Abstract
Background and Aim
Despite the well-established involvement of the gut microbiome in inflammatory bowel disease (IBD), less is known about how the gut microbiome changes over time and how it varies with clinical disease activity and fecal calprotectin (f-calprotectin). To address this gap, we utilized samples from the population-based inception cohort of the Inflammatory Bowel Disease in South-Eastern Norway III (IBSEN III) study.
Methods
Data and stool samples from study participants with IBD and symptomatic controls were collected at diagnosis and after 3, 6, and 12 months. Microbiome profiling of stool samples was performed targeting the V3-V4 region of the 16S rRNA gene, and a consensus-based approach of mixed models was employed for the longitudinal microbiome analysis.
Results
We included 1251 samples from 744 patients with ulcerative colitis, 618 samples from 356 patients with Crohn’ s disease and 266 samples from 164 symptomatic non-IBD controls. In the IBD population, we observed that levels of f-calprotectin decreased over time, as did the patient-reported disease activity (P < .001). Distinct changes in the gut microbiome of IBD patients were observed throughout the first year, such as increased alpha diversity (P < .001) and significant taxonomic changes.
Notably, there was no covariation between the changes in alpha diversity and f-calprotectin or symptom score.
Conclusion
The gut microbiome during the first year after IBD diagnosis showed changes that paralleled inflammation and clinical disease activity, albeit without covariation, suggesting that there may be a disease-driving impact of gut microbiome independent of inflammation and inflammation-driven symptoms.
Keywords: microbiome, IBD, longitudinal, calprotectin, symptoms
Key Messages.
What is already known?
The gut microbiome of individuals with IBD shows greater temporal variability compared to that of healthy individuals.
What is new here?
During the first year after diagnosis, the IBD gut microbiome shifts toward increased abundance of beneficial bacterial taxa. However, reductions in fecal calprotectin and clinical symptoms do not coincide with increased microbial diversity, indicating partly independent processes.
How can this study help patient care?
By demonstrating that gut microbiome alterations can occur independently of inflammation, this study identifies the microbiome as a potential therapeutic and monitoring target, paving the way for earlier and more individualized treatment strategies in IBD care.
Introduction
Inflammatory bowel disease (IBD) is a chronic, relapsing inflammatory disorder of the gastrointestinal tract, affecting 0.5%–-1% of the global population.1 The disease is suggested to be initiated by microbial or environmental factors that trigger gut inflammation in genetically susceptible individuals, causing chronic intestinal inflammation.2 Extensive clinical and experimental research has implicated the gut microbiome in IBD pathogenesis.3 Both major subtypes of IBD, Crohns disease (CD) and ulcerative colitis (UC), are characterized by reduced microbial diversity, distinct alterations, and higher variability in microbiome composition compared to healthy individuals.3,4 Moreover, an IBD-like microbiome has been identified in individuals at high risk of developing the disease,5–7 supporting the notion that dysbiosis may precede and contribute to onset of symptoms, independent of overt inflammation. Differences in the IBD gut microbiome depending on disease activity state have been reported, illustrating the dynamic interplay between inflammation and microbial imbalance .8,9 On the other hand, intestinal inflammation increases oxygen levels in the gut lumen and thereby alters the local environment to favor growth of facultative anaerobes.10,11 Therefore, inflammation will push microbiome composition further toward a dysbiosis state, highlighting the difficulties in answering the question of cause or effect in studies of the IBD microbiome.
Despite extensive characterization of the IBD microbiome by subdiagnosis and disease status, the majority of studies are cross-sectional,12 and it is difficult to interpret the potential causal relationship between disease activity and the microbiome. This situation could possibly be improved by studying the relationship between the gut microbiome and disease activity over time. Endoscopy is the gold standard for monitoring IBD. However, fecal calprotectin (f-calprotectin), a maker of mucosal inflammation, is widely used as a less invasive option relevant for repeated measurements.13 Although f-calprotectin is nonspecific and exhibits a high degree of intra-individual variation and variation between different measurement methods, levels below 100 μg/g indicate mucosal remission, while levels above 250 μg/g suggest significant inflammation.14 Symptoms are also potential measurements of disease activity. The correlation between mucosal inflammation and patient-reported symptoms is typically poor in CD, though better in UC.15 For quantifying symptoms, patient-reported outcomes (PROs) are becoming the standard of measure. For CD, the recommended PRO2 measure is the sum of the weighted daily stool frequency (SF) and abdominal pain, whereas for UC it is SF and rectal bleeding.15
In this study, we used the population-based inception cohort IBSEN III with treatment-naive and newly diagnosed patients with IBD to study changes in markers of inflammation, symptoms, and gut microbiome during the first year after diagnosis. The aim was to study the co-correlation between disease activity and the microbiome to understand better whether these variables are independent factors or if gut microbiome alterations are driven by intestinal inflammation.
