Discovery and validation of mucosal TNF expression combined with histological score - a biomarker for personalized treatment in ulcerative colitis

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Florholmen, Kay-Martin Johnsen, Meyer Renate, Trine Olsen, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-25116/v3 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2020 Read the published version in BMC Gastroenterology → Version 3 posted 2 You are reading this latest preprint version Show more versions Abstract Background There are no accurate markers that can predict clinical outcome in ulcerative colitis at time of diagnosis. The aim of this study was to explore a comprehensive data set to identify and validate predictors of clinical outcome in the first year following diagnosis. Methods Treatment naive-patients with ulcerative colitis were included at time of initial diagnosis from 2004-2014, followed by a validation study from 2014-2018. Patients were treated according to clinical guidelines following a standard step-up regime. Patients were categorized according to the treatment level necessary to achieve clinical remission: mild, moderate and severe. The biopsies were assessed by Robarts histopathology index (RHI) and TNF gene transcripts. Results We included 66 patients in the calibration cohort and 89 patients in the validation. Mucosal TNF transcripts showed high test reliability for predicting severe outcome in UC. When combined with histological activity (RHI) scores the test improved its diagnostic reliability. Based on the cut-off values of mucosal TNF and RHI scores from the calibration cohort, the combined test had still high reliability in the validation cohort (specificity 0.99, sensitivity 0.44, PPV 0.89, NPV 0.87) and a diagnostic odds-ratio (DOR) of 54. Conclusions The combined test using TNF transcript and histological score at debut of UC can predict severe outcome and the need for anti-TNF therapy with a high level of precision. These validated data may be of great clinical utility and contribute to a personalized medical approach with the possibility of top-down treatment for selected patients. Gastroenterology & Hepatology antiTNF calprotectin cytokines diagnostic odds ratio Robarts Histopathology Index Figures Figure 1 Figure 2 Figure 3 Background Ulcerative colitis (UC) is one of the two main disease entities of inflammatory bowel disease (IBD). UC is a chronic inflammatory disease believed to result from a dysregulated immune response caused by a combination of environmental and genetic factors causing loss of immunotolerance in the gut. 1 Many patients experience severe outcomes of disease with significant reduction in quality of life. The need for surgery is reported in 8 % and 9.7 % after 5 and 11 years, respectively. 2, 3 Definitions of clinical outcomes and prognosis in IBD are poorly defined, with little agreement on primary and secondary endpoints. 4 The IBSEN study is one of the most well-known prospective studies on clinical outcomes in UC, where the patients were divided into 4 predefined patterns of disease. 2 In a recently published review, the extent of disease and high disease activity were predictors of a more severe progression of disease. 5 The Montréal guidelines classify UC disease activity into four categories; clinical remission, mild, moderate and severe disease. 6, 7 Different guidelines for medical and surgical treatment are available for both UC and CD in Europe and America, European Crohn’s and Colitis Organization (ECCO) guidelines and American Gastroenterological Association (AGA) clinical care pathway repectively. 8, 9 Danese et al have created a modified algorithm with a medical step-up approach for the treatment of UC with the goal of achieving clinical remission. 10 In short, 5-ASA and local steroids are used in mild disease, with additional oral steroids, immunosuppressive and biological therapy in moderate to severe disease, consecutively. In contrast, a so-called top-down therapy has previously been documented to induce long term clinical remission of Crohn’s disease. 11 From a clinical point of view, there is a need to find good predictive markers at onset of disease that enables clinicians to individually tailor therapy. There is an increasing interest for a biomarker approach. In various diseases, such as breast cancer, four gene subtypes of human epidermal growth factor receptor 2 ( HER2) forms the basis of a molecular reclassification of disease according to risk factors. 12 Although there are an increasing number of reports and reviews for clinical and biochemical biomarkers at onset of disease, none have been able to predict future clinical outcome with great certainty . 13-18 In our research group we have published reports on mucosal transcript levels of tumor necrosis factor (TNF) as a biomarker for response to and when to stop anti-TNF thereapy, 19-21 However, most of the studies are of retrospective design and there is a lack of validated studies of prognostic biomarkers to predict the clinical outcome in IBD with high reliability. Moreover, a personalized therapy approach initiated at the time of disease diagnosis, may have an impact on the natural course of IBD. This is so far unsettled due to the lack of long- term studies. 22-24 There is increasing knowledge of the pathophysiological events mediating the mucosal inflammation in IBD including cytokine and chemokine responses. 25, 26 So far there are few reports on how these crucial mediators can be used as biomarkers. 19-22 Therefore, the aims of this study were, first, based on a calibration cohort of newly diagnosed patients with ulcerative colitis from 2004-2014, to discover potential clinical, biochemical, histological and mucosal gene transcripts to predict one year level of treatment to obtain remission. Second, to validate these parameters in a cohort study from 2014-2018. Methods The main goal of the study was to detect and validate potential predictors of treatment level 1 year after disease onset of UC. In principle, to do a proper validation of a predictor(s) it is general accepted that this should be a two-step procedure. First, we have to study a calibration (discover) cohort, followed by a study of a validation cohort to validate the candidate predictors from the discovery study. Inclusion criteria for both the discovery and validation cohort were patients with newly diagnosed, treatment- naive UC aged ≥ 18 years. Patients were excluded if they were lost to follow in the first year after diagnosis, patients with severe medical disease other than UC, pregnancy and lactation; and patients who first were diagnosed UC but later developed an indeterminate form of IBD. In addition to the UC patients with newly diagnosed, treatment-naïve disease, a group of healthy subjects performing a cancer screening examination with no clinical, endoscopic or histological signs of intestinal disease were included as controls. Cohorts examined Calibration cohort: Patients attending the Gastrointestinal Unit at the University Hospital of North Norway, Tromsø, Norway, were recruited from the project Immunopathogenesis in inflammatory bowel disease in the time period January 2004 –March 2014. Validation cohort: Patients were recruited in the time period March 2014 –March 2018 attending 6 clinical centers in Norway (Gastrointestinal units at the hospitals of Kirkenes, Hammerfest, University Hospital North Norway, Tromsø, Bodø, Vestre Viken (Ringerike and Drammen)) as a part of an ongoing prospective study - Advanced Study of Inflammatory Bowel disease (ASIB- study). Diagnosis, clinical grading and clinical outcome after 1 year The clinical grading of UC was based on evaluation of clinical activity at 1 year. The biopsies were histologically assessed by an experienced pathologist (SWS) using Robarts histopathology index (RHI) score. 27 The clinical outcomes of UC are based on the required treatment level to obtain disease remission, using the step-up algorithm guidelines ECCO and the three levels proposed by Danese et al. 10, 28 In this study we used three disease outcome levels after 1 year; mild, moderate and severe. These outcomes were defined by the treatment level needed for clinical remission; 5-ASA per oral or local (mild), need of oral steroids and/or thiopurines (moderate) and need of anti-TNF and/or surgery (severe)(see figure 1). Clinical remission was defined by ulcerative colitis clinical score (UCCS) <2 29 and/or calprotectin level < 100 mg/kg according to Feagan et al. Faecal calprotectin was measured by an ELISA kit from Calpro Norway (Oslo, Norway). Tissue samples Colonic mucosal biopsies were sampled from the region with the most severe inflammation. In healthy controls, biopsies were sampled from the sigmoid. Biopsy specimens for RNA extraction were immediately immersed in RNA later (Qiagen) and stored at room temperature overnight, then at -20 ◦ C until RNA isolation. Cytokine transcript measurements Total RNA was isolated from patient biopsies using Trizol until July 1, 2008; later the Allprep DNA/RNA Mini Kit (Qiagen, Hilden, Germany, Cat No: 80204) and the automated QIAcube instrument (Qiagen, Hilden, Germany) according to the manufacturer’s recommendations. Quantity and purity of the extracted RNA were determined using the Qubit 3 Fluorometer (Cat No: Q33216; Invitrogen by Thermo Fisher Scientific, Waltham, MA, USA). Reverse transcription of the total RNA was performed using the QuantiTect Reverse Transcription Kit (Cat. No: 205314; Qiagen, Hilden, Germany). Mucosal TNF gene transcript was measured by real-time PCR procedures previously described in detail. 30-33 Statistics The following factors were evaluated as predictors: extent of disease, UCDAI score and endoscopic sub-score, histological activity score, fecal calprotectin and mucosal cytokine transcripts. All baseline predictors were standardized and centered for exploring combinations of two variables. To evaluate predictors of outcome, ROC curves were constructed. Optimal cut-off values were picked by maximal Youden’s J. 34 Test characteristics were derived by confusion matrices and diagnostic odds ratios. 