Distinct clinical phenotypes for Crohn's disease derived from patient surveys

Low-dose dimethylfumarate attenuates colitis-associated cancer in mice through M2 macrophage polarization and blocking oxidative stress

Colitis-associated cancer (CAC) is an aggressive subtype of colorectal cancer that can develop in ulcerative colitis patients and is driven by chronic inflammation and oxidative stress. Current chemotherapy for CAC, based on 5-fluorouracil and oxalipltin, is not fully effective and displays severe side effects, prompting the search for alternative therapies. Dimethylfumarate (DMF), an activator of the nuclear factor erythroid 2-related factor 2 (NRF2), is a potent antioxidant and immunomodelatrory drug used in the treatment of multiple sclerosis and showed a strong anti-inflammatory effect on experimental colitis. Here, we investigated the chemotherapeutic effect of DMF on an experimental model of CAC. Male NMRI mice were given two subcutaneous injections of 1,2 Dimethylhydrazine (DMH), followed by three cycles of dextran sulfate sodium (DSS). Low-dose (DMF30) and high-dose of DMF (DMF100) or oxaliplatin (OXA) were administered from the 8th to 12th week of the experiment, and then the colon tissues were analysed histologically and biochemically. DMH/DSS induced dysplastic aberrant crypt foci (ACF), oxidative stress, and severe colonic inflammation, with a predominance of pro-inflammatory M1 macrophages. As OXA, DMF30 reduced ACF multiplicity and crypt dysplasia, but further restored redox status, and reduced colitis severity by shifting macrophages towards the anti-inflammatory M2 phenotype. Surprisingly, DMF100 exacerbated ACF multiplicity, oxidative stress, and colon inflammation, likely through NRF2 and p53 overexpression in colonic inflammatory cells. DMF had a dual effect on CAC. At low dose, DMF is chemotherapeutic and acts as an antioxidant and immunomodulator, whereas at high dose, DMF is pro-oxidant and exacerbates colitis-associated cancer.

Cholestasis: exploring the triangular relationship of gut microbiota-bile acid-cholestasis and the potential probiotic strategies

Cholestasis is a condition characterized by the abnormal production or excretion of bile, and it can be induced by a variety of causes, the factors of which are extremely complex. Although great progress has been made in understanding cholestasis pathogenesis, the specific mechanisms remain unclear. Therefore, it is important to understand and distinguish cholestasis from different etiologies, which will also provide indispensable theoretical support for the development of corresponding therapeutic drugs. At present, the treatment of cholestasis mainly involves several bile acids (BAs) and their derivatives, most of which are in the clinical stage of development. Multiple lines of evidence indicate that ecological disorders of the gut microbiota are strongly related to the occurrence of cholestasis, in which BAs also play a pivotal role. Recent studies indicate that probiotics seem to have certain effects on cholestasis, but further confirmation from clinical trials is required. This paper reviews the etiology of and therapeutic strategies for cholestasis; summarizes the similarities and differences in inducement, symptoms, and mechanisms of related diseases; and provides information about the latest pharmacological therapies currently available and those under research for cholestasis. We also reviewed the highly intertwined relationship between gut microbiota-BA-cholestasis, revealing the potential role and possible mechanism of probiotics in the treatment of cholestasis.

Inflammatory Bowel Disease Treatments and Predictive Biomarkers of Therapeutic Response

Inflammatory bowel disease (IBD) is a chronic immune-mediated inflammation of the gastrointestinal tract with a highly heterogeneous presentation. It has a relapsing and remitting clinical course that necessitates lifelong monitoring and treatment. Although the availability of a variety of effective therapeutic options including immunomodulators and biologics (such as TNF, CAM inhibitors) has led to a paradigm shift in the treatment outcomes and clinical management of IBD patients, some patients still either fail to respond or lose their responsiveness to therapy over time. Therefore, according to the recent Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE-II) recommendations, continuous disease monitoring from symptomatic relief to endoscopic healing along with short- and long-term therapeutic responses are critical for providing IBD patients with a tailored therapy algorithm. Moreover, considering the high unmet need for novel therapeutic approaches for IBD patients, various new modulators of cytokine signaling events (for example, JAK/TYK inhibitors), inhibitors of cytokines (for example IL-12/IL-23, IL-22, IL-36, and IL-6 inhibitors), anti-adhesion and migration strategies (for example, β7 integrin, sphingosine 1-phosphate receptors, and stem cells), as well as microbial-based therapeutics to decolonize the bed buds (for example, fecal microbiota transplantation and bacterial inhibitors) are currently being evaluated in different phases of controlled clinical trials. This review aims to offer a comprehensive overview of available treatment options and emerging therapeutic approaches for IBD patients. Furthermore, predictive biomarkers for monitoring the therapeutic response to different IBD therapies are also discussed.

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

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.

Stool multi-omics for the study of host-microbe interactions in inflammatory bowel disease

Inflammatory Bowel Disease (IBD) is a chronic immune-mediated inflammatory disease of the gastrointestinal tract that is a growing public burden. Gut microbes and their interactions with hosts play a crucial role in disease pathogenesis and progression. These interactions are complex, spanning multiple physiological systems and data types, making comprehensive disease assessment difficult, and often overwhelming single-omic capabilities. Stool-based multi-omics is a promising approach for characterizing host-gut microbiome interactions using deep integration of technologies such as 16S rRNA sequencing, shotgun metagenomics, meta-transcriptomics, metabolomics, and metaproteomics. The wealth of information generated through multi-omic studies is poised to usher in advancements in IBD research and precision medicine. This review highlights historical and recent findings from stool-based muti-omic studies that have contributed to unraveling IBD's complexity. Finally, we discuss common pitfalls, issues, and limitations, and how future pipelines should address them to standardize multi-omics in IBD research and beyond.