Methods
Study population, clinical definitions, and biochemical markers
The Inflammatory Bowel Disease in South-Eastern Norway (IBSEN) III study is a population-based observational inception cohort study that recruited participants suspected of having IBD. Participants were included during 2017-2019 at hospitals belonging to the South-Eastern health region in Norway, as detailed by Kristensen et al.16 All participants underwent standardized examinations according to study standard operating procedures (SOPs) at baseline and 1-year follow-up, including endoscopy, clinical assessments, and collection of biological samples. At 3 and 6 months there were no scheduled study visits at the hospitals, but participants responded to electronic questionnaires and participants from the largest centers were asked to provide fecal samples for microbiome analysis by mail. This study population comprised all adult (aged ≥18 years at time of diagnosis) participants in the IBSEN III cohort classified as having IBD or symptomatic controls who donated at least 1fecal sample.
Fecal sample collection
Fecal samples were collected at home using a stool collection tube with DNA stabilizer (Invitek Diagnostics) for microbiome analysis and 1 dry tube for f-calprotectin analysis. Samples were returned to the hospital by mail and stored at −80 °C. A subset of the participants received a form for declaring the Bristol Stool Scale score of their fecal sample.
Fecal sample processing and sequencing
Fecal samples were processed as described previously.17 In short, DNA was extracted in 2 batches using the QIAcube system. Protocol published by Fadrosh et al.18 was used for amplification of the V3-V4 region when preparing for 16S rRNA sequencing. The normalized and quality-controlled amplicons were pooled in 12 libraries and sequenced on the Illumina MiSeq platform using the v3 kit (Illumina) at the Norwegian Sequencing Centre in Oslo. Samples were kept randomized to minimize batch effects, although most baseline samples were processed in the first batch of extraction and sequencing libraries. Samples yielding less than 10 000 reads were resequenced.
Bioinformatics processing
The full bioinformatics pipeline has been described previously.17 In short, the sequences were filtered, demultiplexed, trimmed, and merged using bbduk, cutadapt, and bbmerge.19–21 Denoising was conducted as implemented in QIIME2 2021.4 by Deblur22,23 and classification was performed using SILVA 138.24–26 Contaminants were removed and the remaining sequences were collapsed at the genus level before the output was exported from QIIME2 for subsequent analysis.
Classification and definitions
The participants completed a symptom questionnaire that included questions regarding number of liquid stools, blood in stool (a factor, with 4 levels), defecation urgency, and stomach pain during (a factor, with 4 levels) the last 24 hours. The original PRO2 score relies on the number of stools,15 not liquid stools; thus, we calculated a modified PRO2 (mPRO2). For UC the score was composed by the sum of blood in stool and number of liquid stools, and for CD the score was composed of the sum of experienced stomach pain and number of watery stools. For f-calprotectin classification we used the cutoff at 250 µg/g to stratify between remission and ongoing disease activity.27
At the 1-year follow-up, we classified the participants with IBD into those having a severe or indolent disease course on the basis of a previously described composite endpoint composed of difficulties in controlling the inflammation and the development of IBD-related complications.17,28,29
IBD treatment data were collected at baseline and 1-year follow-up. Medication start date was recorded for all IBD treatments except for courses of steroids and budesonide. IBD medications were categorized into different groups as previously described,30,31 except for the medication 5-aminosalicylic acid (5-ASA) for which local and oral treatments were combined due to the expected effect on the microbiome.32
Statistics
Data were handled within the safe environment for sensitive data (Services for Sensitive Data) at the University of Oslo. Linear mixed models were applied to account for repeated measures and confounders, with age, sex, and baseline antibiotic use as fixed effects and patient ID as a random effect, implemented in R (lme4 v1.1-35.3; lmerTest v3.1-3). To investigate longitudinal changes and time-dependent associations, time-lagged mixed models were used for handling repeated samples and confounders as described above. The intraindividual (alpha) diversity was measured by the Shannon diversity index, which was calculated on the rarefied dataset (10 003 reads per sample) using the phyloseq package in R (version 1.48.0). Differences in alpha diversity were assessed by linear- and time-lagged mixed models. Compositionality and stability (beta diversity) of samples cross-sectionally and over time were examined using the Bray-Curtis dissimilarity with the vegan (version 2.6-6.1), SEtools (version 1.12.0), and miaTime (version 0.99.8) packages. Stability in the gut microbiome over time was assessed by changes in beta diversity from baseline and from each previous time point. Differential abundance analysis was conducted using MaAsLin2 (version 1.18.0) and LinDA (version 0.2.0), both being mixed models that allow for handling repeated samples and adjustment for confounders. Only taxa that are differentially abundant with both methods are reported. The presented q values (P values adjusted for the false discovery rate) and model coefficients were taken from results obtained by using the LinDA program. Correlation analysis was conducted with the base R stats package, and Spearman’s rho was used for calculating correlation coefficients. Correlation refers to cross-sectional correlation, whereas covariation refers to the correlation between changes. Tables were made using gtsummary (version 1.7). Visualizations were made with ggplot2 (version 3.5.1) and combined by the package ggpubr (version 0.6.0). All analyses were conducted in RStudio using R version 4.2.3.
