scholarly journals Treatment-Specific Composition of the Gut Microbiota Is Associated With Disease Remission in a Pediatric Crohn’s Disease Cohort

2019 ◽  
Vol 25 (12) ◽  
pp. 1927-1938 ◽  
Author(s):  
Daniel Sprockett ◽  
Natalie Fischer ◽  
Rotem Sigall Boneh ◽  
Dan Turner ◽  
Jarek Kierkus ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Random Forest ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Area Under The Curve ◽  
Disease Remission ◽  
Beneficial Effects ◽  
Forest Models ◽  
Random Forest Models ◽  
The Impact

Abstract Background The beneficial effects of antibiotics in Crohn’s disease (CD) depend in part on the gut microbiota but are inadequately understood. We investigated the impact of metronidazole (MET) and metronidazole plus azithromycin (MET+AZ) on the microbiota in pediatric CD and the use of microbiota features as classifiers or predictors of disease remission. Methods 16S rRNA-based microbiota profiling was performed on stool samples from 67 patients in a multinational, randomized, controlled, longitudinal, 12-week trial of MET vs MET+AZ in children with mild to moderate CD. Profiles were analyzed together with disease activity, and then used to construct random forest models to classify remission or predict treatment response. Results Both MET and MET+AZ significantly decreased diversity of the microbiota and caused large treatment-specific shifts in microbiota structure at week 4. Disease remission was associated with a treatment-specific microbiota configuration. Random forest models constructed from microbiota profiles before and during antibiotic treatment with metronidazole accurately classified disease remission in this treatment group (area under the curve [AUC], 0.879; 95% confidence interval, 0.683–0.9877; sensitivity, 0.7778; specificity, 1.000; P < 0.001). A random forest model trained on pre-antibiotic microbiota profiles predicted disease remission at week 4 with modest accuracy (AUC, 0.8; P = 0.24). Conclusions MET and MET+AZ antibiotic regimens in pediatric CD lead to distinct gut microbiota structures at remission. It may be possible to classify and predict remission based in part on microbiota profiles, but larger cohorts will be needed to realize this goal.

scholarly journals Treatment-Specific Composition of Gut Microbiota Is Associated with Disease Remission in a Pediatric Crohn’s Disease Cohort

2018 ◽  
Author(s):  
Daniel Sprockett ◽  
Natalie Fischer ◽  
Rotem Sigall Boneh ◽  
Dan Turner ◽  
Jarek Kierkus ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Random Forest ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Disease Remission ◽  
Beneficial Effects ◽  
Forest Models ◽  
Random Forest Models ◽  
Stool Samples ◽  
The Impact

AbstractBackgroundThe beneficial effects of antibiotics in Crohn’s disease (CD) depend in part on the gut microbiota but are inadequately understood. We investigated the impact of metronidazole (MET) and metronidazole plus azithromycin (MET+AZ) on the microbiota in pediatric CD, and the use of microbiota features as classifiers or predictors of disease remission.Methods16S rRNA-based microbiota profiling was performed on stool samples from 67 patients in a multinational, randomized, controlled, longitudinal, 12-week trial of MET vs. MET+AZ in children with mild to moderate CD. Profiles were analyzed together with disease activity, and then used to construct Random Forest models to classify remission or predict treatment response.ResultsBoth MET and MET+AZ significantly decreased diversity of the microbiota and caused large treatment-specific shifts in microbiota structure at week 4. Disease remission was associated with a treatment-specific microbiota configuration. Random Forest models constructed from microbiota profiles pre- and during antibiotic treatment with metronidazole accurately classified disease remission in this treatment group (AUC of 0.879, 95% CI 0.683, 0.9877; sensitivity 0.7778; specificity 1.000, P < 0.001). A Random Forest model trained on preantibiotic microbiota profiles predicted disease remission at week 4 with modest accuracy (AUC of 0.8, P = 0.24).ConclusionsMET and MET+AZ antibiotic regimens in pediatric CD lead to distinct gut microbiota structures at remission. It may be possible to classify and predict remission based in part on microbiota profiles, but larger cohorts will be needed to realize this goal.SummaryWe investigated the impact of metronidazole and metronidazole plus azithromycin on the gut microbiota in pediatric Crohn’s disease. Disease remission was associated with a treatment-specific microbiota configuration, and could be predicted based on pre-antibiotic microbiota profiles.

