the percentage of individuals without disease who are
classified as not having the disease) (Fisher & Belle,
Biostatistics: A Methodology for the Health Sciences,
Wiley-Interscience, p. 206 (1993)). Less commonly, the metric may
also involve positive predictive value (ppv, the percentage of
individuals with a positive test who have the disease) and negative
predictive value (npv, the percentage of individuals with a
negative test who do not have the disease).
[0400] The following is a list of available metrics: accuracy;
sensitivity (alone); specificity (alone); the arithmetic mean of
sensitivity and specificity; the geometric mean of sensitivity and
specificity; the minimum of sensitivity and specificity; and the
maximum of sensitivity and specificity. A similar set of metrics
may be used with ppv and npv: ppv/npv alone; arithmetic mean;
geometric mean; max; and min. It is also possible to define metrics
which combine sensitivity, specificity, ppv, and npv (e.g., the
arithmetic mean of those four values). It is also possible to
define specific penalties for false positives and false negatives,
in which case the score is to be minimized rather than
maximized.
2.2.2 Search Method
[0401] For any of the scoring metrics defined above, it is possible
to evaluate any algorithm (including random forest, logistic
regression, discriminant analysis, and others) by exhaustively
enumerating every possible subset of features in the input vector.
In cases where this is unacceptably computationally intensive, it
is possible to conduct a stepwise search in which individual
features are added (a forward search) or removed (a backwards
search) one by one, in a series of rounds (Petrie & Sabin,
Medical Statistics at a Glance, 2nd ed., Blackwell Publishing, p.
89 (2005)).
[0402] In a forward search, features (e.g., biomarkers, symptoms,
etc.) are added one by one, in rounds. In the first round, an input
vector consisting of one feature is evaluated on the training data,
and the best feature (defined by the metric described above) is
identified. In the second round, a new set of input features is
constructed and evaluated. Each set has two features, one of which
is the "best" feature from the first round of evaluation. The best
pair of features from the second round is chosen, and becomes the
basis for the third round, in which all input vectors have three
features, two of which are the ones identified in the second round,
and so forth. This procedure is carried out iteratively, with the
number of rounds equal to the number of possible features in the
input vector. At the conclusion, the best input vector (i.e., set
of features), as defined by the metric, is selected.
[0403] A backward search is similar, but follows a process of model
simplification rather than model expansion (Crawley, Statistics: An
Introduction Using R, Wiley, p. 105 (2005)). The starting point is
the input vector with a complete set of features. In each round,
one parameter is chosen for deletion, as evaluated by the metric
described above.
[0404] In addition to exhaustive forward and backward searches, it
is possible to search stochastically. One method is to randomly
generate a set of features, which are used as seeds. Each seed may
then be evaluated both forward and backward, and the best resulting
set of inputs may be used. An alternative method is to carry out
multiple forward and/or backward searches, but in each round,
rather than deterministically choosing the best feature addition or
deletion, probabilistically choosing the feature to include or
delete by a formula which monotonically decreases/increases the
probability of addition/deletion based on the ranking in the last
round.
3. Algorithm-Specific Techniques
[0405] Having discussed methods for feature selection which are
applicable to any algorithm, this section describes methods which
are specific to particular algorithms. Three representative
algorithms are discussed: random forests; logistic regression; and
discriminant analysis.
3.1 Random Forests
[0406] For random forests, two metrics are available to describe
the importance of features: permutation importance (Strobl et al.,
BMC Bioinformatics, 8:25 (2007)) and gini importance (Breiman et
al., Classification and Regression Trees, Chapman & Hall/CRC,
p. 146 (1984)).
[0407] For permutation importance, the idea is to compare the
scoring of a full forest to the scoring produced by a forest in
which the input values for one feature have been scrambled.
Intuitively, the more important the feature, the more the scoring
will be reduced if the values of that feature have been randomly
permuted. The decrease in score is the permutation importance; by
evaluating all the features in this way, their importance may be
ranked.
[0408] For gini importance, the idea is to take a weighted mean of
the individual trees' improvement in the "gini gain" splitting
criterion produced by each feature. Every time a split of a node is
made on a certain feature, the gini impurity criterion for the two
descendent nodes is less than the parent node. Adding up the gini
decreases for each individual feature over all trees in the forest
gives a measure of feature importance.
3.2 Logistic Regression
[0409] Logistic regression is used in cases where the dependent
variable (e.g., diagnosis) is categorical/nominal. (Agresti, An
Introduction to Categorical Data Analysis, 2nd ed.,
Wiley-Interscience, Chapter 4 (2007)). An extensive literature
describes techniques for feature/model selection in multiple
regression (Maindonald & Braun, Data Analysis and Graphics
Using R, 2nd ed., Cambridge University Press, Chapter 6
(2003)).
[0410] In logistic and other types of regression, the significance
of individual features may be assessed by testing the hypothesis
that the corresponding regression coefficient is zero (Kachigan,
Multivariate Statistical Analysis, A Conceptual Introduction, 2nd
ed., Radius Press, p. 178 (1991)). It is also possible to assess a
group of features on the basis of a deletion test, e.g., using an F
test to assess the significance of the increase in deviance that
results when a given term is removed from a regression model
(Crawley, Statistics: An Introduction Using R, Wiley, p. 103
(2005); Devore, Probability and Statistics for Engineering and the
Sciences, 4th ed., Brooks/Cole, p. 560 (1995)).
