Big data in IBD: a look into the future

Target Trial Emulation: Improving the Quality of Observational Studies in Inflammatory Bowel Disease Using the Principles of Randomized Trials

The past decade has seen a substantial increase in the number of randomized controlled trials (RCTs) conducted in inflammatory bowel disease (IBD). Randomized controlled trials are the gold standard method for generating robust evidence of drug safety and efficacy but are expensive, time-consuming, and may have ethical implications. Observational studies in IBD are often used to fill the gaps in evidence but are typically hindered by significant bias. There are several approaches for making statistical inferences from observational data with some that focus on study design and others on statistical techniques. Target trial emulation is an emerging methodological process that aims to bridge this gap and improve the quality of observational studies by applying the principles of an ideal, or "target," randomized trial to routinely collected clinical data. There has been a rapid expansion of observational studies that have emulated trials over the past 5 years in other medical fields, but this has yet to be adopted in gastroenterology and IBD. The wealth of nonrandomized clinical data available through electronic health records, patient registries, and administrative health databases afford innumerable hypothesis-generating opportunities for IBD research. This review outlines the principles of target trial emulation, discusses the merits to IBD observational studies in reducing the most common biases and improving confidence in causality, and details the caveats of using this approach.

Baseline Data and Measurement Instruments Reported in Observational Studies in Inflammatory Bowel Disease: Results from a Systematic Review

BACKGROUND

Heterogeneity in demographic and outcomes data with corresponding measurement instruments [MIs] creates barriers to data pooling and analysis. Several core outcome sets have been developed in inflammatory bowel disease [IBD] to homogenize outcomes data. A parallel Minimum Data Set [MDS] for baseline characteristics is lacking. We conducted a systematic review to develop the first MDS.

METHODS

A systematic review was made of observational studies from three databases [2000-2021]. Titles and abstracts were screened, full-text articles were reviewed, and data were extracted by two reviewers. Baseline data were grouped into ten domains: demographics, clinical features, disease behaviour/complications, biomarkers, endoscopy, histology, radiology, healthcare utilization and patient-reported data. Frequency of baseline data and MIs within respective domains are reported.

RESULTS

From 315 included studies [600 552 subjects], most originated from Europe [196; 62%] and North America [59; 19%], and were published between 2011 and 2021 [251; 80%]. The most frequent domains were demographics [311; 98.7%] and clinical [289; 91.7%]; 224 [71.1%] studies reported on the triad of sex [306; 97.1%], age [289; 91.7%], and disease phenotype [231; 73.3%]. Few included baseline data for radiology [19; 6%], healthcare utilization [19; 6%], and histology [17; 5.4%]. Ethnicity [19; 6%], race [17; 5.4%], and alcohol/drug consumption [6; 1.9%] were the least reported demographics. From 25 MIs for clinical disease activity, the Harvey-Bradshaw Index [n = 53] and Mayo score [n = 37] were most frequently used.

CONCLUSIONS

Substantial variability exists in baseline population data reporting. These findings will inform a future consensus for MDS in IBD to enhance data harmonization and credibility of real-world evidence.

Big data approach in the field of gastric and colorectal cancer research

Big data is characterized by three attributes: volume, variety,, and velocity. In healthcare setting, big data refers to vast dataset that is electronically stored and managed in an automated manner and has the potential to enhance human health and healthcare system. In this review, gastric cancer (GC) and postcolonoscopy colorectal cancer (PCCRC) will be used to illustrate application of big data approach in the field of gastrointestinal cancer research. Helicobacter pylori (HP) eradication only reduces GC risk by 46% due to preexisting precancerous lesions. Apart from endoscopy surveillance, identifying medications that modify GC risk is another strategy. Population-based cohort studies showed that long-term use of proton pump inhibitors (PPIs) associated with higher GC risk after HP eradication, while aspirin and statins associated with lower risk. While diabetes mellitus conferred 73% higher GC risk, metformin use associated with 51% lower risk, effect of which was independent of glycemic control. Nonetheless, nonsteroidal anti-inflammatory drugs (NA-NSAIDs) are not associated with lower GC risk. CRC can still occur after initial colonoscopy in which no cancer was detected (i.e. PCCRC). Between 2005 and 2013, the rate of interval-type PCCRC-3y (defined as CRC diagnosed between 6 and 36 months of index colonoscopy which was negative for CRC) was 7.9% in Hong Kong, with >80% being distal cancers and higher cancer-specific mortality compared with detected CRC. Certain clinical and endoscopy-related factors were associated with PCCRC-3 risk. Medications shown to have chemopreventive effects on PCCRC include statins, NA-NSAIDs, and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers.

Peptide-Based Hydrogel for Nanosystems Encapsulation: the Next Generation of Localized Delivery Systems for the Treatment of Intestinal Inflammations

Conventional therapies for inflammatory bowel diseases are mainly based on systemic treatments which cause side effects and toxicity over long-term administration. Nanoparticles appear as a valid alternative to allow a preferential accumulation in inflamed tissues following oral administration while reducing systemic drug exposure. To increase their residence time in the inflamed intestine, the nanoparticles are here associated with a hydrogel matrix. A bioadhesive peptide-based hydrogel is mixed with nanoemulsions, creating a hybrid lipid-polymer nanocomposite. Mucopenetrating nanoemulsions of 100 nm are embedded in a scaffold constituted of the self-assembling peptide hydrogel product PuraStat. The nanocomposite is fully characterized to study the impact of lipid particles in the hydrogel structure. Rheological measurements and circular dichroism analyses are performed to investigate the system's microstructure and physical properties. Biodistribution studies demonstrate that the nanocomposite acts as a depot in the stomach and facilitates the slow release of the nanoemulsions in the intestine. Efficacy studies upon oral administration of the drug-loaded system show the improvement of the disease score in a mouse model of intestinal inflammation.

Oyster Peptides Ameliorate Dextran Sulfate Sodium-Induced Ulcerative Colitis via Modulating the Gut Microbiota and Inhibiting the TLR4/NF-κB Pathway

Ulcerative colitis (UC) is an inflammatory bowel disease with an increasing prevalence year over year, and the medications used to treat patients with UC clinically have severe side effects. Oyster peptides (OPs) have anti-inflammatory and antioxidant properties as functional foods that can alleviate a wide range of inflammatory conditions. However, the application of oyster peptides in ulcerative colitis is not well studied. In this work, an animal model of acute colitis was established using 3% dextran sulfate sodium (DSS), and the impact of OP therapy on colitis in mice was examined. Supplementing with OPs prevented DSS-induced colitis from worsening, reduced the expression of oxidative stress and inflammatory markers, and restored the intestinal barrier damage caused by DSS-induced colitis in mice. The 16S rDNA results showed that the OP treatment improved the gut microbiota structure of the UC mice, including increasing microbial diversity, increasing beneficial bacteria, and decreasing harmful bacteria. In the UC mice, the OP therapy decreased the relative abundance of Family_XIII_AD3011_group and Prevotella_9 and increased the relative abundance of Alistipes. In conclusion, OP treatment can inhibit the TLR4/NF-κB pathway and improve the intestinal microbiota in UC mice, which in turn alleviates DSS-induced colitis, providing a reference for the treatment of clinical UC patients.

Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease

Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.

Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review

BACKGROUND

There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies.

METHODS

We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST).

RESULTS

Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images.

CONCLUSION

The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.

