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New Biomarkers for Diagnosing Inflammatory Bowel Disease and Assessing Treatment Outcomes

Barnes, Edward L. MD, MPH; Burakoff, Robert MD, MPH

doi: 10.1097/MIB.0000000000000903
Clinical Review Articles

Abstract: Despite advances in our understanding of the pathophysiology underlying inflammatory bowel disease, there remains a significant need for biomarkers that can differentiate between Crohn's disease and ulcerative colitis with high sensitivity and specificity, in a cost-efficient manner. As the focus on personalized approaches to the delivery of medical treatment increases, new biomarkers are being developed to predict an individual's response to therapy and their overall disease course. In this review, we will outline many of the existing and recently developed biomarkers, detailing their role in the assessment of patients with inflammatory bowel disease. We will identify opportunities for improvement in our biomarkers, including better differentiation between the subtypes of inflammatory bowel disease. We will also discuss new targets and strategies in biomarker development, including combining modalities to create biomarker signatures to improve the ability to predict disease courses and response to therapy among individual patients.

Article first published online 18 October 2016.

Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts.

Address correspondence to: Robert Burakoff, MD, MPH, Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, One Brigham Circle 3002-QQ, 1620 Tremont Street, Boston, MA 02120 (e-mail:

E. L. Barnes is supported by the National Institutes of Health (T32 DK007533-29).

The authors have no conflict of interest to disclose.

Received June 22, 2016

Accepted July 20, 2016

Although great strides have occurred in our understanding of the epidemiology and pathophysiology of inflammatory bowel disease (IBD) in recent years, we continue to seek a set of ideal biomarkers that would allow us to improve our diagnostic and therapeutic approaches in assessing and treating patients with ulcerative colitis (UC) and Crohn's disease (CD). The initial diagnosis of UC or CD can be made using a combination of phenotypic and serologic information;1–3 however, distinguishing the initial presentation of an IBD from an acute colitis of another etiology, or even distinguishing between UC and CD can at times be difficult. Furthermore, monitoring patients over time and potentially predicting clinical outcomes among individual patients require a more nuanced and personalized approach.

The ideal biomarker is readily available, noninvasive, accurate, sensitive, specific, and affordable such that it can be used in clinical settings. Traditionally, the assessment of patients with IBD has been somewhat complicated by the necessary, but rather invasive nature of evaluation, including endoscopic procedures with biopsies. This has prompted investigators to seek noninvasive biomarkers that can be used in both the initial diagnosis of IBD and in monitoring the disease course. These efforts have led to the emergence of multiple serologic and stool biomarkers of varying degrees of utility, although many of these biomarkers still have underlying weaknesses that limit their widespread use.

To date, no ideal biomarker for the assessment and management of IBD has been identified. However, the newer biomarkers that have been developed in recent years have several strengths that should be noted. In this review, we will outline many of the existing biomarkers, including a more detailed analysis of the recently developed biomarkers and their role in the assessment of patients with IBD. We will also identify opportunities for improvement in our biomarkers, including better differentiation between subtypes of IBD, and improvements in predictions of disease course and response to therapy among individual patients. Finally, we will discuss novel approaches to biomarker development and what targets biomarkers may focus on in the coming years.

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Markers of Inflammation

Erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are 2 nonspecific markers of inflammation that can be elevated in patients with active UC and CD. Under normal circumstances, hepatocyte production of CRP is low. CRP has demonstrated utility in differentiating IBD from other noninflammatory gastrointestinal conditions;4 however, both ESR and CRP can be elevated in other conditions,5–8 and thus reliance on these biomarkers alone in the evaluation of a patient with suspected or established IBD can be challenging. Although CRP is thought to increase in majority of patients with active CD, up to 50% of patients with an active flare of UC can demonstrate normal CRP levels.9 Even among a subset of patients with endoscopically active CD, normal CRP levels can be noted,10 as biomarker levels are not necessarily correlated with mucosal lesions noted on endoscopy. Additionally, some patients with CD can demonstrate persistently low CRP levels despite active disease, including patients with a low body mass index or a purely ileal disease distribution.11

In contrast to serologic biomarkers, fecal biomarkers such as fecal calprotectin (FC) and fecal lactoferrin are more specific for intestinal inflammation. FC is released by activated neutrophils, and thus serves as an indirect estimate of the neutrophil infiltrate in the gastrointestinal tract. In the initial evaluation of a patient with suspected IBD, FC can be used as a screening tool for identifying patients who are likely to need endoscopy for further evaluation.12 Among patients with established IBD, FC serves as a reliable indicator of disease activity,13–19 can serve as a marker of mucosal healing,16,20,21 can predict relapse of disease,22–25 and among patients with CD, FC can predict endoscopic recurrence after intestinal resection.26 Although FC has demonstrated significant utility in differentiating IBD from other chronic abdominal syndromes such as irritable bowel syndrome,4,27 FC does not reliably differentiate between UC and CD.28 Recent studies have also demonstrated that intraindividual variability of FC can occur throughout the day, which may indicate that the time of assessment is also critical.29

Lactoferrin, a sensitive and specific marker of inflammation among patients with IBD,30 is a major component of granules of neutrophilic granulocytes and is released during the process of neutrophil degradation.31 Levels of fecal lactoferrin are typically elevated in patients with active IBD, and tend to correlate well with FC levels.32 In addition to the identification of active disease,15,16 similar to FC, fecal lactoferrin can serve as a marker of mucosal healing.14,21

S100A12 is a calcium-binding calgranulin protein that is expressed in activated neutrophils. The expression of S100A12 is more restricted to granulocytes, with release occurring at the site of inflammation among patients with IBD.33–35 S100A12 is an attractive candidate as a diagnostic biomarker, given its high sensitivity and specificity in differentiating pediatric patients with CD from healthy controls.36 However, despite these findings and the ability of S100A12 to distinguish IBD from irritable bowel syndrome,34 S100A12 is also elevated in other inflammatory conditions such as Kawasaki disease37 and inflammatory arthritis.38

Lipocalin-2 (Lcn-2), also referred to as neutrophil gelatinase–associated lipocalin, is stored in neutrophil granules and released at sites of inflammation.39 Lcn-2 has demonstrated utility as a biomarker of active UC,39 and has been reported as upregulated in both feces40 and colonic mucosa41,42 of patients with UC. Although Lcn-2 correlates with other markers of inflammation, it does not seem to significantly distinguish between UC and CD.43 Furthermore, Lcn-2 can be elevated in other conditions such as kidney disease,44 ovarian cancer,45 acute pancreatitis,46 chronic obstructive pulmonary disease,47 and cardiovascular disease48 which limits its utility as an IBD-specific biomarker.