35 A sequential test for mucosal TNF transcript and RHI score was constructed: Observations with a positive TNF test were run in a new ROC analysis for RHI score, which resulted in a two-step combined model with one cut-off value for mucosal TNF transcript and another cut-off value for RHI score following a positive TNF test. As a global test, Kruskal Wallis one-way ANOVA was performed, then Mann-Whitney U test with Bonferroni correction. For categorical values Chi-square test with Bonferroni correction was utilized. All statistical analyses were carried out in IBM SPSS Statistics 24 (IBM Corporation, Armonk, New York, USA). Results Healthy controls Thirty-eight healthy controls were included, 13 females and 25 men aged 43-69 years. The median TNF value was 4450 copies/µg mRNA. Calibration cohort Baseline characteristics and outcome groups Sixty-six patients were included as a follow up mainly from an earlier report. 19 At 1 year follow-up patients were categorized into mild (n= 23), moderate (n= 18) and severe (n=25) disease outcomes based on a step-up treatment level algorithm. In the moderate outcome group, no patients needed continuous steroid treatment and two patients were treated with azathioprine. In the severe outcome group, all patients were on anti-TNF treatment including one patient that later was in the need of colectomy. Sixteen patients were on concomitant treatment with azathioprine and one patient on methotrexate. An overview of baseline characteristics for each outcome group is shown in Table 1. There were significant differences between the three treatment groups for mucosal TNF and UCDAI scores (p< 0.017). Discovery of potential biomarkers With three defined treatment outcomes we made two sets of ROC curves, one set to discriminate between mild and moderate/severe and one set to discriminate between mild/moderate and severe. There were no baseline predictors that showed good test performance for discriminations between mild and moderate/severe (data not shown). However, there was a tendency towards increasing concentrations of the mucosal TNF transcripts with increasing treatment level (figure 2A). Severe outcome. Baseline predictors of severe outcome are shown in table 1 and figure 3 presenting clinical parameters (Calprotectin, UCDAI, Mayo endoscopic score), RHI score and mucosal TNF transcripts. Selected predictors including cut off values are shown in table 3. Of individual factors, mucosal TNF transcript had the best test performance with a sensitivity, specificity and diagnostic odds ratio (DOR) of 0.81, 0.91 and 43 respectively. Clinical data including fecal calprotectin, UCDAI and RHI -score, yielded a high sensitivity but poor specificity (table 2, figure 3), and therefore a poorer test performance than mucosal TNF transcript. To increase the test performance, we then combined mucosal TNF transcript and RHI score in a sequential setup: subjects with mucosal TNF transcript above cut-off were subjected to a second ROC curve using RHI score as predictor. The combined sequential test of mucosal TNF transcript and RHI score showed a superior test performance for specificity and DOR, however lower sensitivity (table 2). No other clinical, biochemical, histological or immunological combinations could improve the test performance of prediction of severe outcome (supplement material figure 4). Validation cohort Baseline characteristics and outcome groups At one year follow up patients were categorized into mild (n= 36), moderate (n= 31) and severe (n=22) disease outcomes based on a step-up treatment level algorithm. In the moderate outcome group, no patients needed continuous steroid treatment and five subjects were treated with azathioprine. In the severe outcome group, 22 patients were on anti-TNF treatment whereas two of these patients were later in the need of colectomy. Thirty-eight healthy controls were included. An overview of baseline characteristics for each outcome group is shown in Table 3. There were significant differences between the three treatment groups for mucosal TNF, UCDAI, RHI scores and fecal calprotectin (table 3, figure 2B). Validation of predictors of severe outcome The cut off values from the discovery study (TNF≥18000, RHI≥9) were used for test performance. The baseline predictors of severe outcome presenting mucosal TNF transcripts and RHI score are shown in table 4. Mucosal TNF transcript had a test performance with sensitivity, specificity and DOR of 0.5, 0.9 and 9 respectively. RHI transcript had a test performance with sensitivity, specificity and DOR of 0.72, 0.69 and 6, respectively. When combined TNF and RHI the specificity increased to high 0.99, whereas the DOR was still high as 54. Moreover, the low sensitivity of 0.44 represents most likely the overlapping TNF and RHI score values to the mild/moderate outcome groups (table 3). Discussion We present a combined discovery study (from 2004) and a validation study (from 2014) in a prospective design (the transomic Advanced Study of Inflammatory Bowel Disease) where clinical, biochemical, histological and transcript data where retrospectively tested to identify biomarkers of clinical outcome 1 year after disease diagnosis of UC. Mucosal TNF transcripts showed high test reliability for predicting severe outcome after 1 year in UC in both studies but was not ideal to discriminate between mild, moderate and severe disease. Moreover, when the TNF transcripts were combined with histological activity (RHI) scores, the test improved its diagnostic reliability. Mucosal cut-off values for TNF and RHI scores determined in the calibration cohort displayed a high test performance with specificity of 0.99 and a diagnostic odds-ratio (DOR) of 54 in the prospective validation study. Thus, mucosal TNF transcript combined with a histological score at debut of disease can likely identify patients who experience severe outcomes during the first year. This is an important step towards personalizing treatment in IBD and may be used as a criterion for selecting candidates for top-down treatment of anti-TNF. However, this awaits further studies. We have tested a broad spectrum of potential factors that could, alone, or in combinations, predict clinical outcome in the first year of diagnosis. The clinical outcomes were defined as the highest treatment level required for achieving disease remission during the first year of disease, in a step-up treatment approach. The broad/wide selection of variables including various combinations did not have the necessary precision to discriminate between mild, moderate and severe outcomes. However mucosal TNF transcript in combination with the histological RHI score was able to predict, with high precision, the most severe colitis outcomes needing biological or surgical treatment, within the first year of disease. The validated cut-off values (TNF ≥18000, RHI≥9) showed a high specificity to predict severe outcome and a DOR as high as 54. From a clinical point of view, these cut–off values indicate a need of anti-TNF therapy during the first year after diagnosis with high reliability, and therefore of high clinical value and utility in the management of IBD/UC. In order to use a biomarker for selection for top-down treatment, a high PPV is necessary to avoid excessive use of biologics. Our proposed biomarker shows a PPV of 0.89 meaning that 9 out of 10 positives will be correctly identified as severe outcome. A step-up treatment approach represents well-established international guidelines. 8-10 One drawback of this approach is that patients in the severe outcome group often experience a period of poor response during the gradual escalation of treatment intensity until an adequate response is obtained. In some cases, one may lose an important window of opportunity for optimal effect of biologics leading to permanent structural damage and/or need of surgery. The impact of early treatment before development of severe disease is not completely investigated. However, the top down approach published by D’Haens et al indicated that immunosuppressive therapy was superior to a step-up approach in patients with Crohn’s disease. 36 Moreover, it is well documented that induction of treatment to remission reduces later hospitalization, whereas conflicting results exist for colectomy in two studies. 37, 38 The use of molecular data from the mucosa represents a novel approach and is an easily available tool, with high utility for clinicians to individually tailor therapy in UC. Endoscopic biopsies are routinely taken at diagnosis and surveillance of IBD. Thus, the logistics of measuring mucosal TNF transcript are simple, as biopsies are readily available and samples do not require freezing prior to analysis. 31 Our study contributes with new knowledge in the scientific field of personalized therapy in UC. 15 16 We know that treatment to remission improves long-term clinical outcome. 39, 40 The main question is: Can a top-down therapy of the most severe forms of disease have an effect on the natural course of disease? This awaits future studies. The strength of this prospective designed, combined discovery and validation study is that we have retrospectively searched for and validated biomarkers for treatment at debut of UC, using a broad search of clinical, histological and analytical factors including mucosal immune transcripts. Moreover, this is part of the transomic Advanced Study of Inflammatory Bowel disease (ASIB) study where parallel studies of the epigenome, transcriptome, proteome and metabolome are ongoing. 33, 41-45 . This transomic approach at debut of UC will be performed and correlated to long-term clinical outcome. Therefore, the upcoming transomic data from the ASIB study and from several ongoing studies such as the PREDICTS study will not only search for therapeutic but also prognostic and natural course biomarkers. 