To Örebro University

OBJECTIVES: Diagnostic delay is common in pediatric inflammatory bowel disease (PIBD), and fecal calprotectin (FCP) is often limited by challenges with sample collection. Therefore, we aimed to identify and validate a blood-based diagnostic protein signature of PIBD.

METHODS: Proteins were analyzed using proximity extension assay in plasma samples from treatment-naïve pediatric patients in a Swedish inception cohort referred for suspected IBD and validated in an independent Norwegian population-based pediatric inception cohort. Diagnostic performance was estimated by the area under the curve (AUC) with 95% confidence intervals (CIs).

RESULTS: The discovery cohort included 58 patients with PIBD and 36 symptomatic controls without evidence of IBD, while the validation cohort consisted of 79 patients with PIBD and 37 symptomatic controls. In total, 154 proteins were examined. Univariable analyses identified 26 differentially regulated proteins for PIBD versus symptomatic controls in the discovery cohort (q < 0.05), whereas 29 proteins were differentially regulated in the validation cohort. Using regularized logistic regression, we identified a diagnostic model of 31 proteins that differentiated PIBD from symptomatic controls in the discovery cohort (AUC = 0.83; 95% CI: 0.74-0.90). The protein signature was further reduced to a clinically relevant biomarker consisting of high-sensitivity C-reactive protein (hsCRP) and seven other proteins with diagnostic capacity (AUC = 0.85, 95% CI: 0.78-0.92) outperforming hsCRP in the validation cohort (p = 0.006).

CONCLUSIONS: We identified and validated a blood-based protein signature for PIBD with superior diagnostic performance compared to hsCRP. Given the challenges of fecal sample collection, further assay development may enable integration of these biomarkers into diagnostic pathways for PIBD.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02727959.

Background: The disease course of patients with newly diagnosed ulcerative colitis (UC) is highly uncertain, and there is a lack of validated prognostic biomarkers that could aid in clinical decision making.

Methods: Newly diagnosed, mainly treatment naïve patients with UC from three large inception cohorts were used to develop and validate a serum proteomics-based risk score for prognostication of disease course during the first year from diagnosis. In the discovery cohort (n = 161) and validation cohort 1 (n = 186) an aggressive disease course was defined as the presence of any IBD-related surgery, hospital admission for active disease, treatment refractoriness towards targeted therapies (i.e. biologics, JAK-inhibitors or S1P receptor modulators), and >2 courses or high cumulative doses of systemic corticosteroids. In validation cohort 2 (n = 120), an aggressive disease course was defined as the need for a biologic, ciclosporin or surgery. 178 proteins were measured on Olink platforms, and a machine learning algorithm (i.e. regularised regression) was applied to the discovery cohort to develop an UC risk score comprising 23 proteins. The performance of the UC risk score was assessed in the two external validation cohorts. For validation cohort 2, a condensed version of the UC risk score was applied, as only 14 of the original 23 proteins were available. Cox regression estimated hazard ratios (HR) for the association between the UC risk score at diagnosis, time to escalation to targeted therapy (validation cohort I) and time to the defining episode of an aggressive disease course (validation cohort II).

Results: Based on univariate analyses, we identified 59 proteins associated with an aggressive disease course in the discovery cohort (PFDR <0.10; Figure 1A). Twenty could be validated in validation cohort 1, and nine remained in validation cohort 2 (PFDR <0.10; Figure 1B-C).

In the discovery cohort, the machine learning model showed a high predictive capacity, with an area under the curve (AUC) of 0.81 (Figure 1D) and was numerically superior compared with a clinical model comprising sex, age and CRP (AUC = 0.72). Next, the performance of the UC risk score was confirmed in the two external validation cohorts, displaying AUC:s of 0.77 (Figure 1E-F). Patients with a higher UC risk score at diagnosis had increased risk of initiating targeted therapy (HR 4.26, 95% CI 1.91–9.49; Figure 2A). The HR for having an aggressive disease course was 9.12 (95% CI 3.04–27.3; Figure 2B).

