A blood-based prognostic biomarker in IBD

Composite outcomes in observational studies of Crohn's disease: a systematic review and meta-analysis

BACKGROUND

This systematic review and meta-analysis aims to assess composite and aggregate outcomes of observational studies in Crohn's disease and to evaluate whether the number and type of variables included affect the frequency of the outcome.

METHODS

MEDLINE [via PubMed], Scopus and Web of Science were searched to identify observational studies that enrolled patients with Crohn's disease and evaluated a composite or aggregate outcome. The proportion of patients achieving the outcome was determined and a random-effects meta-analysis was performed to evaluate how the frequency of each outcome varies according to the reporting of predefined variables.

RESULTS

From 10,257 identified records, 46 were included in the qualitative analysis and 38 in the meta-analysis. The frequency for composite and aggregate outcomes was 0.445 [95% confidence interval (CI): 0.389-0.501] and 0.140 (95% CI: 0.000-0.211), respectively. When comparing composite outcomes by number of included variables, the frequency was 0.271 (95% CI: 0.000-0.405) and 0.698 (95% CI: 0.651-0.746), for one and six variables, respectively. The frequency of the composite outcome varied according to the identity of the variables being reported. Specific pairs of predefined variables had a significant effect in the frequency of composite outcomes.

CONCLUSION

Composite outcomes with increasing number of predefined variables show an increase in frequency. Outcomes including variables such as 'Surgery' and 'Steroids' had higher frequencies when compared with the ones that did not include these variables. These results show that the frequency of composite outcomes is dependent on the number and type of variables being reported.

miR-374a-5p regulates inflammatory genes and monocyte function in patients with inflammatory bowel disease

MicroRNAs are critical regulators of gene expression controlling cellular processes including inflammation. We explored their role in the pathogenesis of inflammatory bowel disease (IBD) and identified reduced expression of miR-374a-5p in IBD monocytes that correlated with a module of up-regulated genes related to the inflammatory response. Key proinflammatory module genes, including for example TNFα, IL1A, IL6, and OSM, were inversely correlated with miR-374a-5p and were validated in vitro. In colonic biopsies, miR-374a-5p was again reduced in expression and inversely correlated with the same inflammatory module, and its levels predicted subsequent response to anti-TNF therapy. Increased miR-374a-5p expression was shown to control macrophage-driven inflammation by suppressing proinflammatory mediators and to reduce the capacity of monocytes to migrate and activate T cells. Our findings suggest that miR-374a-5p reduction is a central driver of inflammation in IBD, and its therapeutic supplementation could reduce monocyte-driven inflammation in IBD or other immune-mediated diseases.

A systematic review and functional bioinformatics analysis of genes associated with Crohn's disease identify more than 120 related genes

BACKGROUND

Crohn's disease is one of the two categories of inflammatory bowel diseases that affect the gastrointestinal tract. The heritability estimate has been reported to be 0.75. Several genes linked to Crohn's disease risk have been identified using a plethora of strategies such as linkage-based studies, candidate gene association studies, and lately through genome-wide association studies (GWAS). Nevertheless, to our knowledge, a compendium of all the genes that have been associated with CD is lacking.

METHODS

We conducted functional analyses of a gene set generated from a systematic review where genes potentially related to CD found in the literature were analyzed and classified depending on the genetic evidence reported and putative biological function. For this, we retrieved and analyzed 2496 abstracts comprising 1067 human genes plus 22 publications regarding 133 genes from GWAS Catalog. Then, each gene was curated and categorized according to the type of evidence associated with Crohn's disease.

RESULTS

We identified 126 genes associated with Crohn's disease risk by specific experiments. Additionally, 71 genes were recognized associated through GWAS alone, 18 to treatment response, 41 to disease complications, and 81 to related diseases. Bioinformatic analysis of the 126 genes supports their importance in Crohn's disease and highlights genes associated with specific aspects such as symptoms, drugs, and comorbidities. Importantly, most genes were not included in commercial genetic panels suggesting that Crohn's disease is genetically underdiagnosed.

CONCLUSIONS

We identified a total of 126 genes from PubMed and 71 from GWAS that showed evidence of association to diagnosis, 18 to treatment response, and 41 to disease complications in Crohn's disease. This prioritized gene catalog can be explored at http://victortrevino.bioinformatics.mx/CrohnDisease .

Time to Revisit Disease Classification in Inflammatory Bowel Disease: Is the Current Classification of Inflammatory Bowel Disease Good Enough for Optimal Clinical Management?

Inflammatory bowel disease (IBD), historically subdivided into Crohn's disease and ulcerative colitis, is a very heterogeneous condition. While the tendency in medicine is to try to reduce complexity, IBD is a disease that cannot justify a one-size-fits-all principle. Our current clinical classification tools are suboptimal and need further refinement to capture, at least in part, the variety of phenotypes encountered in daily clinical practice. Although these revised classification tools alone will not be sufficient and should be complemented by more detailed molecular subclassifications, optimized clinical phenotypes can contribute to improved trial designs, future translational research approaches, and better treatment outcomes. In the current review, we discuss key clinical features important in IBD disease heterogeneity, tackle limitations of the current classification systems, propose some potential improvements, and raise priorities for future research in this domain.

Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?

Artificial intelligence (AI) has arrived and it will directly impact how we assess, monitor, and manage inflammatory bowel disease (IBD). Advances in the machine learning methodologies that power AI have produced astounding results for replicating expert judgment and predicting clinical outcomes, particularly in the analysis of imaging. This review will cover general concepts for AI in IBD, with descriptions of common machine learning methods, including decision trees and neural networks. Applications of AI in IBD will cover recent achievements in endoscopic image interpretation and scoring, new capabilities for cross-sectional image analysis, natural language processing for automated understanding of clinical text, and progress in AI-powered clinical decision support tools. In addition to detailing current evidence supporting the capabilities of AI for replicating expert clinical judgment, speculative commentary on how AI may advance concepts of disease activity assessment, care pathways, and pathophysiologic mechanisms of IBD will be addressed.

How Do We Predict a Patient's Disease Course and Whether They Will Respond to Specific Treatments?

Gastroenterologists will be all too familiar with the difficult decisions that managing inflammatory bowel disease often presents. How aggressively should I treat this patient? Do I expect them to have a mild or aggressive form of disease? Do they need a biologic? If so, which one? And when should I start it? The reality is that the answers that would be right for one patient might be disastrous for another. The growing therapeutic armamentarium will only make these decisions more difficult, and yet, we have seen how other specialties have begun to use the molecular heterogeneity in their diseases to provide some answers. Here, we review the progress that has been made in predicting the future for any given patient with inflammatory bowel disease-whether that is the course of disease that they will experience or whether or not they will respond to, or indeed tolerate, a particular therapy.

