The Clinical Significance of Eosinophils in Ulcerative Colitis: A Systematic Review

A comprehensive immunobiology review of IBD: With a specific glance to Th22 lymphocytes development, biology, function, and role in IBD

The two primary forms of inflammatory disorders of the small intestine andcolon that make up inflammatory bowel disease (IBD) are ulcerative colitis (UC) and Crohn's disease (CD). While ulcerative colitis primarily affects the colon and the rectum, CD affects the small and large intestines, as well as the esophagus,mouth, anus, andstomach. Although the etiology of IBD is not completely clear, and there are many unknowns about it, the development, progression, and recurrence of IBD are significantly influenced by the activity of immune system cells, particularly lymphocytes, given that the disease is primarily caused by the immune system stimulation and activation against gastrointestinal (GI) tract components due to the inflammation caused by environmental factors such as viral or bacterial infections, etc. in genetically predisposed individuals. Maintaining homeostasis and the integrity of the mucosal barrier are critical in stopping the development of IBD. Specific immune system cells and the quantity of secretory mucus and microbiome are vital in maintaining this stability. Th22 cells are helper T lymphocyte subtypes that are particularly important for maintaining the integrity and equilibrium of the mucosal barrier. This review discusses the most recent research on these cells' biology, function, and evolution and their involvement in IBD.

Endoscopic, Histologic, and Composite Endpoints in Patients With Ulcerative Colitis Treated With Etrasimod

BACKGROUND & AIMS

Histologic remission, a potentially important treatment target in ulcerative colitis (UC), is associated with favorable long-term outcomes. Etrasimod is an oral, once-daily, selective sphingosine 1-phosphate (S1P)1,4,5 receptor modulator for the treatment of moderately to severely active UC. This post-hoc analysis of the ELEVATE UC program evaluated the efficacy of etrasimod according to histologic and composite (histologic/endoscopic/symptomatic) endpoints and examined their prognostic value.

METHODS

Patients with moderately to severely active UC were randomized 2:1 to once-daily oral etrasimod 2 mg or placebo. Histologic and composite endpoints, including disease clearance (endoscopic/histologic/symptomatic remission), were assessed at Weeks 12 (ELEVATE UC 52; ELEVATE UC 12) and 52 (ELEVATE UC 52). Logistic regressions examined associations between baseline and Week 12 histologic/composite endpoints and Week 52 outcomes.

RESULTS

At Weeks 12 and 52, significant improvements with etrasimod vs placebo were observed in histologic/composite outcomes, including endoscopic improvement-histologic remission and disease clearance. The proportion of patients treated with etrasimod achieving clinical remission at Week 52 was higher among those with disease clearance at Week 12 vs those without disease clearance (73.9% [17/23] vs 28.3% [71/251]). Histologic improvement and endoscopic improvement at Week 12 were moderately and strongly associated with clinical remission at Week 52 (odds ratio [OR], 2.37; 95% confidence interval [CI], 1.27-4.41; and OR, 6.36; 95% CI, 3.47-11.64, respectively). Histologic remission and endoscopic improvement at Week 12 were strongly associated with endoscopic improvement-histologic remission at Week 52 (OR, 3.21; 95% CI, 1.70-6.06 and OR, 5.47; 95% CI, 2.89-10.36, respectively).

CONCLUSIONS

Etrasimod was superior to placebo for achievement of stringent histologic and composite endpoints.

CLINICALTRIALS

gov, Number: NCT03945188; ClinicalTrials.gov, Number: NCT03996369.

Eosinophils and risk of ulcerative colitis in European population: Evidence from Mendelian randomization study

BACKGROUND

Observational studies have indicated that peripheral blood eosinophil count is elevated in individuals diagnosed with ulcerative colitis (UC) and correlates with the disease activity of UC. However, this conclusion contradicts with findings from other studies. Therefore, we employed Mendelian randomization (MR) method to assess the genetic link between eosinophil count and UC.

METHOD

This MR study utilized summary data from genome-wide association studies (GWAS) on eosinophil count and UC. The main approach used for conducting MR analysis was the inverse variance weighted (IVW) method. Meta-analysis of the IVW results was performed alongside multiple sensitivity analyses to confirm the robustness of the MR analysis results.

RESULTS

The IVW method unveiled a causal relationship between eosinophil count and UC (OR = 1.18, 95% CI: 1.04-1.33, p = .01) in the discovery cohort. This finding was further corroborated by the replication cohorts (OR = 1.16, 95% CI: 1.04-1.29, p = .01; OR = 1.12, 95% CI: 1.01-1.24, p = .03). The meta-analysis indicated that the overall odds ratio (OR) for all studies was 1.15 (common effect model, 95% CI: 1.08-1.23, p < .01). Sensitivity analysis suggested the absence of heterogeneity and horizontal pleiotropy in all MR analyses.