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Biomarkers that Differentiate CD from UC

Multiple serologic biomarkers have been evaluated for their association with CD, including anti–Saccharomyces cerevisiae antibodies (ASCAs) and antibodies to bacterial proteins such as outer membrane protein C (OmpC), I2, and flagellin (CBir1). Increased titers of ASCA have demonstrated high specificity, but low sensitivity in the evaluation of patients with suspected CD.49 Perinuclear antineutrophil cytoplasmic antibodies (pANCAs) were first reported as present in the sera of patients with UC, but not CD.50 However, later studies demonstrated that elevated levels of pANCA can be seen in patients with both UC and patients with CD who have colonic disease in a UC-like presentation.49

Although the combined use of pANCA and ASCA could be of benefit in the evaluation of patients with IBD,51 low sensitivity limits their overall utility. When evaluated in a meta-analysis of 60 studies, the most sensitive combination of these 2 tests for the evaluation of CD was ASCA+/pANCA-, which was 55% sensitive and 93% specific.52 Among patients with UC, a pANCA+/ASCA- combination demonstrated a sensitivity of 51.3% and a specificity of 94.3%.52 The characteristics of many of the biomarkers currently being used in the care of patients with IBD are summarized in Table 1.



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Biomarker Signatures

Given that no single serologic or fecal biomarker has demonstrated the necessary sensitivity and specificity to operate as a stand-alone tool in the evaluation of suspected or established IBD, recent attention has been focused on the potential for development of groups of biomarkers operating in a pattern or signature that may increase diagnostic utility.

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Gene Expression Analysis

The use of whole-blood mRNA gene expression techniques is one area where such biomarker signatures may be useful. Recent advances have allowed for the evaluation of mRNA extracted from whole blood.53,54 Among patients with IBD, gene expression profiles obtained from whole blood have been used to differentiate active from inactive CD.55 Additionally, gene expression profiles have demonstrated the ability to differentiate between CD, UC, and other noninflammatory diarrheal conditions.56 More recently, the Affymetrix GeneChip technology was used to generate genome-wide expression profiles to predict disease activity in patients with UC and CD.57 In this study, whole-blood gene panels determined the activity of disease with high sensitivity and specificity, while reliably distinguishing between UC and CD.57 Although these initial results are promising, most studies evaluating the utility of whole-blood gene expression analysis have been performed in small populations, and thus larger studies remain necessary for further evaluation of this modality.58

An earlier study demonstrated high accuracy in distinguishing UC from CD when using transcriptional profiling of peripheral blood mononuclear cell RNA.59 In a separate study of 58 patients, peripheral blood mononuclear mRNA expression levels were used to reliably differentiate patients with IBD, rheumatoid arthritis, and psoriasis from healthy controls.60 Similarly, in a comparison with healthy controls, patients with IBD in clinical remission demonstrated distinct gene expression profiles obtained from peripheral blood leukocytes.61

Gene expression profiling from mucosal biopsies has also stimulated interest as a potentially attractive means of identifying new biomarkers in the evaluation of IBD. Gene expression profiles obtained from mucosal biopsies have been used to differentiate patients with UC from healthy controls62 and to differentiate patients with both subtypes of IBD from infectious colitis63 and normal controls.64 Another previous study used gene expression profiling from mucosal biopsies to identify discriminative signatures to differentiate between colon adenoma, colorectal cancer, and IBD.65 Although each of these studies is indicative of the significant promise for gene expression analysis as a tool in differentiating IBD from other colonic diseases and potentially predicting disease activity, the requirement of mucosal biopsy makes the noninvasive option of whole-blood gene analysis potentially more attractive.

When evaluating specific patterns identified by gene expression profiling, trends along biological processes have been identified (Tables 2 and 3). In a study using gene expression analysis of mucosal biopsies to evaluate response to infliximab (IFX) among patients with UC, a specific gene profile involved in signaling along several pathways was identified, including the adaptive immune response, inflammation, and the tumor necrosis factor (TNF) pathway.66 In a separate evaluation comparing the gene sets used in this study to those identified in patients with the colitis subtype of CD, there was considerable overlap.67 A similar focus around immune function has been demonstrated when analyzing those patterns identified by whole-blood gene expression analysis. Among a 4-gene panel used to differentiate UC from CD, CD300A which potentially plays a role in modulating proinflammatory stimuli among neutrophils and IL1R2 which is involved in cytokine–cytokine receptor interactions were identified as potential markers.56 In an evaluation of the prediction of disease activity among patients with UC and CD, a number of genes that were identified among patients with active disease had previously been associated with UC and CD in other studies.39 These potential marker genes included NLRP12 (a member of the NOD-like receptor family) and TAGAP, one of the 22 genes previously identified as downregulated among responders to IFX in the Active Ulcerative Colitis Trial 1 (ACT 1).68





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Circulating microRNA (miRNA) levels are another potential method of assessing disease activity among patients with IBD. In a comparison of patients with active and inactive UC and CD with controls, peripheral blood miRNAs were able to distinguish active UC and CD from healthy controls.69 Although specific patterns were identified to allow for delineation between active UC and CD, in this evaluation there was significant overlap between several of the miRNAs in both CD and UC. The blood expression of miRs-199a-5p, -362-3p, -340*, -532-3p, and miRplus-1271 was elevated in both subtypes of IBD as compared with healthy controls, which may indicate an overall inflammatory state found in both UC and CD.69 In a similar evaluation, 11 miRNAs were elevated in pediatric patients with active CD when compared with healthy controls.70 A later study suggested that several miRNAs could accurately distinguish UC from CD, in addition to differentiating both subtypes of IBD from controls.71 Importantly, the authors of this study noted that among patients with CD, their miRNA profiles were consistent with the earlier patterns indicated by Wu et al69 and Zahm et al.70 When patients with UC were compared with controls, a distinct signature consisting of 31 miRNAs was identified which could differentiate patients with UC from controls with high specificity, sensitivity, and accuracy.72

Tissue miRNA profiling has also been used to differentiate subtypes of IBD and to differentiate patients with IBD from controls.73–79 Wu, et al. were among the first to analyze the potential role of miRNA obtained from colonic biopsies, in their description of the differential expression of 11 miRNAs among patients with active UC.73 A separate study by Wu et al identified 5 miRNAs associated with active CD of the sigmoid colon and 4 miRNAs that were increased among patients with CD affecting the terminal ileum.75 The 5 miRNAs associated with active colonic CD were later studied to assess their ability to differentiate CD from UC and indeterminate colitis. In this evaluation, all 5 miRNAs were statistically different when comparing patients with CD to those with indeterminate colitis, whereas no difference was noted when patients with UC were compared with those with indeterminate colitis.80 The ability to identify similar miRNA expression profiles across multiple studies is encouraging; however, the need for invasive testing with endoscopic examination and biopsy may limit the utility of colonic tissue miRNA profiling as a biomarker.

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In an era of increased focus on the potential for personalized medicine, the emphasis on strategies for the development of better biomarkers in IBD will continue to exist. In addition to the identification of specific disease subtypes within IBD, a renewed focus on predictors of the disease course is paramount. Improving the ability to not only diagnose patients with IBD, but also to predict their disease activity and their response to therapy will significantly improve the care of patients with IBD. Additionally, by avoiding costly therapies that may be of minimal benefit, more precise therapy choices may lead to significant reductions in health care costs and resource utilization over time.