Transcriptome analysis identifies genetic risk markers and explores the pathogenesis for inflammatory bowel disease

Inflammatory bowel disease (IBD) is an incurable and disabling bowel disease driven by multiple risk factors that severely limit patients' quality of life. We integrated the RNA-sequencing data of 1238 IBD patients, and investigated the pathogenesis of IBD by combining transcriptional element prediction analysis and immune-related analysis. Here, we first determined that KIAA1109 is inhibited in IBD patients. The expression of KIAA1109 and NOD2, the key receptor of NOD-like receptors, showed a negative correlation. The NOD-like receptor signaling pathway is activated and exerts transcriptional regulation on the chemokines CXCL1 and CXCL2 through the activation of the transcription factors NFκB and AP1. Analysis of immune infiltration revealed that the expression of chemokines CXCL1 and CXCL2 may regulate the inflammatory response induced by immune cells. These findings suggest that the KIAA1109-NOD2-NFκB/AP1-CXCL1/CXCL2 regulatory axis is the molecular mechanism of IBD pathogenesis, which will provide a new perspective for the diagnosis, treatment and management of IBD patients.

Pharmacological induction of autophagy reduces inflammation in macrophages by degrading immunoproteasome subunits

Defective autophagy is linked to proinflammatory diseases. However, the mechanisms by which autophagy limits inflammation remain elusive. Here, we found that the pan-FGFR inhibitor LY2874455 efficiently activated autophagy and suppressed expression of proinflammatory factors in macrophages stimulated by lipopolysaccharide (LPS). Multiplex proteomic profiling identified the immunoproteasome, which is a specific isoform of the 20s constitutive proteasome, as a substrate that is degraded by selective autophagy. SQSTM1/p62 was found to be a selective autophagy-related receptor that mediated this degradation. Autophagy deficiency or p62 knockdown blocked the effects of LY2874455, leading to the accumulation of immunoproteasomes and increases in inflammatory reactions. Expression of proinflammatory factors in autophagy-deficient macrophages could be reversed by immunoproteasome inhibitors, confirming the pivotal role of immunoproteasome turnover in the autophagy-mediated suppression on the expression of proinflammatory factors. In mice, LY2874455 protected against LPS-induced acute lung injury and dextran sulfate sodium (DSS)-induced colitis and caused low levels of proinflammatory cytokines and immunoproteasomes. These findings suggested that selective autophagy of the immunoproteasome was a key regulator of signaling via the innate immune system.

Anemoside B4, a new pyruvate carboxylase inhibitor, alleviates colitis by reprogramming macrophage function

OBJECTIVES

Colitis is a global disease usually accompanied by intestinal epithelial damage and intestinal inflammation, and an increasing number of studies have found natural products to be highly effective in treating colitis. Anemoside B4 (AB4), an abundant saponin isolated from Pulsatilla chinensis (Bunge), which was found to have strong anti-inflammatory activity. However, the exact molecular mechanisms and direct targets of AB4 in the treatment of colitis remain to be discovered.

METHODS

The anti-inflammatory activities of AB4 were verified in LPS-induced cell models and 2, 4, 6-trinitrobenzene sulfonic (TNBS) or dextran sulfate sodium (DSS)-induced colitis mice and rat models. The molecular target of AB4 was identified by affinity chromatography analysis using chemical probes derived from AB4. Experiments including proteomics, molecular docking, biotin pull-down, surface plasmon resonance (SPR), and cellular thermal shift assay (CETSA) were used to confirm the binding of AB4 to its molecular target. Overexpression of pyruvate carboxylase (PC) and PC agonist were used to study the effects of PC on the anti-inflammatory and metabolic regulation of AB4 in vitro and in vivo.

RESULTS

AB4 not only significantly inhibited LPS-induced NF-κB activation and increased ROS levels in THP-1 cells, but also suppressed TNBS/DSS-induced colonic inflammation in mice and rats. The molecular target of AB4 was identified as PC, a key enzyme related to fatty acid, amino acid and tricarboxylic acid (TCA) cycle. We next demonstrated that AB4 specifically bound to the His879 site of PC and altered the protein's spatial conformation, thereby affecting the enzymatic activity of PC. LPS activated NF-κB pathway and increased PC activity, which caused metabolic reprogramming, while AB4 reversed this phenomenon by inhibiting the PC activity. In vivo studies showed that diisopropylamine dichloroacetate (DADA), a PC agonist, eliminated the therapeutic effects of AB4 by changing the metabolic rearrangement of intestinal tissues in colitis mice.

CONCLUSION

We identified PC as a direct cellular target of AB4 in the modulation of inflammation, especially colitis. Moreover, PC/pyruvate metabolism/NF-κB is crucial for LPS-driven inflammation and oxidative stress. These findings shed more light on the possibilities of PC as a potential new target for treating colitis.

Therapeutic application and potential mechanism of plant-derived extracellular vesicles in inflammatory bowel disease

BACKGROUND

Plant-derived extracellular vesicles (PDEVs) are membrane vesicles characterized by a phospholipid bilayer as the basic skeleton that is wrapped by various functional components of proteins and nucleic acids. An increasing number of studies have confirmed that PDEVs can be a potential treatment of inflammatory bowel disease (IBD) and can, to some extent, compensate for the limitations of existing therapies.

AIM OF REVIEW

This review summarizes the recent advances and potential mechanisms underlying PDEVs obtained from different sources to alleviate IBD. In addition, the review discusses the possible applications and challenges of PDEVs, providing a theoretical basis for exploring novel and practical therapeutic strategies for IBD.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In IBD, the crosstalk mechanism of PDEVs may regulate the intestinal microenvironment homeostasis, especially immune responses, the intestinal barrier, and the gut microbiota. In addition, drug loading enhances the therapeutic potential of PDEVs, particularly regarding improved tissue targeting and stability. In the future, not only immunotherapy based on PDEVs may be an effective treatment for IBD, but also the intestinal barrier and intestinal microbiota will be a new direction for the treatment of IBD.

Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome

OBJECTIVE

The increasing implementation of electronic health records allows the use of advanced text-mining methods for establishing new patient phenotypes and stratification, and for revealing outcome correlations. In this study, we aimed to explore the electronic narrative clinical reports of a cohort of patients with Dravet syndrome (DS) longitudinally followed at our center, to identify the capacity of this methodology to retrace natural history of DS during the early years.

METHODS

We used a document-based clinical data warehouse employing natural language processing to recognize the phenotype concepts in the narrative medical reports. We included patients with DS who have a medical report produced before the age of 2 years and a follow-up after the age of 3 years ("DS cohort," 56 individuals). We selected two control populations, a "general control cohort" (275 individuals) and a "neurological control cohort" (281 individuals), with similar characteristics in terms of gender, number of reports, and age at last report. To find concepts specifically associated with DS, we performed a phenome-wide association study using Cox regression, comparing the reports of the three cohorts. We then performed a qualitative analysis of the surviving concepts based on their median age at first appearance.

RESULTS

A total of 76 concepts were prevalent in the reports of children with DS. Concepts appearing during the first 2 years were mostly related with the epilepsy features at the onset of DS (convulsive and prolonged seizures triggered by fever, often requiring in-hospital care). Subsequently, concepts related to new types of seizures and to drug resistance appeared. A series of non-seizure-related concepts emerged after the age of 2-3 years, referring to the nonseizure comorbidities classically associated with DS.

SIGNIFICANCE

The extraction of clinical terms by narrative reports of children with DS allows outlining the known natural history of this rare disease in early childhood. This original model of "longitudinal phenotyping" could be applied to other rare and very rare conditions with poor natural history description.