Innovation in Gastroenterology-Can We Do Better?

The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird's eye view of gastroenterology's innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.

Innovation in Gastroenterology—Can We Do Better?

The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird’s eye view of gastroenterology’s innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.

Clinical Significance of Novel Neutrophil-Based Biomarkers in the Diagnosis and Prediction of Response to Infliximab Therapy in Crohn’s Disease

With the increasing incidence and prevalence, Crohn’s disease (CD) has become one of the most challenging diseases in both diagnosis and treatment of gastroenterology. Evaluation of the disease activity and mucosal healing guides clinical decisions regarding subsequent therapy for CD. In this study, we enrolled a total of 144 patients with CD and 239 healthy controls were enrolled. Clinical characteristics and laboratory parameters of enrolled subjects were retrieved from the electronic medical record database of our hospital. Serum cytokine levels were measured by enzyme-linked immunosorbent assay (ELISA). Mucosa expression levels of inflammatory agents were measured by quantitative RT-PCR (qRT-PCR). We identified two neutrophil-based indexes, the neutrophil-to-albumin ratio (NAR) and neutrophil-to-bilirubin ratio (NBR), both of which had not yet been explored in CD or UC. NAR and NBR were significantly increased in patients with CD compared to those in healthy controls, and both indexes showed significantly positive correlations with CD activity and inflammatory load. In note, NAR and NBR showed better performance than blood neutrophil percentage, serum albumin, or bilirubin alone in these scenarios. More importantly, both NAR and NBR discriminated CD patients who completely or partially responded to infliximab (IFX) induction therapy from those with primary non-response. Our observations suggest that NAR and NBR may serve as promising biomarkers in the diagnosis and prediction of response to IFX therapy in CD.

The effect of infliximab dose escalation in inflammatory bowel disease patients with antibodies to infliximab

BACKGROUND

Infliximab dose escalation (DE) can be used in inflammatory bowel disease patient; however, the long-term benefit remains unclear, especially in those with antibodies to infliximab (ATI). The aim was to assess the effect of DE in patients with ATI on drug level, clinical response and ATI status.

METHODS

All patients undergoing infliximab DE (a reduction in dose interval between infusions <8 weeks ± an increase in dose up to 10 mg/kg) at a referral centre between April 2016 and August 2019 were included.

RESULTS

Ninety-two patients were DE: 51 were men, 50 had CD and 63 were receiving immunosuppression. A total of 87 people received DE for a median of 44 weeks (range 4-176). Five stopped infliximab after 1 dose of DE: 2 for loss of response and 3 for infusion reaction. In patients with ATI ≤10 vs. >10 AU/mL, DE significantly increased drug levels: median infliximab levels of 1.4 and 0.9 at baseline, respectively, to 3.2 and 3.5 at week 24. After DE, 21/35 ATI-positive patients had a fall in ATI ≤10 AU/mL. At week 24 following DE 62/92 patients were in clinical remission. Duration of clinical remission was shorter in those with ATI >10 AU/mL (median 24 weeks, range 0-88) than in those with transient/ATI ≤10 AU/mL (median 36 weeks, range 0-126, P = 0.06).

CONCLUSIONS

A strategy of DE for selected patients receiving infliximab is associated with an increase in drug levels and reduced ATI positivity. This is associated with clinical remission in approximately 70% of patients at 6 months.

Mucosal Gene Transcript Signatures in Treatment Naïve Inflammatory Bowel Disease: A Comparative Analysis of Disease to Symptomatic and Healthy Controls in the European IBD-Character Cohort

Background

Studies of the mucosal transcriptomic landscape have given new insight into the pathogenesis of inflammatory bowel disease (IBD). Recently, the predictive biomarker potential of gene expression signatures has been explored. To further investigate the mucosal gene expression in IBD, we recruited a cohort of treatment naïve patients and compared them to both symptomatic and healthy controls.

Methods

Altogether, 323 subjects were included: Crohn’s disease (N = 75), ulcerative colitis (N = 87) and IBD unclassified (N = 3). Additionally, there were two control groups: symptomatic controls (N = 131) and healthy controls (N = 27). Mucosal biopsies were collected during ileocolonoscopy and gene expression in inflamed and non-inflamed mucosa was explored. Gene expression profiling was performed using Agilent G3 Human Gene Expression 860K v3 One-Color microarray. We recorded information about treatment escalation to anti-TNF agents or surgery, and anti-TNF response, to explore predictive opportunities of the mucosal transcriptome.

Results

Gene expression profiles in symptomatic controls in whom IBD had been excluded resembled that of IBD patients and diverged from that of healthy controls. In non-inflamed Crohn’s disease and ulcerative colitis, gene set enrichment analysis revealed dysregulation of pathways involved in basic cellular biological processes. Mitochondria-associated pathways were dysregulated both in non-inflamed and inflamed Crohn’s disease and ulcerative colitis (>2.6 normalized enrichment scores <−1.8). Gene expression signatures of Crohn’s disease and ulcerative colitis did not predict time for treatment escalation (p = 0.175). No significant association was found between gene expression signatures and anti-TNF response.

Conclusion

Non-inflamed samples are probably superior to inflamed samples when exploring gene expression signatures in IBD and might reveal underlying mechanisms central for disease initiation. The gene expression signatures of the control groups were related to if they were symptomatic or not, which may have important implications for future study designs.

Unmet needs in inflammatory bowel disease

Despite the recent developments in the diagnosis and management of inflammatory bowel diseases (IBD), patients still suffer from disabling bowel symptoms and significant disease complications and many questions remain to improve their care. IBD is a chronic disease, whose management could be divided into the five different stages of chronic diseases, ranging from the pre-treatment evaluation phase to the induction therapy, maintenance therapy, monitor and re-establishment of control and the cessation of the disease. Reconciling these phases with the current unmet needs in IBD could help tailor priorities for research. In this review, some of the unanswered questions in the management of both Crohn’s Disease and Ulcerative Colitis will be addressed, by following this paradigm of chronic diseases’ management.

Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort

Introduction

Smoking is known to affect whole-blood expression and methylation profiles. Although whole-genome methylation studies indicated that effects observed in blood may be driven by changes within leukocyte subtypes, these phenomena have not been explored using expression profiling.

Material and methods

This study reanalyzed data from the Correlated Expression and Disease Association Research (CEDAR) patient cohort recruited by Momozawa et al. (E-MTAB-6667). Data from gene expression profiling of immunomagnetically sorted CD4⁺, CD8⁺, CD14⁺, CD15⁺, and CD19⁺ cells were processed. Differential expression analyses were conducted in each immune cell type, followed by gene ontology analysis and supplementary investigations.