17 The weakness of the study includes the lack of endoscopic diagnosis at one year, which would have given insight into endoscopic status and endoscopic remission rates according to treatment levels. Additionally, the decision to use or not use steroids at time of diagnosis is dependent on the subjective decision of the clinicians. This may be one explanation for the small differences detected between the mild and moderate treatment group. Conclusions The combined information of mucosal TNF transcription and histological score at debut of UC can predict severe outcome and the need for anti-TNF therapy. This is of great clinical utility and may contribute to a personalized medicine approach in UC. Abbreviations Robarts histopathology index (RHI), diagnostic odds-ratio (DOR), Ulcerative colitis (UC), Inflammatory bowel disease (IBD), Tumor necrosis factor (TNF), Advanced Study of Inflammatory Bowel disease (ASIB- study), Ulcerative colitis clinical score (UCCS) Declarations Ethics approval and consent to participate All participants were informed and signed a written consent to participate and publication. Approval including the use of biobank was granted by the Regional Committee of Medical Ethics of Northern Norway Ref no: 14/2004 and 1349/2012 Consent for publication All authors have approved the final manuscript for publication Availability of data and material Data are available from the authors upon reasonable request due to privacy/ethical restrictions Competing interests None declared from all authors. Funding This work was supported by Northern Norway Regional Health Authority, ID SFP-50-04, SFP-888-09 and SFP-1136-13 Authors' contributions Planning and conducting: JRF, KMJ, RG, KJ, RM, TO, SWS, ØKM, PT, MDG, JMK, TL, GR, CV Collecting or interpreting data: JRF, KMJ, RG, KJ, RM, TO, SWS, ØKM, PT, MDG, JMK, TL, GR, CV Drafting of manuscript: JRF, KMJ, RG, KJ, RM, TO, SWS, ØKM, PT, MDG, JMK, TL, GR, CV Acknowledgments The publication charges for this article have been funded by a grant from the publication fund of UiT The Arctic University of Norway. We thank Ingrid Christiansen, Marian Remijn and Line Wilsgaard for expert technical assistance. References Zhang YZ, Li YY. Inflammatory bowel disease: pathogenesis. World J Gastroenterol 2014;20:91-9. Henriksen M, Jahnsen J, Lygren I, et al. Ulcerative colitis and clinical course: results of a 5-year population-based follow-up study (the IBSEN study). Inflamm Bowel Dis 2006;12:543-50. Solberg IC, Hoivik ML, Cvancarova M, et al. 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Schniers A, Goll R, Pasing Y, et al. Ulcerative colitis: functional analysis of the in-depth proteome. Clin Proteomics 2019;16:4. Diab J, Hansen T, Goll R, et al. Lipidomics in Ulcerative Colitis Reveal Alteration in Mucosal Lipid Composition Associated With the Disease State. Inflamm Bowel Dis 2019;25:1780-1787. Tables Table 1 Baseline characteristics of patients in the calibration cohort with ulcerative colitis according to one-year treatment outcome level. Patients groups Mild N=23 Moderate N=18 Severe N=25 Age med(IQR) 41 (35-54) 35 (24-55) 41 (27-54) Sex Female 15 (65%) 7 (39%) 9 (36%) Male 8 (35%) 11 (61%) 16 (64%) Colonic area involved Proctitis 9 (39%) 3(17%) 3 (12%) Left side 9 (39%) 7 (39%) 10 (40%) Extensive 5 (22%) 8 (44%) 12 (48%) Smoking 14 21 12 Current smoker 4 (29%) 2 (17%) 2 (10%) Non-smoker 10 (71%) 10 (83%) 18 (90%) Mucosal TNF* 10500 (4600-11900) 12000 (8000-17200) 26900 (18700-40400) UCDAI med(IQR)* at debut 7 (5-8) 9 (8-12) 12 (9-12) Calprotectin med(IQR) 590 (400-1100) 790 (470-1540) 2300 (670-2500) RHI med(IQR) 9 (5-10) 7 (6-10) 9 (7-12) UCCS score 1-year med(IQR) 0 (0-0) 0 (0-2) 0 (0-2) Calprotectin 1-year med(IQR) 60 (25-85) 50 (25-100) 25 (0-160) *p<0,017 between groups, Mann-Whitney U test with Bonferroni correction. Med(IQR) = median (Interquartile range); RHI= Robarts histopathology index. Mucosal TNF in copies/µg RNA: Fecal calprotectin in mg/kg. Table 2. Factors at debut of ulcerative colitis in the calibration cohort to predict severe treatment outcomes at one year of disease. Factors Youden's J Cut-off value Sensitivity Specificity PPV NPV DOR TNF* 0,72 ≥18000 0,81 0,91 0,85 0,89 43 RHI** 0,23 ≥9 0,71 0,52 0,48 0,74 3 Combined TNF RHI 0,57 ≥18000 and ≥9 0,57 1 1 0,79 ∞ UCDAI 0,4 ≥9 0,79 0,61 0,54 0,83 6 Mayo subscore 0,45 3 0,72 0,73 0,62 0,81 7 Calprotectin 0,51 ≥2000 0,6 0,91 0,86 0,72 15 Diagnostic odds ratio PPV: Positive predictive value NPV: Negative predictive value * copies/µg mRNA **Robarts histopathology index score Table 3 Baseline characteristics of patients with ulcerative colitis in the validation cohort according to one-year treatment outcome level. Patient groups Mild N=36 Moderate N=31 Severe N=22 Age med(IQR) 36 (24-49) 30 (24-41) 26 (22-47) Sex Female 17 (47%) 8 (26%) 10 (46%) Male 19 (53%) 23 (74%) 12 (54%) Colonic area involved Proctitis 5 (14%) 1(3%) 1 (4%) Left side 25 (69%) 18 (58%) 10 (46%) Extensive 6 (17%) 12 (39%) 11 (50%) Smoking 28 21 12 Current smoker 1 (4%) 2 (10%) 1 (8%) Non-smoker 27 (96%) 19 (90%) 11 (92%) Mucosal TNF* 8800 (6100-12800) 10500 (7400-13200) 17400 (15100-26800) UCDAI med(IQR)* at debut 7 (5-9) 9 (8-11) 10 (7-11) Calprotectin med(IQR)* 570 (200-970) 1000 (340-2000) 1100 (830-1400) RHI med(IQR)* 6 (2-10) 6 (4-11) 14 (9-27) UCCS score 1-year med(IQR) 0 (0-0) 0 (0-0) 0 (0-8) Calprotectin 1-year med(IQR) 40 (25-94) 50 (0-140) 25 (20-60) *p<0,017 between groups, Mann-Whitney U test with Bonferroni correction. Med(IQR): median (Interquartile range); RHI: Robarts histopathology index. Mucosal TNF in copies/µg RNA; Fecal calprotectin in mg/kg Table 4. Factors at debut of ulcerative colitis in the validation cohort to predict severe treatment outcomes at one year of disease based on cut off values from the discovery cohort. Factors Youden's J Cutt off value Sensitivity Specificity PPV NPV DOR TNF* 0,40 ≥18000 0,50 0,90 0,56 0,87 9 RHI** 0,41 ≥9 0,72 0,69 0,38 0,90 6 Combined TNF RHI 0,43 18000 ≥9 0,44 0,99 0,89 0,87 54 DOR: Diagnostic odds ratio PPV: Positive predictive value NPV: Negative predictive value * copies/µg mRNA **Robarts histopathology index score Supplementary Files rocmildoutcome.tif Figure 4: Supplement figure with ROC curves of predictors of mild outcome from calibration cohort Cite Share Download PDF Status: Published Journal Publication published 02 Oct, 2020 Read the published version in BMC Gastroenterology → Version 3 posted Submission checks completed at journal 10 Sep, 2020 Editorial decision: Accept 09 Sep, 2020 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-25116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":2220116,"identity":"cac34f12-93e6-4d1b-976c-b1d157606e4d","order_by":0,"name":"Jon R. Florholmen","email":"","orcid":"","institution":"Universitetssykehuset Nord-Norge","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jon","middleName":"R.","lastName":"Florholmen","suffix":""},{"id":2220117,"identity":"27474617-1a1b-4e72-9540-314ae5598c97","order_by":1,"name":"Kay-Martin Johnsen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACCRiDDYgNGBtATOYDRGmRQNLClkCcFjAJ0cJjgFeLZPsZA6YbNffq+KQbGAo+7rhjzy/d8026cAdDHj8OLdI8OQbMOceKJdhkDjAYzjzzLHHmnLPbpGeeYSiWbMCuRY4hx/x3DluCBJtEAoMxb9vhBIMbudukedsYEjccwKGF/w3Qln8ILfb2N3Ke4dUiLQF0WG4bQgvjBokcNrxaJGc8K2DO7UuQbJNIbDCc2XY4ccaNNGNrXiB3Jg6/SJxP3sCc8y2BX35G8jGDj0CH8c9Ifnibt80msR9HiDEwcMBigbENOT4ksKmFAvYHMBbzA1xqRsEoGAWjYGQDACElUoOX5fXQAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7530-2666","institution":"Universitetssykehuset Nord-Norge","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kay-Martin","middleName":"","lastName":"Johnsen","suffix":""},{"id":2220118,"identity":"7e39cbab-7c8e-4ac5-a276-6697c7bc887d","order_by":2,"name":"Meyer Renate","email":"","orcid":"","institution":"Universitetssykehuset Nord-Norge","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meyer","middleName":"","lastName":"Renate","suffix":""},{"id":2220119,"identity":"3b7dc7a5-ded3-4eed-a5cd-28597d930817","order_by":3,"name":"Trine Olsen","email":"","orcid":"","institution":"Universitetssykehuset Nord-Norge","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Trine","middleName":"","lastName":"Olsen","suffix":""},{"id":2220120,"identity":"9e99ecfe-4cd5-4921-9ee7-3fdf810bacb2","order_by":4,"name":"Øystein Kittel Moe","email":"","orcid":"","institution":"Universitetssykehuset Nord-Norge","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Øystein","middleName":"Kittel","lastName":"Moe","suffix":""},{"id":2220121,"identity":"1ee9cc18-df8c-4749-931b-d7d1d9c31a36","order_by":5,"name":"Petter Tandberg","email":"","orcid":"","institution":"Vestre Viken HF","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Petter","middleName":"","lastName":"Tandberg","suffix":""},{"id":2220122,"identity":"171a8087-86e3-43bd-8a66-c6f37f8ec067","order_by":6,"name":"Mona D. 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Sørbye","email":"","orcid":"","institution":"Universitetssykehuset Nord-Norge","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sveinung","middleName":"W.","lastName":"Sørbye","suffix":""},{"id":2220129,"identity":"a26e25ca-883c-4a88-9bc3-2a13bed3fe2b","order_by":13,"name":"Rasmus Goll","email":"","orcid":"","institution":"Universitetssykehuset Nord-Norge","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rasmus","middleName":"","lastName":"Goll","suffix":""}],"badges":[],"createdAt":"2020-04-24 11:02:26","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-25116/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-25116/v3","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12876-020-01447-0","type":"published","date":"2020-10-02T12:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2372972,"identity":"f7630562-ba48-45a5-8641-e0b46459d57c","added_by":"auto","created_at":"2020-09-12 02:37:21","extension":"tif","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169353,"visible":true,"origin":"","legend":"In this and the following figures data from patients with ulcerative colitis at debut of disease are grouped after 1-year treatment level outcome, Step-up algorithm according to clinical treatment outcomes (mild, moderate, severe). Modified after Danese et al., see ref 8.","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-25116/v3/Figure1.tif"},{"id":2372973,"identity":"508f337b-fb12-40a3-a427-23e92ba319ee","added_by":"auto","created_at":"2020-09-12 02:37:21","extension":"tif","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7421,"visible":true,"origin":"","legend":"Mucosal TNF transcript in treatment outcome groups and in healthy normal controls in the calibration cohort (A) and the validation group (B). ","description":"","filename":"boxplot.tif","url":"https://assets-eu.researchsquare.com/files/rs-25116/v3/boxplot.tif"},{"id":2372974,"identity":"00aaa7c9-3ac6-45c1-8414-4105e3da9415","added_by":"auto","created_at":"2020-09-12 02:37:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29860,"visible":true,"origin":"","legend":"ROC curves of predictors of severe outcome in calibration cohorte. ","description":"","filename":"3.PNG","url":"https://assets-eu.researchsquare.com/files/rs-25116/v3/3.PNG"},{"id":13590703,"identity":"4a72eb70-a9d5-46db-8dd9-98430ce14dd3","added_by":"auto","created_at":"2021-09-17 05:05:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":486113,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-25116/v3/95802baf-5318-4358-938e-b349b42ef4d0.pdf"},{"id":2372976,"identity":"d052eb62-d994-4e0e-a47d-deac982472f3","added_by":"auto","created_at":"2020-09-12 02:37:22","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2269934,"visible":true,"origin":"","legend":"Figure 4: Supplement figure with ROC curves of predictors of mild outcome from calibration cohort \n\n","description":"","filename":"rocmildoutcome.tif","url":"https://assets-eu.researchsquare.com/files/rs-25116/v3/rocmildoutcome.tif"}],"financialInterests":"","formattedTitle":"Discovery and validation of mucosal TNF expression combined with histological score - a biomarker for personalized treatment in ulcerative colitis","fulltext":[{"header":"Background","content":"\u003cp\u003eUlcerative colitis (UC) is one of the two main disease entities of inflammatory bowel disease (IBD). UC is a chronic inflammatory disease believed to result from a dysregulated immune response caused by a combination of environmental and genetic factors causing loss of immunotolerance in the gut.