scholarly journals A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

2022 ◽  
Vol 21 (1) ◽  
Author(s):  
Luca Boniardi ◽  
Federica Nobile ◽  
Massimo Stafoggia ◽  
Paola Michelozzi ◽  
Carla Ancona
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Air Pollution ◽  
Random Forest ◽  
Citizen Science ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Forest Models ◽  
Random Forest Models ◽  
The Impact

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

scholarly journals PWE-082 The Impact Of Nod2 Variants On Gut Microbiota In Crohn’s Disease And Healthy Controls

Gut ◽  
2014 ◽  
Vol 63 (Suppl 1) ◽  
pp. A159.2-A160 ◽  
Author(s):  
NA Kennedy ◽  
AW Walker ◽  
SH Berry ◽  
CA Lamb ◽  
S Lewis ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Healthy Controls ◽  
The Impact

Mo1816 The Impact of NOD2 Variants on Gut Microbiota in Crohn's Disease and Healthy Controls

2015 ◽  
Vol 148 (4) ◽  
pp. S-718
Author(s):  
Nicholas A. Kennedy ◽  
Alan W. Walker ◽  
Susan H. Berry ◽  
Christopher A. Lamb ◽  
Sophie Lewis ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Healthy Controls ◽  
The Impact

scholarly journals The Impact of Multiple Sclerosis Disease Status and Subtype on Hematological Profile

2021 ◽  
Vol 18 (6) ◽  
pp. 3318
Author(s):  
Jacob M. Miller ◽  
Jeremy T. Beales ◽  
Matthew D. Montierth ◽  
Farren B. Briggs ◽  
Scott F. Frodsham ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Random Forest ◽  
Demyelinating Disease ◽  
Complete Blood Count ◽  
Disease Status ◽  
Multiple Sclerosis Disease ◽  
Forest Models ◽  
Random Forest Models ◽  
The Central Nervous System ◽  
The Impact

Multiple sclerosis (MS) is an immune-mediated, demyelinating disease of the central nervous system. In this study, an MS cohort and healthy controls were stratified into Caucasian and African American groups. Patient hematological profiles—composed of complete blood count (CBC) and complete metabolic panel (CMP) test values—were analyzed to identify differences between MS cases and controls and between patients with different MS subtypes. Additionally, random forest models were used to determine the aggregate utility of common hematological tests in determining MS disease status and subtype. The most significant and relevant results were increased bilirubin and creatinine in MS cases. The random forest models achieved some success in differentiating between MS cases and controls (AUC values: 0.725 and 0.710, respectively) but were not successful in differentiating between subtypes. However, larger samples that adjust for possible confounding variables, such as treatment status, may reveal the value of these tests in differentiating between MS subtypes.

Predicting and explaining inflammation in Crohn’s disease patients using predictive analytics methods and electronic medical record data

2018 ◽  
Vol 25 (4) ◽  
pp. 1201-1218 ◽  
Author(s):  
Bhargava K Reddy ◽  
Dursun Delen ◽  
Rupesh K Agrawal
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Area Under The Curve ◽  
Gradient Boosting ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
The Impact ◽  
Disease Location

Crohn’s disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn’s disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn’s disease patients.

The impact of Crohn's disease genes on healthy human gut microbiota: a pilot study

Gut ◽  
2013 ◽  
Vol 62 (6) ◽  
pp. 952.1-954 ◽  
Author(s):  
Christopher Quince ◽  
Elin E Lundin ◽  
Anna N Andreasson ◽  
Dario Greco ◽  
Joseph Rafter ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Pilot Study ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Disease Genes ◽  
Human Gut ◽  
Healthy Human ◽  
Human Gut Microbiota ◽  
The Impact

scholarly journals Modulation of CD39 and Exogenous APT102 Correct Immune Dysfunction in Experimental Colitis and Crohn’s Disease