Identification of OLA1 as a Novel Protein Target of Vitexin to Ameliorate Dextran Sulfate Sodium-Induced Colitis with Tissue Thermal Proteome Profiling

Vitexin, which exists in various medicinal plants and food sources, has recently received increasing attention because of its anti-inflammatory properties. This study aims to identify the protein target of vitexin that ameliorates dextran sulfate sodium (DSS)-induced colitis. The results showed that vitexin not only alleviated the clinical symptoms and colonic damage in mice with DSS-induced colitis but also suppressed the colonic production of inflammatory cytokines (IL-1β, IL-6, ICAM, and VCAM) and enhanced the expression of barrier-associated proteins (ZO-1, Occludin, and E-cadherin). Based on tissue thermal proteome profiling (Tissue-TPP) and molecular docking, OLA1 was creatively identified as a potential protein target for vitexin. Further siRNA-mediated knockdown of the OLA1 gene in Caco-2 cells demonstrated the ability of OLA1 to increase Nrf2 protein expression and, thus, mediated the anti-inflammatory effects of vitexin. Interaction of the OLA1-vitexin complex with Keap1 protein to disrupt the Keap1-Nrf2 interaction may be required for activating Nrf2. Our findings revealed a novel role for OLA1 as a protein target of vitexin that contributes to its anti-inflammatory action by activating Nrf2, which may provide a promising molecular mechanism for novel therapeutic strategies to treat colitis and the associated systemic inflammation.

Cocreation to Facilitate Communication and Collaboration Between Multidisciplinary Stakeholders in eHealth Research and Development: Case Study of the CARRIER (Coronary Artery Disease: Risk Estimations and Interventions for Prevention and Early Detection) Consortium

BACKGROUND

Collaboration with diverse stakeholders in eHealth research is fundamental yet complex. Stakeholders from various disciplines do not "speak the same language" and have different levels of power and interest, resulting in contrasting objectives, priorities, and expectations. An approach to constructive communication and collaboration is necessary to overcome this complex dynamic. Cocreation, known in the field of eHealth most often to involve end users, may also be suitable for facilitating stakeholder engagement and alignment.

OBJECTIVE

This paper provides insights into the application of cocreation, specifically in the early phases of research that focus on involving and aligning relevant stakeholders from different academic and professional backgrounds.

METHODS

The case for this study was a group discussion with members of a multidisciplinary consortium that works on developing a personalized eHealth intervention for atherosclerotic cardiovascular disease. Using stakeholder mapping, health and medicine experts, big data scientists, software developers, and an innovation manager (N=8) were invited to participate. The discussion was based on a user scenario and structured according to the Six Thinking Hats of de Bono, representing 6 different types of thinking. The discussion was recorded, transcribed verbatim, and analyzed thematically with the use of ATLAS.ti software.

RESULTS

First, informative and intuitive thinking served the preparatory purpose of familiarization with the project details and other participants. Second, positive and critical thinking constituted the body of the discussion and resulted in an in-depth conversation. Third, creative and organizational thinking were action oriented and focused on solutions and planning to safeguard future progress. The participants repeatedly reflected on various intervention-related themes, ranging from intervention content to technical functionalities and from legal requirements to implementation in practice. Moreover, project-related matters were discussed, including stakeholder management and time and budget constraints.

CONCLUSIONS

This paper demonstrates how cocreation can be of value for multidisciplinary stakeholder engagement and alignment. Based on stakeholder mapping (with whom to discuss), a dream user scenario (what to discuss), and the Six Thinking Hats of de Bono (how to discuss), the participants shared information, discussed differences, searched for solutions, and moved toward a collective approach regarding intervention development. The lessons learned may further improve the understanding of how cocreation can contribute to multidisciplinary collaboration.

Treatment strategy changes for inflammatory bowel diseases in biologic era: results from a multicenter cohort in Japan, Far East 1000

Many molecular targeted agents, including biologics, have emerged for inflammatory bowel diseases (IBD), but their high prices have prevented their widespread use. This study aimed to reveal the changes in patient characteristics and the therapeutic strategies of IBD before and after the implementation of biologics in Japan, where the unique health insurance system allows patients with IBD and physicians to select drugs with minimum patient expenses. The analysis was performed using a prospective cohort, including IBD expert and nonexpert hospitals in Japan. In this study, patients were classified into two groups according to the year of diagnosis based on infliximab implementation as the prebiologic and biologic era groups. The characteristics of therapeutic strategies in both groups were evaluated using association analysis. This study analyzed 542 ulcerative colitis (UC) and 186 Crohn's disease (CD). The biologic era included 53.3% of patients with UC and 76.2% with CD, respectively. The age of UC (33.9 years vs. 38.8 years, P < 0.001) or CD diagnosis (24.3 years vs. 31.9 years, P < 0.001) was significantly higher in the biologic era group. The association analysis of patients with multiple drug usage histories revealed that patients in the prebiologic era group selected anti-tumor necrosis factor (TNF)-α agents, whereas those in the biologic era group preferred biologic agents with different mechanisms other than anti-TNF-α. In conclusion, this study demonstrated that both patient characteristics and treatment preferences in IBD have changed before and after biologic implementation.

GRK2 Mediates Macrophage Polarization by Regulating EP4-cAMP-pCREB Signaling in Ulcerative Colitis and the Therapeutic Effect of Paroxetine on Mice with DSS-Induced Colitis

G protein-coupled receptor kinase 2 (GRK2) is one of the cytosolic enzymes, and GRK2 translocation induces prostaglandin E2 receptor 4 (EP4) over-desensitization and reduces the level of cyclic adenosine monophosphate (cAMP) to regulate macrophage polarization. However, the role of GRK2 in the pathophysiology of ulcerative colitis (UC) remains unclear. In this study, we investigated the role of GRK2 in macrophage polarization in UC, using biopsies from patients, a GRK2 heterozygous mouse model with dextran sulfate sodium (DSS)-induced colitis, and THP-1 cells. The results showed that a high level of prostaglandin E2 (PGE2) stimulated the receptor EP4 and enhanced the transmembrane activity of GRK2 in colonic lamina propria mononuclear cells (LPMCs), resulting in a down-regulation of membrane EP4 expression. Then, the suppression of cAMP-cyclic AMP responsive element-binding (CREB) signal inhibited M2 polarization in UC. Paroxetine is acknowledged as one of the selective serotonin reuptake inhibitors (SSRI), which is also considered as a potent GRK2 inhibitor with a high selectivity for GRK2. We found that paroxetine could alleviate symptoms of DSS-induced colitis in mice by regulating GPCR signaling to affect macrophage polarization. Taken together, the current results show that GRK2 may act as a novel therapeutic target in UC by regulating macrophage polarization, and paroxetine as a GRK2 inhibitor may have therapeutic effect on mice with DSS-induced colitis.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Development of a multimodal machine-learning fusion model to non-invasively assess ileal Crohn's disease endoscopic activity

BACKGROUND AND OBJECTIVE

Recurrent attentive non-invasive observation of intestinal inflammation is essential for the proper management of Crohn's disease (CD). The goal of this study was to develop and evaluate a multi-modal machine-learning (ML) model to assess ileal CD endoscopic activity by integrating information from Magnetic Resonance Enterography (MRE) and biochemical biomarkers.

METHODS

We obtained MRE, biochemical and ileocolonoscopy data from the multi-center ImageKids study database. We developed an optimized multimodal fusion ML model to non-invasively assess terminal ileum (TI) endoscopic disease activity in CD from MRE data. We determined the most informative features for model development using a permutation feature importance technique. We assessed model performance in comparison to the clinically recommended linear-regression MRE model in an experimental setup that consisted of stratified 2-fold validation, repeated 50 times, with the ileocolonoscopy-based Simple Endoscopic Score for CD at the TI (TI SES-CD) as a reference. We used the predictions' mean-squared-error (MSE) and the receiver operation characteristics (ROC) area under curve (AUC) for active disease classification (TI SEC-CD≥3) as performance metrics.