Conclusion: We develop a UC risk score for prognostication of disease course and confirmed its high predictive performance by external validation in two independent cohorts. The risk score is a promising tool for quantifying the risk of having an aggressive disease course in UC.

Background: The clinical course of inflammatory bowel disease (IBD) is highly heterogeneous. Previously we identified a protein-based prognostic signature in serum using the proximity extension assay (PEA). To enable clinical translation, further assay development is required, including the establishment of standard curves and clinically actionable cut-offs. Therefore, we aimed to develop, optimise and validate a serum-based assay that quantifies absolute protein concentrations at diagnosis to identify individuals at higher risk of an aggressive course of IBD.

Methods: Proteins were selected from the commercially available Olink inflammation and oncology II panels as well as from five custom-made multiplex panels comprising 460 proteins based on genes in 163 IBD-risk loci (the IBD Character project1). Serum from 71 newly diagnosed IBD patients (Crohn’s disease, n = 29; ulcerative colitis, n = 42) (SIC-IBD) was analysed. Proteins associated with an aggressive course: I) need for biologics, ciclosporin, or surgery for flare after initial induction or II) hospitalisation, refractoriness to targeted therapy, excessive corticosteroid use or surgery within one year, were identified using penalised logistic regression. From these proteins a focused prognostic panel was developed and analytically optimized for absolute quantification. Model performance was evaluated by nested cross-validation (area under the curve [AUC]) and externally validated in 329 treatment-naïve IBD patients from the population-based IBSEN III cohort. Time to the composite outcome was analysed by estimating hazard rations (HR) using Cox regression.

Results: Thirteen proteins (CCL19, XPNPEP2, TGFα, VGFA, DLL1, PSGL1, OSM, CSF1, FURIN, IL-18, EpCAM, and TNFRSF9) met the pre-defined criteria and formed the final prognostic signature. In the discovery cohort, the model achieved an AUC of 0.76, with no interaction with age, sex or IBD subtype. External validation in the IBSEN III cohort yielded an AUC of 0.66 (95%CI 0.57-0.75), sensitivity 49% and specificity 70% at the discovery cohort derived optimal cut-off. Among patients classified as high risk, compared to low risk, the HR for the composite endpoint of aggressive disease course was 1.81 (P = 0.0002, Figure 1).

Conclusion: We developed and validated the first PEA-based serum panel enabling absolute quantitation of prognostic proteins for clinical use. At diagnosis, IBD-patients classified as high risk based on the 13-protein signature had an 81% higher relative risk of an aggressive disease course, supporting further evaluation.

Effective early warning systems for aquatic contamination require monitoring strategies capable of detecting subtle, long-term shifts in mixture-driven biological activity. Suspended particulate matter (SPM) serves as a carrier and reservoir for complex contaminant mixtures, facilitating their transport and persistence in aquatic systems, yet systematic toxicological time series for archived SPM remain scarce. Regulatory monitoring predominantly targets Priority Substances and River Basin Specific Pollutants, leaving the temporal trends of particle-associated mixture toxicity largely unresolved. Leveraging 18 years (2005–2022) of cryogenically archived annual SPM composites from the Rhine River, we conducted a spatiotemporal effect-based assessment integrating receptor-mediated effects, oxidative stress analysis and untargeted Cell Painting phenomics. This integrated toolbox enabled evaluation of pathway-specific responses and multi-compartment cellular perturbations associated with particle-bound contaminant mixtures. Polar SPM-associated chemicals elicited oxidative stress response and caused endocrine disruption through estrogen receptor α (ERα) activation and androgen receptor inhibition (anti-AR). Trend analysis showed spatiotemporal variation along the river, with statistically increasing trends of oxidative stress and anti-AR activity over time at Koblenz, driven by polar chemicals. Both polar and non-polar SPM extracts activated the aryl hydrocarbon receptor (AhR), indicating presence of compounds capable of triggering xenobiotic response pathways. Several subcellular compartments were affected, with mitochondrial features being among the most affected. These findings demonstrate that SPM-associated chemicals elicit diverse toxicological effects by acting on several receptors and impacting diverse cellular structures. Combining targeted and phenomics-based effect approaches provided comprehensive mechanistic insights and valuable information to support the early warning systems for chemical contamination in aquatic environments.

