The primary aim is to identify molecular markers (e.g. in the blood and stools) for
discrimination between individuals diagnosed with inflammatory bowel disease (IBD) inclusive
Crohn's disease (CD), ulcerative colitis (UC) and inflammatory bowel disease unclassified
(IBD-U), and those without (non-IBD). Participants with suspected IBD at baseline, with
various disease pathways, will be evaluated again using a 1-year cohort study. Diagnosis and
clinical outcome will be evaluated at referral and after 1 year of observation.
The secondary aims are, in addition to the molecular information, to investigate whether the
inclusion of information on clinical and lifestyle factors as well as combination hereof
(e.g. gene-environment interaction analyses) can improve the predictive potential of
identifying IBD and distinguish the prognosis.
Study design: A prospective Nordic multicenter study on prognostic factors for the diagnosis
and characterization of IBD among patients referred to the hospital on suspicion of IBD. A
panel of possible prognostic biomarkers for diagnostic purposes will be applied to all
participants.
Setting: All patients referred due to a suspicion of IBD to the departments of
gastroenterology in Odense University Hospital, Svendborg Hospital, Vejle Hospital, Esbjerg
Hospital and potentially Hospital of Southern Jutland, Aabenraa will be invited to take part
in the study. Participants will be included from January 2022 for a 1-year period or until
800 participants in the Nordic study (up to 400 in Denmark) have been included. The follow-up
period is one-year including visits and questionnaires and an additional nine years of
follow-up by the use of register data. Biological material will be obtained four times for
participants with IBD, at week-2/0, and 12, 26 and 52 after the diagnosis has been
established. Participants where IBD is not established (non-IBD) will only have biological
material obtained at baseline (that is visit -2/0) and will have a clinical interview after
52 weeks. All participants are treated according to standard clinical practice by the
clinical departments.
Clinical data consist of personal data, data on health and disease, lifestyle, laboratory
measures, and disease activity scores including patient-reported outcome measures (PROMs),
clinical assessments, and laboratory data. Each participant will fill out validated
questionnaires on disease activity, quality of life, and lifestyle using electronic links.
Data management: Study data registered by clinicians, study nurses and technicians will be
stored in the web-based case report form (eCRF) Viedoc (Viedoc™, Uppsala, Sweden) or REDCap
(Open Patient data Explorative Network (OPEN) at Odense University Hospital) for the diet
questionnaire. The questionnaires are in Danish and the participants will have access to the
questionnaires via an electronic link sent to their personal, electronic mailbox (REDCap) or
via an investigator provided link to MyViedoc. All data will be stored in secure research
storage facilities.
Statistical methods: We will develop multivariable prediction models relating multiple
predictors for a particular individual to the probability of or risk for the presence
(diagnosis of IBD at baseline) or future occurrence (prognosis) of a particular outcome such
as severe IBD within the first year. Predictors such as biomarkers are covariates, explored
as prognostic factors (independent variables).
The primary hypothesis is that the final biomarker will define the participant population
into two groups each consisting of 50%: a biomarker positive group whereof 80% will be
diagnosed with IBD and a biomarker negative group whereof 20% will be diagnosed with IBD.
For a comparison of two independent binomial proportions using the likelihood ratio statistic
with a Chi-square approximation with a two-sided significance level of 0.05, a total sample
size of 800 assuming a ratio biomarker positivity-to-negativity of 1 to 1 has an approximate
power of 100% when the proportions of being diagnosed with IBD are 0.8 and 0.2. If we assume
that we will have 800 study participants, we will potentially ("rule of thumb") be able to
build a statistical model with as many as 80 covariates in the multivariable model.
The associations of the suspected important biomarkers with other variables will be tested
with non-parametric tests: with Spearman rank correlation (rs) for continuous variables, and
the Wilcoxon rank-sum test or Kruskal-Wallis test, including a Wilcoxon-type test for trend
across ordered groups where appropriate, for categorical variables. In general, logistic
regression models will be used with individual marker as the exposure variables and the
clinical response as the outcome (dependent variable). The analyses will be adjusted
simultaneously for sex, age, and prescribed targeted therapy. Potential interaction between
biomarker status (positive/negative) and specific drug type will be analyzed. Covariates
(biomarkers) consist of various measures within genetics, transcriptomic, microbiomes, and
proteomics.
Sample size considerations: Assuming that biomarker positivity constitutes 50% of the
individuals enrolled at baseline, if our event rate is 10% on average, a total sample size of
800 patients (i.e. 400 biomarker positive) we will have a very good statistical power (99.7%)
to detect a difference between proportions having surgery of 10% points (15% and 5%,
respectively). If we decide to split the data set into two (2×400 individuals), in order to
first build the model, and subsequently validate it in the second independent dataset, we
will have 91.8% statistical power to detect a difference between groups. If the prognostic
value of our biomarkers is not that effective separating the number of patients with severe
IBD at week 52, we will still have more than 90% power to detect a difference between
biomarker groups of 6% points (e.g. 10% and 4%, respectively).
Another consideration is the number of events (individuals having severe IBD at week 52) per
variable (EPV) considered for inclusion in the multivariable model. For logistic regression
modelling the EPV should be at least ten times the number of potential prognostic variables
that could be included in the model. As a consequence of this logic, our expected sample size
of 800 individuals (having 80 events) will, with reasonable confidence, allow us to create a
multivariable model with up to 8 covariates simultaneously.
Project organization: NORDTREAT is part of a larger Nordic project (DK, SE, NO and IS) where
regular meetings will be held between the partners. Collaborative research and material
transfer agreements will be conducted with the national and international collaborators.
In addition to the scientific reporting of results, major findings with translational
implications will be communicated to health professionals, patient organizations, public
health policy makers, and to the general public through various media and news activities.