RESULTS

121 subjects out of the 240 subjects in the ImageKids study cohort had all required information (Non-active CD: 62 [51%], active CD: 59 [49%]). Length of disease segment and normalized biochemical biomarkers were the most informative features. The optimized fusion model performed better than the clinically recommended model determined by both a better median test MSE distribution (7.73 vs. 8.8, Wilcoxon test, p<1e-5) and a better aggregated AUC over the folds (0.84 vs. 0.8, DeLong's test, p<1e-9).

CONCLUSIONS

Optimized ML models for ileal CD endoscopic activity assessment have the potential to enable accurate and non-invasive attentive observation of intestinal inflammation in CD patients. The presented model is available at https://tcml-bme.github.io/ML_SESCD.html.

Electronic health record-based facilitation of familial hypercholesterolaemia detection sensitivity of different algorithms in genetically confirmed patients

Aims

Familial hypercholesterolaemia (FH) is a disorder of LDL cholesterol clearance, resulting in increased risk of cardiovascular disease. Recently, we developed a Dutch Lipid Clinic Network (DLCN) criteria-based algorithm to facilitate FH detection in electronic health records (EHRs). In this study, we investigated the sensitivity of this and other algorithms in a genetically confirmed FH population.

Methods and results

All patients with a healthcare insurance-related coded diagnosis of 'primary dyslipidaemia' between 2018 and 2020 were assessed for genetically confirmed FH. Data were extracted at the time of genetic confirmation of FH (T1) and during the first visit in 2018-2020 (T2). We assessed the sensitivity of algorithms on T1 and T2 for DLCN ≥ 6 and compared with other algorithms [familial hypercholesterolaemia case ascertainment tool (FAMCAT), Make Early Diagnoses to Prevent Early Death (MEDPED), and Simon Broome (SB)] using EHR-coded data and using all available data (i.e. including non-coded free text). 208 patients with genetically confirmed FH were included. The sensitivity (95% CI) on T1 and T2 with EHR-coded data for DLCN ≥ 6 was 19% (14-25%) and 22% (17-28%), respectively. When using all available data, the sensitivity for DLCN ≥ 6 was 26% (20-32%) on T1 and 28% (22-34%) on T2. For FAMCAT, the sensitivity with EHR-coded data on T1 was 74% (67-79%) and 32% (26-39%) on T2, whilst sensitivity with all available data was 81% on T1 (75-86%) and 45% (39-52%) on T2. For Make Early Diagnoses to Prevent Early Death MEDPED and SB, using all available data, the sensitivity on T1 was 31% (25-37%) and 17% (13-23%), respectively.

Conclusions

The FAMCAT algorithm had significantly better sensitivity than DLCN, MEDPED, and SB. FAMCAT has the best potential for FH case-finding using EHRs.

Robust 2D MoS2 Artificial Synapse Device Based on a Lithium Silicate Solid Electrolyte for High-Precision Analogue Neuromorphic Computing

High-precision artificial synaptic devices compatible with existing CMOS technology are essential for realizing robust neuromorphic hardware systems with reliable parallel analogue computation beyond the von Neumann serial digital computing architecture. However, critical issues related to reliability and variability, such as nonlinearity and asymmetric weight updates, have been great challenges in the implementation of artificial synaptic devices in practical neuromorphic hardware systems. Herein, a robust three-terminal two-dimensional (2D) MoS₂ artificial synaptic device combined with a lithium silicate (LSO) solid-state electrolyte thin film is proposed. The rationally designed synaptic device exhibits excellent linearity and symmetry upon electrical potentiation and depression, benefiting from the reversible intercalation of Li ions into the MoS₂ channel. In particular, extremely low cycle-to-cycle variations (3.01%) during long-term potentiation and depression processes over 500 pulses are achieved, causing statistical analogue discrete states. Thus, a high classification accuracy of 96.77% (close to the software baseline of 98%) is demonstrated in the Modified National Institute of Standards and Technology (MNIST) simulations. These results provide a future perspective for robust synaptic device architecture of lithium solid-state electrolytes stacked with 2D van der Waals layered channels for high-precision analogue neuromorphic computing systems.

Research hotspot and trend analysis in the diagnosis of inflammatory bowel disease: A machine learning bibliometric analysis from 2012 to 2021

Aims

This study aimed to conduct a bibliometric analysis of the relevant literature on the diagnosis of inflammatory bowel disease (IBD), and show its current status, hot spots, and development trends.

Methods

The literature on IBD diagnosis was acquired from the Science Citation Index Expanded of the Web of Science Core Collection. Co-occurrence and cooperation relationship analysis of authors, institutions, countries, journals, references, and keywords in the literature were carried out through CiteSpace software and the Online Analysis platform of Literature Metrology. At the same time, the relevant knowledge maps were drawn, and the keywords cluster analysis and emergence analysis were performed.

Results

14,742 related articles were included, showing that the number of articles in this field has increased in recent years. The results showed that PEYRIN-BIROULET L from the University Hospital of Nancy-Brabois was the author with the most cumulative number of articles. The institution with the most articles was Mayo Clin, and the United States was far ahead in the article output and had a dominant role. Keywords analysis showed that there was a total of 818 keywords, which were mainly focused on the research of related diseases caused or coexisted by IBD, such as colorectal cancer and autoimmune diseases, and the diagnosis and treatment methods of IBD. Emerging analysis showed that future research hotspots and trends might be the treatment of IBD and precision medicine.

Conclusion

This research was the first bibliometric analysis of publications in the field of IBD diagnosis using visualization software and data information mining, and obtained the current status, hotspots, and development of this field. The future research hotspot might be the precision medicine of IBD, and the mechanism needed to be explored in depth to provide a theoretical basis for its clinical application.

Exploring the Underlying Mechanism of Ren-Shen-Bai-Du Powder for Treating Inflammatory Bowel Disease Based on Network Pharmacology and Molecular Docking

Ren-Shen-Bai-Du Powder (RSBDP) is currently used for inflammatory bowel disease (IBD) therapy in China. However, its potential mechanism against IBD remains unknown. In this study, we initially identified potential targets of RSBDP against IBD through network pharmacology analysis and molecular docking. Afterwards, the DSS-induced colitis mice model was employed to assess the effects of RSBDP. The results of network pharmacology indicated that a total of 39 main active ingredients in RSBDP generated 309 pairs of drug-ingredient and ingredient-target correspondences through 115 highly relevant targets of IBD. The primary ingredients (quercetin, kaempferol, luteolin, naringenin, and sitosterol) exerted functions through multiple targets that include CYP1B1, CA4/7, and ESR1/2, etc. GO functional enrichment analysis revealed that the targets related to IBD were significantly enriched in the oxidation-reduction process, protein binding, and cytosol. Per the KEGG pathway analysis, pathways in cancer, adherens junction, and nitrogen metabolism were pivotal in the RSBDP’s treatment of IBD. Additionally, molecular docking demonstrated that a set of active ingredients and their targets displayed good bonding capabilities (e.g., kaempferol and AhR with combined energy < 5 kcal/mol). For the animal experiment, oral RSBDP promoted weight recovery, reduced intestinal inflammation, and decreased serum IL-1, IL-6, and IL-8 concentrations in the DSS + RSBDP group. Meanwhile, oral RSBDP significantly up-regulated the mRNA levels of CA7, CPY1B1, and PTPN11; in particular, the expression level of CYP1B1 in the DSS + RSBDP group was up-regulated by as high as 9-fold compared to the DSS group. Western blot results indicated that the protein levels of AKR1C1, PI3K, AKT, p-AKT, and Bcl-2 were significantly down-regulated, and Bax was significantly up-regulated in the DSS + RSBDP group. Compared to the DSS and control groups, the Bax/Bcl-2 value in the DSS + RSBDP group increased 4-fold and 8-fold, respectively, which suggested that oral RSBDP promotes apoptosis of intestinal epithelial cells. In short, this study established quercetin, kaempferol, luteolin, naringenin, and sitosterol as the primary key active ingredients of RSBDP that exert synergistic therapeutic effects against IBD through modulating the AhR/CYP1B1 and AKR1C1/PI3K/AKT pathways.