\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;\u0026nbsp; Many patients experience severe outcomes of disease with significant reduction in quality of life. The need for surgery is reported in 8 % and 9.7 % after 5 and 11 years, respectively.\u003csup\u003e2, 3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eDefinitions of clinical outcomes and prognosis in IBD are poorly defined, with little agreement on primary and secondary endpoints.\u003csup\u003e4\u003c/sup\u003e The IBSEN study is one of the most well-known prospective studies on clinical outcomes in UC, where the patients were divided into 4 predefined patterns of disease.\u003csup\u003e2\u003c/sup\u003e In a recently published review, the extent of disease and high disease activity were predictors of a more severe progression of disease.\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe Montr\u0026eacute;al guidelines classify UC disease activity into four categories; clinical remission, mild, moderate and severe disease.\u003csup\u003e6, 7\u003c/sup\u003e\u0026nbsp; Different guidelines for medical and surgical treatment are available for both UC and CD in Europe and America, European Crohn\u0026rsquo;s and Colitis Organization (ECCO) guidelines and American Gastroenterological Association (AGA) clinical care pathway repectively.\u003csup\u003e8, 9\u003c/sup\u003e Danese et al have created a modified algorithm with a medical step-up approach for the treatment of UC with the goal of achieving clinical remission.\u003csup\u003e10\u003c/sup\u003e In short, 5-ASA and local steroids are used in mild disease, with additional oral steroids, immunosuppressive and biological therapy in moderate to severe disease, consecutively. In contrast, a so-called top-down therapy has previously been documented to induce long term clinical remission of Crohn\u0026rsquo;s disease.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFrom a clinical point of view, there is a need to find good predictive markers at onset of disease that enables clinicians to individually tailor therapy. There is an increasing interest for a biomarker approach. In various diseases, such as breast cancer, four gene subtypes of human epidermal growth factor receptor 2 (\u003cem\u003eHER2)\u003c/em\u003e forms the basis of a molecular reclassification of disease according to risk factors.\u003csup\u003e12\u003c/sup\u003e \u0026nbsp;Although there are an increasing number of reports and reviews for clinical and biochemical biomarkers at onset of disease, none have been able to predict future clinical outcome with great certainty .\u003csup\u003e13-18\u003c/sup\u003e In our research group we have published reports on mucosal transcript levels of tumor necrosis factor (TNF) as a biomarker for response to and when to stop anti-TNF thereapy,\u003csup\u003e19-21\u003c/sup\u003e However, most of the studies are of retrospective design and there is a lack of validated studies of prognostic biomarkers to predict the clinical outcome in IBD with high reliability. Moreover, a personalized therapy approach initiated at the time of disease diagnosis, may have an impact on the natural course of IBD. This is so far unsettled due to the lack of long- term studies.\u003csup\u003e22-24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There is increasing knowledge of the pathophysiological events mediating the mucosal inflammation in IBD including cytokine and chemokine responses.\u003csup\u003e25, 26\u003c/sup\u003e So far there are few reports on how these crucial mediators can be used as biomarkers.\u003csup\u003e19-22\u003c/sup\u003e Therefore, the aims of this study were, first, based on a calibration cohort of newly diagnosed patients with ulcerative colitis from 2004-2014, to discover potential clinical, biochemical, histological and mucosal gene transcripts to predict one year level of treatment to obtain remission. Second, to validate these parameters in a cohort study from 2014-2018.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe main goal of the study was to detect and validate potential predictors of treatment level 1 year after disease onset of UC. In principle, to do a proper validation of a predictor(s) it is general accepted that this should be a two-step procedure. \u0026nbsp;First, we have to study a calibration (discover) cohort, followed by a study of a validation cohort to validate the candidate predictors from the discovery study. Inclusion criteria for both the discovery and validation cohort were patients with newly diagnosed, treatment- naive UC aged \u0026ge; 18 years. Patients were excluded if they were lost to follow in the first year after diagnosis, patients with severe medical disease other than UC, pregnancy and lactation; and patients who first were diagnosed UC but later developed an indeterminate form of IBD.\u003c/p\u003e\n\u003cp\u003eIn addition to the UC patients with newly diagnosed, treatment-na\u0026iuml;ve disease, a group of healthy subjects performing a cancer screening examination with no clinical, endoscopic or histological signs of intestinal disease were included as controls. \u003cbr /\u003e \u003cbr /\u003e \u003cu\u003eCohorts examined\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCalibration cohort: \u003c/em\u003ePatients attending the Gastrointestinal Unit at the University Hospital of North Norway, Troms\u0026oslash;, Norway, were recruited from the project \u003cem\u003eImmunopathogenesis in inflammatory bowel disease\u003c/em\u003e in the time period January 2004 \u0026ndash;March 2014. \u003cem\u003eValidation cohort:\u003c/em\u003e Patients were recruited in the time period March 2014 \u0026ndash;March 2018 attending 6 clinical centers in Norway (Gastrointestinal units at the hospitals of Kirkenes, Hammerfest, University Hospital North Norway, Troms\u0026oslash;, Bod\u0026oslash;, Vestre Viken (Ringerike and Drammen)) as a part of an ongoing prospective study - \u003cem\u003eAdvanced Study of Inflammatory Bowel disease\u003c/em\u003e (ASIB- study).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDiagnosis, clinical grading and clinical outcome after 1 year\u003c/u\u003e\u003cstrong\u003e\u003cbr /\u003e \u003c/strong\u003eThe clinical grading of UC was based on evaluation of clinical activity at 1 year. The biopsies were histologically assessed by an experienced pathologist (SWS) using Robarts histopathology index (RHI) score.\u003csup\u003e27\u003c/sup\u003e\u003cbr /\u003eThe clinical outcomes of UC are based on the required treatment level to obtain disease remission, using the step-up algorithm guidelines ECCO and the three levels proposed by Danese et al.\u003csup\u003e10, 28\u003c/sup\u003e In this study we used three disease outcome levels after 1 year; mild, moderate and severe. These outcomes were defined by the treatment level needed for clinical remission; 5-ASA per oral or local (mild), need of oral steroids and/or thiopurines (moderate) and need of anti-TNF and/or surgery (severe)(see figure 1). Clinical remission was defined by ulcerative colitis clinical score (UCCS) \u0026lt;2 \u003csup\u003e29\u003c/sup\u003e and/or calprotectin level \u0026lt; 100 mg/kg according to Feagan et al. Faecal calprotectin was measured by an ELISA kit from Calpro Norway (Oslo, Norway).\u003cu\u003e\u003cbr /\u003e \u003cbr /\u003e Tissue samples\u003cbr /\u003e \u003c/u\u003eColonic mucosal biopsies were sampled from the region with the most severe inflammation. In healthy controls, biopsies were sampled from the sigmoid. Biopsy specimens for RNA extraction were immediately immersed in RNA \u003cem\u003elater \u003c/em\u003e(Qiagen) and stored at room temperature overnight, then at -20\u003csup\u003e◦\u003c/sup\u003eC until RNA isolation. \u003cbr /\u003e \u003cbr /\u003e \u003cu\u003eCytokine transcript measurements\u003cbr /\u003e \u003c/u\u003eTotal RNA was isolated from patient biopsies using Trizol until July 1, 2008; later the Allprep DNA/RNA Mini Kit (Qiagen, Hilden, Germany, Cat No: 80204) and the automated QIAcube instrument (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s recommendations. Quantity and purity of the extracted RNA were determined using the Qubit 3 Fluorometer (Cat No: Q33216; Invitrogen by Thermo Fisher Scientific, Waltham, MA, USA). Reverse transcription of the total RNA was performed using the QuantiTect Reverse Transcription Kit (Cat. No: 205314; Qiagen, Hilden, Germany). Mucosal TNF gene transcript was measured by real-time PCR procedures previously described in detail. \u003csup\u003e30-33\u003c/sup\u003e\u003cu\u003e\u003cbr /\u003e \u003cbr /\u003e Statistics \u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe following factors were evaluated as predictors: extent of disease, UCDAI score and endoscopic sub-score, histological activity score, fecal calprotectin and mucosal cytokine transcripts. All baseline predictors were standardized and centered for exploring combinations of two variables. To evaluate predictors of outcome, ROC curves were constructed. Optimal cut-off values were picked by maximal Youden\u0026rsquo;s J.\u003csup\u003e34\u003c/sup\u003e Test characteristics were derived by confusion matrices and diagnostic odds ratios.