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Novel Approaches to Biomarker Development

Given the lack of sensitivity and specificity associated with ESR and CRP, a significant opportunity exists for the development of disease-specific serologic markers of inflammation. Further attention may be focused on specific genotypes associated with CD or UC as a means of identifying better targets for biomarker development. For example, defensins such as β-defensin 2 and antimicrobial peptides such as cathelicidin may be increased among patients with CD where bacterial DNA is present in blood samples, and mediated through a wild-type NOD2/CARD15 genotype.81

Metabolic profiling has been proposed as another area of great promise in the evaluation of patients with suspected IBD and in the differentiation of UC from CD.82 Multiple specimen types can be analyzed through metabolomic methods, including mucosal biopsies, stool, and urine samples.83–88 One of the more unique metabolomic profiles recently suggested is a breathprint that can differentiate children with IBD from healthy controls. In a study of 117 patients, the authors used selected-ion flow-tube mass spectrometry to identify patterns of volatile organic compounds in the exhaled breath of children with IBD, demonstrating the potential utility for breath testing as a noninvasive method of evaluating a patient with suspected IBD.89

Protein profiling of serum, plasma, and tissue samples may also reveal distinct patterns among those patients with IBD. A variety of techniques for proteomic analysis have been proposed,90,91 with pilot studies indicating that proteomic profiling may be useful in the differentiation of patients with IBD from healthy controls,92,93 and in the differentiation of subtypes of IBD.94 Early studies also suggest that protein profiling may also have a role in the prediction of response to biologic therapy among patients with IBD.95

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Differentiation Between Subtypes of IBD

There has been continued interest in the development of biomarkers to aid in the differentiation between subtypes of IBD given the low sensitivity associated with serologic tests such as pANCA and ASCA. Our ability to explore genetic associations with clinical presentations of disease has improved considerably over the past decade, holding great promise for such evaluations. Recently, the largest genotype–phenotype study of patients with IBD was published.96 In an analysis of 29,838 patients with IBD, 3 gene loci (NOD2, MHC, and MST1 3p21) were identified which were associated with subphenotypes of IBD. These findings led to the recommendation that based on genetic factors, IBD may be better classified into 3 distinct subphenotypes (ileal CD, colonic CD, and UC).96 In an accompanying editorial, more systematic evaluation of the gene–environment connections was suggested as one means of improving our understanding of disease pathogenesis.97 Another recent genetic evaluation identified nearly 200 single-nucleotide polymorphisms that were associated with IBD, many of which overlapped between patients with UC and CD.98

Other efforts have been focused on improving the sensitivity of more established tests with previously detailed high specificity. Given the inability of pANCA alone to distinguish UC from CD, combining pANCA with other biomarkers has been proposed as a means of better delineation of disease subtype. In a study of 484 patients, Targan et al. found anti-CBir1 positivity in 44% of pANCA-positive patients with CD compared with 4% of pANCA-positive patients with UC,99 suggesting that the combination of these markers (pANCA+/anti-CBir1+) may be part of a biomarker signature suggestive of a specific, perhaps more complicated or UC-like phenotype of CD. In another approach, the genetic marker TNFSF15 was combined with ASCA IgA to increase the power of predicting a stenosing or perforating phenotype of CD.100

Perhaps most indicative of the potential power of using a multifaceted biomarker signature or panel was the comparison by Plevy, et al. of a panel of serological markers (ASCA-IgA, ASCA-IgG, ANCA, pANCA, OmpC, and CBir1) to a panel that included the same serological markers as well as inflammatory markers (including CRP), gene variants, and 2 additional serological markers (A4-Fla2 and FlaX).101 In this evaluation, the larger panel improved both the ability to differentiate IBD from non-IBD as well as the discrimination between CD and UC.101 As the utilization of serological biomarkers, genetic analysis, inflammatory and potentially environmental factors would seem to offer the greatest hope for increasing the ability to differentiate patients with IBD from those without IBD as well as to differentiate UC from CD, the creation of multifaceted biomarker signatures is an area that will likely continue to expand in the near future.

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Predicting Disease Course

Although complicated due to the inherent multifactorial nature, the prediction of an individual patient's disease course is one area where improvement in biomarker performance is most desired. The potential use of biomarkers in the prediction of disease course has been demonstrated for over 15 years, beginning with the association of high ASCA levels with fibrostenosing and penetrating disease among patients with CD.102 Other studies have suggested that the sum of antibodies is an important factor in the evaluation of disease progression among patients with CD.103 Early prospective studies by Dubinsky et al104 demonstrated that the frequency of internal penetrating or stricturing disease increased as the presence of immune response to microbial antigens such as I2, OmpC, CBir1, and ASCA increased. In a larger study of 796 pediatric patients with CD, Dubinsky et al demonstrated that the rate of complicated CD (penetrating, stricturing, or surgery requiring disease) increased as the number and magnitude of reactivity to antibodies increased, with those patients expressing immune reactivity demonstrating a significantly faster disease progression.105 In a study of sera from 100 military personnel with CD, 65 patients were positive for at least 1 CD-associated antimicrobial antibody (ASCA-IgA, ASCA-IgG, anti-OmpC, anti-CBir1, anti-A4-Fla2, or anti-FlaX) at a median of 6 years before a diagnosis of CD.106 Additionally, the proportion of positive antimicrobial antibodies before diagnosis was higher among patients who developed complicated CD when compared with those who developed noncomplicated CD.106 Genotyping may also suggest the potential for a more severe disease course, as the NOD2 genotype has been associated with stricturing small bowel disease among patients with CD and more rapid disease progression.107

After the initial success in identifying serologic and genotypic predictors of disease course, more recent efforts have been focused on combining methods to create even stronger predictive models. In an evaluation of 1721 patients with CD, Kaur et al demonstrated that combining clinical and genetic data led to improved performance in determining an association with perianal CD.108 Additionally, the development of models incorporating genotype, serologic, and clinical information into a multivariable model for prediction of disease progression offers great promise for better predictions of the disease course of patients with CD.109,110 These models are particularly attractive given their web-based nature allowing for real-time discussions of predictions of disease prognosis with individual patients.

In addition to the demonstrated abilities to assess inflammation and mucosal healing, FC has also emerged as a noninvasive assessment of prediction of disease relapse among patients with UC.111 In a study of 70 patients in remission at study entry, an elevated FC was associated with an increased risk of relapse at both 6 and 12 months, whereas histologic inflammation, CRP, and length of remission were not predictive of relapse.111 In patients with severe UC, multiple methods have been proposed for the identification of patients at the greatest risk of colectomy. In one study, an elevated CRP alone was associated with an increased likelihood of colectomy.112 However, a more recent study used a risk matrix model to identify the extent of disease, age, need for systemic steroids, and CRP or ESR at diagnosis as reliable predictors of need for colectomy both individually and in combination.113

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Predicting Response to Therapy

Given the success of combination approaches to predicting the disease course of patients with both CD110 and UC,113 it would seem that further development and refining of these prediction models holds the greatest potential for better identification of patients at risk for a more severe disease course, allowing for an earlier and more personalized approach to therapy.

