Fecal bacteria and short-chain fatty acids in irritable bowel syndrome: Relations to subtype.

BACKGROUND: The relationship between gut microbiota and irritable bowel syndrome (IBS) subtype is unclear. We aimed to explore whether differences in fecal bacteria composition and short-chain fatty acid (SCFA) levels were associated with subtypes and symptoms of IBS. METHODS: All participants delivered fecal samples and self-reports on IBS Symptom Severity Score (IBS-SSS), Bristol Stool Scale (BSS), and Gastrointestinal Symptom Rating Scale (GSRS). Fecal bacteria composition was assessed by the GA-map® Dysbiosis Test based on 16S rRNA sequences of bacterial species/groups. SCFAs were analyzed by vacuum distillation followed by gas chromatography. KEY RESULTS: Sixty patients with IBS were included (mean age 38 years, 46 [77%] females): Twenty-one patients were classified as IBS-D (diarrhea), 31 IBS-M (mixed diarrhea and constipation), and eight IBS-C (constipation). Forty-two healthy controls (HCs) (mean age 35 years, 27 [64%] females) were included. Patients had a significantly higher relative frequency of dysbiosis, lower levels of Actinobacteria, and higher levels of Bacilli than HCs. Eight bacterial markers were significantly different across IBS subgroups and HCs, and 13 bacterial markers were weakly correlated with IBS symptoms. Clostridia and Veillonella spp. had a weak negative correlation with constipation scores (GSRS) and a weak positive correlation with loose stools (BSS). Diarrhea scores (GSRS) and looser stool (BSS) were weakly correlated with levels of total SCFAs, acetic and butyric acid. Levels of total SCFAs and acetic acid were weakly correlated with symptom severity (IBS-SSS). CONCLUSIONS & INFERENCES: Patients with IBS had a different fecal bacteria composition compared to HCs, and alterations of SCFAs may contribute to the subtype.

Nutritional safety and status following a 12-week strict low FODMAP diet in patients with irritable bowel syndrome.

BACKGROUND: A low FODMAP diet (LFD) is an established dietary treatment for patients with irritable bowel syndrome (IBS). However, knowledge on the extended effects of the restriction phase regarding nutrient intake, symptom severity, and quality of life (QoL) is sparse. Therefore, our objectives were to evaluate the safety of a dietitian-led 12-week strict LFD on measures of blood biochemistry, nutritional status, symptom severity, and QoL. METHODS: In this open-label dietitian-led 12-week strict LFD intervention for IBS patients with predominantly diarrhea or mixed stool pattern (IBS-D/-M), we collected data on diet intake (3-day dietary record), overnight fasting routine blood samples, body weight, IBS symptoms (IBS Severity Scoring System (IBS-SSS)), and IBS-related QoL (IBS-QoL) at baseline and after 12 weeks. KEY RESULTS: Thirty-six participants completed the 12-week follow-up (mean age: 37 years, 67% women, IBS-SSS: 242 (101)). All blood parameters measured were within established reference values at both time points. We found no change in intake of macro- or micronutrients, but several micronutrients were below the recommendations both before and after 12 weeks. BMI slightly decreased, primarily driven by participants with BMI >25 (p < 0.005). QoL improved among most subdomains (p ≤ 0.002), except food avoidance and social reaction. CONCLUSION: An extended dietitian-guided LFD (12 weeks) is not inferior to the participants' baseline diet, since no clinically meaningful changes in nutritionally related blood samples and no changes in macro- or micronutrient intake were observed. However, the intake of several nutrients was below the recommendations at both time points indicating low diet quality.

A Systematic Review: Fecal Bacterial Profile in Patients with Irritable Bowel Syndrome Analyzed with the GA-Map Dysbiosis Test Based on the 16S rRNA Gene of Bacterial Species or Groups.

Purpose: The diagnosis of irritable bowel syndrome (IBS) is based on symptom-based criteria due to lack of reliable disease-specific biomarkers. Gut microbiota is perturbed in IBS and when comparing different methods used to analyze gut microbiota, the results might be obscured. Therefore, in this systematic review we aimed to investigate the profile of fecal bacterial markers and dysbiosis index (DI) in patients with IBS and IBS subgroups compared to healthy controls (HCs) conducted by the same method (GA-map Dysbiosis Test based on16S rRNA sequencing). Material and Method: We searched PubMed, EMBASE (Ovid) and Cochrane Library for case-control studies comparing fecal gut microbiota analyzed with the GA-map® Dysbiosis Test (Oslo, Norway) in patients with IBS and HCs. Our outcomes were the difference in fecal bacterial markers and DI in patients with IBS and IBS subgroups compared to HCs. Results: The search identified 28 citations; five articles were included. Most studies evaluated fecal bacterial markers and DI in patients with diarrhea-predominant IBS (IBS-D). Results of fecal bacteria profile in IBS and IBS subgroups compared to HCs are inconsistent, however, two studies showed increased levels of Ruminococcus gnavus in IBS-D compared to HCs and results of DI indicated IBS and IBS subgroups (especially IBS-D) having higher DI compared to HCs. Conclusion: This systematic review revealed inconsistent findings in respect to differences in bacterial markers between IBS and IBS subgroups with HCs in studies using the GA-map Dysbiosis Test based on 16S rRNA sequencing. However, the test is quite novel, and few studies have used the method so far. More research comparing fecal microbiota profile differences in IBS and IBS subgroups compared to HCs utilizing the same method of analysis is needed to give us further insight into the gut bacteria profile in IBS and the clinical consequences of intestinal dysbiosis.

A psychological symptom based machine learning model for clinical evaluation of irritable bowel syndrome.

Background: Irritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder characterized by recurrent abdominal pain associated with alterations  in stool form and/or stool frequency. Co-morbidities such as anxiety, depression, fatigue, and insomnia are frequently reported by patients suffering from IBS. Identification of these symptoms should thus be an integral part of an IBS assessment.      However, an optimal tool to screen for core psychological symptoms in IBS is still  missing. Here, we aim to develop a psychological symptom based machine learning model to efficiently help clinicians to identify patients suffering from IBS. Methods: We developed a machine learning workflow to select the most significant psychological features associated with IBS in a dataset including 49 patients with IBS and 35 healthy controls. These features were used to train three different types of machine learning models: logistic regression, decision trees and support vector machine classifiers; which were validated on a holdout validation dataset and an unseen test set. The performance of these models was compared in terms of balanced accuracy scores. Results: A logistic regression model including a combination of symptom features associated with anxiety and fatigue resulted in a balanced accuracy score of 0.93 (0.81-1.0) on unseen test data and outperformed the other comparable models. The same model correctly identified all patients with IBS in a test set (recall score 1) and misclassified one non-IBS subject (precision score 0.91). A complementary post-hoc leave-one-out cross validation analysis including the same symptom features showed similar, but slightly inferior results (balanced accuracy 0.84, recall 0.88, precision 0.86). Conclusions: Inclusion of machine learning based psychological evaluation can complement and improve existing clinical procedure for diagnosis of IBS.