Results

Ninety-four differentially expressed genes were found (CD8⁺ n = 58, CD14⁺ n = 20, CD4⁺ n = 14, CD19⁺ n = 2). Two key smoking-related genes were overexpressed in specific cell types: LRRN3 (CD4⁺, CD8⁺) and MMP25 (CD8⁺, CD14⁺). In CD4⁺ cells smoking was associated with reduced expression of the NK cell receptor KLRB1, suggesting CD4⁺ subpopulation shifts and differences in interferon signaling (reduced IRF1 and IL18RAP in smokers). Key results and their integration with an immune protein-protein interaction network revealed that smoking influences integrins in CD8⁺ cells (ITGB7, ITGAL, ITGAM, ITGB2). C-type lectin CLEC4A was reduced in CD8⁺ cells and CLEC10A was increased in CD14⁺ cells from smokers; moreover, CLEC5A (CD8⁺), CLEC7A (CD8⁺) and CLEC9A (CD19⁺) were related to smoking in supplementary analyses. CD14⁺ cells from smokers exhibited overexpression of LDLR and the formyl peptide receptor FPR3.

Conclusions

Smoking specifically alters vital immune regulation genes in lymphocyte subtypes, especially CD4⁺, CD8⁺ and CD14⁺ cells.

Biomarker discovery for personalized therapy selection in inflammatory bowel diseases: Challenges and promises

The past decades witnessed a significant stride in deciphering the pathophysiology of inflammatory bowel disease, which further advanced drug development adding several new biologicals and small molecules to the arsenal of available therapies. Surprisingly, this wealth in therapeutic options did not yield the aspired high durable response rates. In addition, the increase in therapeutic availabilities ignited an increase in research toward biomarkers that could help assign therapies to patients with the highest probability of response. Luckily, major steps have been undertaken in this domain which resulted in the discovery of some interesting biomarkers that are still under validation. However, the pace in which this domain is progressing, the discordance between short-term endpoints in biomarker discovery studies and the ambition of the disease community in modifying disease course, and the uncertainties about the validity of discovered biomarkers highlight the need for a critical appraisal of research conduct in this domain. In this review, we shed light on areas of improvement in biomarker discovery studies that will help optimize the use of available therapies and break the current therapeutic ceiling.

Identification of diagnostic signatures in ulcerative colitis patients via bioinformatic analysis integrated with machine learning

Ulcerative colitis (UC) is an immune-related disorder with enhanced prevalence globally. Early diagnosis is critical for the effective treatment of UC. However, it still lacks specific diagnostic signatures. The aim of our study was to explore efficient signatures and construct the diagnostic model for UC. Microarray data of GSE87473 and GSE48634, which were obtained from tissue biopsy samples, were downloaded from the Gene Expression Omnibus (GEO), and differently expressed genes (DEGs), GO, and KEGG analyses were performed. We constructed the PPI network via STRING database. The immune infiltration of the samples was evaluated using CIBERSORT methods combined with the LM22 feature matrix. The logistic regression model was constructed, with the expression of selected genes as the predictor variable, and the UC occurrence as the responsive variable. As a result, a total of 126 DEGs between the UC patients and normal counterparts were identified. The GO and KEGG analysis revealed that multiple biological processes, such as antimicrobial humoral immune response mediated by antimicrobial peptide and IL-17 signaling pathway, were enriched. The infiltration of eight immune cell types (B cells naive, Dendritic.cells.activated, Macrophages.M0, Macrophages.M2, Mast.cells.resting, Neutrophils, Plasma.cells, and T.cells.follicular.helper) was significantly different between patients with UC and normal counterparts. The top 50 most significant DEGs were selected for the construction of the PPI network. The average AUC of the logistic regression model in the fivefold cross-validation was 0.8497 in the training set, GSE87473. The AUC of another independent verification set of GSE48634 from the GEO database was 0.7208. In conclusion, we identified potential hub genes, including REG3A, REG1A, DEFA6, REG1B, and DEFA5, which might be significantly associated with UC progression. The logistic regression model based on the five genes could reliably diagnose UC patients.

Composite outcomes in observational studies of ulcerative colitis: A systematic review and meta-analysis

Background

Ulcerative colitis (UC) has been the focus of numerous observational studies over the years and a common strategy employed in their design is the use of composite and aggregate outcomes.

Objective

This systematic review and meta‐analysis aims to identify composite and aggregate outcomes of observational studies in UC and to evaluate how the number and type of variables included and the length of follow‐up affect the frequency of patients that achieve these outcomes.

Methods

A systematic literature search was carried out using MEDLINE [via PubMed], Scopus, and Web of Science online databases. Observational studies that included UC patients and reported composite or aggregate outcomes were identified. A set of variables considered to be representative of progressive or disabling UC was defined, the proportion of patients attaining the outcomes was determined and a random‐effects meta‐analysis was performed by dividing the identified studies into subgroups according to different criteria of interest.

Results

A total of 10,264 records were identified in the systematic search, of which 33 were retained for qualitative analysis and 20 were included in the meta‐analysis. The mean frequency for composite outcomes was 0.363 [95% confidence interval (CI) 0.323‐0.403]. The frequency of composite outcome for the subgroup of studies that included the variable “Biologics” was significantly higher than for those in which this variable was not reported [0.410; 95% CI 0.364‐0.457 versus 0.298; 95% CI 0.232‐0.364; p = 0.006]. Composite outcomes were also more frequent as the follow‐up duration increased.

Conclusion

The frequency of composite outcomes in observational studies of UC is dependent on the specific identity of the variables being reported. Moreover, longer follow‐up periods are associated with higher frequencies of composite outcomes. The evidence provided here is useful for the design of future observational studies of UC that aim to maximize the frequency of patients that achieve composite outcomes.

Summarize the established knowledge on this subject

Observational studies have become a valuable source of information but also present a remarkable heterogeneity.

The inconsistency on the reported outcomes between individual studies and the potential for reporting biases has led to calls for the development of core outcome sets (COS).

What are the significant and/or new findings of this study?

This is the first systematic review and meta‐analysis of outcomes in observational studies of Ulcerative colitis (UC).

The frequency of composite outcomes in observational studies of UC is dependent on the specific identity of the variables being reported and on the follow‐up duration.

Reporting of the variable “Biologics” significantly increased the frequency of composite outcomes.

These findings may be useful for the design of future observational studies of UC.