Ideally, biomarkers would be used as a predictive means to guide the initial decisions regarding the initiation of one therapeutic agent over another among patients with active CD and UC. However, to this point, many biomarkers have demonstrated utility in predicting response or remission only after initiation of an agent, which may lead to trials of multiple therapies before a successful maintenance regimen is established. In addition to an increasing focus on the utility of pharmacodynamic and pharmacokinetic monitoring of patients being treated with biological therapy,114–118 multiple biomarkers have been identified (Table 4).



CRP has been used in a variety of studies to predict response to biological therapy.119–122 The overall importance of CRP in the prediction of response to biological therapy has been discussed in many scenarios, with particular questions centered around CRP's role as an independent biomarker predicting clinical response or remission to therapy as opposed to a more general indicator of inflammation.123 CRP has also been described as a predictor of low IFX level, and subsequent loss of response among patients with CD being treated with IFX.124 Among patients being treated with thiopurines, CRP can also serve as a predictor of relapse.125,126 Elevated CRP has also served as a predictor of relapse after withdrawal of IFX therapy in patients being treated with combination therapy.127

Other serologic measures such as mean platelet volume128 and erythrocyte mean corpuscular volume129 have also demonstrated utility in predicting response to therapy. More recently, Arias et al. used a risk panel to predict long-term relapse-free or colectomy-free survival among patients with UC. This risk panel incorporated 5 factors including baseline CRP and albumin, pANCA, and clinical factors manifested as absence of short-term clinical response and absence of short-term mucosal healing.130

Given concerns that blood-based tests such as CRP might reflect an overall state of inflammation, there has been continued interest in the role of fecal tests that may be more directly associated with mucosal inflammation. High FC at baseline have been associated with increased risk of disease relapse among patients with CD,24 whereas FC levels that normalize after induction therapy have been associated with sustained clinical remission among patients with CD and UC.131,132 Lower FC levels have been associated with response to biological therapy and clinical outcomes including clinical remission and mucosal healing.133,134 Perhaps most useful, among patients in remission, FC has been reported to increase earlier and remain elevated before clinical or endoscopic relapse of disease,135 which may indicate a role for prospective or routine monitoring with FC to identify those patients at the greatest risk of relapse.

Genome-wide association studies have been used to identify predictors of response to anti-TNF therapy among patients with IBD. In an evaluation of 94 patients with IBD, Dubinsky et al136 found an association between 6 known susceptibility loci and primary nonresponse to an anti-TNF therapy. In the final predictive model used in this study, only the 21q22.2/BRWDI loci demonstrated a significant association, along with pANCA and a diagnosis of UC.136

Gene expression analysis has also been used as a predictor of response to anti-TNF therapy in patients with both UC66 and CD.67,137 Gene expression analysis offers a particularly attractive tool, as it could be performed before initiation of therapy and thus offers a prediction of response before use of therapy that may ultimately provide a less than desirable treatment effect. Techniques using analysis of “metagenes,” transcript sets that have been derived to reflect ongoing biological change within a mucosal biopsy have also demonstrated utility in the identification of predictors of the response to IFX therapy among patients with UC.138 Whole-blood gene expression analysis techniques are perhaps more attractive, as they allow for prediction of response using a minimally invasive approach as compared with the need for biopsies for gene expression analysis of mucosal tissue. Given the preference for a less invasive, blood-based predictor of response, there are ongoing studies of whole-blood gene expression analysis to identify predictors of response to therapy with IFX and adalimumab.

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Further development of biomarkers to assist in the care of patients with UC and CD is an area that is primed for progression in the near future. As we move toward an ultimate goal of precision medicine, where treatment decisions can be individualized through the use of clinical, genetic, and phenotypic information, there will be further emphasis on the initial identification of patients with IBD, as well as predictors of disease course and responses to individual treatment regimens. Given the initial successes in combining multiple testing modalities, there is hope that the ultimate development of a biomarker signature may yield significant advances in our ability to identify those patients with the greatest risk for severe disease, and thus would benefit most from aggressive and individualized therapies. Although the ideal biomarker for the care of patients with UC and CD does not exist at this point, there is hope that we can build on the initial foundations of serologic and stool tests to identify a more sensitive and specific biomarker or biomarker signature with low cost and increased availability.