Machine Learning Can Predict the Probability of Biologic Therapy in Patients with Inflammatory Bowel Disease

Background: Inflammatory bowel disease (IBD) is of high medical and socioeconomic relevance. Moderate and severe disease courses often require treatment with biologics. The aim of this study was to evaluate machine learning (ML)-based methods for the prediction of biologic therapy in IBD patients using a large prescription database. Methods: The present retrospective cohort study utilized a longitudinal prescription database (LRx). Patients with at least one prescription for an intestinal anti-inflammatory agent from a gastroenterologist between January 2015 and July 2021 were included. Patients who had received an initial biologic therapy prescription (infliximab, adalimumab, golimumab, vedolizumab, or ustekinumab) were categorized as the “biologic group”. The potential predictors included in the machine learning-based models were age, sex, and the 100 most frequently prescribed drugs within 12 months prior to the index date. Six machine learning-based methods were used for the prediction of biologic therapy. Results: A total of 122,089 patients were included in this study. Of these, 15,824 (13.0%) received at least one prescription for a biologic drug. The Light Gradient Boosting Machine had the best performance (accuracy = 74%) and was able to correctly identify 78.5% of the biologics patients and 72.6% of the non-biologics patients in the testing dataset. The most important variable was prednisolone, followed by lower age, mesalazine, budesonide, and ferric iron. Conclusions: In summary, this study reveals the advantages of ML-based models in predicting biologic therapy in IBD patients based on pre-treatment and demographic variables. There is a need for further studies in this regard that take into account individual patient characteristics, i.e., genetics and gut microbiota, to adequately address the challenges of finding optimal treatment strategies for patients with IBD.

Nutraceuticals for the Treatment of IBD: Current Progress and Future Directions

Inflammatory bowel disease (IBD) is a chronic relapsing-remitting inflammatory disease of the gastrointestinal tract. Patients are usually diagnosed in adolescence and early adulthood and need lifelong treatment. In recent years, it has been found that diet plays an important role in the pathogenesis of IBD. Diet can change intestinal barrier function, affect the structure and function of intestinal flora, and promote immune disorder, thus promoting inflammation. Many patients believe that diet plays a role in the onset and treatment of the disease and changes their diet spontaneously. This review provides some insights into how nutraceuticals regulate intestinal immune homeostasis and improve intestinal barrier function. We reviewed the research results of dietary fiber, polyphenols, bioactive peptides, and other nutraceuticals in the prevention and treatment of IBD and sought better alternative or supplementary treatment methods for IBD patients.

Commensal bacteria-derived extracellular vesicles suppress ulcerative colitis through regulating the macrophages polarization and remodeling the gut microbiota

Background

The extracellular vesicles (EVs) traffic constitutes an essential pathway of cellular communication. And the molecules in EVs produced by procaryotes help in maintaining homeostasis, addressing microbial imbalance and infections, and regulating the immune system. Despite the fact that Clostridium butyricum (C. butyricum) is commonly used for treating ulcerative colitis (UC), the potential role of C. butyricum-secreted EVs in commensals-host crosstalk remains unclear.

Results

Here, we performed flow cytometry, western blot, immunohistochemistry and 16S rRNA analysis to explore the role of C. butyricum-derived EVs on macrophage polarization and gut microbiota composition in a dextran sulfate sodium (DSS)-induced UC mouse model. The antibiotic cocktail-induced microbiome depletion and faecal transplantations were used to further investigate the mechanisms by which EVs regulate macrophage balance. Our findings showed that C. butyricum-derived EVs improved the remission of murine colitis and polarized the transformation of macrophages to the M2 type. Furthermore, C. butyricum-derived EVs restored gut dysbiosis and altered the relative abundance of Helicobacter, Escherichia-Shigella, Lactobacillus, Akkermansia and Bacteroides, which, in turn, faecal transplantations from EVs-treated mice relieved the symptoms of UC and improved the impact of EVs on the reprogramming of the M2 macrophages.

Conclusion

C. butyricum-derived EVs could protect against DSS-induced colitis by regulating the repolarization of M2 macrophages and remodelling the composition of gut microbiota, suggesting the potential efficacy of EVs from commensal and probiotic Clostridium species against UC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01812-6.

Fast continuous streaming sort in big streaming data environment under fixed-size single storage

Big streaming data environment concerns a complicated scenario where data to be processed continuously flow into a processing unit and certainly cause a memory overflow problem. This obstructs the adaptation of deploying all existing classic sorting algorithms because the data to be sorted must be entirely stored inside the fixed-size storage including the space in internal and external storage devices. Generally, it is always assumed that the size of each data chunk is not larger than the size of storage (M) but in fact the size of the entire stream (n) is usually much larger than M. In this paper, a new fast continuous streaming sorting is proposed to cope with the constraint of storage overflow. The algorithm was tested with various real data sets consisting of 10,000 to 17,000,000 numbers and different storage sizes ranging from 0.01n to 0.50n. It was found that the feasible lower bound of storage size is 0.35n with 100% sorting accuracy. The sorting time outperforms bubble sort, quick sort, insertion sort, and merge sort when data size is greater than 1,000,000 numbers. Remarkably, the sorting time of the proposed algorithm is 1,452 times less than the sorting time of external merge sort and 28.1767 times less than the sorting time of streaming data sort. The time complexity of proposed algorithm is O(n) while the space complexity is O(M).

Therapeutic effect of indirubin-loaded bovine serum albumin nanoparticules on ulcerative colitis

Indirubin is considered to have promising potential in the treatment of ulcerative colitis (UC). However, poor aqueous solubility and low bioavailability limit its clinical application. We produced indirubin-loaded bovine serum albumin nanoparticles (INPs) and characterized their drug encapsulation efficiency, drug-loading capacity, capacity to release indirubin in vitro and short-term physical stability. We also investigated the pharmacokinetics of INPs in mice. We then compared the curative effects of INPs and indirubin against dextran sulfate sodium-induced colitis in mice and 3D cultured biopsies from patients with UC. In the mouse model, the outcomes of INP treatment, including the disease activity index and serous levels of interleukin (IL)-1β and IL-10, were significantly different from those of indirubin treatment. Similarly, when we administered INPs and indirubin to the ex vivo colonic tissues of patients with UC, the effect of INPs was stronger than that of indirubin for most antioxidant and anti-inflammatory biomarkers. The results of both the animal trial and ex vivo experiment indicate that the therapeutic effect of indirubin was further enhanced by the carrier system, making it a highly promising medical candidate for UC.

Artificial intelligence-rationalized balanced PPARα/γ dual agonism resets dysregulated macrophage processes in inflammatory bowel disease

A computational platform, Boolean network explorer (BoNE), has recently been developed to infuse AI-enhanced precision into drug discovery; it enables invariant Boolean Implication Networks of disease maps for prioritizing high-value targets. Here we used BoNE to query an Inflammatory Bowel Disease (IBD)-map and prioritize a therapeutic strategy that involves dual agonism of two nuclear receptors, PPARα/γ. Balanced agonism of PPARα/γ was predicted to modulate macrophage processes, ameliorate colitis, 'reset' the gene expression network from disease to health. Predictions were validated using a balanced and potent PPARα/γ-dual-agonist (PAR5359) in Citrobacter rodentium- and DSS-induced murine colitis models. Using inhibitors and agonists, we show that balanced-dual agonism promotes bacterial clearance efficiently than individual agonists, both in vivo and in vitro. PPARα is required and sufficient to induce the pro-inflammatory cytokines and cellular ROS, which are essential for bacterial clearance and immunity, whereas PPARγ-agonism blunts these responses, delays microbial clearance; balanced dual agonism achieved controlled inflammation while protecting the gut barrier and 'reversal' of the transcriptomic network. Furthermore, dual agonism reversed the defective bacterial clearance observed in PBMCs derived from IBD patients. These findings not only deliver a macrophage modulator for use as barrier-protective therapy in IBD, but also highlight the potential of BoNE to rationalize combination therapy.

