The precise role of periostin, an extra-cellular matrix protein, in inflammatory bowel disease (IBD) is unclear. Here, we investigated periostin in paediatric IBD including its relationship with ...disease activity, clinical outcomes, genomic variation and expression in the colonic tissue. Plasma periostin was analysed using ELISA in 144 paediatric patients and 38 controls. Plasma levels were assessed against validated disease activity indices in IBD and clinical outcomes. An immuno-fluorescence for periostin and detailed isoform-expression analysis in the colonic tissue was performed in 23 individuals. We integrated a whole-gene based burden metric 'GenePy' to assess the impact of variation in POSTN and 23 other genes functionally connected to periostin. We found that plasma periostin levels were significantly increased during remission compared to active Crohn's disease. The immuno-fluorescence analysis demonstrated enhanced peri-cryptal ring patterns in patients compared to controls, present throughout inflamed, as well as macroscopically non-inflamed colonic tissue. Interestingly, the pattern of isoforms remained unchanged during bowel inflammation compared to healthy controls. In addition to its role during the inflammatory processes in IBD, periostin may have an additional prominent role in mucosal repair. Additional studies will be necessary to understand its role in the pathogenesis, repair and fibrosis in IBD.
Abstract
Crohn’s disease (CD) is characterised by chronic inflammation. We aimed to identify a relationship between plasma inflammatory metabolomic signature and genomic data in CD using blood plasma ...metabolic profiles. Proton NMR spectroscopy were achieved for 228 paediatric CD patients. Regression (OPLS) modelling and machine learning (ML) approaches were independently applied to establish the metabolic inflammatory signature, which was correlated against gene-level pathogenicity scores generated for all patients and functional enrichment was analysed. OPLS modelling of metabolomic spectra from unfasted patients revealed distinctive shifts in plasma metabolites corresponding to regions of the spectrum assigned to
N
-acetyl glycoprotein, glycerol and phenylalanine that were highly correlated (R
2
= 0.62) with C-reactive protein levels. The same metabolomic signature was independently identified using ML to predict patient inflammation status. Correlation of the individual peaks comprising this metabolomic signature of inflammation with pathogenic burden across 15,854 unselected genes identified significant enrichment for genes functioning within ‘intrinsic component of membrane’ (
p
= 0.003) and ‘inflammatory bowel disease (IBD)’ (
p
= 0.003). The seven genes contributing IBD enrichment are critical regulators of pro-inflammatory signaling. Overall, a metabolomic signature of inflammation can be detected from blood plasma in CD. This signal is correlated with pathogenic mutation in pro-inflammatory immune response genes.
Monogenic inflammatory bowel disease (IBD) comprises rare Mendelian causes of gut inflammation, often presenting in infants with severe and atypical disease. This study aimed to identify clinically ...relevant variants within 68 monogenic IBD genes in an unselected pediatric IBD cohort.
Whole exome sequencing was performed on patients with pediatric-onset disease. Variants fulfilling the American College of Medical Genetics criteria as "pathogenic" or "likely pathogenic" were assessed against phenotype at diagnosis and follow-up. Individual patient variants were assessed and processed to generate a per-gene, per-individual, deleteriousness score.
Four hundred one patients were included, and the median age of disease-onset was 11.92 years. In total, 11.5% of patients harbored a monogenic variant. TRIM22-related disease was implicated in 5 patients. A pathogenic mutation in the Wiskott-Aldrich syndrome (WAS) gene was confirmed in 2 male children with severe pancolonic inflammation and primary sclerosing cholangitis. In total, 7.3% of patients with Crohn's disease had apparent autosomal recessive, monogenic NOD2-related disease. Compared with non-NOD2 Crohn's disease, these patients had a marked stricturing phenotype (odds ratio 11.52, significant after correction for disease location) and had undergone significantly more intestinal resections (odds ratio 10.75). Variants in ADA, FERMT1, and LRBA did not meet the criteria for monogenic disease in any patients; however, case-control analysis of mutation burden significantly implicated these genes in disease etiology.
Routine whole exome sequencing in pediatric patients with IBD results in a precise molecular diagnosis for a subset of patients with IBD, providing the opportunity to personalize therapy. NOD2 status informs risk of stricturing disease requiring surgery, allowing clinicians to direct prognosis and intervention.
Abstract
Background
Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential ...to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time.
Methods
On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure (“machine learning” OR “artificial intelligence”) AND (“Crohn* Disease” OR “Ulcerative Colitis” OR “Inflammatory Bowel Disease”). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research.