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1. Gasche C, Scholmerich T, Brynskov J, et al.. A simple classification of Crohn's disease: report of the Working Party for the World Congresses of Gastroenterology, Vienna 1998. Inflamm Bowel Dis. 2000;6:8–15.
2. Silverberg MS, Satsangi J, Ahmad T, et al.. Toward an integrated clinical, molecular and serological classification of inflammatory bowel disease: report of a Working Party of the 2005 Montreal World Congress of Gastroenterology. Can J Gastroenterol. 2005;19(suppl A):5A–36A.
3. Dassopoulos T, Nguyen GC, Bitton A, et al.. Assessment of reliability and validity of IBD phenotyping within the National Institutes of Diabetes and Digestive and Kidney Diseases (NIDDK) IBD Genetics Consortium (IBDGC). Inflamm Bowel Dis. 2007;13:975–983.
4. Menees SB, Powell C, Kurlander J, et al.. A meta-analysis of the utility of C-reactive protein, erythrocyte sedimentation rate, fecal calprotectin, and fecal lactoferrin to exclude inflammatory bowel disease in adults with IBS. Am J Gastroenterol. 2015;110:444–454.
5. Agassandian M, Shurin GV, Ma Y, et al.. C-reactive protein and lung diseases. Int J Biochem Cell Biol. 2014;53:77–88.
6. Yi JH, Wang D, Li ZY, et al.. C-reactive protein as a prognostic factor for human osteosarcoma: a meta-analysis and literature review. PLoS One. 2014;9:e94632.
7. Pathak S, Nunes QM, Daniels IR, et al.. Is C-reactive protein useful in prognostication for colorectal cancer? A systematic review. Colorectal Dis. 2014;16:769–776.
8. Kaptoge S, Di Angelantonio E, Pennells L, et al.. C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. 2012;367:1310–1320.
9. Lewis JD. The utility of biomarkers in the diagnosis and therapy of inflammatory bowel disease. Gastroenterology. 2011;140:1817–1826.e2.
10. Denis MA, Reenaers C, Fontaine F, et al.. Assessment of endoscopic activity index and biological inflammatory markers in clinically active Crohn's disease with normal C-reactive protein serum level. Inflamm Bowel Dis. 2007;13:1100–1105.
11. Florin TH, Paterson EW, Fowler EV, et al.. Clinically active Crohn's disease in the presence of a low C-reactive protein. Scand J Gastroenterol. 2006;41:306–311.
12. van Rheenen PF, Van de Vijver E, Fidler V. Faecal calprotectin for screening of patients with suspected inflammatory bowel disease: diagnostic meta-analysis. BMJ. 2010;341:c3369.
13. Røseth AG, Aadland E, Jahnsen J, et al.. Assessment of disease activity in ulcerative colitis by faecal calprotectin, a novel granulocyte marker protein. Digestion. 1997;58:176–180.
14. Sipponen T, Kärkkäinen P, Savilahti E, et al.. Correlation of faecal calprotectin and lactoferrin with an endoscopic score for Crohn's disease and histological findings. Aliment Pharmacol Ther. 2008;28:1221–1229.
15. Sipponen T, Savilahti E, Kolho KL, et al.. Crohn's disease activity assessed by fecal calprotectin and lactoferrin: correlation with Crohn's disease activity index and endoscopic findings. Inflamm Bowel Dis. 2008;14:40–46.
16. Sipponen T, Savilahti E, Kärkkäinen P, et al.. Fecal calprotectin, lactoferrin, and endoscopic disease activity in monitoring anti-TNF-alpha therapy for Crohn's disease. Inflamm Bowel Dis. 2008;14:1392–1398.
17. Schoepfer AM, Beglinger C, Straumann A, et al.. Ulcerative colitis: correlation of the Rachmilewitz endoscopic activity index with fecal calprotectin, clinical activity, C-reactive protein, and blood leukocytes. Inflamm Bowel Dis. 2009;15:1851–1858.
18. Lin JF, Chen JM, Zuo JH, et al.. Meta-analysis: fecal calprotectin for assessment of inflammatory bowel disease activity. Inflamm Bowel Dis. 2014;20:1407–1415.
19. D'Haens G, Ferrante M, Vermeire S, et al.. Fecal calprotectin is a surrogate marker for endoscopic lesions in inflammatory bowel disease. Inflamm Bowel Dis. 2012;18:2218–2224.
20. Røseth AG, Aadland E, Grzyb K. Normalization of faecal calprotectin: a predictor of mucosal healing in patients with inflammatory bowel disease. Scand J Gastroenterol. 2004;39:1017–1020.
21. Sipponen T, Björkesten CGAF, Färkkilä M, et al.. Faecal calprotectin and lactoferrin are reliable surrogate markers of endoscopic response during Crohn's disease treatment. Scand J Gastroenterol. 2010;45:325–331.
22. Tibble JA, Sigthorsson G, Bridger S, et al.. Surrogate markers of intestinal inflammation are predictive of relapse in patients with inflammatory bowel disease. Gastroenterology. 2000;119:15–22.
23. D'Incà R, Dal Pont E, Di Leo V, et al.. Can calprotectin predict relapse risk in inflammatory bowel disease? Am J Gastroenterol. 2008;103:2007–2014.
24. Gisbert JP, Bermejo F, Pérez-Calle JL, et al.. Fecal calprotectin and lactoferrin for the prediction of inflammatory bowel disease relapse. Inflamm Bowel Dis. 2009;15:1190–1198.
25. Kallel L, Ayadi I, Matri S, et al.. Fecal calprotectin is a predictive marker of relapse in Crohn's disease involving the colon: a prospective study. Eur J Gastroenterol Hepatol. 2010;22:340–345.
26. Wright EK, Kamm MA, De Cruz P, et al.. Measurement of fecal calprotectin improves monitoring and detection of recurrence of Crohn's disease after surgery. Gastroenterology. 2015;148:938–947.e1.
27. Kopylov U, Rosenfeld G, Bressler B, et al.. Clinical utility of fecal biomarkers for the diagnosis and management of inflammatory bowel disease. Inflamm Bowel Dis. 2014;20:742–756.
28. Tibble JA, Sigthorsson G, Foster R, et al.. Use of surrogate markers of inflammation and Rome criteria to distinguish organic from nonorganic intestinal disease. Gastroenterology. 2002;123:450–460.
29. Lasson A, Stotzer POO, Ohman L, et al.. The intra-individual variability of faecal calprotectin: a prospective study in patients with active ulcerative colitis. J Crohns Colitis. 2015;9:26–32.
30. Kane SV, Sandborn WJ, Rufo PA, et al.. Fecal lactoferrin is a sensitive and specific marker in identifying intestinal inflammation. Am J Gastroenterol. 2003;98:1309–1314.
31. Buderus S, Boone J, Lyerly D, et al.. Fecal lactoferrin: a new parameter to monitor infliximab therapy. Dig Dis Sci. 2004;49:1036–1039.
32. Scarpa M, D'Incà R, Basso D, et al.. Fecal lactoferrin and calprotectin after ileocolonic resection for Crohn's disease. Dis Colon Rectum. 2007;50:861–869.
33. Hofmann MA, Drury S, Fu C, et al.. RAGE mediates a novel proinflammatory axis: a central cell surface receptor for S100/calgranulin polypeptides. Cell. 1999;97:889–901.
34. Kaiser T, Langhorst J, Wittkowski H, et al.. Faecal S100A12 as a non-invasive marker distinguishing inflammatory bowel disease from irritable bowel syndrome. Gut. 2007;56:1706–1713.
35. Foell D, Wittkowski H, Ren Z, et al.. Phagocyte-specific S100 proteins are released from affected mucosa and promote immune responses during inflammatory bowel disease. J Pathol. 2008;216:183–192.
