Dietary Exposures and Interventions in Inflammatory Bowel Disease: Current Evidence and Emerging Concepts.

Diet is intimately linked to the gastrointestinal (GI) tract and has potent effects on intestinal immune homeostasis. Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the GI tract. The therapeutic implications of diet in patients with IBD have received significant attention in recent years. In this review, we provide a contemporary and comprehensive overview of dietary exposures and interventions in IBD. Epidemiological studies suggest that ultra-processed foods, food additives, and emulsifiers are associated with a higher incidence of IBD. Exclusion and elimination diets are associated with improved symptoms in patients with IBD, but no effects on objective markers of inflammation. Specific dietary interventions (e.g., Mediterranean, specific carbohydrate, high fiber, ketogenic, anti-inflammatory diets) have been shown to reduce symptoms, improve inflammatory biomarkers, and quality of life metrics to varying degrees, but these studies are limited by study design, underpowering, heterogeneity, and confounding. To date, there is no robust evidence that any dietary intervention alone may replace standard therapies in patients with IBD. However, diet may play an adjunct role to induce or maintain clinical remission with standard IBD therapies. The results of novel dietary trials in IBD such as personalized fiber, intermittent fasting, and time-restricted diets are eagerly awaited.

Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records.

Importance: Physicians are required to work with rapidly growing amounts of medical data. Approximately 62% of time per patient is devoted to reviewing electronic health records (EHRs), with clinical data review being the most time-consuming portion. Objective: To determine whether an artificial intelligence (AI) system developed to organize and display new patient referral records would improve a clinician's ability to extract patient information compared with the current standard of care. Design, Setting, and Participants: In this prognostic study, an AI system was created to organize patient records and improve data retrieval. To evaluate the system on time and accuracy, a nonblinded, prospective study was conducted at a single academic medical center. Recruitment emails were sent to all physicians in the gastroenterology division, and 12 clinicians agreed to participate. Each of the clinicians participating in the study received 2 referral records: 1 AI-optimized patient record and 1 standard (non-AI-optimized) patient record. For each record, clinicians were asked 22 questions requiring them to search the assigned record for clinically relevant information. Clinicians reviewed records from June 1 to August 30, 2020. Main Outcomes and Measures: The time required to answer each question, along with accuracy, was measured for both records, with and without AI optimization. Participants were asked to assess overall satisfaction with the AI system, their preferred review method (AI-optimized vs standard), and other topics to assess clinical utility. Results: Twelve gastroenterology physicians/fellows completed the study. Compared with standard (non-AI-optimized) patient record review, the AI system saved first-time physician users 18% of the time used to answer the clinical questions (10.5 [95% CI, 8.5-12.6] vs 12.8 [95% CI, 9.4-16.2] minutes; P = .02). There was no significant decrease in accuracy when physicians retrieved important patient information (83.7% [95% CI, 79.3%-88.2%] with the AI-optimized vs 86.0% [95% CI, 81.8%-90.2%] without the AI-optimized record; P = .81). Survey responses from physicians were generally positive across all questions. Eleven of 12 physicians (92%) preferred the AI-optimized record review to standard review. Despite a learning curve pointed out by respondents, 11 of 12 physicians believed that the technology would save them time to assess new patient records and were interested in using this technology in their clinic. Conclusions and Relevance: In this prognostic study, an AI system helped physicians extract relevant patient information in a shorter time while maintaining high accuracy. This finding is particularly germane to the ever-increasing amounts of medical data and increased stressors on clinicians. Increased user familiarity with the AI system, along with further enhancements in the system itself, hold promise to further improve physician data extraction from large quantities of patient health records.

Bacterial colonization reprograms the neonatal gut metabolome.

Initial microbial colonization and later succession in the gut of human infants are linked to health and disease later in life. The timing of the appearance of the first gut microbiome, and the consequences for the early life metabolome, are just starting to be defined. Here, we evaluated the gut microbiome, proteome and metabolome in 88 African-American newborns using faecal samples collected in the first few days of life. Gut bacteria became detectable using molecular methods by 16 h after birth. Detailed analysis of the three most common species, Escherichia coli, Enterococcus faecalis and Bacteroides vulgatus, did not suggest a genomic signature for neonatal gut colonization. The appearance of bacteria was associated with reduced abundance of approximately 50 human proteins, decreased levels of free amino acids and an increase in products of bacterial fermentation, including acetate and succinate. Using flux balance modelling and in vitro experiments, we provide evidence that fermentation of amino acids provides a mechanism for the initial growth of E. coli, the most common early colonizer, under anaerobic conditions. These results provide a deep characterization of the first microbes in the human gut and show how the biochemical environment is altered by their appearance.