Methods for diagnosing irritable bowel syndrome
View Patent ↗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 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 two diagnostic markers including brain-derived neurotrophic factor (BDNF) and an anti-flagellin antibody in said sample;
(b) a data processing module configured to process the data set by applying a learning statistical classifier system 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.
2. The system of claim 1 , 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 learning statistical classifier system 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.
3. The system of claim 1 , wherein said learning statistical classifier system comprises a combination of at least two learning statistical classifier systems.
4. The system of claim 1 , wherein said diagnostic marker profile is determined by detecting the presence or level of at least three, four, five, or six diagnostic markers.
5. The system of claim 1 , wherein the presence or level of said at least two diagnostic markers is detected using a hybridization assay, amplification-based assay, immunoassay, or immunohistochemical assay.
6. The system of claim 2 , 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.
7. The system of claim 2 , wherein the presence or severity of said at least one symptom is identified using a questionnaire.
8. The system of claim 7 , 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.
9. The system of claim 2 , 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.
10. The system of claim 2 , wherein said symptom profile is determined by identifying the presence or severity of at least two, three, four, five, or six symptoms.
11. The system of claim 1 , wherein said sample is selected from the group consisting of serum, plasma, whole blood, and stool.
12. The system of claim 1 , 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.
13. The system of claim 1 , wherein said system further comprises classifying said non-IBS sample as a normal, inflammatory bowel disease (IBD), or non-IBD sample.
14. The system of claim 1 , wherein said system further comprises sending the results from said classification to a clinician.
15. The system of claim 1 , wherein said system further provides a diagnosis in the form of a probability that said individual has IBS.
16. The system of claim 1 , wherein said system 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.
17. The system of claim 1 , wherein said system further comprises ruling out intestinal inflammation.
18. The system of claim 1 , wherein said diagnostic marker profile indicates the presence or level of at least one additional 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.
19. The system of claim 18 , 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.
20. The system of claim 18 , 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), amphiregulin (SDGF), and combinations thereof.
21. The system of claim 18 , 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.
22. The system of claim 18 , wherein said ASCA is selected from the group consisting of ASCA-IgA, ASCA-IgG, and combinations thereof.
23. The system of claim 18 , wherein said antimicrobial antibody is selected from the group consisting of an anti-outer membrane protein C (anti-OmpC) antibody, anti-I2 antibody, and combinations thereof.
24. The system of claim 18 , wherein said lipocalin is selected from the group consisting of neutrophil gelatinase-associated lipocalin (NGAL), an NGAL/MMP-9 complex, and combinations thereof.
25. The system of claim 18 , wherein said MMP is MMP-9.
26. The system of claim 18 , wherein said TIMP is TIMP-1.
27. The system of claim 18 , wherein said alpha-globulin is selected from the group consisting of alpha-2-macroglobulin, haptoglobin, orosomucoid, and combinations thereof.
28. The system of claim 18 , wherein said actin-severing protein is gelsolin.
29. The system of claim 18 , wherein said S100 protein is calgranulin.
30. The system of claim 18 , wherein said fibrinopeptide is fibrinopeptide A (FIBA).
31. The system of claim 1 , wherein said anti-flagellin antibody is an anti-Cbir-1 flagellin antibody.