Results
Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation.
Discussion
Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.
Summary
Background
Anti‐tumour necrosis factor‐α (anti‐TNF) therapy use has risen in paediatric‐onset inflammatory bowel disease (PIBD). Whether this has translated into preventing/delaying childhood ...surgery is uncertain. The Wessex PIBD cohort was analysed for trends in anti‐TNF‐therapy and surgery.
Aim
To assess patients diagnosed with PIBD within Wessex from 1997 to 2017. The prevalence of anti‐TNF‐therapy and yearly surgery rates (resection and perianal) during childhood (<18 years) were analysed (Pearson's correlation, multivariate regression, Fisher's exact).
Results
Eight‐hundred‐and‐twenty‐five children were included (498 Crohn's disease, 272 ulcerative colitis, 55 IBD‐unclassified), mean age at diagnosis 13.6 years (1.6‐17.6), 39.6% female. The prevalence of anti‐TNF‐treated patients increased from 5.1% to 27.1% (2007‐2017), P = 0.0001. Surgical resection‐rate fell (7.1%‐1.5%, P = 0.001), driven by a decrease in Crohn's disease resections (8.9%‐2.3%, P = 0.001). Perianal surgery and ulcerative colitis resection‐rates were unchanged. Time from diagnosis to resection increased (1.6‐2.8 years, P = 0.028) but mean age at resection was unchanged. Patients undergoing resections during childhood were diagnosed at a younger age in the most recent 5 years (2007‐2011 = 13.1 years, 2013‐2017 = 11.9 years, P = 0.014).
Resection‐rate in anti‐TNF‐therapy treated (16.1%) or untreated (12.2%) was no different (P = 0.25). Patients started on anti‐TNF‐therapy <3 years post‐diagnosis (11.6%) vs later (28.6%) had a reduction in resections, P = 0.047. Anti‐TNF‐therapy prevalence was the only significant predictor of resection‐rate using multivariate regression (P = 0.011).
Conclusions
The prevalence of anti‐TNF‐therapy increased significantly, alongside a decrease in surgical resection‐rate. Patients diagnosed at younger ages still underwent surgery during childhood. Anti‐TNF‐therapy may reduce the need for surgical intervention in childhood, thereby influencing the natural history of PIBD.
Abstract
Background
Inflammatory bowel disease IBD is a chronic inflammatory disorder with two main subtypes: Crohn’s disease CD and ulcerative colitis UC. Prompt subtype diagnosis enables the ...correct treatment to be administered. Using genomic data, we aimed to assess machine learning ML to classify patients according to IBD subtype.
Methods
Whole exome sequencing WES from paediatric/adult IBD patients was processed using an in-house bioinformatics pipeline. These data were condensed into the per-gene, per-individual genomic burden score, GenePy. Data were split into training and testing datasets 80/20. Feature selection with a linear support vector classifier, and hyperparameter tuning with Bayesian Optimisation, were performed training data. The supervised ML method random forest was utilised to classify patients as CD or UC, using three panels: 1 all available genes; 2 autoimmune genes; 3 ‘IBD’ genes. ML results were assessed using area under the receiver operating characteristics curve AUROC, sensitivity, and specificity on the testing dataset.
Results
A total of 906 patients were included in analysis 600 CD, 306 UC. Training data included 488 patients, balanced according to the minority class of UC. The autoimmune gene panel generated the best performing ML model AUROC = 0.68, outperforming an IBD gene panel AUROC = 0.61. NOD2 was the top gene for discriminating CD and UC, regardless of the gene panel used. Lack of variation in genes with high GenePy scores in CD patients was the best classifier of a diagnosis of UC.
Discussion
We demonstrate promising classification of patients by subtype using random forest and WES data. Focusing on specific subgroups of patients, with larger datasets, may result in better classification.