36. de Jong NSH, Leach ST, Day AS. Fecal S100A12: a novel noninvasive marker in children with Crohn's disease. Inflamm Bowel Dis. 2006;12:566–572.
37. Ye F, Foell D, Hirono K, et al.. Neutrophil-derived S100A12 is profoundly upregulated in the early stage of acute Kawasaki disease. Am J Cardiol. 2004;94:840–844.
38. Perera C, McNeil HP, Geczy CL. S100 Calgranulins in inflammatory arthritis. Immunol Cell Biol. 2010;88:41–49.
39. Stallhofer J, Friedrich M, Konrad-Zerna A, et al.. Lipocalin-2 is a disease activity marker in inflammatory bowel disease regulated by IL-17A, IL-22, and TNF-α and modulated by IL23R genotype status. Inflamm Bowel Dis. 2015;21:2327–2340.
40. Nielsen OH, Gionchetti P, Ainsworth M, et al.. Rectal dialysate and fecal concentrations of neutrophil gelatinase-associated lipocalin, interleukin-8, and tumor necrosis factor-alpha in ulcerative colitis. Am J Gastroenterol. 1999;94:2923–2928.
41. Nielsen BS, Borregaard N, Bundgaard JR, et al.. Induction of NGAL synthesis in epithelial cells of human colorectal neoplasia and inffammatory bowel diseases. Gut. 1996;38:414–420.
42. Carlson M, Raab Y, Sevéus L, et al.. Human neutrophil lipocalin is a unique marker of neutrophil inflammation in ulcerative colitis and proctitis. Gut. 2002;50:501–506.
43. Oikonomou KA, Kapsoritakis AN, Theodoridou C, et al.. Neutrophil gelatinase-associated lipocalin (NGAL) in inflammatory bowel disease: association with pathophysiology of inflammation, established markers, and disease activity. J Gastroenterol. 2012;47:519–530.
44. Malyszko J, Bachorzewska-Gajewska H, Sitniewska E, et al.. Serum neutrophil gelatinase-associated lipocalin as a marker of renal function in non-diabetic patients with stage 2-4 chronic kidney disease. Ren Fail. 2008;30:625–628.
45. Cho H, Kim JH. Lipocalin2 expressions correlate significantly with tumor differentiation in epithelial ovarian cancer. J Histochem Cytochem. 2009;57:513–521.
46. Chakraborty S, Kaur S, Muddana V, et al.. Elevated serum neutrophil gelatinase-associated lipocalin is an early predictor of severity and outcome in acute pancreatitis. Am J Gastroenterol. 2010;105:2050–2059.
47. Eagan TM, Damås JK, Ueland T, et al.. Neutrophil gelatinase-associated lipocalin: a biomarker in COPD. Chest. 2010;138:888–895.
48. Furuya F, Shimura H, Yokomichi H, et al.. Neutrophil gelatinase-associated lipocalin levels associated with cardiovascular disease in chronic kidney disease patients. Clin Exp Nephrol. 2014;18:778–783.
49. Ruemmele FM, Targan SR, Levy G, et al.. Diagnostic accuracy of serological assays in pediatric inflammatory bowel disease. Gastroenterology. 1998;115:822–829.
50. Rump JA, Schölmerich J, Gross V, et al.. A new type of perinuclear anti-neutrophil cytoplasmic antibody (p-ANCA) in active ulcerative colitis but not in Crohn's disease. Immunobiology. 1990;181:406–413.
51. Quinton JF, Sendid B, Reumaux D, et al.. Anti-Saccharomyces cerevisiae mannan antibodies combined with antineutrophil cytoplasmic autoantibodies in inflammatory bowel disease: prevalence and diagnostic role. Gut. 1998;42:788–791.
52. Reese GE, Constantinides VA, Simillis C, et al.. Diagnostic precision of anti-Saccharomyces cerevisiae antibodies and perinuclear antineutrophil cytoplasmic antibodies in inflammatory bowel disease. Am J Gastroenterol. 2006;101:2410–2422.
53. Rockett JC, Burczynski ME, Fornace AJ, et al.. Surrogate tissue analysis: monitoring toxicant exposure and health status of inaccessible tissues through the analysis of accessible tissues and cells. Toxicol Appl Pharmacol. 2004;194:189–199.
54. Liew CC, Ma J, Tang HC, et al.. The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J Lab Clin Med. 2006;147:126–132.
55. Burakoff R, Hande S, Ma J, et al.. Differential regulation of peripheral leukocyte genes in patients with active Crohn's disease and Crohn's disease in remission. J Clin Gastroenterol. 2010;44:120–126.
56. Burakoff R, Chao S, Perencevich M, et al.. Blood-based biomarkers can differentiate ulcerative colitis from Crohn's disease and noninflammatory diarrhea. Inflamm Bowel Dis. 2011;17:1719–1725.
57. Burakoff R, Pabby V, Onyewadume L, et al.. Blood-based biomarkers used to predict disease activity in Crohn's disease and ulcerative colitis. Inflamm Bowel Dis. 2015;21:1132–1140.
58. Barnes EL, Liew CC, Chao S, et al.. Use of blood based biomarkers in the evaluation of Crohn's disease and ulcerative colitis. World J Gastrointest Endosc. 2015;7:1233–1237.
59. Burczynski ME, Peterson RL, Twine NC, et al.. Molecular classification of Crohn's disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. J Mol Diagn. 2006;8:51–61.
60. Mesko B, Poliska S, Szegedi A, et al.. Peripheral blood gene expression patterns discriminate among chronic inflammatory diseases and healthy controls and identify novel targets. BMC Med Genomics. 2010;3:15.
61. Van Lierop PPE, Swagemakers SM, De Bie CI, et al.. Gene expression analysis of peripheral cells for subclassification of pediatric inflammatory bowel disease in remission. PLoS One. 2013;8:1–8.
62. Dieckgraefe BK, Stenson WF, Korzenik JR, et al.. Analysis of mucosal gene expression in inflammatory bowel disease by parallel oligonucleotide arrays. Physiol Genomics. 2000;4:1–11.
63. Wu F, Dassopoulos T, Cope L, et al.. Genome-wide gene expression differences in Crohn's disease and ulcerative colitis from endoscopic pinch biopsies: insights into distinctive pathogenesis. Inflamm Bowel Dis. 2007;13:807–821.
64. Arijs I, De Hertogh G, Machiels K, et al.. Mucosal gene expression of cell adhesion molecules, chemokines, and chemokine receptors in patients with inflammatory bowel disease before and after infliximab treatment. Am J Gastroenterol. 2011;106:748–761.
65. Galamb O, Györffy B, Sipos F, et al.. Inflammation, adenoma and cancer: objective classification of colon biopsy specimens with gene expression signature. Dis Markers. 2008;25:1–16.
66. Arijs I, Li K, Toedter G, et al.. Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis. Gut. 2009;58:1612–1619.
67. Arijs I, Quintens R, Van Lommel L, et al.. Predictive value of epithelial gene expression profiles for response to infliximab in Crohn's disease. Inflamm Bowel Dis. 2010;16:2090–2098.
68. Toedter G, Li K, Marano C, et al.. Gene expression profiling and response signatures associated with differential responses to infliximab treatment in ulcerative colitis. Am J Gastroenterol. 2011;106:1272–1280.
69. Wu F, Guo NJ, Tian H, et al.. Peripheral blood MicroRNAs distinguish active ulcerative colitis and Crohn's disease. Inflamm Bowel Dis. 2011;17:241–250.