Tumour necrosis factor inhibitors in inflammatory bowel disease: the story continues

In the 1990s, tumour necrosis factor-α inhibitor therapy ushered in the biologic therapy era for inflammatory bowel disease, leading to marked improvements in treatment options and patient outcomes. There are currently four tumour necrosis factor-α inhibitors approved as treatments for ulcerative colitis and/or Crohn's disease: infliximab, adalimumab, golimumab and certolizumab pegol. Despite the clear benefits of tumour necrosis factor-α inhibitors, a subset of patients with inflammatory bowel disease either do not respond, experience a loss of response after initial clinical improvement or report intolerance to anti-tumour necrosis factor-α therapy. Optimizing outcomes of these agents may be achieved through earlier intervention, the use of therapeutic drug monitoring and thoughtful switching within class. To complement these approaches, evolving predictive biomarkers may help inform and optimize clinical decision making by identifying patients who might potentially benefit from an alternative treatment strategy. This review will focus on the current use of tumour necrosis factor-α inhibitors in inflammatory bowel disease and the application of personalized medicine to improve future outcomes for all patients.

Precision Medicine in Pediatric Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) describes a heterogenous group of diseases characterized by chronic inflammation of the intestinal tract. The IBD subtypes, Crohn's disease, ulcerative colitis, and IBD-Unspecified, each have characteristic features, but heterogeneity remains even among the subtypes. There has been an explosion of new knowledge on the possible pathogenesis of IBD over the last 2 decades mirroring innovation and refinement in technology, particularly the generation of large scale - "-omic" data. This knowledge has fostered a veritable renaissance of novel diagnostics, prognostics, and therapeutics, with patients with IBD seeing hope bloom in the increasingly large armamentarium of IBD therapies. However, while there are increased numbers of therapies and more pathways being targeted, the number of medications for IBD is still finite and the efficacy has reached a plateau. Precision medicine (PM) is much needed to rationally select and optimize IBD therapies in the new reality of wider but still limited choice with a concurrent, increasingly fine resolution on the significance and utility of clinical, genetic, microbial, and proteomic characteristics that define individual patients. PM is a rapidly changing art, but this review will strive to detail the current state and future directions of PM in pediatric IBD.

Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department

BACKGROUND

Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, frequently surgical. However, repeated APCT's includes increased ionizing radiation exposure. Guidance regarding APCT performance is mostly clinical and empiric.

AIMS

We used a machine learning (ML) approach for predicting CAF on APCT in the ED.

METHODS

We performed a retrospective cohort study of patients with CD who presented to the ED and underwent APCT. CAF were defined as bowel obstruction, perforation, intra-abdominal abscess or complicated fistula. ML was used to predict the probability of having CAF on APCT, using routine clinical variables.

RESULTS

Of 101 admissions included, 44 (43.5%) had CAF on APCT. ML successfully identified patients at low (NPV 91.6%, CI-95% 90.6-92.5) and high (PPV 92.8%, CI-95%, 92.3-93.2) risk for CAF (AUROC = 0.774), using beats-per-minute, mean arterial pressure, neutrophil-to-lymphocyte ratio and sex. This allowed the construction of a risk stratification scheme according to patients' probability for CAF on APCT.

CONCLUSION

We present a novel artificial intelligence-based approach, utilizing readily available clinical variables to better select patients with CD in the ED for APCT. This might reduce the number of APCTs performed, avoiding related hazards while ensuring high-risk patients undergo APCT.

HLA-DQA1*05 Associates with Extensive Ulcerative Colitis at Diagnosis: An Observational Study in Children

The human leukocyte antigen (HLA) allele group HLA-DQA1*05 predisposes to ulcerative colitis (UC) and is associated with the development of antibodies against infliximab in patients with inflammatory bowel disease (IBD). Therefore, we hypothesized that the presence of HLA-DQA1*05 correlates with characteristics of pediatric IBD. Within a multi-center cohort in Poland, the phenotype at diagnosis and worst flare was established and HLA-DQA1*05 status was assessed enabling genotype-phenotype analyses. HLA-DQA1*05 was present in 221 (55.1%) out of 401 children with IBD (UC n = 188, Crohn's disease n = 213). In UC, the presence of HLA-DQA1*05 was moderately associated with a large extent of colonic inflammation at diagnosis (E4 55% more frequent in HLA-DQA1*05-positive patients, p = 0.012). PUCAI at diagnosis (p = 0.078) and the time from UC diagnosis to the first administration of biologic treatment (p = 0.054) did not differ depending on HLA-DQA1*05 status. The number of days of hospitalization for exacerbation was analyzed in 98 patients for whom sufficient follow-up was available and did not differ depending on HLA-DQA1*05 carriership (p = 0.066). HLA-DQA1*05 carriers with CD were less likely to present with both stenosing and penetrating disease (B2B3, p = 0.048) and to have active disease proximal to the ligament of Treitz (L4a) at the worst flare (p = 0.046). Future research focusing on explaining and preventing anti-TNF immunogenicity should take into account that ADA may develop not only as an isolated reaction to anti-TNF exposure but also as a consequence of intrinsic differences in the early course of UC.

Research-Based Product Innovation to Address Critical Unmet Needs of Patients with Inflammatory Bowel Diseases

Despite progress in recent decades, patients with inflammatory bowel diseases face many critical unmet needs, demonstrating the limitations of available treatment options. Addressing these unmet needs will require interventions targeting multiple aspects of inflammatory bowel disease pathology, including disease drivers that are not targeted by available therapies. The vast majority of late-stage investigational therapies also focus primarily on a narrow range of fundamental mechanisms. Thus, there is a pressing need to advance to clinical stage differentiated investigational therapies directly targeting a broader range of key mechanistic drivers of inflammatory bowel diseases. In addition, innovations are critically needed to enable treatments to be tailored to the specific underlying abnormal biological pathways of patients; interventions with improved safety profiles; biomarkers to develop prognostic, predictive, and monitoring tests; novel devices for nonpharmacological approaches such as minimally invasive monitoring; and digital health technologies. To address these needs, the Crohn's & Colitis Foundation launched IBD Ventures, a venture philanthropy-funding mechanism, and IBD Innovate®, an innovative, product-focused scientific conference. This special IBD Innovate® supplement is a collection of articles reflecting the diverse and exciting research and development that is currently ongoing in the inflammatory bowel disease field to deliver innovative and differentiated products addressing critical unmet needs of patients. Here, we highlight the pipeline of new product opportunities currently advancing at the preclinical and early clinical development stages. We categorize and describe novel and differentiated potential product opportunities based on their potential to address the following critical unmet patient needs: (1) biomarkers for prognosis of disease course and prediction/monitoring of treatment response; (2) restoration of eubiosis; (3) restoration of barrier function and mucosal healing; (4) more effective and safer anti-inflammatories; (5) neuromodulatory and behavioral therapies; (6) management of disease complications; and (7) targeted drug delivery.