\u003csup\u003e35\u003c/sup\u003e A sequential test for mucosal TNF transcript and RHI score was constructed: Observations with a positive TNF test were run in a new ROC analysis for RHI score, which resulted in a two-step combined model with one cut-off value for mucosal TNF transcript and another cut-off value for RHI score following a positive TNF test. \u003cbr /\u003e As a global test, Kruskal Wallis one-way ANOVA was performed, then Mann-Whitney U test with Bonferroni correction. For categorical values Chi-square test with Bonferroni correction was utilized. \u003cbr /\u003eAll statistical analyses were carried out in IBM SPSS Statistics 24 (IBM Corporation, Armonk, New York, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003e \u003c/strong\u003e\u003cu\u003eHealthy controls\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThirty-eight healthy controls were included, 13 females and 25 men aged 43-69 years. The median TNF value was 4450 copies/\u0026micro;g mRNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCalibration cohort\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eBaseline characteristics and outcome groups\u003cbr /\u003e \u003c/u\u003eSixty-six patients were included as a follow up mainly from an earlier report.\u003csup\u003e19\u003c/sup\u003e At 1 year follow-up patients were categorized into mild (n= 23), moderate (n= 18) and severe (n=25) disease outcomes based on a step-up treatment level algorithm. In the moderate outcome group, no patients needed continuous steroid treatment and two patients were treated with azathioprine. In the severe outcome group, all patients were on anti-TNF treatment including one patient that later was in the need of colectomy. Sixteen patients were on concomitant treatment with azathioprine and one patient on methotrexate. An overview of baseline characteristics for each outcome group is shown in Table 1. There were significant differences between the three treatment groups for mucosal TNF and UCDAI scores (p\u0026lt; 0.017).\u003c/p\u003e\n\n\u003cp\u003e\u003cu\u003eDiscovery of potential biomarkers\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWith three defined treatment outcomes we made two sets of ROC curves, one set to discriminate between mild and moderate/severe and one set to discriminate between mild/moderate and severe. There were no baseline predictors that showed good test performance for discriminations between mild and moderate/severe (data not shown). However, there was a tendency towards increasing concentrations of the mucosal TNF transcripts with increasing treatment level (figure 2A).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSevere outcome.\u003c/u\u003e \u003cbr /\u003e Baseline predictors of severe outcome are shown in table 1 and figure 3 presenting clinical parameters (Calprotectin, UCDAI, Mayo endoscopic score), RHI score and mucosal TNF transcripts. Selected predictors including cut off values are shown in table 3. Of individual factors, mucosal TNF transcript had the best test performance with a sensitivity, specificity and diagnostic odds ratio (DOR) of 0.81, 0.91 and 43 respectively. Clinical data including fecal calprotectin, UCDAI and RHI -score, yielded a high sensitivity but poor specificity (table 2, figure 3), and therefore a poorer test performance than mucosal TNF transcript. To increase the test performance, we then combined mucosal TNF transcript and RHI score in a sequential setup: subjects with mucosal TNF transcript above cut-off were subjected to a second ROC curve using RHI score as predictor. The combined sequential test of mucosal TNF transcript and RHI score showed a superior test performance for specificity and DOR, however lower sensitivity (table 2). No other clinical, biochemical, histological or immunological combinations could improve the test performance of prediction of severe outcome (supplement material figure 4). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eValidation cohort\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eBaseline characteristics and outcome groups\u003cbr /\u003e \u003c/u\u003eAt one year follow up patients were categorized into mild (n= 36), moderate (n= 31) and severe (n=22) disease outcomes based on a step-up treatment level algorithm. In the moderate outcome group, no patients needed continuous steroid treatment and five subjects were treated with azathioprine. In the severe outcome group, 22 patients were on anti-TNF treatment whereas two of these patients were later in the need of colectomy. Thirty-eight healthy controls were included. An overview of baseline characteristics for each outcome group is shown in Table 3. There were significant differences between the three treatment groups for mucosal TNF, UCDAI, RHI scores and fecal calprotectin (table 3, figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eValidation of predictors of severe outcome \u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe cut off values from the discovery study (TNF\u0026ge;18000, RHI\u0026ge;9) were used for test performance.\u0026nbsp; The baseline predictors of severe outcome presenting mucosal TNF transcripts and RHI score are shown in table 4. Mucosal TNF transcript had a test performance with sensitivity, specificity and DOR of 0.5, 0.9 and 9 respectively. RHI transcript had a test performance with sensitivity, specificity and DOR of 0.72, 0.69 and 6, respectively. When combined TNF and RHI the specificity increased to high 0.99, whereas the DOR was still high as 54. Moreover, the low sensitivity of 0.44 represents most likely the overlapping TNF and RHI score values to the mild/moderate outcome groups (table 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe present a combined discovery study (from 2004) and a validation study (from 2014) in a prospective design (the transomic Advanced Study of Inflammatory Bowel Disease) where clinical, biochemical, histological and transcript data where retrospectively tested to identify biomarkers of clinical outcome 1 year after disease diagnosis of UC. Mucosal TNF transcripts showed high test reliability for predicting severe outcome after 1 year in UC in both studies but was not ideal to discriminate between mild, moderate and severe disease. Moreover, when the TNF transcripts were combined with histological activity (RHI) scores, the test improved its diagnostic reliability. Mucosal cut-off values for TNF and RHI scores determined in the calibration cohort displayed a high test performance with specificity of 0.99 and a diagnostic odds-ratio (DOR) of 54 in the prospective validation study. Thus, mucosal TNF transcript combined with a histological score at debut of disease can likely identify patients who experience severe outcomes during the first year. This is an important step towards personalizing treatment in IBD and may be used as a criterion for selecting candidates for top-down treatment of anti-TNF. However, this awaits further studies.\u003cbr /\u003eWe have tested a broad spectrum of potential factors that could, alone, or in combinations, predict clinical outcome in the first year of diagnosis. The clinical outcomes were defined as the highest treatment level required for achieving disease remission during the first year of disease, in a step-up treatment approach. The broad/wide selection of variables including various combinations did not have the necessary precision to discriminate between mild, moderate and severe outcomes. However mucosal TNF transcript in combination with the histological RHI score was able to predict, with high precision, the most severe colitis outcomes needing biological or surgical treatment, within the first year of disease. The validated cut-off values (TNF \u0026ge;18000, RHI\u0026ge;9) showed a high specificity to predict severe outcome and a DOR as high as 54. From a clinical point of view, these cut\u0026ndash;off values indicate a need of anti-TNF therapy during the first year after diagnosis with high reliability, and therefore of high clinical value and utility in the management of IBD/UC. In order to use a biomarker for selection for top-down treatment, a high PPV is necessary to avoid excessive use of biologics. Our proposed biomarker shows a PPV of 0.89 meaning that 9 out of 10 positives will be correctly identified as severe outcome.\u003cbr /\u003eA step-up treatment approach represents well-established international guidelines.\u003csup\u003e8-10\u003c/sup\u003e One drawback of this approach is that patients in the severe outcome group often experience a period of poor response during the gradual escalation of treatment intensity until an adequate response is obtained. In some cases, one may lose an important window of opportunity for optimal effect of biologics leading to permanent structural damage and/or need of surgery. The impact of early treatment before development of severe disease is not completely investigated. However, the top down approach published by D\u0026rsquo;Haens et al indicated that immunosuppressive therapy was superior to a step-up approach in patients with Crohn\u0026rsquo;s disease.\u003csup\u003e36\u003c/sup\u003e Moreover, it is well documented that induction of treatment to remission reduces later hospitalization, whereas conflicting results exist for colectomy in two studies.\u003csup\u003e37, 38\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe use of molecular data from the mucosa represents a novel approach and is an easily available tool, with high utility for clinicians to individually tailor therapy in UC.\u0026nbsp; Endoscopic biopsies are routinely taken at diagnosis and surveillance of IBD. Thus, the logistics of measuring mucosal TNF transcript are simple, as biopsies are readily available and samples do not require freezing prior to analysis.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur study contributes with new knowledge in the scientific field of personalized therapy in UC.\u003csup\u003e15\u003c/sup\u003e \u003csup\u003e16\u003c/sup\u003e We know that treatment to remission improves long-term clinical outcome.\u003csup\u003e39, 40\u003c/sup\u003e The main question is: Can a top-down therapy of the most severe forms of disease have an effect on the natural course of disease? This awaits future studies.\u003c/p\u003e\n\u003cp\u003eThe strength of this prospective designed, combined discovery and validation study is that we have retrospectively searched for and validated biomarkers for treatment at debut of UC, using a broad search of clinical, histological and analytical factors including mucosal immune transcripts. Moreover, this is part of the transomic Advanced Study of Inflammatory Bowel disease (ASIB) study where parallel studies of the epigenome, transcriptome, proteome and metabolome are ongoing.