Results
Population
The study population included 1264 participants: 744 with UC, 356 with CD, and 164 symptomatic non-IBD controls, each with at least 1 sample (Table 1). Among the participants diagnosed with UC, 204 provided samples at both baseline and 1 year, and 74 of these patients also donated samples at the 3- and 6-month follow-ups. Among participants with CD, 108 contributed samples at both baseline and 1 year, and 36 of these also provided samples at 3 and 6 months. Among the symptomatic non-IBD controls, 22 provided samples at both baseline and 1 year, while 18 provided samples at all 4 time points. The number of samples at each time point is shown in Figure 1. Compared to the overall adult IBSEN population, this study population was slightly older and had a higher proportion of females (Table S1).
Table 1.
Characteristics of the study population.
| Characteristic | CD (n = 356) | UC (n = 744) | Symptomatic controls (n = 164) |
|---|---|---|---|
| Age, median (IQR), y | 40 (28–53) | 39 (28–52) | 31 (24–40) |
| Sex, n (%) | |||
| Female | 208 (58) | 356 (48) | 82 (50) |
| Male | 148 (42) | 388 (52) | 82 (50) |
| Treatment-naive sample, n (%) | |||
| 0 | 263 (74) | 653 (88) | 1 (0.6) |
| 1 | 93 (26) | 91 (12) | 163 (99) |
| Validated bacteria data at baseline and 12 m, n (%) | |||
| 0 | 248 (70) | 540 (73) | 142 (87) |
| 1 | 108 (30) | 204 (27) | 22 (13) |
| Validated bacteria data at baseline, 3, 6, and 12 mo, n (%) | |||
| 0 | 320 (90) | 670 (90) | 146 (89) |
| 1 | 36 (10) | 74 (9.9) | 18 (11) |
| Severe Disease Course, n (%) | |||
| Severe course | 51 (16) | 67 (9.6) | 0 (0) |
| Indolent course | 265 (84) | 628 (90) | 164 (100) |
| Unknown | 40 | 49 | 0 |
Abbreviations: CD, Crohns disease; UC, ulcerative colitis.
Figure 1.
Flow chart of the number of participants who donated fecal samples at each time point. In total, the study population included 1264 individuals: 744 with UC, 356 with CD, and 164 symptomatic non-IBD controls.
Microbiome during the first year
The intraindividual (alpha) diversity of the gut microbiome increased significantly over the first year in both CD (P = .001) and UC patients (P < .001, Figure 2A) but did not increase among the symptomatic controls. When assessing the global microbiome composition (beta diversity) and its variability over time, a significant difference in the degree of variability was observed between patients with UC and CD and the symptomatic controls, with the highest variability in UC and the lowest in symptomatic controls (Figure 2B). In differential abundance analysis, 9 taxa differed significantly over the first year in patients with CD (Figure 2C) and 27 taxa in those with UC (Figure 2D), while no taxa changed significantly in symptomatic controls.
Figure 2.
A. Alpha diversity (Shannon index) over time by diagnosis. Each dot represents a study participant; lines indicate the median per class. *denotes P < .05 from mixed-effects models. B. Bray-Curtis dissimilarity from baseline. Each line represents 1 patient; bold lines indicate the median per class. Variability was significantly higher in UC and CD compared with symptomatic non-IBD controls (adonis2, P < .05). C. Bacterial taxa with significant consensus-based differential abundance (MaAsLin2 and LinDA, q < 0.05) over time among patients with CD. Lines represent the median abundance per taxon, color coded by increasing or decreasing trend. D. Bacterial taxa with significant consensus-based differential abundance over time among patients with UC (MaAsLin2 and LinDA, q < 0.05). Lines represent the median abundance per taxon, color-coded by increasing or decreasing trend. CD, Crohns disease; UC, ulcerative colitis.
F-calprotectin levels and symptom severity
The levels of F-calprotectin decreased throughout the first year in both CD and UC patients, while there was no change in samples from the symptomatic controls (Figure 3A). A Bristol stool score (BSS) was available for 48.6% of the samples at baseline and was weakly correlated with f-calprotectin (r = 0.13, P < .001) (Figure S1).