Immunological Networks Defining the Heterogeneity of Inflammatory Bowel Diseases

Current practice in IBD is to classify patients based on clinical signs and symptoms and provide treatments accordingly. However, the response of IBD patients to available treatments is highly variable, highlighting clinically significant heterogeneity among patients. Thus, more accurate patient stratification is urgently needed to more effectively target therapeutic interventions to specific patients. Here we review the degree of heterogeneity in IBD, discussing how the microbiota, genetics, and immune system may contribute to the variation among patients. We highlight how molecular heterogeneity may relate to clinical phenotype, but in other situations may be independent of clinical phenotype, encouraging future studies to fill the gaps. Finally, we discuss novel stratification methodologies as a foundation for precision medicine, in particular a novel stratification strategy based on conserved genes across species. All of these dimensions of heterogeneity have potential to provide strategies for patient stratification and move IBD practice towards personalised medicine.

Screening of characteristic genes in ulcerative colitis by integrating gene expression profiles

Background

This study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients.

Methods

Four datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves.

Results

Twelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively).

Conclusions

An SVM classifier based on feature genes might correctly identify healthy people or UC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01940-0.

New Insights on CD8+ T Cells in Inflammatory Bowel Disease and Therapeutic Approaches

CD8⁺ T cells are involved in the pathogenesis of inflammatory bowel disease (IBD), a complex multifactorial chronic disease. Here, we present an overview of the current research with the controversial findings of CD8⁺ T cell subsets and discuss some possible perspectives on their therapeutic value in IBD. Studies on the role of CD8⁺ T cells in IBD have contradictory outcomes, which might be related to the heterogeneity of the cells. Recent data suggest that cytotoxic CD8⁺ T cells (Tc1) and interleukin (IL) 17-producing CD8⁺ (Tc17) cells contribute to the pathogenesis of IBD. Moreover, subsets of regulatory CD8⁺ T cells are abundant at sites of inflammation and can exhibit pro-inflammatory features. Some subsets of tissue resident memory CD8⁺ T cells (Trm) might be immunosuppressant, whereas others might be pro-inflammatory. Lastly, exhausted T cells might indicate a positive outcome for patients. The function and plasticity of different subsets of CD8⁺ T cells in health and IBD remain to be further investigated in a challenging field due to the limited availability of mucosal samples and adequate controls.

Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

Genetic Variants of DMBT1 and SFTPD and Disease Severity in Paediatric Inflammatory Bowel Disease—A Polish Population-Based Study

Deleted in malignant brain tumours 1 protein (DMBT1) and surfactant protein D (SFTPD) are antimicrobial peptides previously linked to inflammatory bowel disease (IBD) susceptibility. This study attempts to link the most potential IBD-associated polymorphisms in DMBT1 and SFTPD with the disease severity in children. A total of 406 IBD patients (Crohn’s disease (CD) n = 214 and ulcerative colitis (UC) n = 192) were genotyped using hydrolysis probe assay. Clinical expression was described by disease activity scales, albumin and C-reactive protein levels, localisation and behaviour (Paris classification), systemic steroid, immunosuppressive, biological, and surgical treatment, number of exacerbation-caused hospitalisations, relapses and nutritional status. IBD patients with the risk genotype (AA) in DMBT1 rs2981804 had more frequent biological treatment (AA: vs. AG/GG; p = 0.012), concomitant diseases (AA vs. AG vs. GG; p = 0.015) and cutaneous manifestations (AA vs. AG/GG, p = 0.008). In UC, rs2981804 genotypes might be linked with albumin concentrations at diagnosis (AA vs. AG vs. GG; p = 0.009). In CD, DMBT1 rs2981745 was significantly associated with the number of severe relapses per year of disease (p = 0.020) and time-to-immunosuppression (p = 0.045). SFTPD was seemingly found to be associated with age at first immunosuppression in IBD (CC vs. CT vs. TT; p = 0.048). In conclusion, selected polymorphisms of DMBT1 and SFTPD might be associated with some disease severity measures in children with IBD. However, the magnitude of associations and their clinical relevance might be minor.

Extracellular Matrix Components as Diagnostic Tools in Inflammatory Bowel Disease

Simple Summary

For decades, the extracellular matrix (ECM) has been defined as a structure component playing a rather neglected role in the human body. In recent years, research has shed light on the role of ECM within cellular processes, including proliferation, migration and differentiation, as well as in inflammation. In inflammation, ECM composition is constantly being remodeled and undergoes dynamic and rapid changes. Tracking these changes could serve as a novel diagnostic tool. Inflammatory bowel disease is accompanied by complications such as fibrosis, stenosis and fistulas. All of these structural complications involve excessive synthesis or degradation of ECM. With this review, we explored whether the analysis of ECM composition can be of support in diagnosing inflammatory bowel disease and whether changes within ECM can help to predict a complicated disease course early on.

Abstract

Work from the last years indicates that the extracellular matrix (ECM) plays a direct role in various cellular processes, including proliferation, migration and differentiation. Besides homeostatic processes, its regulatory function in inflammation becomes more and more evident. In inflammation, such as inflammatory bowel disease, the ECM composition is constantly remodeled, and this can result in a structuring of fistulizing disease course. Thus, tracking early ECM changes might bear the potential to predict the disease course. In this review, we provide an overview of relevant diagnostic methods, focusing on ECM changes.

ECCO Topical Review: Refractory Inflammatory Bowel Disease

Inflammatory bowel disease is a chronic disease with variable degrees of extent, severity, and activity. A proportion of patients will have disease that is refractory to licensed therapies, resulting in significant impairment in quality of life. The treatment of these patients involves a systematic approach by the entire multidisciplinary team, with particular consideration given to medical options including unlicensed therapies, surgical interventions, and dietetic and psychological support. The purpose of this review is to guide clinicians through this process and provide an accurate summary of the available evidence for different strategies.