70. Zahm AM, Thayu M, Hand NJ, et al.. Circulating microRNA is a biomarker of pediatric Crohn disease. J Pediatr Gastroenterol Nutr. 2011;53:26–33.
71. Paraskevi A, Theodoropoulos G, Papaconstantinou I, et al.. Circulating MicroRNA in inflammatory bowel disease. J Crohns Colitis. 2012;6:900–904.
72. Duttagupta R, DiRienzo S, Jiang R, et al.. Genome-wide maps of circulating miRNA biomarkers for ulcerative colitis. PLoS One. 2012;7:e31241.
73. Wu F, Zikusoka M, Trindade A, et al.. MicroRNAs are differentially expressed in ulcerative colitis and alter expression of macrophage inflammatory peptide-2 alpha. Gastroenterology. 2008;135:1624–1635.e24.
74. Nguyen HTT, Dalmasso G, Yan Y, et al.. MicroRNA-7 modulates CD98 expression during intestinal epithelial cell differentiation. J Biol Chem. 2010;285:1479–1489.
75. Wu F, Zhang S, Dassopoulos T, et al.. Identification of microRNAs associated with ileal and colonic Crohn's disease. Inflamm Bowel Dis. 2010;16:1729–1738.
76. Fasseu M, Tréton X, Guichard C, et al.. Identification of restricted subsets of mature microRNA abnormally expressed in inactive colonic mucosa of patients with inflammatory bowel disease. PLoS One. 2010;5. pii: e13160.
77. Takagi T, Naito Y, Mizushima K, et al.. Increased expression of microRNA in the inflamed colonic mucosa of patients with active ulcerative colitis. J Gastroenterol Hepatol. 2010;25(suppl 1):S129–S133.
78. Pekow JR, Dougherty U, Mustafi R, et al.. MiR-143 and miR-145 are downregulated in ulcerative colitis: putative regulators of inflammation and protooncogenes. Inflamm Bowel Dis. 2012;18:94–100.
79. Iborra M, Bernuzzi F, Correale C, et al.. Identification of serum and tissue micro-RNA expression profiles in different stages of inflammatory bowel disease. Clin Exp Immunol. 2013;173:250–258.
80. Lin J, Cao Q, Zhang J, et al.. MicroRNA expression patterns in indeterminate inflammatory bowel disease. Mod Pathol. 2013;26:148–154.
81. Gutierrez A, Holler E, Zapater P, et al.. Antimicrobial peptide response to blood translocation of bacterial DNA in Crohn's disease is affected by NOD2/CARD15 genotype. Inflamm Bowel Dis. 2011;17:1641–1650.
82. Sands BE. Biomarkers of inflammation in inflammatory bowel disease. Gastroenterology. 2015;149:1275–1285.
83. Marchesi JR, Holmes E, Khan F, et al.. Rapid and noninvasive metabonomic characterization of inflammatory bowel disease. J Proteome Res. 2007;6:546–551.
84. Bjerrum JT, Nielsen OH, Hao F, et al.. Metabonomics in ulcerative colitis: diagnostics, biomarker identification, and insight into the pathophysiology. J Proteome Res. 2010;9:954–962.
85. Patel NR, McPhail MJW, Shariff MIF, et al.. Biofluid metabonomics using (1)H NMR spectroscopy: the road to biomarker discovery in gastroenterology and hepatology. Expert Rev Gastroenterol Hepatol. 2012;6:239–251.
86. Schicho R, Shaykhutdinov R, Ngo J, et al.. Quantitative metabolomic profiling of serum, plasma, and urine by (1)H NMR spectroscopy discriminates between patients with inflammatory bowel disease and healthy individuals. J Proteome Res. 2012;11:3344–3357.
87. Storr M, Vogel HJ, Schicho R. Metabolomics: is it useful for inflammatory bowel diseases? Curr Opin Gastroenterol. 2013;29:378–383.
88. Kohashi M, Nishiumi S, Ooi M, et al.. A novel gas chromatography mass spectrometry-based serum diagnostic and assessment approach to ulcerative colitis. J Crohns Colitis. 2014;8:1010–1021.
89. Patel N, Alkhouri N, Eng K, et al.. Metabolomic analysis of breath volatile organic compounds reveals unique breathprints in children with inflammatory bowel disease: a pilot study. Aliment Pharmacol Ther. 2014;40:498–507.
90. Han NY, Kim EH, Choi J, et al.. Quantitative proteomic approaches in biomarker discovery of inflammatory bowel disease. J Dig Dis. 2012;13:497–503.
91. Poulsen NA, Andersen V, Møller JC, et al.. Comparative analysis of inflamed and non-inflamed colon biopsies reveals strong proteomic inflammation profile in patients with ulcerative colitis. BMC Gastroenterol. 2012;12:76.
92. Li N, Wang X, Zhang Y, et al.. Comparative proteomics analysis of serum proteins in ulcerative colitis patients. Mol Biol Rep. 2012;39:5659–5667.
93. Han NY, Choi W, Park JM, et al.. Label-free quantification for discovering novel biomarkers in the diagnosis and assessment of disease activity in inflammatory bowel disease. J Dig Dis. 2013;14:166–174.
94. Townsend P, Zhang Q, Shapiro J, et al.. Serum Proteome profiles in stricturing Crohn's disease: a pilot study. Inflamm Bowel Dis. 2015;21:1935–1941.
95. Meuwis MA, Fillet M, Lutteri L, et al.. Proteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study. Clin Biochem. 2008;41:960–967.
96. Cleynen I, Boucher G, Jostins L, et al.. Inherited determinants of Crohn's disease and ulcerative colitis phenotypes: a genetic association study. Lancet. 2015;387:156–167.
97. Torres J, Colombel JF. Genetics and phenotypes in inflammatory bowel disease. Lancet. 2015;387:98–100.
98. Chen GB, Lee SH, Brion MJA, et al.. Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data. Hum Mol Genet. 2014;23:4710–4720.
99. Targan SR, Landers CJ, Yang H, et al.. Antibodies to CBir1 flagellin define a unique response that is associated independently with complicated Crohn's disease. Gastroenterology. 2005;128:2020–2028.
100. Tung CC, Wong JM, Lee WC, et al.. Combining TNFSF15 and ASCA IgA can be used as a predictor for the stenosis/perforating phenotype of Crohn's disease. J Gastroenterol Hepatol. 2014;29:723–729.
101. Plevy S, Silverberg MS, Lockton S, et al.. Combined serological, genetic, and inflammatory markers differentiate non-IBD, Crohn's disease, and ulcerative colitis patients. Inflamm Bowel Dis. 2013;19:1139–1148.
102. Vasiliauskas EA, Kam LY, Karp LC, et al.. Marker antibody expression stratifies Crohn's disease into immunologically homogeneous subgroups with distinct clinical characteristics. Gut. 2000;47:487–496.
103. Dubinsky M, Braun J. Diagnostic and prognostic microbial biomarkers in inflammatory bowel diseases. Gastroenterology. 2015;149:1265–1274.
104. Dubinsky MC, Lin YC, Dutridge D, et al.. Serum immune responses predict rapid disease progression among children with Crohn's disease: immune responses predict disease progression. Am J Gastroenterol. 2006;101:360–367.
105. Dubinsky MC, Kugathasan S, Mei L, et al.. Increased immune reactivity predicts aggressive complicating Crohn's disease in children. Clin Gastroenterol Hepatol. 2008;6:1105–1111.
106. Choung RS, Princen F, Stockfisch TP, et al.. Serologic microbial associated markers can predict Crohn's disease behaviour years before disease diagnosis. Aliment Pharmacol Ther. 2016;43:1300–1310.