Figure 3.
A. F-calprotectin over time by sub diagnosis. Each dot represents a participant; lines indicate the median per class. *Denotes P < .05 from mixed-effects models. B. mPRO2 scores over time by sub diagnosis. Same format as (A). C. Venn diagram of samples with f-calprotectin < 250 µg/g and/or mPRO2 < 1 among patients with CD. D. Venn diagram of samples with f-calprotectin < 250 µg/g and/or mPRO2 < 1 among patients with UC. E. Correlation between change in mPRO2 from baseline to 12 months and change in f-calprotectin in CD and UC (Spearman correlation; CD: r = 0.21, P = .0005; UC: r = 0.22, P = .0004). CD, Crohns disease; UC, ulcerative colitis.
The majority of the included participants (88.7%) completed a questionnaire of symptoms at the time of stool sampling. Throughout the first year, there was a significant decline in the reported number of liquid stools, stomach pain, urgency to defecate, and reported blood in stool (Figure S2), and thereafter the mPRO2 score decreased significantly (P < .001, Figure 3B).
Considering all time points in a cross-sectional analysis, f-calprotectin and mPRO2 were moderately correlated (r = 0.33, P < .001, Figure S3), and among patients with UC a higher average f-calprotectin level was significantly associated with a higher average mPRO2 score, as evaluated by the time-lagged mixed model (P = .012). These increases were not observed in samples from CD patients (P = .51). Among all samples, 64% from CD and 59% from UC patients had an f-calprotectin below 250 µg/g and reported symptoms or reported no symptoms but had f-calprotectin above 250 µg/g (Figure 3C and D). There was a weak correlation (r = 0.21–0.22, P < .001) between the changes (deltas) in f-calprotectin and mPRO2 score from baseline to 1 year (Figure 3E), with similar values in the subset with baseline f-calprotectin above 250 µg/g (r = 0.19, P = .04 and r = 0.24, P = .01 for CD and UC, respectively, Figure S4). Through the time-lagged analysis, we observed that a reduction in f-calprotectin significantly predicted a lower mPRO2 score at the next visit (P = .011). When stratified by IBD subtype, this association was not significant among participants with CD (P = .445) but remained significant in UC (P = .011).
Relationship between microbiome, f-calprotectin, and symptoms
In the time-lagged analysis, participants with overall higher microbiome diversity had lower fecal calprotectin levels (P = .022), but intra-individual (alpha) diversity only weakly correlated with levels of f-calprotectin (r = −0.14, P < .001, Figure S5A). There was no correlation between the observed change (delta) in alpha diversity and f-calprotectin over the first year (r = −0.03, P = .6, Figure 4A), and changes in alpha diversity did not predict change in levels of f-calprotectin (P = .52). The levels of f-calprotectin were highly stable over time and were best explained by its previous measurement (P < .001). Multiple bacterial taxa associated significantly with levels of f-calprotectin (105 taxa q < 0.05, Figure 4B and Table S2) when adjusting for repeated samples and confounders, with Escherichia-Shigella as the statistically strongest association.
Figure 4.
A. Correlation between change in alpha diversity (Shannon index) from baseline to 12 months and change in f-calprotectin. Spearman correlation: r = −0.03, P = .6. B. Bacterial taxa with significant consensus-based differential abundance by f-calprotectin, independent of time point (MaAsLin2 and LinDA, q < 0.05). The x-axis represents the log2 fold change and the y-axis the -log10 of the q-value. C. Correlation between change in alpha diversity (Shannon index) from baseline to 12 months and change in mPRO2. Spearman correlation: r = −0.04, P = .5.
Patients with higher overall diversity had lower symptom burden (P = .027), and a weak correlation pattern was found between alpha diversity and the symptom score mPRO2 at any given time point (r = −0.2, P < .001, Figure S5B). However, there was no correlation between the changes (deltas) in alpha diversity and mPRO2 the first year (r = −0.04, P = .5, Figure 4C). Furthermore, a change in alpha diversity did not predict change in mPRO2 score (P = .64), and the symptom scoring was significantly persistent over time (P < .001). Multiple taxa were differentially abundant based on the mPRO2 score while adjusting for repeated samples and confounders (Table S3 and S4).