Shifting to a plant-based diet naturally alters protein source choices. In many countries, protein from yellow pea is widely used as a main ingredient in meat alternatives. Still, its biological effects, especially regarding gastrointestinal health, remain incompletely understood. The aim of our study was to investigate how a weekly increase in the intake of a well-characterized pea protein isolate affects surrogate markers of health, fecal short-chain fatty acids and gut microbiota composition in healthy individuals. Male and female adults (N = 29) participated in this exploratory intervention study. A 4-week pre-intervention period for questionnaires and fecal samples collection was followed by a 4-week supplementation. Participants consumed isolated pea protein in weekly increasing amounts, starting from 0.25 g per kg body mass per day in week 5 to 1.00 g per kg body mass per day in week 8. Questionnaire data, fecal samples as well as fasting blood and 24 h urine samples were collected weekly. Data from biological samples and questionnaires confirmed a healthy study population and compliance. Fecal calprotectin levels significantly increased only in a subset of participants, which was accompanied by higher fecal water cytotoxicity in vitro. Short-chain fatty acids mainly rose in those subjects with stable calprotectin levels. Relative abundances of Limosilactobacillus frumenti, Odoribacter splanchnicus and Lactobacillus crispatus increased significantly in the total population during the intervention while the relative abundance of Bifidobacterium longum and Bifidobacterium catenulatum decreased. Our results indicate that an increased intake of pea protein isolate affects the growth of certain beneficial bacterial strains and differentially influences markers related to gut inflammation in healthy individuals.

Background: Minimally invasive biomarkers that can reliably distinguish adults with inflammatory bowel disease (IBD) from symptomatic controls are still lacking. We investigated whether serum lipidomics can provide a reproducible diagnostic signature for IBD and how such a signature performs in relation to relevant inflammatory markers.

Methods: Untargeted lipidomic profiling was conducted on serum from the Swedish SIC-IBD inception cohort, comprising adults referred for suspected IBD, symptomatic controls, as well as healthy controls. Candidate diagnostic lipids were identified using supervised machine-learning models in the discovery cohort and then evaluated in the independent, population-based Norwegian IBSEN III inception cohort of treatment-naïve adults. We examined the diagnostic performance of the lipidomic signature in combination with high-sensitivity C-reactive protein (hs-CRP) and fecal calprotectin (FCP).

Results: The discovery cohort included 96 IBD patients, 66 non-IBD symptomatic controls, and 48 healthy controls, whereas the validation cohort comprised 349 patients with IBD and 198 symptomatic controls (Table 1). In the discovery cohort, a serum signature of hs-CRP and the two top lipids, lactosyl ceramide (d18:1/16:0) and phosphatidylcholine (O-44:5), demonstrated a high diagnostic performance (area under the curve [AUC] 0.80, 95% CI 0.73-0.87) compared with hs-CRP alone (AUC 0.70, 95% CI 0.63-0.79). When applied to the validation cohort, hs-CRP combined with the serum lipid signature significantly enhanced discrimination between IBD and symptomatic controls (AUC 0.79, 95% CI 0.75-0.83) compared with hs-CRP alone (AUC 0.68, 95% CI 0.64-0.73; P < 0.0001). As illustrated in Figure 1, among patients with available stool samples, combining FCP with hs-CRP and the lipid signature numerically improved diagnostic accuracy (AUC 0.89, 95% CI 0.86-0.92) compared with FCP alone (AUC 0.86, 95% CI 0.83–0.90).

Conclusion: We identified and externally validated a serum lipidomic signature that improves the diagnostic prediction of IBD when combined with hs-CRP. Although the blood-based model of hs-CRP, lactosyl ceramide (d18:1/16:0), and phosphatidylcholine (O-44:5) did not outperform FCP alone, it approached its diagnostic performance and further enhanced accuracy when integrated with FCP. These findings support added value these lipids and may offer insights into lipid-related pathways underlying IBD pathogenesis.