CONCLUSION

Based on bidirectional two-sample MR analysis, there is an indication that elevated eosinophil count may increase the risk of UC. However, potential confounding factors cannot be ruled out, and further research is necessary to explore how eosinophils contribute to the onset and progression of UC.

Using Deep Learning to Automate Eosinophil Counting in Pediatric Ulcerative Colitis Histopathological Images

Background

Accurate identification of inflammatory cells from mucosal histopathology images is important in diagnosing ulcerative colitis. The identification of eosinophils in the colonic mucosa has been associated with disease course. Cell counting is not only time-consuming but can also be subjective to human biases. In this study we developed an automatic eosinophilic cell counting tool from mucosal histopathology images, using deep learning.

Method

Four pediatric IBD pathologists from two North American pediatric hospitals annotated 530 crops from 143 standard-of-care hematoxylin and eosin (H & E) rectal mucosal biopsies. A 305/75 split was used for training/validation to develop and optimize a U-Net based deep learning model, and 150 crops were used as a test set. The U-Net model was then compared to SAU-Net, a state-of-the-art U-Net variant. We undertook post-processing steps, namely, (1) the pixel-level probability threshold, (2) the minimum number of clustered pixels to designate a cell, and (3) the connectivity. Experiments were run to optimize model parameters using AUROC and cross-entropy loss as the performance metrics.

Results

The F1-score was 0.86 (95%CI:0.79-0.91) (Precision: 0.77 (95%CI:0.70-0.83), Recall: 0.96 (95%CI:0.93-0.99)) to identify eosinophils as compared to an F1-score of 0.2 (95%CI:0.13-0.26) for SAU-Net (Precision: 0.38 (95%CI:0.31-0.46), Recall: 0.13 (95%CI:0.08-0.19)). The inter-rater reliability was 0.96 (95%CI:0.93-0.97). The correlation between two pathologists and the algorithm was 0.89 (95%CI:0.82-0.94) and 0.88 (95%CI:0.80-0.94) respectively.

Conclusion

Our results indicate that deep learning-based automated eosinophilic cell counting can obtain a robust level of accuracy with a high degree of concordance with manual expert annotations.

Machine learning using clinical data at baseline predicts the medium-term efficacy of ustekinumab in patients with ulcerative colitis

Predicting the therapeutic response to biologics before administration is a key clinical challenge in ulcerative colitis (UC). We previously reported a model for predicting the efficacy of vedolizumab (VDZ) for UC using a machine-learning approach. Ustekinumab (UST) is now available for treating UC, but no model for predicting its efficacy has been developed. When applied to patients with UC treated with UST, our VDZ prediction model showed positive predictive value (PPV) of 56.3% and negative predictive value (NPV) of 62.5%. Given this limited predictive ability, we aimed to develop a UST-specific prediction model with clinical features at baseline including background factors, clinical and endoscopic activity, and blood test results, as we did for the VDZ prediction model. The top 10 features (Alb, monocytes, height, MCV, TP, Lichtiger index, white blood cell count, MCHC, partial Mayo score, and CRP) associated with steroid-free clinical remission at 6 months after starting UST were selected using random forest. The predictive ability of a model using these predictors was evaluated by fivefold cross-validation. Validation of the prediction model with an external cohort showed PPV of 68.8% and NPV of 71.4%. Our study suggested the importance of establishing a drug-specific prediction model.

Serological Biomarker-Based Machine Learning Models for Predicting the Relapse of Ulcerative Colitis

Purpose

To explore whether machine learning models using serological markers can predict the relapse of Ulcerative colitis (UC).

Patients and Methods

This clinical cohort study included 292 UC patients, and serological markers were obtained when patients were discharged from the hospital. Subsequently, four machine learning models including the random forest (RF) model, the logistic regression model, the decision tree, and the neural network were compared to predict the relapse of UC. A nomogram was constructed, and the performance of these models was evaluated by accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).

Results

Based on the patients' characteristics and serological markers, we selected the relevant variables associated with relapse and developed a LR model. The novel model including gender, white blood cell count, percentage of leukomonocyte, percentage of monocyte, absolute value of neutrophilic granulocyte, and erythrocyte sedimentation rate was established for predicting the relapse. In addition, the average AUC of the four machine learning models was 0.828, of which the RF model was the best. The AUC of the test group was 0.889, the accuracy was 76.4%, the sensitivity was 78.5%, and the specificity was 76.4%. There were 45 variables in the RF models, and the relative weight coefficients of these variables were determined. Age has the greatest impact on classification results, followed by hemoglobin concentration, white blood cell count, and platelet distribution width.

Conclusion

Machine learning models based on serological markers had high accuracy in predicting the relapse of UC. The model can be used to noninvasively predict patient outcomes and can be an effective tool for determining personalized treatment plans.