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 - 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-1β, TNF-related weak inducer of apoptosis (TWEAK), leptin, osteoprotegerin (OPG), MIP-3β, GROα, 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.