Background: Substantial variation exists in early inflammatory bowel disease (IBD) disease management. Biomarkers predicting disease trajectories may enable personalised treatment. The NORDTREAT strategy-trial was designed to compare “top-down” treatment with standard clinical care in patients at increased risk of an aggressive IBD course, as defined by the prognostic NORDTREAT protein signature.

Methods: NORDTREAT (NORdic inflammatory bowel disease TREATment; NCT05180175) was a randomised, multicentre, biomarker-strategy design, open-label, controlled trial conducted across 15 Nordic hospitals. Following screening, eligible adult with newly diagnosed IBD were randomised (1:1) to either access or non-access to the protein signature. In the access arm, patients displaying a high-risk profile received “top-down” (anti-TNF +/- immunomodulator) treatment, whereas low-risk patients were excluded. The non-access arm received standard care with stepwise treatment escalation at the investigator’s discretion (“clinical management”). The primary endpoint was corticosteroid-free clinical and endoscopic remission at week 52, with surgery considered treatment failure. Secondary endpoints included endoscopic remission, cumulative corticosteroid exposure, and serious adverse events. Analyses were performed in the intention to treat population.

Results: 313 IBD patients (CD, n = 133 and UC/IBD-unclassified, n = 180) were randomised between February 2022 and March 2024. Median time from diagnosis to enrolment was 7 days. Among access-arm patients (n = 157), 24 (15%) had a high-risk profile. In the non-access arm, 29/156 (19%) patients were found after trial completion to have had a high-risk profile at baseline and of these 16 (55%) received advanced therapy after a median period of 15 days. The primary endpoint was achieved in 10/24 (42%) “top-down” and 8/29 (27%) “clinical management” patients (absolute difference 15%; 95%CI -11-41, p = 0.26). Endoscopic remission rates at week 52 were comparable (53% vs 53%, p = 0.99). Median cumulative cortisone equivalents was 1232 mg in top-down vs 1651 mg in standard care, p = 0.07). Table 1 provides information on serious adverse events. Post-hoc analyses showed numerically higher rates of achieving the primary endpoint in CD, 50% vs 11% (p = 0.09) UC, whereas rates were comparable in UC 36% vs 35% (p = 0.97).

Conclusion: A biomarker-guided “top-down” approach did not increase corticosteroid-free clinical and endoscopic remission at week 52 compared with standard care. However, a possible benefit was seen in CD, suggesting that early “top-down” could improve outcomes in this subgroup of patients even in the era of widespread use of advanced therapies.

Background: IgG autoantibodies against integrin αvβ6 have been associated with preclinical ulcerative colitis (UC),1,2 but data on their emergence and validation are still scarce. Therefore, we aimed to establish and validate a cutoff of anti-αvβ6 for predicting future UC across two independent preclinical cohorts. Additionally, we examined their development in early-life, assessed the influence of genetic-, environmental risk factors and inflammation on anti-αvβ6.

Methods: Within large population-based cohorts (n > 180,000), prediagnostic blood samples from individuals who later in life were diagnosed with UC were compared with age- and sex-matched controls who remained UC-free during follow-up (Figure 1). To explore early-life presence and genetic and shared environmental influences, we analysed serum from ABIS, a population-based birth cohort (n = 17,055) and the Swedish Twin Registry (n > 97,000). Anti-αvβ6 levels were measured using an in-house automated fluorescence enzyme immunoassay. Relative estimates of proteins were measured on the Olink platform3.