107. Lichtenstein GR, Targan SR, Dubinsky MC, et al.. Combination of genetic and quantitative serological immune markers are associated with complicated Crohn's disease behavior. Inflamm Bowel Dis. 2011;17:2488–2496.
108. Kaur M, Panikkath D, Yan X, et al.. Perianal Crohn's disease is associated with distal colonic disease, stricturing disease behavior, IBD-associated serologies and genetic variation in the JAK-STAT pathway. Inflamm Bowel Dis. 2016;22:862–869.
109. Siegel CA, Siegel LS, Hyams JS, et al.. Real-time tool to display the predicted disease course and treatment response for children with Crohn's disease. Inflamm Bowel Dis. 2011;17:30–38.
110. Siegel CA, Horton H, Siegel LS, et al.. A validated web-based tool to display individualised Crohn's disease predicted outcomes based on clinical, serologic and genetic variables. Aliment Pharmacol Ther. 2016;43:262–271.
111. Theede K, Holck S, Ibsen P, et al.. Fecal calprotectin predicts relapse and histological mucosal healing in ulcerative colitis. Inflamm Bowel Dis. 2016;22:1042–1048.
112. Travis SP, Farrant JM, Ricketts C, et al.. Predicting outcome in severe ulcerative colitis. Gut. 1996;38:905–910.
113. Solberg IC, Hoivik ML, Cvancarova M, et al.. Risk matrix model for prediction of colectomy in a population-based study of ulcerative colitis patients (the IBSEN study). Scand J Gastroenterol. 2015;50:1456–1462.
114. Afif W, Loftus EV, Faubion Wa, et al.. Clinical utility of measuring infliximab and human anti-chimeric antibody concentrations in patients with inflammatory bowel disease. Am J Gastroenterol. 2010;105:1133–1139.
115. Bortlik M, Duricova D, Malickova K, et al.. Infliximab trough levels may predict sustained response to infliximab in patients with Crohn's disease. J Crohns Colitis 2013;7:736–743.
116. Vande Casteele N, Khanna R, Levesque BG, et al.. The relationship between infliximab concentrations, antibodies to infliximab and disease activity in Crohn's disease. Gut. 2015;64:1539–1545.
117. Cornillie F, Hanauer SB, Diamond RH, et al.. Postinduction serum infliximab trough level and decrease of C-reactive protein level are associated with durable sustained response to infliximab: a retrospective analysis of the ACCENT I trial. Gut. 2014;63:1721–1727.
118. Huang VW, Prosser C, Kroeker KI, et al.. Knowledge of fecal calprotectin and infliximab trough levels alters clinical decision-making for IBD outpatients on maintenance infliximab therapy. Inflamm Bowel Dis. 2015;21:1359–1367.
119. Iwasa R, Yamada A, Sono K, et al.. C-reactive protein level at 2 weeks following initiation of infliximab induction therapy predicts outcomes in patients with ulcerative colitis: a 3 year follow-up study. BMC Gastroenterol. 2015;15:103.
120. Reinisch W, Wang Y, Oddens BJ, et al.. C-reactive protein, an indicator for maintained response or remission to infliximab in patients with Crohn's disease: a post-hoc analysis from ACCENT I. Aliment Pharmacol Ther. 2012;35:568–576.
121. Jürgens M, Mahachie John JM, Cleynen I, et al.. Levels of C-reactive protein are associated with response to infliximab therapy in patients with Crohn's disease. Clin Gastroenterol Hepatol. 2011;9:421–427.e1.
122. Magro F, Rodrigues-Pinto E, Santos-Antunes J, et al.. High C-reactive protein in Crohn's disease patients predicts nonresponse to infliximab treatment. J Crohns Colitis 2014;8:129–136.
123. Murdoch TB, Msc M, O'donnell S, et al.. Biomarkers as potential treatment targets in inflammatory bowel disease: a systematic review. Can J Gastroenterol Hepatol. 2015;29:203–208.
124. Hibi T, Sakuraba A, Watanabe M, et al.. C-reactive protein is an indicator of serum infliximab level in predicting loss of response in patients with Crohn's disease. J Gastroenterol. 2014;49:254–262.
125. Park JJ, Cheon JH, Hong SP, et al.. Outcome predictors for thiopurine maintenance therapy in patients with Crohn's disease. Dig Dis Sci. 2012;57:133–141.
126. Treton X, Bouhnik Y, Mary JY, et al.. Azathioprine withdrawal in patients with Crohn's disease maintained on prolonged remission: a high risk of relapse. Clin Gastroenterol Hepatol. 2009;7:80–85.
127. Louis E, Mary JY, Vernier-Massouille G, et al.. Maintenance of remission among patients with Crohn's disease on antimetabolite therapy after infliximab therapy is stopped. Gastroenterology. 2012;142:63–70.e5; quiz e31.
128. Sobolewska A, Włodarczyk M, Stec-Michalska K, et al.. Mean platelet volume in Crohn's disease patients predicts sustained response to a 52-Week infliximab therapy: a pilot study. Dig Dis Sci. 2015;61:542–549.
129. Bouguen G, Sninsky C, Tang KL, et al.. Change in erythrocyte mean corpuscular volume during combination therapy with azathioprine and infliximab is associated with mucosal healing. Inflamm Bowel Dis. 2015;21:606–614.
130. Arias MT, Vande Casteele N, Vermeire S, et al.. A panel to predict long-term outcome of infliximab therapy for patients with ulcerative colitis. Clin Gastroenterol Hepatol. 2015;13:531–538.
131. Molander P, af Björkesten CG, Mustonen H, et al.. Fecal calprotectin concentration predicts outcome in inflammatory bowel disease after induction therapy with TNFα blocking agents. Inflamm Bowel Dis. 2012;18:2011–2017.
132. Boschetti G, Garnero P, Moussata D, et al.. Accuracies of serum and fecal S100 proteins (calprotectin and calgranulin C) to predict the response to TNF antagonists in patients with Crohn's disease. Inflamm Bowel Dis. 2015;21:331–336.
133. Sandborn WJ, Panés J, Zhang H, et al.. Correlation between concentrations of fecal calprotectin and outcomes of patients with ulcerative colitis in a phase 2 trial. Gastroenterology. 2016;150:96–102.
134. Guidi L, Marzo M, Andrisani G, et al.. Faecal calprotectin assay after induction with anti-Tumour Necrosis Factor α agents in inflammatory bowel disease: prediction of clinical response and mucosal healing at one year. Dig Liver Dis. 2014;46:974–979.
135. Molander P, Färkkilä M, Ristimäki A, et al.. Does fecal calprotectin predict short-term relapse after stopping TNFα-blocking agents in inflammatory bowel disease patients in deep remission? J Crohns Colitis. 2015;9:33–40.
136. Dubinsky MC, Mei L, Friedman M, et al.. Genome Wide Association (GWA) predictors of anti-TNFα therapeutic responsiveness in pediatric inflammatory bowel disease. Inflamm Bowel Dis. 2010;16:1357–1366.
137. Medrano LM, Taxonera C, González-artacho C, et al.. Response to infliximab in Crohn's disease: genetic analysis supporting expression profile. Mediators Inflamm. 2015;2015:318207.
138. Halloran B, Chang J, Shih DQ, et al.. Molecular patterns in human ulcerative colitis and correlation with response to infliximab. Inflamm Bowel Dis. 2014;20:2353–2363.

inflammatory bowel disease; biomarker signatures; gene expression

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