Insights From Single Cell RNA Sequencing Into the Immunology of Type 1 Diabetes- Cell Phenotypes and Antigen Specificity

In the past few years, huge advances have been made in techniques to analyse cells at an individual level using RNA sequencing, and many of these have precipitated exciting discoveries in the immunology of type 1 diabetes (T1D). This review will cover the first papers to use scRNAseq to characterise human lymphocyte phenotypes in T1D in the peripheral blood, pancreatic lymph nodes and islets. These have revealed specific genes such as IL-32 that are differentially expressed in islet -specific T cells in T1D. scRNAseq has also revealed wider gene expression patterns that are involved in T1D and can predict its development even predating autoantibody production. Single cell sequencing of TCRs has revealed V genes and CDR3 motifs that are commonly used to target islet autoantigens, although truly public TCRs remain elusive. Little is known about BCR repertoires in T1D, but scRNAseq approaches have revealed that insulin binding BCRs commonly use specific J genes, share motifs between donors and frequently demonstrate poly-reactivity. This review will also summarise new developments in scRNAseq technology, the insights they have given into other diseases and how they could be leveraged to advance research in the type 1 diabetes field to identify novel biomarkers and targets for immunotherapy.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-Disease Outcome and Response to Therapy

Inflammatory bowel diseases [IBD] are a heterogeneous spectrum with two extreme phenotypes, Crohn's disease [CD] and ulcerative colitis [UC], which both represent numerous phenotypical variations. Hence, we should no longer approach all IBD patients similarly, but rather aim to rethink clinical classifications and modify treatment algorithms to usher in a new era of precision medicine in IBD. This scientific ECCO workshop aims to provide a state-of-the-art overview on prognostic and predictive markers, shed light on key questions in biomarker development, propose best practices in IBD biomarker development [including trial design], and discuss the potential for multi-omic data integration to help drive further advances to make precision medicine a reality in IBD.

Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

Technological readiness and implementation of genomic-driven precision medicine for complex diseases

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease

Assessment of endoscopic disease activity can be difficult in patients with inflammatory bowel disease (IBD) [comprises Crohn's disease (CD) and ulcerative colitis (UC)]. Endoscopic assessment is currently the foundation of disease evaluation and the grading is pivotal for the initiation of certain treatments. Yet, disharmony is found among experts; even when reassessed by the same expert. Some studies have demonstrated that the evaluation is no better than flipping a coin. In UC, the greatest achieved consensus between physicians when assessing endoscopic disease activity only reached a Kappa value of 0.77 (or 77% agreement adjustment for chance/accident). This is unsatisfactory when dealing with patients at risk of surgery or disease progression without proper care. Lately, across all medical specialities, computer assistance has become increasingly interesting. Especially after the emanation of machine learning – colloquially referred to as artificial intelligence (AI). Compared to other data analysis methods, the strengths of AI lie in its capability to derive complex models from a relatively small dataset and its ability to learn and optimise its predictions from new inputs. It is therefore evident that with such a model, one hopes to be able to remove inconsistency among humans and standardise the results across educational levels, nationalities and resources. This has manifested in a handful of studies where AI is mainly applied to capsule endoscopy in CD and colonoscopy in UC. However, due to its recent place in IBD, there is a great inconsistency between the results, as well as the reporting of the same. In this opinion review, we will explore and evaluate the method and results of the published studies utilising AI within IBD (with examples), and discuss the future possibilities AI can offer within IBD.

Inflammatory bowel disease and psoriasis: modernizing the multidisciplinary approach

Psoriasis and inflammatory bowel disease (IBD) are immune-mediated diseases occurring in barrier organs whose main task is to protect the organism from attack. These disorders are highly prevalent especially in northern Europe where psoriasis has a prevalence of around 3-4% and IBD around 0.3%. The prevalence of IBD in North America has been estimated at around 0.4%. The total incidence rates in northern Europe have been estimated at around 6 for Crohn's disease and 11 for ulcerative colitis per 100 000 person-years, compared with an incidence rate of around 280 per 100 000 person-years for psoriasis. Both diseases are less common in countries with a lower index of development. The rise in IBD appears to occur as populations adopt a westernized lifestyle, whereas psoriasis seems more stable and prevalence differences may derive more from variation in genetic susceptibility. The gut microbiota is clearly an important driver of IBD pathogenesis; in psoriasis, changes in gut and skin microbiota have been reported, but it is less clear whether and how these changes contribute to the pathogenesis. Large studies show that most identified genes are involved in the immune system. However, psoriasis and IBD are highly heterogeneous diseases and there is a need for more precise and deeper phenotyping to identify specific subgroups and their genetic, epigenetic and molecular signatures. Epigenetic modifications of DNA such as histone modifications, noncoding RNA effects on transcription and translation and DNA methylation are increasingly recognized as the mechanism underpinning much of the gene-environment interaction in the pathogenesis of both IBD and psoriasis. Our understanding of underlying pathogenetic mechanisms has deepened fundamentally over the past decades developing hand in hand with novel therapies targeting pathways and proinflammatory cytokines incriminated in disease. There is not only substantial overlap between psoriasis and IBD, but also there are differences with implication for therapy. In psoriasis, drugs targeting interleukin-23 and interleukin-17 have shown superior efficacy compared with anti-TNFs, whilst in IBD, drugs targeting interleukin-17 may be less beneficial. The therapeutic toolbox for psoriasis is impressive and is enlarging also for IBD. Still, there are unmet needs reflecting the heterogeneity of both diseases and there is a need for closer molecular diagnostics to allow for the development of precise therapeutics.

Early Indolent Course of Crohn's Disease in Newly Diagnosed Patients Is Not Rare and Possibly Predictable

BACKGROUND & AIMS

The early stages of Crohn's disease (CD) course are heterogeneous, and it is a challenge to predict the course of disease in patients with new diagnosis.

METHODS

We performed an observational longitudinal study of 156 adults (79 male; median age, 27.7 years; 57 treatment naïve) with newly diagnosed CD (within 6 months of enrollment), referred from medical centers and community clinics in Israel from 2013 through 2017. Study participants each received semi-annual scheduled evaluations. Indolent disease was defined as a disease course without need for strict interventions to control complicated course of CD (hospitalization or surgery, or decision to start steroid, immunomodulator, or biologic therapy). Cox regression and receiver operating characteristic analyses were used to identify factors associated with early indolent or complicated course of CD. We validated our findings in an independent cohort of patients with CD from a separate medical center in Israel in 2018.

RESULTS

Over a median follow-up period of 17.2 months (interquartile range, 8.8-23.8 months), 52 patients (33.3%) had an indolent course of CD, 29 (18.5%) required hospitalizations, and 75 (48%) were recommended to start steroid, immunomodulator, or biologic therapies. The median time to first intervention was 3.4 months (95% CI, 2.4-4.4). We developed a model based on clinical factors that identified 4 factors associated with complicated course in treatment-naïve patients: body mass index <25 kg/m² (hazard ratio [HR], 2.45; 95% CI, 1.07-5.43; P = .033), serum level of vitamin B12 <350 pg/mL (HR, 2.78; 95% CI, 1.21-6.41; P = .016), white blood cells ≥7 × 10³/μL (HR, 2.419; 95% CI, 1.026-5.703; P = .044), and serum level of ALT ≥25 IU/L (HR, 2.680; 95% CI, 1.186-6.058; P = .018). This model discriminated between patients with vs without a complicated course of disease with 90% and 89% accuracy at 6 and 12 months after diagnosis, respectively. A validation cohort demonstrated a discriminatory ability of 79% at 3 months after diagnosis, and a nomogram was constructed.