Results: In the discovery cohort (Table 1), anti-αvβ6 antibodies differentiated preclinical UC from controls (area under the curve [AUC]=0.68). Applying the model to the validation cohort yielded an AUC of 0.79, with a sensitivity of 46% and a specificity of 93%, at the optimal cut-off (109 UA/l) derived from the discovery cohort. Predictive capacity increased closer to diagnosis but remained significant for samples collected ≥20 years before UC diagnosis (AUC=0.67). In the birth cohort, anti-αvβ6 levels at birth appeared elevated in children who later developed UC (OR = 2.13; 95%CI, 0.84-5.38), likely reflecting transplacental antibody transfer, but also after 5 years (OR = 2.58; 95%CI, 0.83-8.03) and 8 years (OR = 2.50; 95%CI, 0.30-21.00). When assessing the level of agreement within twin pairs, low intra-class correlation coefficients were observed irrespective of zygosity and disease status, suggesting limited influence of genetic and shared environmental exposures. However, anti-αvβ6 levels differed across smoking categories in the preclinical cohorts, but inclusion of smoking status added little to model performance (AUC=0.82). By calculating a composite proteomic score, a progressive increase in the score was observed from the lowest to highest quartile of αvβ6 levels in preclinical UC, whereas most controls were found in the lowest quartiles for both the proteomic score and autoantibody levels.

Conclusion: Anti-αvβ6 autoantibodies represent a reproducible biomarker for preclinical UC. Their early-life presence, environmental modulation, and correlation with immuno-inflammatory proteins highlight their potential utility for risk stratification.

Background: Paediatric inflammatory bowel disease (IBD), comprising the subtypes Crohn’s disease (CD) and ulcerative colitis (UC), is believed to result from an interplay between genetic predisposition and environmental exposures. Examining exposome metabolome may help to identify environmental exposures, including host-derived metabolites, with potential relevance in paediatric IBD.

Methods: Relative concentrations of metabolites were measured in serum samples from treatment-naïve patients with IBD (N = 80) and symptomatic, non-IBD controls (N = 37) included in the IBSENIII cohort (Table 1). Ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS) was performed. Authentic standards or the Human Metabolome Database were used for annotation. Associations with IBD, with CD (N = 53), and with UC including IBD-unclassified (N = 27), were assessed using logistic regression while adjusting for sex, age, and BMI. In addition, correlations with serum proteins (proximity extension assay technology) were examined. A false discovery rate (PFDR) threshold of < 0.1 was applied.

Results: We observed positive association of aryl sulfate (phenol sulfate; PFDR=0.03) with IBD versus symptomatic controls (Figure 1). Within the IBD population, aryl sulfate was positively correlated with several serum proteins, including CCL20 (r = 0.48, PFDR=0.004), MCP-3 (r = 0.47, PFDR=0.004), and TNF-α (r = 0.45, PFDR=0.001). We observed inverse associations of four perfluoroalkyl substances (PFAS); (linear-perfluorooctane sulfonate (PFOS-L), branched-perfluorohexanesulfonate (PFHxS-B), perfluorooctanoate (PFOA), and branched-perfluorooctane sulfonate (PFOS-B)) with IBD, UC, or both. In IBD, PFOA, PFOS-L, PFHxS-B positively correlated with Delta/Notch-like epidermal growth factor (EGF)-related receptor (DNER) protein levels. The secondary bile acid 6-ketholithocholic acid was inversely associated with UC, while the primary bile acids glycochenodeoxycholic acid-3-O-sulfate, cholic acid and glycocholic acid were positively associated with UC (all PFDR<0.1).

Conclusion: Several metabolites including aryl sulfate were associated with paediatric IBD and correlated with inflammation-related proteins. These findings confirm recent results from preclinical Crohn’s disease1 and may point towards a role of aryl sulfate at IBD diagnosis.

Formation of the NLRP3 inflammasome occurs in response to a wide range of triggers and leads to pyroptosis and release of IL-1 beta and IL-18. This has widely been considered an "all or nothing" response leading to similar cellular outcomes each time, once the inflammasome has formed, a view that has been challenged over the years. This assumption has largely been based on endpoint measurements at the population level, producing metrics that fail to adequately capture dynamic responses. Herein, we utilize live-cell imaging combined with an algorithmic approach to track individual ASC-GFP specks over time, formed in response to the triggers ATP, monosodium urate and nigericin. Using this approach, we report trigger-dependent differences in speck lifetime. The use of the proposed algorithm requires a relatively small dataset, while still providing insights into NLRP3 inflammasome dynamics downstream of inflammasome activation at the single-cell level.