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.

1 . 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 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 in said sample; and    (b) classifying said sample as an IBS sample or non-IBS sample using an algorithm based upon said diagnostic marker profile.    
     
     
         2 . The method of  claim 1 , wherein said cytokine is selected from the group consisting of IL-8, IL-1β, TNF-related weak inducer of apoptosis (TWEAK), leptin, osteoprotegerin (OPG), MIP-3β, GROα, CXCL4/PF-4, CXCL7/NAP-2, and combinations thereof.  
     
     
         3 . The method of  claim 1 , wherein said growth factor is selected from the group consisting of epidermal growth factor (EGF), vascular endothelial growth factor (VEGF), pigment epithelium-derived factor (PEDF), brain-derived neurotrophic factor (BDNF), amphiregulin (SDGF), and combinations thereof.  
     
     
         4 . The method of  claim 1 , 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.  
     
     
         5 . The method of  claim 1 , wherein said ASCA is selected from the group consisting of ASCA-IgA, ASCA-IgG, and combinations thereof.  
     
     
         6 . The method of  claim 1 , 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.  
     
     
         7 . The method of  claim 1 , wherein said lipocalin is selected from the group consisting of neutrophil gelatinase-associated lipocalin (NGAL), an NGAL/MMP-9 complex, and combinations thereof.  
     
     
         8 . The method of  claim 1 , wherein said MMP is MMP-9.  
     
     
         9 . The method of  claim 1 , wherein said TIMP is TIMP-1.  
     
     
         10 . The method of  claim 1 , wherein said alpha-globulin is selected from the group consisting of alpha-2-macroglobulin, haptoglobin, orosomucoid, and combinations thereof.  
     
     
         11 . The method of  claim 1 , wherein said actin-severing protein is gelsolin.  
     
     
         12 . The method of  claim 1 , wherein said S100 protein is calgranulin.  
     
     
         13 . The method of  claim 1 , wherein said fibrinopeptide is fibrinopeptide A (FIBA).  
     
     
         14 . The method of  claim 1 , wherein said diagnostic marker profile is determined by detecting the presence or level of at least two, three, four, five, or six diagnostic markers.  
     
     
         15 . The method of  claim 1 , 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.  
     
     
         16 . The method of  claim 1 , 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 or non-IBS sample using an algorithm based upon said diagnostic marker profile and said symptom profile.  
     
     
         17 . The method of  claim 16 , wherein said at least one symptom is 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.  
     
     
         18 . The method of  claim 16 , wherein the presence or severity of said at least one symptom is identified using a questionnaire.  
     
     
         19 . The method of  claim 18 , 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, a set of questions asking said individual about the presence or severity of negative thoughts or feelings associated with having pain or discomfort, and combinations thereof.  
     
     
         20 . The method of  claim 16 , 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.  
     
     
         21 . The method of  claim 16 , wherein said symptom profile is determined by identifying the presence or severity of at least two, three, four, five, or six symptoms.  
     
     
         22 . The method of  claim 1 , wherein said sample is selected from the group consisting of serum, plasma, whole blood, and stool.  
     
     
         23 . The method of  claim 1 , wherein said algorithm comprises a statistical algorithm.  
     
     
         24 . The method of  claim 23 , wherein said statistical algorithm comprises a learning statistical classifier system.  
     
     
         25 . The method of  claim 24 , 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.  
     
     
         26 . The method of  claim 23 , wherein said statistical algorithm comprises a single learning statistical classifier system.  
     
     
         27 . The method of  claim 23 , wherein said statistical algorithm comprises a combination of at least two learning statistical classifier systems.  
     
     
         28 . The method of  claim 1 , wherein said method further comprises classifying said non-IBS sample as a normal, inflammatory bowel disease (IBD), or non-IBD sample.  
     
     
         29 . The method of  claim 1 , wherein said method further comprises sending the results from said classification to a clinician.  
     
     
         30 . The method of  claim 1 , wherein said method further provides a diagnosis in the form of a probability that said individual has IBS.  
     
     
         31 . The method of  claim 1 , 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.  
     
     
         32 . The method of  claim 1 , wherein said method further comprises ruling out intestinal inflammation.  
     