CONCLUSIONS

In an observational longitudinal study of 156 patients with newly diagnosed CD, we found that one third have an early indolent course of disease. We identified factors that can be measured at diagnosis to identify patients at risk for an early complicated course-these might be used in patient management and selection of treatment.

Artificial intelligence in gastrointestinal diseases

Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.

Deconvolution of monocyte responses in inflammatory bowel disease reveals an IL-1 cytokine network that regulates IL-23 in genetic and acquired IL-10 resistance

OBJECTIVE

Dysregulated immune responses are the cause of IBDs. Studies in mice and humans suggest a central role of interleukin (IL)-23-producing mononuclear phagocytes in disease pathogenesis. Mechanistic insights into the regulation of IL-23 are prerequisite for selective IL-23 targeting therapies as part of personalised medicine.

DESIGN

We performed transcriptomic analysis to investigate IL-23 expression in human mononuclear phagocytes and peripheral blood mononuclear cells. We investigated the regulation of IL-23 expression and used single-cell RNA sequencing to derive a transcriptomic signature of hyperinflammatory monocytes. Using gene network correlation analysis, we deconvolved this signature into components associated with homeostasis and inflammation in patient biopsy samples.

RESULTS

We characterised monocyte subsets of healthy individuals and patients with IBD that express IL-23. We identified autosensing and paracrine sensing of IL-1α/IL-1β and IL-10 as key cytokines that control IL-23-producing monocytes. Whereas Mendelian genetic defects in IL-10 receptor signalling induced IL-23 secretion after lipopolysaccharide stimulation, whole bacteria exposure induced IL-23 production in controls via acquired IL-10 signalling resistance. We found a transcriptional signature of IL-23-producing inflammatory monocytes that predicted both disease and resistance to antitumour necrosis factor (TNF) therapy and differentiated that from an IL-23-associated lymphocyte differentiation signature that was present in homeostasis and in disease.

CONCLUSION

Our work identifies IL-10 and IL-1 as critical regulators of monocyte IL-23 production. We differentiate homeostatic IL-23 production from hyperinflammation-associated IL-23 production in patients with severe ulcerating active Crohn's disease and anti-TNF treatment non-responsiveness. Altogether, we identify subgroups of patients with IBD that might benefit from IL-23p19 and/or IL-1α/IL-1β-targeting therapies upstream of IL-23.

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.

Serum proteomic profiling at diagnosis predicts clinical course, and need for intensification of treatment in inflammatory bowel disease

BACKGROUND

Success in personalized medicine in complex disease is critically dependent on biomarker discovery. We profiled serum proteins using a novel proximity extension assay [PEA] to identify diagnostic and prognostic biomarkers in inflammatory bowel disease [IBD].

METHODS

We conducted a prospective case-control study in an inception cohort of 552 patients [328 IBD, 224 non-IBD], profiling proteins recruited across six centres. Treatment escalation was characterized by the need for biological agents or surgery after initial disease remission. Nested leave-one-out cross-validation was used to examine the performance of diagnostic and prognostic proteins.

RESULTS

A total of 66 serum proteins differentiated IBD from symptomatic non-IBD controls, including matrix metallopeptidase-12 [MMP-12; Holm-adjusted p = 4.1 × 10-23] and oncostatin-M [OSM; p = 3.7 × 10-16]. Nine of these proteins are associated with cis-germline variation [59 independent single nucleotide polymorphisms]. Fifteen proteins, all members of tumour necrosis factor-independent pathways including interleukin-1 (IL-1) and OSM, predicted escalation, over a median follow-up of 518 [interquartile range 224-756] days. Nested cross-validation of the entire data set allowed characterization of five-protein models [96% comprising five core proteins ITGAV, EpCAM, IL18, SLAMF7 and IL8], which define a high-risk subgroup in IBD [hazard ratio 3.90, confidence interval: 2.43-6.26], or allowed distinct two- and three-protein models for ulcerative colitis and Crohn's disease respectively.

CONCLUSION

We have characterized a simple oligo-protein panel that has the potential to identify IBD from symptomatic controls and to predict future disease course. Further prospective work is required to validate our findings.

Prognostic tools for identification of high risk in people with Crohn's disease: systematic review and cost-effectiveness study

BACKGROUND

Crohn's disease is a lifelong condition that can affect any segment of the gastrointestinal tract. Some people with Crohn's disease may be at higher risk of following a severe course of disease than others and being able to identify the level of risk a patient has could lead to personalised management.

OBJECTIVE

To assess the prognostic test accuracy, clinical impact and cost-effectiveness of two tools for the stratification of people with a diagnosis of Crohn's disease by risk of following a severe course of disease.

DATA SOURCES

The data sources MEDLINE, EMBASE, the Cochrane Database of Systematic Reviews and the Cochrane Central Register of Controlled Trials were searched to inform the systematic reviews on prognostic accuracy, clinical impact of the prognostic tools, and economic evaluations. Additional data sources to inform the review of economic evaluations were NHS Economic Evaluation Database, Database of Abstracts of Reviews of Effects and the Health Technology Assessment Database.

REVIEW METHODS

Systematic reviews of electronic databases were carried out from inception to June 2019 for studies assessing the prognostic accuracy and clinical impact of the IBDX® (Crohn's disease Prognosis Test; Glycominds Ltd, Lod, Israel) biomarker stratification tool and the PredictSURE-IBD™ (PredictImmune Ltd, Cambridge, UK) tool. Systematic reviews of studies reporting on the cost-effectiveness of treatments for Crohn's disease were run from inception to July 2019. Two reviewers independently agreed on studies for inclusion, assessed the quality of included studies and validated the data extracted from studies. Clinical and methodological heterogeneity across studies precluded the synthesis of data for prognostic accuracy. A de novo economic model was developed to compare the costs and consequences of two treatment approaches - the 'top-down' and 'step-up' strategies, with step-up considered standard care - in people at high risk of following a severe course of Crohn's disease. The model comprised a decision tree and a Markov cohort model.