Review article: randomised controlled trials in inflammatory bowel disease-common challenges and potential solutions

BACKGROUND

Recruitment rates for Crohn's disease and ulcerative colitis clinical trials continue to decrease annually. The inability to reach recruitment targets and complete trials has serious implications for stakeholders in the inflammatory bowel disease (IBD) community. Action is required to ensure patients with an unmet medical need have access to new therapies to improve the management of their IBD.

AIMS

Identify challenges contributing to recruitment decline in IBD clinical trials and propose potential solutions.

METHODS

PubMed and Google were used to identify literature, regulatory guidelines and conference proceedings related to IBD clinical trials and related concepts. Data on IBD clinical trials conducted between 1989 and 2020 were extracted from the Trialtrove database.

RESULTS

Key aspects that may improve recruitment rates were identified. An increasingly patient-centric approach should be taken to study design including improvements to the readability of key trial documentation and inclusion of patient representatives in trial planning. Placebo is unappealing to patients; approaches including platform trials should be explored to minimise placebo exposure. Non-invasive imaging, biomarkers and novel digital endpoints should continue to be examined to reduce the burden on patients. Reducing the administrative burden associated with trials via the use of electronic signatures, for example, may benefit study sites and investigators. Changes implemented to IBD trials during the COVID-19 pandemic provided examples of how trial conduct can be rapidly and constructively adapted.

CONCLUSIONS

To improve recruitment in Crohn's disease and ulcerative colitis trials, the IBD community should address a broad range of issues related to clinical trial conduct.

Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project

Background

The exploration of clinically relevant information in the free text of electronic health records (EHRs) holds the potential to positively impact clinical practice as well as knowledge regarding Crohn disease (CD), an inflammatory bowel disease that may affect any segment of the gastrointestinal tract. The EHRead technology, a clinical natural language processing (cNLP) system, was designed to detect and extract clinical information from narratives in the clinical notes contained in EHRs.

Objective

The aim of this study is to validate the performance of the EHRead technology in identifying information of patients with CD.

Methods

We used the EHRead technology to explore and extract CD-related clinical information from EHRs. To validate this tool, we compared the output of the EHRead technology with a manually curated gold standard to assess the quality of our cNLP system in detecting records containing any reference to CD and its related variables.

Results

The validation metrics for the main variable (CD) were a precision of 0.88, a recall of 0.98, and an F1 score of 0.93. Regarding the secondary variables, we obtained a precision of 0.91, a recall of 0.71, and an F1 score of 0.80 for CD flare, while for the variable vedolizumab (treatment), a precision, recall, and F1 score of 0.86, 0.94, and 0.90 were obtained, respectively.

Conclusions

This evaluation demonstrates the ability of the EHRead technology to identify patients with CD and their related variables from the free text of EHRs. To the best of our knowledge, this study is the first to use a cNLP system for the identification of CD in EHRs written in Spanish.

Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning

BACKGROUND

Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.

AIM

To accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice.

METHODS

We built a training model cohort and validated our result in a separate cohort. We used LASSO and Ridge regressions, Support Vector Machines, Random Forests and Neural Networks to balance between complexity and interpretability and analyzed their relative performances and reported the strongest predictors to the respective models. The participants in our study were patients with IBD selected from The OptumLabs® Data Warehouse (OLDW), a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data.

RESULTS

We included 72,178 and 69,165 patients in the training and validation set, respectively. In total, 4.1% of patients in the validation set were hospitalized, 2.9% needed IBD-related surgeries, 17% used long-term steroids and 13% of patients were initiated with biological therapy. Of the AI models we tested, the Random Forest and LASSO resulted in high accuracies (AUCs 0.70-0.92). Our artificial neural network performed similarly well in most of the models (AUCs 0.61-0.90).

CONCLUSIONS

This study demonstrates feasibility of accurately predicting adverse outcomes using complex and novel AI models on large longitudinal data sets of patients with IBD. These models could be applied for risk stratification and implementation of preemptive measures to avoid adverse outcomes in a clinical setting.

Digital Health Technologies for Remote Monitoring and Management of Inflammatory Bowel Disease: A Systematic Review

INTRODUCTION

Digital health technologies may be useful tools in the management of chronic diseases. We performed a systematic review of digital health interventions in the management of patients with inflammatory bowel diseases (IBD) and evaluated its impact on (i) disease activity monitoring, (ii) treatment adherence, (iii) quality of life (QoL) measures, and/or (iv) health care utilization.

METHODS

Through a systematic review of multiple databases through August 31, 2020, we identified randomized controlled trials in patients with IBD comparing digital health technologies vs standard of care (SoC) for clinical management and monitoring and reporting impact on IBD disease activity, treatment adherence, QoL, and/or health care utilization or cost-effectiveness. We performed critical qualitative synthesis of the evidence supporting digital health interventions in patients with IBD and rated certainty of evidence using Grading of Recommendations Assessment, Development and Evaluation.

RESULTS

Overall, we included 14 randomized controlled trials (median, 98 patients; range 34-909 patients; follow-up <12 months) that compared web-based interventions, mobile applications, and different telemedicine platforms with SoC (clinic-based encounters). Although overall disease activity and risk of relapse were comparable between digital health technologies and SoC (very low certainty of evidence), digital health interventions were associated with lower rate of health care utilization and health care costs (low certainty of evidence). Digital health interventions did not significantly improve patients' QoL and treatment adherence compared with SoC (very low certainty of evidence). Trials may have intrinsic selection bias due to nature of digital interventions.

DISCUSSION

Digital health technologies may be effective in decreasing health care utilization and costs, though may not offer advantage in reducing risk of relapse, QoL, and improving treatment adherence in patients with IBD. These techniques may offer value-based care for population health management.

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.

A recent update on the use of Chinese medicine in the treatment of inflammatory bowel disease

BACKGROUND

Inflammatory bowel disease (IBD) is a chronic idiopathic disease that is characterized by inflammation of the gastrointestinal tract. Proper management of IBD requires both early diagnosis and novel therapies and management programs. Many reports have suggested that Chinese medicine has unique properties favorable to the treatment of IBD. However, there are no systematic analyses on this topic.

PURPOSE

This review summarizes recent studies that assessed the effects and mechanisms of Chinese medicine in the treatment of IBD in order to fully understand the advantages of Chinese medicine in the management of IBD.

METHODS

A literature search was conducted using peer-reviewed and clinical databases, including PubMed, Web of Science, ClinicalTrials.gov, MEDLINE, EMBASE, Springer LINK, Wan-fang database, the Chinese Biomedicine Database, and the China National Knowledge Infrastructure (CNKI). Keywords used were inflammatory bowel disease (including Ulcerative colitis or Crohn's disease) and Chinese medicine. All selected articles were from 1997 to 2021, and each were assessed critically for our exclusion criteria. Studies describing the pathogenesis of IBD, the effects and mechanisms of Chinese medicine in the treatment of IBD, in particular their roles in immune regulation, intestinal flora regulation, and improvement of intestinal barrier function, were included.