Medications per se as a contributing factor to microbiome changes
The participants donated their fecal samples shortly after starting treatment. For CD patients the median was 17 days, whereas for UC patients the median was 9 days. To investigate the effect of medications, we focused only on individuals with a treatment-naive sample available and at least 1 paired sample at a later point. Given the variable treatment strategies the first year after the IBD diagnosis and limited numbers of follow-up samples, we looked specifically at the largest first-treatment group, UC patients treated with 5-ASA (Figure S6). Treatment-naive fecal samples were available from 77 patients before they started oral or local 5-ASA therapy. There was no difference in alpha diversity before vs after 5-ASA initiation (P = .80), but we observed global compositional differences (P = .030). Differential abundance analysis revealed 4 genera to differ significantly when we compared samples collected before and after 5-ASA therapy (increased Blautia and decreased Akkermansia, Hydrogenoanaerobacterium, uncultured Firmicutes).
Increased gut microbiome variability in patients with severe disease course
We classified participants based on stable-high or stable-low levels of f-calprotectin and found that the stable-high group had significantly higher variability in microbiome composition over time when adjusting for diagnosis (P = .001, Figure 5A).
Figure 5.
A. Bray-Curtis dissimilarity from the previous time point stratified by stable f-calprotectin above vs below 250 µg/g. Variability differed significantly (adonis2, P < .05). B. Alpha diversity (Shannon index) over time by subdiagnosis and disease course. Significant increases were observed in indolent CD and UC, but not in aggressive disease. C. Bray-Curtis dissimilarity from baseline by subdiagnosis and disease course. Variability differed significantly in UC (adonis2, P < .05). D. Consensus-based differentially abundant taxa distinguishing UC with low activity and symptomatic non-IBD controls. The x-axis represents the log2 fold change and y-axis the -log10 of the q-value. CD, Crohns disease; UC, ulcerative colitis.
A severe disease course was observed in 17% of patients with CD and 9.5% among patients with UC (Table 1). Alpha diversity was generally lower among those having a severe disease course than those with an indolent course (P < .001), with an initial increase at 3 months but with a subsequent trend toward reduction (Figure 5B). Considering microbial stability by comparing the Bray-Curtis dissimilarity between each sample and its baseline, we found no significant difference in degree of variability over time among CD patients having an indolent or severe disease course (P = .11). In patients with UC the variability in composition over time was significantly higher in those with severe disease than in those with an indolent disease trajectory (P = .009, Figure 5C).
Gut microbiome in patients with low f-calprotectin
To test whether microbiome changes were driven by inflammation, we adjusted the longitudinal differential abundance models for f-calprotectin by including it as a covariate. In CD patients, Eubacterium hallii, Anaerostipes, Blautia, and Adlercreutzia remained significantly altered over the first year. In UC patients, 19 of 27 taxa retained significance after adjustment (Figure S7).
Furthermore, we stratified our population to samples with f-calprotectin levels below 250 µg/g. The alpha diversity was reduced in both CD patients (P = .002) and UC patients (P = .011) compared to symptomatic controls, regardless of timepoint. Among taxa, only Oscillospiraceae UCG-003 differed between the low-calprotectin CD patients and controls, whereas 17 taxa were differentially abundant in the UC patients (Figure 5D).
Discussion
In the present study, we report overall increases in microbial diversity over the first year after IBD diagnosis that parallelled significant reductions in f-calprotectin and symptom scores. Multiple bacteria taxa increased over time, many of which were negatively associated with f-calprotectin and mPRO2. However, the correlations between microbiome diversity, f-calprotectin, and mPRO2 were only weak to moderate, and changes in microbiome composition did not explain changes in f-calprotectin or mPRO2. Initiation of therapy with 5-ASA was associated with unique microbiome changes that did not overlap with the overall changes in taxa over time. Furthermore, in the subgroup of UC patients with a severe disease course, we observed less stable microbiome composition over time compared to patients with an indolent disease course. Finally, in a comparison of IBD patients with low f-calprotectin and symptomatic controls, the diagnosis-specific signal remained in UC patients, while it almost disappeared in CD patients. Taken together, these findings indicate a distinct correlation between gut inflammation, symptoms, and microbiome composition. However, the correlations were not strong, and we detected no covariation, suggesting that the gut microbiome may have a disease-driving impact that is independent of inflammation and inflammation-driven symptoms.