\u003csup\u003e33, 41-45\u003c/sup\u003e. This transomic approach at debut of UC will be performed and correlated to long-term clinical outcome. Therefore, the upcoming transomic data from the ASIB study and from several ongoing studies such as the PREDICTS study will not only search for therapeutic but also prognostic and natural course biomarkers.\u003csup\u003e17\u003c/sup\u003e The weakness of the study includes the lack of endoscopic diagnosis at one year, which would have given insight into endoscopic status and endoscopic remission rates according to treatment levels. Additionally, the decision to use or not use steroids at time of diagnosis is dependent on the subjective decision of the clinicians. This may be one explanation for the small differences detected between the mild and moderate treatment group. \u003cbr /\u003e \u003cbr /\u003e "},{"header":"Conclusions","content":"\n\u003cp\u003eThe combined information of mucosal TNF transcription and histological score at debut of UC can predict severe outcome and the need for anti-TNF therapy. This is of great clinical utility and may contribute to a personalized medicine approach in UC.\u003c/p\u003e"},{"header":"Abbreviations ","content":"\n\u003cp\u003eRobarts histopathology index (RHI), diagnostic odds-ratio (DOR), Ulcerative colitis (UC), Inflammatory bowel disease (IBD), Tumor necrosis factor (TNF), Advanced Study of Inflammatory Bowel disease (ASIB- study), Ulcerative colitis clinical score (UCCS)\u003cstrong\u003e\u003cbr /\u003e \u003cbr /\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were informed and signed a written consent to participate and publication.\u003c/p\u003e\n\u003cp\u003eApproval including the use of biobank was granted by the Regional Committee of Medical Ethics of Northern Norway Ref no: 14/2004 and 1349/2012\u003cbr /\u003e \u003cbr /\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have approved the final manuscript for publication\u003cstrong\u003e\u003cbr /\u003e \u003cbr /\u003e Availability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the authors upon reasonable request due to privacy/ethical restrictions\u003cbr /\u003e \u003cbr /\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared from all authors.\u003cstrong\u003e\u003cbr /\u003e \u003cbr /\u003e Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Northern Norway Regional Health Authority, ID SFP-50-04, SFP-888-09 and SFP-1136-13\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlanning and conducting: JRF, KMJ, RG, KJ, RM, TO, SWS, \u0026Oslash;KM, PT, MDG, JMK, TL, GR, CV \u003cbr /\u003e Collecting or interpreting data: JRF, KMJ, RG, KJ, RM, TO, SWS, \u0026Oslash;KM, PT, MDG, JMK, TL, GR, CV\u003c/p\u003e\n\u003cp\u003eDrafting of manuscript: JRF, KMJ, RG, KJ, RM, TO, SWS, \u0026Oslash;KM, PT, MDG, JMK, TL, GR, CV\u003cstrong\u003e\u003cbr /\u003e \u003cbr /\u003e Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe publication charges for this article have been funded by a grant from the publication fund of UiT The Arctic University of Norway. We thank Ingrid Christiansen, Marian Remijn and Line Wilsgaard for expert technical assistance.\u003c/p\u003e"},{"header":"References","content":"\n\u003col\u003e\n\u003cli\u003eZhang YZ, Li YY. Inflammatory bowel disease: pathogenesis. World J Gastroenterol 2014;20:91-9.\u003c/li\u003e\n\u003cli\u003eHenriksen M, Jahnsen J, Lygren I, et al. Ulcerative colitis and clinical course: results of a 5-year population-based follow-up study (the IBSEN study). Inflamm Bowel Dis 2006;12:543-50.\u003c/li\u003e\n\u003cli\u003eSolberg 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;50:1456-62.\u003c/li\u003e\n\u003cli\u003eWilliet N, Sandborn WJ, Peyrin\u0026ndash;Biroulet L. Patient-Reported Outcomes as Primary End Points in Clinical Trials of Inflammatory Bowel Disease. Clinical Gastroenterology and Hepatology 2014;12:1246-1256.e6.\u003c/li\u003e\n\u003cli\u003eda Silva BC, Lyra AC, Rocha R, et al. Epidemiology, demographic characteristics and prognostic predictors of ulcerative colitis. World J Gastroenterol 2014;20:9458-67.\u003c/li\u003e\n\u003cli\u003eSilverberg 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.\u003c/li\u003e\n\u003cli\u003eSatsangi J, Silverberg MS, Vermeire S, et al. The Montreal classification of inflammatory bowel disease: controversies, consensus, and implications. Gut 2006;55:749-53.\u003c/li\u003e\n\u003cli\u003eDassopoulos T, Cohen RD, Scherl EJ, et al. Ulcerative Colitis Care Pathway. Gastroenterology 2015;149:238-245.\u003c/li\u003e\n\u003cli\u003eMagro F, Gionchetti P, Eliakim R, et al. 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Serologic microbial associated markers can predict Crohn's disease behaviour years before disease diagnosis. Aliment Pharmacol Ther 2016;43:1300-10.\u003c/li\u003e\n\u003cli\u003eHamilton AL, Kamm MA, De Cruz P, et al. Serologic antibodies in relation to outcome in postoperative Crohn's disease. J Gastroenterol Hepatol 2017;32:1195-1203.\u003c/li\u003e\n\u003cli\u003eSpekhorst LM, Imhann F, Festen EAM, et al. Cohort profile: design and first results of the Dutch IBD Biobank: a prospective, nationwide biobank of patients with inflammatory bowel disease. BMJ open 2017;7:e016695-e016695.\u003c/li\u003e\n\u003cli\u003eStevens TW, Matheeuwsen M, Lonnkvist MH, et al. Systematic review: predictive biomarkers of therapeutic response in inflammatory bowel disease-personalised medicine in its infancy. Aliment Pharmacol Ther 2018;48:1213-1231.\u003c/li\u003e\n\u003cli\u003ePorter CK, Riddle MS, Gutierrez RL, et al. Cohort profile of the PRoteomic Evaluation and Discovery in an IBD Cohort of Tri-service Subjects (PREDICTS) study: Rationale, organization, design, and baseline characteristics. Contemporary Clinical Trials Communications 2019;14:100345.\u003c/li\u003e\n\u003cli\u003eDulai PS, Peyrin-Biroulet L, Danese S, et al. Approaches to Integrating Biomarkers Into Clinical Trials and Care Pathways as Targets for the Treatment of Inflammatory Bowel Diseases. Gastroenterology 2019;157:1032-1043.e1.\u003c/li\u003e\n\u003cli\u003eOlsen T, Goll R, Cui G, et al. TNF-alpha gene expression in colorectal mucosa as a predictor of remission after induction therapy with infliximab in ulcerative colitis. Cytokine 2009;46:222-7.\u003c/li\u003e\n\u003cli\u003eRismo R, Olsen T, Cui G, et al. Normalization of mucosal cytokine gene expression levels predicts long-term remission after discontinuation of anti-TNF therapy in Crohn's disease. Scand J Gastroenterol 2013;48:311-9.\u003c/li\u003e\n\u003cli\u003eJohnsen KM, Goll R, Hansen V, et al. Repeated intensified infliximab induction - results from an 11-year prospective study of ulcerative colitis using a novel treatment algorithm. Eur J Gastroenterol Hepatol 2017;29:98-104.\u003c/li\u003e\n\u003cli\u003eFlamant M, Roblin X. Inflammatory bowel disease: towards a personalized medicine. Therap Adv Gastroenterol 2018;11:1756283x17745029.\u003c/li\u003e\n\u003cli\u003eSiegel CA. Refocusing IBD Patient Management: Personalized, Proactive, and Patient-Centered Care. Am J Gastroenterol 2018;113:1440-1443.\u003c/li\u003e\n\u003cli\u003eWeimers P, Munkholm P. The Natural History of IBD: Lessons Learned. Curr Treat Options Gastroenterol 2018;16:101-111.\u003c/li\u003e\n\u003cli\u003ede Souza HSP. Etiopathogenesis of inflammatory bowel disease: today and tomorrow. Curr Opin Gastroenterol 2017;33:222-229.\u003c/li\u003e\n\u003cli\u003eKim DH, Cheon JH. Pathogenesis of Inflammatory Bowel Disease and Recent Advances in Biologic Therapies. Immune Netw 2017;17:25-40.\u003c/li\u003e\n\u003cli\u003eMosli MH, Feagan BG, Zou G, et al. Development and validation of a histological index for UC. Gut 2017;66:50-58.\u003c/li\u003e\n\u003cli\u003eDignass A, Lindsay JO, Sturm A, et al. Second European evidence-based consensus on the diagnosis and management of ulcerative colitis part 2: current management. J Crohns Colitis 2012;6:991-1030.\u003c/li\u003e\n\u003cli\u003eFeagan BG, Greenberg GR, Wild G, et al. Treatment of Ulcerative Colitis with a Humanized Antibody to the \u0026alpha;4\u0026beta;7 Integrin. New England Journal of Medicine 2005;352:2499-2507.\u003c/li\u003e\n\u003cli\u003eOlsen T, Goll R, Cui G, et al. Tissue levels of tumor necrosis factor-alpha correlates with grade of inflammation in untreated ulcerative colitis. Scand J Gastroenterol 2007;42:1312-20.\u003c/li\u003e\n\u003cli\u003eCui G, Olsen T, Christiansen I, et al. Improvement of real-time polymerase chain reaction for quantifying TNF-alpha mRNA expression in inflamed colorectal mucosa: an approach to optimize procedures for clinical use. Scand J Clin Lab Invest 2006;66:249-59.\u003c/li\u003e\n\u003cli\u003eOlsen T, Rismo R, Gundersen MD, et al. Normalization of mucosal tumor necrosis factor-alpha: A new criterion for discontinuing infliximab therapy in ulcerative colitis. Cytokine 2016;79:90-5.\u003c/li\u003e\n\u003cli\u003eDiab J, Al-Mahdi R, Gouveia-Figueira S, et al. A Quantitative Analysis of Colonic Mucosal Oxylipins and Endocannabinoids in Treatment-Naive and Deep Remission Ulcerative Colitis Patients and the Potential Link With Cytokine Gene Expression. Inflamm Bowel Dis 2019;25:490-497.\u003c/li\u003e\n\u003cli\u003eYouden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-5.\u003c/li\u003e\n\u003cli\u003eGlas AS, Lijmer JG, Prins MH, et al. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 2003;56:1129-35.\u003c/li\u003e\n\u003cli\u003eD'Haens G, Baert F, van Assche G, et al. Early combined immunosuppression or conventional management in patients with newly diagnosed Crohn's disease: an open randomised trial. Lancet 2008;371:660-667.\u003c/li\u003e\n\u003cli\u003eColombel JF, Rutgeerts P, Reinisch W, et al. Early mucosal healing with infliximab is associated with improved long-term clinical outcomes in ulcerative colitis. Gastroenterology 2011;141:1194-201.\u003c/li\u003e\n\u003cli\u003eBurisch J, Kiudelis G, Kupcinskas L, et al. Natural disease course of Crohn\u0026rsquo;s disease during the first 5 years after diagnosis in a European population-based inception cohort: an Epi-IBD study. Gut 2018:gutjnl-2017-315568.\u003c/li\u003e\n\u003cli\u003eRutgeerts P, Vermeire S, Van Assche G. Mucosal healing in inflammatory bowel disease: impossible ideal or therapeutic target? Gut 2007;56:453-5.\u003c/li\u003e\n\u003cli\u003eArias 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;13:531-8.\u003c/li\u003e\n\u003cli\u003eSchniers A, Anderssen E, Fenton CG, et al. The Proteome of Ulcerative Colitis in Colon Biopsies from Adults - Optimized Sample Preparation and Comparison with Healthy Controls. Proteomics Clin Appl 2017;11.