RESULTS

Sixteen publications, including eight original studies (n = 1478), were deemed relevant to the review of prognostic accuracy. Documents supplied by the companies marketing the prognostic tools were also reviewed. No study meeting the eligibility criteria reported on the sensitivity or specificity of the IBDX biomarker stratification tool, whereas one study provided estimates of sensitivity, specificity and negative predictive value for the PredictSURE-IBD tool. All identified studies were observational and were considered to provide weak evidence on the effectiveness of the tools. Owing to the paucity of data on the two tools, in the base-case analysis the accuracy of PredictSURE-IBD was assumed to be 100%. Accuracy of IBDX was assumed to be 100% in a scenario analysis, with the cost of the tests being the only difference between the analyses. The incremental analysis of cost-effectiveness demonstrated that top-down (via the use of PredictSURE-IBD in the model) is more expensive and generates fewer quality-adjusted life-years than step-up (via the standard care arm of the model).

LIMITATIONS

Despite extensive systematic searches of the literature, no robust evidence was identified of the prognostic accuracy of the biomarker stratification tools IBDX and PredictSURE-IBD.

CONCLUSIONS

Although the model indicates that standard care dominates the tests, the lack of evidence of prognostic accuracy of the two tests and the uncertainty around the benefits of the top-down and step-up treatment approaches mean that the results should be interpreted as indicative rather than definitive.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42019138737.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Evidence Synthesis programme and will be published in full in Health Technology Assessment; Vol. 25, No. 23. See the NIHR Journals Library website for further project information.

Clinical applications of artificial intelligence and machine learning-based methods in inflammatory bowel disease

Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence. The range of AI's ability to perform tasks that would normally require human intelligence varies from prediction to complex decision-making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.

Predicting Outcome in Acute Severe Colitis-Controversies in Clinical Practice in 2021

Acute severe ulcerative colitis [ASUC] remains a common medical emergency, with 25% of patients with ulcerative colitis experiencing at least one event in their disease course. Despite advances in medical therapy, ASUC continues to be associated with considerable morbidity and mortality, with up to 30% of patients requiring colectomy during initial admission. Our aim was to review the current controversies and recent progress in risk stratification, prediction of outcome, and personalisation of care in ASUC. We re-assess the use of Truelove and Witts' criteria, serum biomarkers, and the use of composite clinical indices in current clinical practice. We explore the potential for endoscopic prediction using defined validated indices for accurate and early prognostication, and the need to define outcome. We also consider the impact of the current COVID-19 pandemic. Finally, we discuss the current research agenda, including the application of new and emerging biomarkers coupled with multi-omics and the implications in management and optimisation of outcome. Research priorities for the prediction of outcome in acute severe colitis include the following. 1. Development of an accurate admission score to guide early medical rescue therapy or colectomy. 2. Utility of point-of-care faecal calprotectin, with determination of optimal cut-off values. 3. Role of serum and faecal infliximab levels to both predict outcome and guide accelerated infliximab dosing. 4. Role of novel biomarkers, including serum calprotectin, in predicting response to corticosteroids or rescue therapy. 5. Specific predictors of response to ciclosporin and infliximab to allow rationalisation of drug use. 6. Utility of validated endoscopic scores. 7. Utility of radiological assessment beyond use of plain abdominal X-ray. 8. The use of multiomics and machine learning to predict risk of Acute Severe Colitis in patients with Ulcerative Colitis.

Machine learning identifies novel blood protein predictors of penetrating and stricturing complications in newly diagnosed paediatric Crohn's disease

BACKGROUND

There is a need for improved risk stratification in Crohn's disease.

AIM

To identify novel blood protein biomarkers associated with future Crohn's disease complications METHODS: We performed a case-cohort study utilising a paediatric inception cohort, the Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn's disease (RISK) study. All patients had inflammatory disease (B1) at baseline. Outcomes were development of stricturing (B2) or penetrating (B3) complications. We assayed 92 inflammation-related proteins in baseline plasma using a proximity extension assay (Olink Proteomics). An ensemble machine learning technique, random survival forests (RSF), selected variables predicting B2 and B3 complications. Selected analytes were compared to clinical variables and serology only models. We examined selected proteins in a single-cell sequencing cohort to analyse differential cell expression in blood and ileum.

RESULTS

We included 265 patients with mean age 11.6 years (standard deviation [SD] 3.2). Seventy-three and 34 patients, respectively, had B2 and B3 complications within mean 1123 (SD 477) days for B2 and 1251 (442) for B3. A model with 5 protein markers predicted B3 complications with an area under the curve (AUC) of 0.79 (95% confidence interval [CI] 0.76-0.82) compared to 0.69 (95% CI 0.66-0.72) for serologies and 0.74 (95% CI 0.71-0.77) for clinical variables. A model with 4 protein markers predicted B2 complications with an AUC of 0.68 (95% CI 0.65-0.71) compared to 0.62 (95% CI 0.59-0.65) for serologies and 0.52 (95% CI 0.50-0.55) for clinical variables. B2 analytes were highly expressed in ileal stromal cells while B3 analytes were prominent in peripheral blood and ileal T cells.

CONCLUSIONS

We identified novel blood proteomic markers, distinct for B2 and B3, associated with progression of paediatric Crohn's disease.

Transcription and DNA Methylation Patterns of Blood-Derived CD8+ T Cells Are Associated With Age and Inflammatory Bowel Disease But Do Not Predict Prognosis

BACKGROUND & AIMS

Gene expression patterns of CD8⁺ T cells have been reported to correlate with clinical outcomes of adults with inflammatory bowel diseases (IBD). We aimed to validate these findings in independent patient cohorts.

METHODS

We obtained peripheral blood samples from 112 children with a new diagnosis of IBD (71 with Crohn's disease and 41 with ulcerative colitis) and 19 children without IBD (controls) and recorded medical information on disease activity and outcomes. CD8⁺ T cells were isolated from blood samples by magnetic bead sorting at the point of diagnosis and during the course of disease. Genome-wide transcription (n = 192) and DNA methylation (n = 66) profiles were generated using Affymetrix and Illumina arrays, respectively. Publicly available transcriptomes and DNA methylomes of CD8⁺ T cells from 3 adult patient cohorts with and without IBD were included in data analyses.

RESULTS

Previously reported CD8⁺ T-cell prognostic expression and exhaustion signatures were only found in the original adult IBD patient cohort. These signatures could not be detected in either a pediatric or a second adult IBD cohort. In contrast, an association between CD8⁺ T-cell gene expression with age and sex was detected across all 3 cohorts. CD8⁺ gene transcription was clearly associated with IBD in the 2 cohorts that included non-IBD controls. Lastly, DNA methylation profiles of CD8⁺ T cells from children with Crohn's disease correlated with age but not with disease outcome.

CONCLUSIONS

We were unable to validate previously reported findings of an association between CD8⁺ T-cell gene transcription and disease outcome in IBD. Our findings reveal the challenges of developing prognostic biomarkers for patients with IBD and the importance of their validation in large, independent cohorts before clinical application.