The symptoms and f-calprotectin levels were reduced over time as the participants joined the study and started therapy. Especially for f-calprotectin, the largest reduction took place from diagnosis to 3 months, which is in line with data from other studies.33,34 This finding is also in line with the observation that the majority of the participants in the investigated IBSEN III cohort reported a disease course best described as highly active initially, followed by remission or mild disease.30,31
Patients with IBD had a more variable microbiome composition than healthy patients, and this finding is to a large degree influenced by location of disease, surgery, and medications.4 Over the first year of investigation of the newly diagnosed patients with IBD, we observed an increasing diversity over time, which could be interpreted as a development toward a “less IBD-like” microbiome. During the first 12 months we also observed more variability (measured by Bray-Curtis dissimilarity) in the IBD microbiome compared to symptomatic controls. Independent of diagnosis, participants with stable high f-calprotectin (above 250 µg/g) throughout the year had higher degree of intra-individual variation in their microbiome composition than those with low f-calprotectin. Through cross-sectional analysis it has been shown that metagenomic variation and lower alpha diversity correlates strongly to f-calprotectin,35 whereas longitudinal analyses revealed minimal effect on the microbiome dynamics of f-calprotectin.4 On the other hand, it was reported that the UC microbiome is highly stable within individuals across disease stages, activity levels, and treatment escalation both at diagnosis and in patients with established IBD, describing the gut microbiome composition with the Bray-Curtis dissimilarity index by characterizing gut microbiome composition.34 Another study, using unweighted UniFrac dissimilarity reported that people with UC in long term remission (defined by Mayo, SCCAI and histology) presents a microbiome profile similar to healthy controls.36 However, the within-patient fluctuations (measured by Bray-Curtis dissimilarity) were shown to differ by disease activity9 and to be larger in IBD than in healthy individuals.4,9 We found a significant change in the IBD gut bacterial composition over time, primarily characterized by an increase in bacteria considered beneficial for gut health. In contrast, a paediatric cohort study (IBD n = 104) that investigated the microbiome from treatment-naive patients to 6 weeks post-treatment reported no significant increase in diversity, though they did observe a decrease in the abundance of Escherichia coli and Proteobacteria among other taxonomic changes.37 In UC, we observed a decrease in the facultatively anaerobic genera Escherichia-Shigella and Haemophilus (both within the Gammaproteobacteria). These organisms can thrive in both oxygen-rich and -poor environments and often outcompete obligate anaerobes. As demonstrated by Winter et al. nitrate produced during the host inflammatory response can be utilized by bacteria such as E. coli, giving them a competitive advantage over obligate anaerobes and explaining their enrichment during inflammation.38 Notably, the increasing taxa in our cohort were predominantly from the Clostridia class and the order Coriobacteriales, both of which are known to contribute to gut homeostasis and are typical members of a healthy microbiome.39,40 The longitudinal changes we observed thus suggest a shift toward a more homeostatic gut environment, and we show that there are changes in the microbiome independent of what can be explained by decrease in f-calprotectin.
Both the symptom score and f-calprotectin had a weak or moderate correlation to gut microbiome composition, and a higher diversity was associated with lower f-calprotectin and symptom burden. However, there was no covariation between symptoms and f-calprotectin and the gut microbiome during the first year. The cross-sectional associations between symptoms/f-calprotectin and gut microbiome are very much in line with previous studies on both symptoms41 and f-calprotectin,35 for which IBD with f-calprotectin above 250 µg/g is associated with reduced microbiome diversity compared to those with IBD and lower f-calprotectin.9 In CD, it has been reported that dysbiosis was associated with clinical score and symptoms (HBI), independent of mucosal inflammation (f-calprotectin).41 Others investigators who have used different definitions of active IBD, such as endoscopic score (Mayo), clinical score (CDAI/HBI), or symptom score (SCCAI), have shown that the gut microbiome in IBD with low f-calprotectin was less different from that in symptomatic controls and reported differences in the fecal microbiome by the different definitions of disease activity.36,41–43
Despite these cross-sectional correlations, changes in microbial diversity did not imply a change in f-calprotectin or change in symptom score by the longitudinal analysis. This finding is in line with those by Halfvarson et al. who reported that the degree of inflammation was did not explain the difference in microbiome composition between healthy participants and patients with IBD.4 Overall, these findings suggests that the processes related to symptoms, inflammation, and microbiome composition are in part independent. Importantly, it is well known that patient-reported symptoms do not necessarily correlate strongly with mucosal inflammation in IBD.15,44
Direct effects of drug therapy may represent another factor changing the gut microbiome.45–47 Among patients with IBD, medication has been shown to explain more variation in microbial composition than IBD subtype, and changes in medication regime may affect the volatility of the microbiome.4,48 The IBD treatment regimens are individualized and vary greatly, and our study design did not allow us to fully explore this interplay. However, we did observe microbiome-specific differences before and after 5-ASA initiation. The complete mechanistic effect of 5-ASA therapy is unknown, but the medication has been shown to alter the conditions in the intestine to a less oxygenic environment, hence improving the conditions for obligative anaerobic bacteria.49 We observed a decrease in Akkermansia, which is in line with studies investigating treated patients with UC.12,36 Eckenberg et al. reported that the health-associated bacteria were affected by medications to a larger extent than the disease-associated bacteria,48 which is in line with our findings.