3.3 Discriminant Analysis
[0411] Discriminant analysis describes a set of techniques in which
the parametric form of a discriminant function is assumed, and the
parameters of the discriminant function are fitted. This is in
contrast to techniques in which the parametric form of the
underlying probability densities are assumed and fitted, rather
than the discriminant function. The canonical example in this
family of techniques is Fisher's linear discriminant analysis
(LDA); related techniques and extensions include quadratic
discriminant analysis (QDA), regularized discriminant analysis,
mixture discriminant analysis, and others (Venables & Ripley,
Modern Applied Statistics with S, 4th ed., Springer, Chapter 12
(2002)). Feature selection for LDA is discussed below; the
discussion is also applicable to related techniques in this
family.
[0412] In LDA, the coefficients of the linear discriminant are
chosen to maximize the class separation, as measured by the ratio
of the between-class variance and the within-class variance
(Everitt & Dunn, Applied Multivariate Data Analysis, 2nd ed.,
Oxford University Press, p. 253 (2001)). In this context, the
redundancy of features may be formally inferred (Flury, A First
Course in Multivariate Statistics, Springer-Verlag, Sections 5.6
and 6.5 (1997)). This is done by testing the hypothesis that the
relevant discriminant function coefficients are zero. By inference
on the discriminant function coefficients, it is possible to
construct tests of sufficiency/redundancy for possible groups of
features.
3.4 Other Algorithms
[0413] A large number of other algorithms are available for
diagnostic classification, including neural networks, support
vector machines, CART (classification and regression trees),
unsupervised clustering (k-means, Gaussian mixtures), k-nearest
neighbors, and many others. For many of these algorithms,
algorithm-specific techniques are available for evaluating and
selecting features. In addition, some techniques focus on feature
extraction (choosing a smaller number of features which may be
linear or nonlinear combinations of the available features). These
techniques include principal component analysis, independent
component analysis, factor analysis, and other variations (Duda et
al., Pattern Classification, 2nd ed., Wiley-Interscience, p. 568
(2001)).
Example 15
Symptom Profile for Predicting IBS
[0414] This example illustrates techniques for use of a
questionnaire to improve accuracy of an IBS diagnostic prediction
algorithm.
[0415] In certain instances, identifying patients with IBS is more
accurately predicted with the use of one or more questions as
predictors to create an alternative algorithm or further input to
provide added sensitivity and specificity.
[0416] In certain instances, questions were generated such as "Are
you currently experiencing any symptoms?," while others were
extracted from known questionnaires such as Rome II, Rome III, the
Pain Catastrophizing Scale (Sullivan et al., The Pain
Catastrophizing Scale: Development and Validation, Psychol.
Assess., 7:524-532 (1995)), and the like. Some questions had
nominal answers (rates degree of some occurrence), while others
were categorical (binary). In the Rome III questions, the nominal
value of all answers from a patient were added to create a single
score that was considered a simplified "disease severity" score. In
certain embodiments, inclusion of this score together with the
biomarker levels improved both the sensitivity and specificity of
an algorithm.
[0417] In one embodiment, the score of each question (e.g., 0-4)
was used as input (predictor) together with all biomarkers. Models
were then created using Random Forests and Neural Networks. Both
Random Forests and Neural Networks have the capability to determine
the most significant questions that improve the accuracy of
algorithm prediction. After having selected the best questions, one
score was used to predict "disease severity," or level of
Catastrophizing, by summing the values of each question for a
particular patient. The data that included the questionnaire scores
were used to train algorithms using Random Forests, Neural Networks
and other statistical classifiers. The questions from Rome II, Rome
III, and the Pain Catastrophizing Scale improved the accuracy of
prediction when used in combination with multiple biomarkers to
identify patients with IBS. In addition, a single question, "Are
you currently experiencing any symptoms?" (yes or no), was in some
instances as important as the score sum of the answers to the
questions in the questionnaire.
[0418] Table 11 shows that a symptom profile can improve the
accuracy of IBS prediction. With the inclusion of various data from
questionnaires as input predictors, specificity and sensitivity can
both be improved.
TABLE-US-00011 TABLE 11 Improvement of accuracy of IBS prediction
by inclusion of various questionnaires as input predictors.
SEVERITY SCALE X X CATASTROPHIZING X X SCALE CURRENT SYMPTOMS X X
CBIR1 X X X X X ANCA ELISA X X X X X EGF X X X X X ASCA-IgG X X X X
X ASCA-IgA X X X X X AGE X X X X X ANTI-OMPC X X X X X IL-8 X X X X
X LACTOFERRIN X X X X X ANTI- X X X X X TRANSGLUTAMINASE
SENSITIVITY 69% 76% 70% 73% 69% SPECIFICITY 44% 89% 87% 63% 94%
[0419] As the data in Table 11 shows, the specificity is increased
with the use of questionnaire data and on average, sensitivity is
also increased. Sensitivity is the probability of a positive test
among patients with IBS, whereas specificity is the probability of
a negative test among patients without IBS.
[0420] Although the foregoing invention has been described in some
detail by way of illustration and example for purposes of clarity
of understanding, one of skill in the art will appreciate that
certain changes and modifications may be practiced within the scope
of the appended claims. In addition, each reference provided herein
is incorporated by reference in its entirety to the same extent as
if each reference was individually incorporated by reference.
Sequence CWU 1
1
2116PRTHomo sapiensfibrinopeptide A, fibrinogen alpha chain (FGA)
N-terminal region 1Ala Asn Ser Gly Glu Gly Asn Phe Leu Ala Glu Gly
Gly Gly Val Arg 1 5 10 15 214PRTHomo sapiensfibrinopeptide B,
fibrinogen beta chain (FGB) N-terminal region 2Glu Gly Val Asn Asp
Asn Glu Glu Gly Phe Phe Ser Ala Arg 1 5 10
* * * * *
References