\u003c/li\u003e\n\u003cli\u003eTaman H, Fenton CG, Hensel IV, et al. Genome-wide DNA Methylation in Treatment-naive Ulcerative Colitis. J Crohns Colitis 2018;12:1338-1347.\u003c/li\u003e\n\u003cli\u003eTaman H, Fenton CG, Hensel IV, et al. Transcriptomic Landscape of Treatment-Naive Ulcerative Colitis. J Crohns Colitis 2018;12:327-336.\u003c/li\u003e\n\u003cli\u003eSchniers A, Goll R, Pasing Y, et al. Ulcerative colitis: functional analysis of the in-depth proteome. Clin Proteomics 2019;16:4.\u003c/li\u003e\n\u003cli\u003eDiab J, Hansen T, Goll R, et al. Lipidomics in Ulcerative Colitis Reveal Alteration in Mucosal Lipid Composition Associated With the Disease State. Inflamm Bowel Dis 2019;25:1780-1787.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Baseline characteristics of patients in the calibration cohort with ulcerative colitis according to one-year treatment outcome level.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"586\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003ePatients groups\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003eMild N=23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eModerate N=18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eSevere N=25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eAge med(IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e41 (35-54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e35 (24-55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e41 (27-54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e15 (65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e7 (39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e9 (36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e8 (35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e11 (61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e16 (64%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eColonic area involved\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eProctitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e9 (39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e3(17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e3 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eLeft side\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e9 (39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e7 (39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e10 (40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eExtensive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e5 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e8 (44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e12 (48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eCurrent smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e4 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e2 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e10 (71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e10 (83%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e18 (90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eMucosal TNF*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e10500\u003cbr /\u003e (4600-11900)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e12000\u003cbr /\u003e (8000-17200)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e26900\u003cbr /\u003e (18700-40400)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eUCDAI med(IQR)* at debut\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e7 (5-8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e9 (8-12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e12 (9-12)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eCalprotectin med(IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e590 (400-1100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e790 (470-1540)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2300 (670-2500)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eRHI med(IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e9 (5-10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e7 (6-10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e9 (7-12)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eUCCS score 1-year med(IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e0 (0-0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e0 (0-2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e0 (0-2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eCalprotectin 1-year med(IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e60 (25-85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e50 (25-100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e25 (0-160)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"586\"\u003e\n\u003cp\u003e*p\u0026lt;0,017 between groups, Mann-Whitney U test with Bonferroni correction.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Med(IQR) = median (Interquartile range); RHI= Robarts histopathology index. Mucosal TNF in copies/\u0026micro;g RNA: Fecal calprotectin in mg/kg.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Factors at debut of ulcerative colitis in the calibration cohort to predict severe treatment outcomes at one year of disease.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"605\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"210\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eFactors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003eYouden's J\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eCut-off value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003ePPV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eNPV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003eDOR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eTNF*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026ge;18000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0,85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eRHI**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026ge;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0,48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eCombined TNF RHI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026ge;18000 and \u0026ge;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e\u0026infin;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eUCDAI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026ge;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0,54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eMayo subscore\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0,62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eCalprotectin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026ge;2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0,86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" width=\"605\"\u003e\n\u003cp\u003eDiagnostic odds ratio PPV: Positive predictive value NPV: Negative predictive value\u003c/p\u003e\n\u003cp\u003e* copies/\u0026micro;g mRNA **Robarts histopathology index score\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Baseline characteristics of patients with ulcerative colitis in the validation cohort according to one-year treatment outcome level.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"614\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003ePatient groups\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eMild N=36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eModerate N=31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eSevere N=22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eAge med(IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e36 (24-49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e30 (24-41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e26 (22-47)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e17 (47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e8 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e10 (46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e19 (53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e23 (74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e12 (54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eColonic area involved\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eProctitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e5 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e1(3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eLeft side\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e25 (69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e18 (58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e10 (46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eExtensive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e6 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e12 (39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e11 (50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eCurrent smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e1 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e27 (96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e19 (90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e11 (92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eMucosal TNF*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e8800\u003cbr /\u003e (6100-12800)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e10500\u003cbr /\u003e (7400-13200)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e17400\u003c/p\u003e\n\u003cp\u003e(15100-26800)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eUCDAI med(IQR)* at debut\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e7 (5-9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e9 (8-11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e10 (7-11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eCalprotectin med(IQR)*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e570 (200-970)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e1000 (340-2000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1100 (830-1400)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eRHI med(IQR)*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e6 (2-10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e6 (4-11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e14 (9-27)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eUCCS score 1-year med(IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e0 (0-0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e0 (0-0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e0 (0-8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eCalprotectin 1-year med(IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e40 (25-94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e50 (0-140)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e25 (20-60)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"614\"\u003e\n\u003cp\u003e*p\u0026lt;0,017 between groups, Mann-Whitney U test with Bonferroni correction.