This study longitudinally assessed gut microbiome shifts during the first year following an IBD diagnosis, leveraging the large cohort of newly diagnosed patients from the IBSEN III study. A major strength of this study is the availability of repeated fecal samples collected from a substantial proportion of these participants. Furthermore, we have used robust consensus-based differential abundance analysis tools, as recommended by Nearing et al.,50 while adjusting for confounders and repeated sampling. The stool samples for microbiome profiling and f-calprotectin quantification were collected simultaneously, whereas the patient-reported data may be from a different previous 24 hours than the stool samples, which may have affected the investigated relationship between the sources. Our study had several limitations. As our study population ranges from treatment-naive individuals with active disease to newly diagnosed patients undergoing treatment and those in stable remission, this adds complexity to our findings, while the use of a slightly modified PRO2 score limits comparisons to other studies. Another limitation is that we did not adjust for important environmental variables known to influence the microbiome composition, such as diet or medications, and the fraction of participants delivering fecal samples at 3 and 6 months was also low. Microbiome profiling was conducted on the taxonomic genus level, a limitation with the 16S rRNA amplicon sequencing. Furthermore, 16S rRNA amplicon sequencing does not allow for functional analyses. Future studies should investigate the longitudinal functional implications of microbiome changes within specific IBD phenotypes.
Conclusions
In patients during the first year after an IBD diagnosis, we observed significant reductions in f-calprotectin levels and symptom burden in parallel with gut microbiome alterations. Although a higher alpha diversity was associated with lower levels of f-calprotectin and symptom burden, a change in diversity did not predict a change in the mPRO2 score or f-calprotectin, suggesting a disease-driving impact of the gut microbiome independent of inflammation and symptoms.
Supplementary Material
Acknowledgments
All authors would like to thank the study nurses and local study personal for persistent data collection into the IBSEN III study, thank the biobank at Ahus and Lovisenberg Diaconal Hospital for excellent material handling, thank Alexandra Götz, Beate Vestad and Hanne Guldsten for expert laboratory support, and to thank Peder R. Braaland and the Hov group for input in the analysis. Thanks to the steering group of IBSEN III for enabling the study. Finally, the authors want to thank the participants of IBSEN III that made this research possible.
Contributor Information
Maria G Maseng, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Gastroenterology, Division of Medicine, Oslo University Hospital, 0450 Oslo, Norway; Bio-Me AS, 0349 Oslo, Norway.
Simen H Hansen, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Norwegian PSC Research Centre, Oslo University Hospital, 0372 Oslo, Norway; Division of Surgery, Inflammatory Diseases and Transplantation, Research Institute of Internal Medicine, Oslo University Hospital, 0450 Oslo, Norway.
Olle Grännö, Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, 701 82, Sweden.
Corinna Bang, Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, 24118 Kiel, Germany.
Charlotte Lund, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Gastroenterology, Division of Medicine, Oslo University Hospital, 0450 Oslo, Norway.
Gert Huppertz-Hauss, Department of Gastroenterology, Telemark Hospital Trust, Skien, 3710 Skien, Norway.
Gøri Perminow, Department of Pediatrics, Oslo University Hospital, Oslo, 0450 Oslo, Norway.
Jørgen Valeur, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Unger-Vetlesen Institute, Lovisenberg Diaconal Hospital, 0456 Oslo, Norway.
May-Bente Bengtson, Department of Gastroenterology, Vestfold Hospital Trust, 3103 Tønsberg, Norway.
Randi Opheim, Department of Gastroenterology, Division of Medicine, Oslo University Hospital, 0450 Oslo, Norway; Department of Public Health, Institute of Health and Society, University of Oslo, 0371 Oslo, Norway.
Raziye Boyar, Department of Medicine, Diakonhjemmet Hospital, 0370 Oslo, Norway.
Svein O Frigstad, Department of Medicine, Bærum Hospital, Vestre Viken Hospital Trust, 1346 Gjettum, Norway.
Tone Bergene Aabrekk, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Gastroenterology, Tønsberg Hospital, Vestfold Hospital Trust, 3103 Tønsberg, Norway.
Trond Espen Detlie, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Gastroenterology, Akershus University Hospital, 1474 Lørenskog, Norway.
Vendel A Kristensen, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Gastroenterology, Division of Medicine, Oslo University Hospital, 0450 Oslo, Norway.
Vibeke Strande, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Unger-Vetlesen Institute, Lovisenberg Diaconal Hospital, 0456 Oslo, Norway; Department of Gastroenterology, Lovisenberg Diaconal Hospital, 0456 Oslo, Norway.
Øistein Hovde, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Internal Medicine, Gjøvik Hospital, Innlandet Hospital Trust, 2380 Brumunddal, Norway.