CONCLUSION

This review highlights recent progress in the use of Chinese medicine in the treatment of IBD. It also provides a reference for further evaluation and exploration of the potential of classical multi-herbal Chinese medicine in the treatment of IBD.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

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.

Predictors: How to Approach the Individualization of Treatment

The "big data" in inflammatory bowel disease opens a route of detection of predictors that can determine the prognosis of each patient and allow the individualized selection of more efficient and safe therapeutic strategies. Immune biomarkers are a promising field to explore thoroughly.

The role of alcohol dehydrogenase 1C in regulating inflammatory responses in ulcerative colitis

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease caused by an interaction of genetics, immune responses, and environmental factors. However, the precise pathogenesis of UC remains poorly understood. The aim of the present study was to explore the potential target genes implicated in UC and to elucidate its underlying pathogenic mechanism. Firstly, three UC-associated microarray datasets (GSE75214, GSE87466, and GSE92517) were obtained to screen the differentially expressed genes (DEGs). As a result, alcohol dehydrogenase 1C (ADH1C) was the most significantly downregulated DEG in UC. We confirmed that ADH1C was downregulated in colonic tissue taken from patients with UC and colitis mice. Moreover, ADH1C expression was also decreased in colonic cell lines (NCM460 and HT29) treated with mixed proinflammatory cytokines (TNF-α, IFN-γ, IL-1β). Interestingly, we found that overexpression of ADH1C could distinctly decrease the production of IL-6 and IL-8. To elucidate the molecular mechanism of ADH1C in UC, gene set enrichment analysis (GSEA) was performed and demonstrated that immune-related pathways were mainly enriched in the low ADH1C group. Further studies displayed that STAT1/NF-κB pathway was activated in colitis mice and inflammatory cell model. Importantly, overexpression of ADH1C could suppress the phosphorylation of STAT1 and IκB. Therefore, ADH1C might regulate inflammatory responses through STAT1/NF-κB pathway. In conclusion, ADH1C was downregulated during inflammation, and its increased expression could inhibit the activation of STAT1/NF-κB pathway, thereby alleviating the secretion of IL-6 and IL-8. These findings indicate that ADH1C may be a potential therapeutic target for UC therapy.

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

BACKGROUND AND AIMS

There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases [IBD]. We synthesised and critically appraised studies comparing machine learning vs traditional statistical models, using routinely available clinical data for risk prediction in IBD.

METHODS

Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harbouring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment [PROBAST] tool.

RESULTS

We included 13 studies on machine learning-based prediction models in IBD, encompassing themes of predicting treatment response to biologics and thiopurines and predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learning models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated.

CONCLUSIONS

Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

Serum exosomal pregnancy zone protein as a promising biomarker in inflammatory bowel disease

BACKGROUND

Inflammatory bowel disease (IBD) is a kind of intestinal immune dysfunction disease, and its occurrence and prevalence are on the rise worldwide. As a chronic gastrointestinal disease, its pathogenesis is still unknown. Exosomes are vesicles in various body fluids that carry a variety of substances. They can mediate intercellular communication and long-distance transport of multiple media. In this study, we investigated the protein profile of serum exosomes from healthy people and IBD patients to explore a new serological biomarker for IBD.

METHODS

Initially, exosomes were extracted from serum samples, and the proteins within the exosomes were identified by label-free liquid chromatography/mass spectrometry (LC-MS/MS). Western blot and ELISA were used to assess the identified protein. To further analyze the target protein, an acute colitis mouse model was established, and exosomes in colonic tissue and serum were extracted to investigate the protein in them.

RESULTS

Firstly, serum exosomes were extracted from samples, and proteins in exosomes were identified by LC-MS/MS. Through statistical analysis, we identified 633 proteins. Among these proteins, pregnancy zone protein (PZP) showed a marked difference between patients with IBD and healthy people, in that its expression level was much higher in the IBD patients This exosomal protein was associated with immunosuppressive effects. Also, the level of PZP in colon tissue exosomes and serum exosomes of acute colitis mice was significantly higher than that of the control group.

CONCLUSIONS

Our findings indicated that serum exosome PZP was present at a high level in the IBD patients. Hence it might be a promising biomarker and enhance auxiliary diagnosis of IBD.

Designing Consumer Health Information Technology to Support Biform and Articulation Work: A Qualitative Study of Diet and Nutrition Management as Patient Work

BACKGROUND

Diet and nutrition management is an integral component of Crohn disease (CD) management. This type of management is highly variable and individualized and, thus, requires personalized approaches. Consumer health information technology (CHIT) designed to support CD management has typically supported this task as everyday life work and, not necessarily, as illness work. Moreover, CHIT has rarely supported the ways in which diet and nutrition management requires coordination between multiple forms of patient work.

OBJECTIVE

The purpose of this study was to investigate diet and nutrition management as biform work, identify components of articulation work, and provide guidance on how to design CHIT to support this work.

METHODS

We performed a qualitative study in which we recruited participants from CD-related Facebook pages and groups.

RESULTS

Semistructured interviews with 21 individuals showed that diet and nutrition management strategies were highly individualized and variable. Four themes emerged from the data, emphasizing the interactions of diet and nutrition with physical, emotional, information, and technology-enabled management.

CONCLUSIONS

This study shows that the extent to which diet and nutrition management is biform work fluctuates over time and that articulation work can be continuous and unplanned. The design guidance specifies the need for patient-facing technologies to support interactions among diet and nutrition and other management activities such as medication intake, stress reduction, and information seeking, as well as to respond to the ways in which diet and nutrition management needs change over time.

Superior treatment persistence with ustekinumab in Crohn's disease and vedolizumab in ulcerative colitis compared with anti-TNF biological agents: real-world registry data from the Persistence Australian National IBD Cohort (PANIC) study

BACKGROUND

Medication persistence contributes real-world evidence about treatment effectiveness, tolerability and prescriber and patient acceptability.

AIMS

To evaluate persistence of biological agents in Crohn's disease (CD) and ulcerative colitis (UC) and the effects of immunomodulator use and treatment lines.

METHODS

Retrospective national population-based data on treatment persistence for adalimumab, infliximab vedolizumab and ustekinumab for CD and UC were analysed from the Australian Pharmaceutical Benefits Scheme using Kaplan-Meier analysis and Cox proportional hazards models.

RESULTS

There were 2499 patients included with 8219 person-years of follow-up. In CD patients ustekinumab had increased persistence compared to anti-TNF agents (HR: 1.79, 95%CI: 1.32-2.38, P < 0.01). Twelve-month CD persistence rates were ustekinumab 80.0%, vedolizumab 73.5%, infliximab 68.1% and adalimumab 64.2% (P = 0.01). In moderate-severe UC vedolizumab had increased persistence compared to anti-TNF agents (HR: 1.67, 95% CI: 1.27-2.18 P < 0.001). Twelve-month UC persistence rates were vedolizumab 73.4%, infliximab 61.1% and adalimumab 45.5% (P < 0.001). Immunomodulator co-therapy did not significantly increase persistence in non-anti-TNF therapy (P > 0.05). Thiopurines increased persistence of anti-TNF agents in CD (P < 0.001) and UC (P = 0.03). Methotrexate co-therapy increased persistence of anti-TNF agents in CD (P = 0.001) only. First-line therapy was superior to non-first line in persistence (P < 0.001). In fistulising CD, the persistence of infliximab and adalimumab was not significantly different (P = 0.11).

CONCLUSION

Persistence was highest in ustekinumab in CD and vedolizumab in UC. Factors which increased the persistence of biological agents are first-line therapy, and immunomodulator co-therapy in anti-TNF agent use.

Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics

Disruptive innovation is an invention that disrupts an existing market and creates a new one by providing a different set of values, which ultimately overtakes the existing market. Typically, when disruptive innovations are introduced, their performance is initially less than existing standard technologies, but because of their ability to bring the cost down, and with gradual improvement, they end up replacing established service standards.Disruptive technologies have their fingerprints in health care. Pathology and laboratory medicine are fertile soils for disruptive innovations because they are heavily reliant on technology. Disruptive innovations have resulted in a revolution of our diagnostic ability and will take laboratory medicine to the next level of patient care. There are several examples of disruptive innovations in the clinical laboratory. Digitizing pathology practice is an example of disruptive technology, with many advantages and an extended scope of applications. Next-generation sequencing can be disruptive in two ways. The first is by replacing an array of laboratory tests, which each requires expensive and specialized instruments and expertise, with a single cost-effective technology. The second is by disrupting the current paradigm of the clinical laboratory as a diagnostic service by taking it into a new era of preventive or primary care pathology. Other disruptive innovations include the use of dry chemistry reagents in chemistry analyzers and also point of care testing. The use of artificial intelligence is another promising disruptive innovation that can transform the future of pathology and laboratory medicine. Another emerging disruptive concept is the integration of two fields of medicine to create an interrelated discipline such as "histogenomics and radiohistomics." Another recent disruptive innovation in laboratory medicine is the use of social media in clinical practice, education, and publication.There are multiple reasons to encourage disruptive innovations in the clinical laboratory, including the escalating cost of health care, the need for better accessibility of diagnostic care, and the increased demand on the laboratory in the era of precision diagnostics. There are, however, a number of challenges that need to be overcome such as the significant resistance to disruptive innovations by current technology providers and governmental regulatory bodies. The hesitance from health care providers and insurance companies must also be addressed.Adoption of disruptive innovations requires a multifaceted approach that involves orchestrated solutions to key aspects of the process, including creating successful business models, multidisciplinary collaborations, and innovative accreditation and regulatory oversight. It also must be coupled with successful commercialization plans and modernization of health care structure. Fostering a culture of disruptive innovation requires establishing unique collaborative models between academia and industry. It also requires uncovering new sources of unconventional funding that are open to high-risk high-reward projects. It should also be matched with innovative thinking, including new approaches for delivery of care and identifying novel cohorts of patients who can benefit from disruptive technology.

Improving early diagnosis of rare diseases using Natural Language Processing in unstructured medical records: an illustration from Dravet syndrome

Background

The growing use of Electronic Health Records (EHRs) is promoting the application of data mining in health-care. A promising use of big data in this field is to develop models to support early diagnosis and to establish natural history. Dravet Syndrome (DS) is a rare developmental and epileptic encephalopathy that commonly initiates in the first year of life with febrile seizures (FS). Age at diagnosis is often delayed after 2 years, as it is difficult to differentiate DS at onset from FS. We aimed to explore if some clinical terms (concepts) are significantly more used in the electronic narrative medical reports of individuals with DS before the age of 2 years compared to those of individuals with FS. These concepts would allow an earlier detection of patients with DS resulting in an earlier orientation toward expert centers that can provide early diagnosis and care.

Methods

Data were collected from the Necker Enfants Malades Hospital using a document-based data warehouse, Dr Warehouse, which employs Natural Language Processing, a computer technology consisting in processing written information. Using Unified Medical Language System Meta-thesaurus, phenotype concepts can be recognized in medical reports. We selected individuals with DS (DS Cohort) and individuals with FS (FS Cohort) with confirmed diagnosis after the age of 4 years. A phenome-wide analysis was performed evaluating the statistical associations between the phenotypes of DS and FS, based on concepts found in the reports produced before 2 years and using a series of logistic regressions.

Results

We found significative higher representation of concepts related to seizures’ phenotypes distinguishing DS from FS in the first phases, namely the major recurrence of complex febrile convulsions (long-lasting and/or with focal signs) and other seizure-types. Some typical early onset non-seizure concepts also emerged, in relation to neurodevelopment and gait disorders.

Conclusions

Narrative medical reports of individuals younger than 2 years with FS contain specific concepts linked to DS diagnosis, which can be automatically detected by software exploiting NLP. This approach could represent an innovative and sustainable methodology to decrease time of diagnosis of DS and could be transposed to other rare diseases.

Dysbiosis of gut microbiota in inflammatory bowel disease: Current therapies and potential for microbiota-modulating therapeutic approaches

There is a growing body of evidence reinforcing the unique connections between the host microbiome, health, and diseases. Due to the extreme importance of the symbiotic relationship between the intestinal microbiome and the host, it is not surprising that any alteration in the gut microbiota would result in various diseases, including inflammatory bowel disease (IBD), Crohn's disease, (CD) and ulcerative colitis (UC). IBD is a chronic, relapsing-remitting condition that is associated with significant morbidity, mortality, compromised quality of life, and costly medical care. Dysbiosis is believed to exacerbate the progression of IBD. One of the currently used treatments for IBD are anti-tumor necrosis factor (TNF) drugs, representing a biologic therapy that is reported to have an impact on the gut microbiota composition. The efficacy of anti-TNF agents is hindered by the possibility of non-response, which occurs in 10-20% of treated patients, and secondary loss of response, which occurs in up to 30% of treated patients. This underscores the need for novel therapies and studies that evaluate the role of the gut microbiota in these conditions. The success of any therapeutic strategy for IBD depends on our understanding of the interactions that occur between the gut microbiota and the host. In this review, the health and disease IBD-associated microbiota patterns will be discussed, in addition to the effect of currently used therapies for IBD on the gut microbiota composition, as well as new therapeutic approaches that can be used to overcome the current treatment constraints.

Serum IgG4 Subclass Deficiency Defines a Distinct, Commonly Encountered, Severe Inflammatory Bowel Disease Subtype

BACKGROUND

Immunoglobulin G subclass 4 (IgG4) is hypothesized to play an immunomodulatory role, downregulating humoral immune responses. The role of this anti-inflammatory molecule in inflammatory bowel disease (IBD) has not been fully characterized. We sought to define alterations in serum IgG4 in patients with IBD and their association with multiyear disease severity.

METHODS

We analyzed metadata derived from curated electronic health records from consented patients with IBD prospectively followed at a tertiary center over a 10-year time period. Patients with IBD with IgG4 serum levels available formed the study population. Demographics and multiyear clinical data were collected and analyzed. We stratified patients with IBD with low, normal, or high serum IgG4 levels.

RESULTS

We found IgG4 characterized in 1193 patients with IBD and low IgG4 levels in 233 patients (20%) and elevated IgG4 levels in 61 patients (5%). An IgG4 deficiency did not significantly correlate with other antibody deficiencies. In a multiple Poisson regression analysis, low IgG4 was associated with more years on biologic agents (P = 0.002) and steroids (P = 0.049) and more hospital admissions (P < 0.001), clinic visits (P = 0.010), outpatient antibiotic prescriptions (P < 0.001), and CD-related surgeries (P = 0.011) during the study period after controlling for certain confounders. Elevated IgG4 was only associated with primary sclerosing cholangitis (P = 0.011). A cohort of patients with IgG4-deficient severe IBD received intravenous Ig replacement therapy, which benefited and was continued in 10 out of 11 individuals.

CONCLUSIONS

An IgG4 subclass deficiency, distinct from other antibody deficiencies, occurred commonly in a referral IBD population and was associated with multiple markers of disease severity. This is the first association of IgG4 subclass deficiency with an inflammatory disease process. Further work is needed to define the mechanistic role of IgG4 deficiency in this severe IBD subgroup.

IBD Systems Biology Is Here to Stay

BACKGROUND

Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results.

METHODS

Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed.

RESULTS

The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies.

CONCLUSIONS

The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.