\u003c/p\u003e\n\u003cp\u003eMed(IQR): median (Interquartile range); RHI: Robarts histopathology index. Mucosal TNF in copies/\u0026micro;g RNA; Fecal calprotectin in mg/kg\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. \u003c/strong\u003eFactors at debut of ulcerative colitis in the validation cohort to predict severe treatment outcomes at one year of disease based on cut off values from the discovery cohort.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"605\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eFactors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003eYouden's J\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eCutt off value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003ePPV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003eNPV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003eDOR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eTNF*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026ge;18000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0,56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eRHI**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026ge;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0,38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eCombined TNF RHI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e0,43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e18000 \u0026ge;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0,99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e0,89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0,87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" width=\"605\"\u003e\n\u003cp\u003eDOR: Diagnostic odds ratio PPV: Positive predictive value NPV: Negative predictive value\u003c/p\u003e\n\u003cp\u003e* copies/\u0026micro;g mRNA **Robarts histopathology index score\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"antiTNF, calprotectin, cytokines, diagnostic odds ratio, Robarts Histopathology Index","lastPublishedDoi":"10.21203/rs.3.rs-25116/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-25116/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003eBackground \u003c/u\u003eThere are no accurate markers that can predict clinical outcome in ulcerative colitis at time of diagnosis. The aim of this study was to explore a comprehensive data set to identify and validate predictors of clinical outcome in the first year following diagnosis. \u003c/p\u003e\u003cp\u003e\u003cu\u003eMethods \u003c/u\u003eTreatment naive-patients with ulcerative colitis were included at time of initial diagnosis from 2004-2014, followed by a validation study from 2014-2018. Patients were treated according to clinical guidelines following a standard step-up regime. Patients were categorized according to the treatment level necessary to achieve clinical remission: mild, moderate and severe. The biopsies were assessed by Robarts histopathology index (RHI) and TNF gene transcripts. \u003c/p\u003e\u003cp\u003e\u003cu\u003eResults \u003c/u\u003eWe included 66 patients in the calibration cohort and 89 patients in the validation. Mucosal TNF transcripts showed high test reliability for predicting severe outcome in UC. When combined with histological activity (RHI) scores the test improved its diagnostic reliability. Based on the cut-off values of mucosal TNF and RHI scores from the calibration cohort, the combined test had still high reliability in the validation cohort (specificity 0.99, sensitivity 0.44, PPV 0.89, NPV 0.87) and a diagnostic odds-ratio (DOR) of 54.\u003c/p\u003e\u003cp\u003e\u003cu\u003eConclusions \u003c/u\u003eThe combined test using TNF transcript and histological score at debut of UC can predict severe outcome and the need for anti-TNF therapy with a high level of precision. These validated data may be of great clinical utility and contribute to a personalized medical approach with the possibility of top-down treatment for selected patients.\u003c/p\u003e","manuscriptTitle":"Discovery and validation of mucosal TNF expression combined with histological score - a biomarker for personalized treatment in ulcerative colitis","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-09-12 02:37:19","doi":"10.21203/rs.3.rs-25116/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2020-09-10T12:00:00+00:00","index":"","fulltext":""},{"type":"decision","content":"Accept","date":"2020-09-09T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2020-09-03 18:00:46","doi":"10.21203/rs.3.rs-25116/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-09-04T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-09-02T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-09-01T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-09-01T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2020-05-07 17:41:52","doi":"10.21203/rs.3.rs-25116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2020-08-13T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nReviewer's report\nTitle: Discovery and validation of mucosal TNF expression combined with histological score - a biomarker for personalized treatment in ulcerative colitis\nMS Number and Version: BMGE-D-20-00476, 1\nDate: 12.08.2020\nReviewer's report:\nThe content of the subject, \"mucosal TNF expression combined with histological score being used as a biomarker for personalized treatment in ulcerative colitis\" is promising and has quite a value of interest. This is a professionally written and well-structured manuscript and I have some minor revisions to improve the manuscript:\n- Minor Revisions\n* Materials and methods (Page 7): The authors have another methods title in methods section and they only talk about the control group and feacal calprotectin here. I would recommend author to put the information about the control group in the first paragraph of materials and methods before \"cohorts examined title\". And get rid of the second method title.\n* Page 7 line 39 authors said highly standardized sampling methods were used to collect patient material. I would recommend them to explain what they did for standardisation. (Again, it could be in the first section after patient and control data before cohort examined title.)\n* Authors mentioned that they measured faecal calprotectin, however they did not present any cut off for faecal calprotectin. Did they also use calprotectin as a remission or inflammation biomarker? Or did they only measure it for comparison. Faecal calprotectin needs a bit more clarification\n* Authors mentioned some result that they did not show in the paper. I would recommend them to present these results as an additional file.\n* I kindly think all the table and figures should be understandable without going back to the main text. I would recommend authors to give short explanation under the tables if necessary, instead of saying see the text for further details. However, I think tables and figures are already clear, maybe they could just remove the sentence saying \"see the text fur further details.\"\n* There are * and ** signs in table 2 but no explanation under the table regarding to that.\n* There are no * or ** signs in table 4 but yet there is an explanation under the table?\n* Please check all the explanations under the table, some abbreviations are missing\n\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **'I declare that I have no competing interests**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"decision","content":"Minor revision","date":"2020-08-13T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-08-10T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nThis is an interesting manuscript, as part of a more vast ongoing research into prognostic factors in UC. Clearly, it is desirable that prognostic factors can be identified early in the disease, that may be useful to tailor the best treatment for optimised outcome. It is not surprising that higher inflammatory activity as assessed by TNF and histology may provide an indication of more severe outcome.\nThe manuscript is not very clear in the different process handling of the calibration and validation cohorts.\nIt is of interest that the authors did not address the extension of the disease into the algorithm. Do they thinnk this is irrelevant for prognosis?\nDespite a high specificy, the proposed combination (TNF RHI) seems to have a rather low sensitivity. The authors rightly mention that topd down strategy seem sto have a relevant role in Crohn's disease, but this is much less obvious in UC, which are the topic of this study.\nThis study is an interesting contribution into the identification of predictive tools to use at diagnosis, but it still remains to be proven that the use of this score may help in the later outcome of the disease by adaption of treatment.* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **No competing interests.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2020-07-23T12:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-05-18T12:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-05-12T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-04-23T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-04-22T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-04-22T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-04-22T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78a7b94b-fefb-43ec-86fb-f2b670579daa","owner":[],"postedDate":"September 12th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":430047,"name":"Gastroenterology \u0026 Hepatology"}],"tags":[],"updatedAt":"2020-10-04T15:02:17+00:00","versionOfRecord":{"articleIdentity":"rs-25116","link":"https://doi.org/10.1186/s12876-020-01447-0","journal":{"identity":"bmc-gastroenterology","isVorOnly":false,"title":"BMC Gastroenterology"},"publishedOn":"2020-10-02 12:00:00","publishedOnDateReadable":"October 2nd, 2020"},"versionCreatedAt":"2020-09-12 02:37:19","video":"","vorDoi":"10.1186/s12876-020-01447-0","vorDoiUrl":"https://doi.org/10.1186/s12876-020-01447-0","workflowStages":[]},"version":"v3","identity":"rs-25116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-25116","identity":"rs-25116","version":["v3"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}