Øyvind Asak, Department of Medicine, Lillehammer Hospital, Innlandet Hospital Trust, 2609 Lillehammer, Norway.
Andre Franke, Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, 24118 Kiel, Germany.
Jonas Halfvarsson, Department of Gastroenterology, Faculty of Medicine and Health, Örebro University, 70182 Örebro, Sweden.
Marte L Høivik, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Gastroenterology, Division of Medicine, Oslo University Hospital, 0450 Oslo, Norway.
Johannes R Hov, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0371 Oslo, Norway; Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Norwegian PSC Research Centre, Oslo University Hospital, 0372 Oslo, Norway; Division of Surgery, Inflammatory Diseases and Transplantation, Research Institute of Internal Medicine, Oslo University Hospital, 0450 Oslo, Norway; Section of Gastroenterology, Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital, 0372 Oslo, Norway.
Author contributions
Study conception and design: J.R.H. and M.L.H. Inclusion of study participants and clinical data: C.L., G.H., G.P., J.V., M.L.H., M.B., R.O., R.B, S.O.F., T.B.A., T.E.D., V.S., V.A.K., Ø.H., and Ø.A. Laboratory procedures: M.G.M., S.H.H., C.B., A.F. Data handling: M.G.M., S.H.H., O.G. Statistical analyses: M.G.M. Study supervision: J.R.H., and M.L.H. Interpretation of data and draft of the manuscript: M.G.M., J.H., J.R.H., and M.L.H. Critical revision of the manuscript for important intellectual content: All authors. Approval of the final manuscript: All authors.
Supplementary material
Supplementary data are available at Inflammatory Bowel Diseases online.
Funding
The IBSEN III study was entirely initiated by the investigators and received financial support from Takeda Pharmaceuticals, Pfizer, Ferring Pharmaceuticals, Tillotts Pharma, Foundation Dam, and the Norwegian South-Eastern Health Authorities. The work was supported by the NordForsk [90569 to J.H.], Vinnova [2019-01185 to JH] and the Research Council of Norway (2988039 to MLH). SHH and JRH were funded by the Regional Health Authorities South-Eastern Norway (No: 2020066). Extraction of stool samples for microbiome sequencing at IKMB received infrastructure support from the DFG Excellence Cluster 2167 “Precision Medicine in Chronic Inflammation” (PMI) and the DFG Research Unit 5042 “miTarget”. MGM funded in part by Research Council of Norway (no: 327634) and in part by Bio-Me AS.
Conflicts of interest
M.G.M. holds shares in Bio-Me AS. V.S. was sponsored by funds from Takeda. S.O.F. reports personal fees from Takeda, Galapagos, Jansen-Cilag, Abbvie, Pharmacosmos, Norgine and Bristol-Myers-Squibb. J.R.H. received a research grant from Biogen and speaker honoraria from Roche, Novartis, Amgen, and has been a consultant for Novartis and Orkla Health, all unrelated to the present work. M.L.H. received investigator-initiated research grants from Takeda, Pfizer, Tillotts, Ferring and Janssen, speaker honoraria from Takeda, Tillotts, Ferring, AbbVie, Pfizer, Galapagos, MSD and Meda and participated in advisory boards for Takeda, Galapagos, MSD, Lilly, Janssen, Pfizer and AbbVie. J.H. has received consulting and/or advisory board fees from: AbbVie, Alfasigma, Aqilion, Bristol Myers Squibb, Celgene, Celltrion, Eli Lilly, Ferring, Galapagos, Gilead, Hospira, Index Pharma, Janssen, Johnson & Johnson, MEDA, Medivir, Medtronic, Merck, Merck Sharp & Dohme, Novartis, Pfizer, Prometheus Laboratories Inc., Sandoz, Shire, STADA, Takeda, Thermo Fisher Scientific, Tillotts Pharma, Vifor Pharma, UCB; and speaker’s fees from: AbbVie, Alfasigma, Bristol Myers Squibb, Celgene, Eli Lilly, Ferring, Galapagos, Gilead, Hospira, Janssen, Johnson & Johnson, Merck Sharp & Dohme, Novartis, Pfizer, Shire, Takeda, Thermo Fisher Scientific, Tillotts Pharma; and research grant support from Janssen, Merck Sharp & Dohme and Takeda. T.E.D. has served as a speaker, consultant or advisory board member for AbbVie, Ferring, Johnson&Johnson, Pfizer, Pharmacosmos, Takeda, Tillotts, and Vifor Pharma. He has received unrestricted research grants from AbbVie and Pharmacosmos. The remaining authors disclose no conflicts.
Data availability
Available upon reasonable request.
Ethical considerations
The IBSEN III study is approved by the regional committee for medical and health research ethics (Ref 2015/946-3).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Available upon reasonable request.