Up to 25% of inflammatory bowel disease (IBD) presents during childhood, often with severe and extensive disease, leading to significant morbidity including delayed growth and nutritional impairment. ...The classical approach to management has centred on differentiation into Crohn's disease (CD) or ulcerative colitis (UC), with subsequent treatment based on symptoms, results and complications. However, IBD is a heterogeneous condition with substantial variation in phenotype, disease course and outcome, so whilst effective treatment exists one size does not fit all. The ability to predict disease course at diagnosis, alongside tailoring medications based on response gives the potential for a more 'personalised approach'. The move to a pre-emptive strategy to prevent IBD-related complications, whilst simultaneously minimising side effects and long-term toxicity from therapy, particularly in those with relatively indolent disease, has the potential to revolutionise care. In very early-onset IBD, personalised approaches to diagnosis and management have become the standard of treatment enabling clinicians to significantly alter the outcomes of the few children with monogenic disease. However, the promise of discoveries in genomics, microbiome and transcriptomics in paediatric IBD has not yet translated to clinical application for the vast majority of patients. Despite this, the opportunity presents itself to apply data gathered at diagnosis and follow-up to predict which patients are likely to progress to complicated disease, which will respond well and which will require additional therapy. Using complex mathematics and innovative, cutting-edge machine learning (ML) techniques gives the potential to use this data to develop personalised clinical care algorithms to treat patients more effectively, reduce toxicity and improve outcome. In this review, we will consider current management of paediatric IBD, discuss how precision medicine is making inroads into clinical practice already, examine the contemporary studies applying data to stratify patients and explore how future management may be revolutionised by personalisation with clinical, genomic and other multi-omic data.
Pediatric inflammatory bowel disease (PIBD) is associated with a diagnostic delay. Blood tests are a routine part of the work-up in children with chronic abdominal symptoms (pain, diarrhea). Normal ...blood tests cannot exclude PIBD. We analyzed blood results at diagnosis over a 5-year period.
Patients diagnosed from 2013 to 2017 were identified from the Southampton-PIBD database. Results were obtained up to 100 days before diagnostic endoscopy. Erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), albumin, hemoglobin, platelets, packed cell volume (PCV), white cell count (WCC), and alanine transferase (ALT) were analyzed. Hierarchical clustering was applied to normalized results.
Two hundred fifty-six patients were included (Crohn's disease CD, 151; ulcerative colitis UC, 95; IBD-unclassified, 10; median age, 13.48 years; 36.7% female). Hierarchical clustering of patients revealed novel groupings enriched for CD and UC, characterized by specific patterns of results. In PIBD, 9% presented with all normal blood tests, 21.9% with normal CRP and ESR. Abnormal results were seen in all tests (ESR, 56.4% of patients; CRP, 53.4%; albumin, 28%; hemoglobin, 61.9%; platelets, 55.6%; PCV, 64.6%; WCC, 22.7%; and ALT, 7.2%). Normal inflammatory markers were more common in UC compared with CD (UC, 34%; CD, 15.8%; P = 0.0035). UC (14.4% normal) presented with all normal results more frequently than CD (CD, 5.3%; P = 0.02). CRP, ESR, and platelets were significantly higher in CD compared with UC. Albumin and hemoglobin were significantly lower.
Most cases of PIBD present with >1 abnormal blood result, although 1/11 patients presents with normal blood tests and 1/5 present with normal inflammatory markers. Hierarchical clustering offers the potential to produce novel groupings to inform disease categorization and best management.
ABSTRACT
Objectives:
The current classification of inflammatory bowel disease (IBD) is based on clinical phenotypes, which is blind to the molecular basis of the disease. The aim of this study was to ...stratify a treatment‐naïve paediatric IBD cohort through specific innate immunity pathway profiling and application of unsupervised machine learning (UML).
Methods:
In order to test the molecular integrity of biological pathways implicated in IBD, innate immune responses were assessed at diagnosis in 22 paediatric patients and 10 age‐matched controls. Peripheral blood mononuclear cells (PBMCs) were selectively stimulated for assessing the functionality of upstream activation receptors including NOD2, toll‐like receptor (TLR) 1‐2 and TLR4, and the downstream cytokine responses (IL‐10, IL‐1β, IL‐6, and TNF‐α) using multiplex assays. Cytokine data generated were subjected to hierarchical clustering to assess for patient stratification.
Results:
Combined immune responses in patients across 12 effector responses were significantly reduced compared with controls (P = 0.003) and driven primarily by “hypofunctional” TLR responses (P values 0.045, 0.010, and 0.018 for TLR4‐mediated IL‐10, IL‐1β, and TNF‐α, respectively; 0.018 and 0.015 for TLR1‐2 ‐mediated IL‐10 and IL‐1β). Hierarchical clustering generated 3 distinct clusters of patients and a fourth group of “unclustered” individuals. No relationship was observed between the observed immune clusters and the clinical disease phenotype.
Conclusions:
Although a clinically useful outcome was not observed through hierarchical clustering, our study provides a rationale for using an UML approach to stratify patients. The study also highlights the predominance of hypo‐inflammatory innate immune responses as a key mechanism in the pathogenesis of IBD.
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