2019 ◽  
Vol 14 (6) ◽  
pp. 818-830 ◽  
Author(s):  
René J Robles ◽  
Samiran Mukherjee ◽  
Marta Vuerich ◽  
Anyan Xie ◽  
Rasika Harshe ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Experimental Colitis ◽  
Therapeutic Effects ◽  
Nucleotide Polymorphisms ◽  
Beneficial Effects ◽  
The Impact ◽  
Tr1 Cells

Abstract Background and Aims CD39/ENTPD1 scavenges pro-inflammatory nucleotides, to ultimately generate immunosuppressive adenosine, which has a central role in immune homeostasis. Global deletion of Cd39 increases susceptibility to experimental colitis while single nucleotide polymorphisms within the human CD39 promoter, and aberrant patterns of expression during experimental hypoxia, predispose to Crohn’s disease. We aimed to define the impact of transgenic human CD39 [hTG] overexpression in experimental colitis and to model therapeutic effects using the recombinant apyrase APT102 in vivo. We also determined the in vitro effects of APT102 on phenotypic and functional properties of regulatory T-lymphocytes derived from patients with Crohn’s disease. Methods Colitis was induced by administration of dextran sulfate sodium in wild-type [WT] or hTG mice, and, in another model, by adoptive transfer of CD45RBhigh cells with or without WT or hTG regulatory T cells [Treg]. In additional experiments, mice were treated with APT102. The effects of APT102 on phenotype and function of Treg and type-1 regulatory T [Tr1] cells were also evaluated, after purification from peripheral blood and lamina propria of Crohn’s disease patients [n = 38]. Results Overexpression of human CD39 attenuated experimental colitis and protected from the deleterious effects of systemic hypoxia, pharmacologically induced by deferoxamine. Administration of APT102 in vivo enhanced the beneficial effects of endogenous Cd39 boosted by the administration of the aryl hydrocarbon receptor [AhR] ligand unconjugated bilirubin [UCB]. Importantly, supplemental APT102 restored responsiveness to AhR stimulation by UCB in Treg and Tr1 cells, obtained from Crohn’s disease patients. Conclusions hCD39 overexpression ameliorated experimental colitis and prevented hypoxia-related damage in vivo. Exogenous administration of APT102 boosted AhR-mediated regulatory effects in vivo while enhancing Treg functions in Crohn’s disease in vitro.

scholarly journals The Modification of the Gut Microbiota via Selected Specific Diets in Patients with Crohn’s Disease

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2125
Author(s):  
Eliza Starz ◽  
Karolina Wzorek ◽  
Marcin Folwarski ◽  
Karolina Kaźmierczak-Siedlecka ◽  
Laura Stachowska ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Gastrointestinal Symptoms ◽  
Positive Influence ◽  
Nutritional Guidelines ◽  
Faecalibacterium Prausnitzii ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
The Impact

Gastrointestinal symptoms in Crohn’s disease (CD) are common and affect the quality of life of patients; consequently, a growing number of studies have been published on diet interventions in this group. The role of the gut microbiota in the pathogenesis and the progression of inflammatory bowel diseases (IBD), including CD, has been widely discussed. Mainly, a decreased abundance of Firmicutes, species of the Bifidobacterium genus, and the Faecalibacterium prausnitzii species as well as a reduced general diversity have been described. In this review article, we summarize available data on the influence of reduction diets on the microbiome of patients with CD. One of the most frequently used elimination diets in CD patients is the low-FODMAP (Fermentable Oligosaccharides, Disaccharides, Monosaccharides, and Polyols) diet. Although many papers show it may reduce abdominal pain, diarrhea, or bloating, it also reduces the intake of prebiotic substances, which can negatively affect the gut microbiota composition, decreasing the abundance of Bifidobacterium species and Faecalibacterium prausnitzii. Other elimination diets used by IBD patients, such as lactose-free or gluten-free diets, have also been shown to disturb the microbial diversity. On the other hand, CDED (Crohn’s disease exclusion diet) with partial enteral nutrition not only induces the remission of CD but also has a positive influence on the microbiota. The impact of diet interventions on the microbiota and, potentially, on the future course of the disease should be considered when nutritional guidelines for IBD patients are designed. Dietetic recommendations should be based not only on the regulation of the symptoms but also on the long-term development of the disease.

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