     
         33 . A method for monitoring the progression or regression of irritable bowel syndrome (IBS) in an individual, 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 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 in said sample; and    (b) determining the presence or severity of IBS in said individual using an algorithm based upon said diagnostic marker profile.    
     
     
         34 . The method of  claim 33 , 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 determining the presence or severity of IBS in said individual using an algorithm based upon said diagnostic marker profile and said symptom profile.  
     
     
         35 . The method of  claim 33 , wherein said algorithm comprises a statistical algorithm.  
     
     
         36 . The method of  claim 35 , wherein said statistical algorithm comprises a single learning statistical classifier system or a combination of at least two learning statistical classifier systems.  
     
     
         37 . The method of  claim 33 , wherein said method further comprises comparing the presence or severity of IBS determined in step (b) to the presence or severity of IBS in said individual at an earlier time.  
     
     
         38 . A method for monitoring drug efficacy in an individual receiving a drug useful for treating 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 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 in said sample; and    (b) determining the effectiveness of said drug using an algorithm based upon said diagnostic marker profile.    
     
     
         39 . The method of  claim 38 , 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 determining the effectiveness of said drug using an algorithm based upon said diagnostic marker profile and said symptom profile.  
     
     
         40 . The method of  claim 38 , wherein said algorithm comprises a statistical algorithm.  
     
     
         41 . The method of  claim 40 , wherein said statistical algorithm comprises a single learning statistical classifier system or a combination of at least two learning statistical classifier systems.  
     
     
         42 . The method of  claim 38 , wherein said method further comprises comparing the effectiveness of said drug determined in step (b) to the effectiveness of said drug in said individual at an earlier time in therapy.  
     
     
         43 . The method of  claim 38 , wherein said drug is selected from the group consisting of a serotonergic agent, antidepressant, chloride channel activator, guanylate cyclase agonist, antibiotic, opioid, neurokinin antagonist, antispasmodic or anticholinergic agent, belladonna alkaloid, barbiturate, free bases thereof, pharmaceutically acceptable salts thereof, derivatives thereof, analogs thereof, and combinations thereof.  
     
     
         44 . A computer-readable medium comprising code for controlling one or more processors to classify whether a sample from an individual is associated with irritable bowel syndrome (IBS), said code comprising: 
 instructions to apply a statistical process to a data set comprising a diagnostic marker profile to produce a statistically derived decision classifying said sample as an IBS sample or non-IBS sample based upon said diagnostic marker profile,    wherein said 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, 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 in said sample.    
     
     
         45 . The computer-readable medium of  claim 44 , wherein said computer-readable medium comprises instructions to apply a statistical process to a data set comprising said diagnostic marker profile in combination with a symptom profile which indicates the presence or severity of at least one symptom in said individual to produce a statistically derived decision classifying said sample as an IBS sample or non-IBS sample based upon said diagnostic marker profile and said symptom profile.  
     
     
         46 . The computer-readable medium of  claim 44 , wherein said statistical process comprises a single learning statistical classifier system.  
     
     
         47 . The computer-readable medium of  claim 44 , wherein said statistical process comprises a combination of at least two learning statistical classifier systems.  
     
     
         48 . A system for classifying whether a sample from an individual is associated with irritable bowel syndrome (IBS), said system comprising: 
 (a) a data acquisition module configured to produce a data set comprising a diagnostic marker profile, wherein said 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, 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 in said sample;    (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 said sample as an IBS sample or non-IBS sample based upon said diagnostic marker profile; and    (c) a display module configured to display the statistically derived decision.    
     
     
         49 . The system of  claim 48 , wherein said system comprises a data acquisition module configured to produce a data set comprising said diagnostic marker profile in combination with a symptom profile which indicates the presence or severity of at least one symptom in said 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 said sample as an IBS sample or non-IBS sample based upon said diagnostic marker profile and said symptom profile; and a display module configured to display the statistically derived decision.  
     
     
         50 . The system of  claim 48 , wherein said statistical process comprises a single learning statistical classifier system.  
     
     
         51 . The system of  claim 48 , wherein said statistical process comprises a combination of at least two learning statistical classifier systems.