Weight, habitual fibre intake, and microbiome composition predict tolerance to fructan supplementation.

Fructans are commonly used as dietary fibre supplements for their ability to promote the growth of beneficial gut microbes. However, fructan consumption has been associated with various dosage-dependent side effects. We characterised side effects in an exploratory analysis of a randomised trial in healthy adults (n = 40) who consumed 18 g/day inulin or placebo. We found that individuals weighing more or habitually consuming higher fibre exhibited the best tolerance. Furthermore, we identified associations between gut microbiome composition and host tolerance. Specifically, higher levels of Christensenellaceae R-7 group were associated with gastrointestinal discomfort, and a machine-learning-based approach successfully predicted high levels of flatulence, with [Ruminococcus] torques group and (Oscillospiraceae) UCG-002 sp. identified as key predictive taxa. These data reveal trends that can help guide personalised recommendations for initial inulin dosage. Our results support prior ecological findings indicating that fibre supplementation has the greatest impact on individuals whose baseline fibre intake is lowest.

Public Perspectives on Direct-to-Consumer Testing Oversight.

The FDA published a final rule for Medical Devices; Laboratory Developed Tests in the Federal Register on May 6, 2024, which aims to ensure the safety and effectiveness of laboratory developed tests (LDTs) by amending current regulations. The rule also includes a policy to phase out the FDA's general enforcement discretion approach for LDTs, aligning them with other In Vitro Diagnostic Devices. Notably, direct-to-consumer (DTC) testing is exempt from this policy shift, as the FDA believes this category of tests has already met applicable requirements. This rule was first proposed in the Federal Register on October 3, 2023. The publication of this proposed rule sparked a considerable volume of public reactions during the comment period of the rule-making process, comprising general sentiment, key concerns, and suggestions. This commentary analyzes these concerns, particularly focusing on DTC tests, and offers recommendations, including reassessing the FDA's enforcement discretion for hybrid DTC tests, advocating for clear guidance on clinical oversight, and prioritizing a risk-based enforcement approach. Additionally, enhancing public education about the risks of DTC testing is crucial for safeguarding public health.

Metaproteomics and DNA metabarcoding as tools to assess dietary intake in humans.

Objective biomarkers of food intake are a sought-after goal in nutrition research. Most biomarker development to date has focused on metabolites detected in blood, urine, skin or hair, but detection of consumed foods in stool has also been shown to be possible via DNA sequencing. An additional food macromolecule in stool that harbors sequence information is protein. However, the use of protein as an intake biomarker has only been explored to a very limited extent. Here, we evaluate and compare measurement of residual food-derived DNA and protein in stool as potential biomarkers of intake. We performed a pilot study of DNA sequencing-based metabarcoding (FoodSeq) and mass spectrometry-based metaproteomics in five individuals' stool sampled in short, longitudinal bursts accompanied by detailed diet records (n=27 total samples). Dietary data provided by stool DNA, stool protein, and written diet record independently identified a strong within-person dietary signature, identified similar food taxa, and had significantly similar global structure in two of the three pairwise comparisons between measurement techniques (DNA-to-protein and DNA-to-diet record). Metaproteomics identified proteins including myosin, ovalbumin, and beta-lactoglobulin that differentiated food tissue types like beef from dairy and chicken from egg, distinctions that were not possible by DNA alone. Overall, our results lay the groundwork for development of targeted metaproteomic assays for dietary assessment and demonstrate that diverse molecular components of food can be leveraged to study food intake using stool samples.

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients. MATERIALS AND METHODS: We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment. RESULTS: The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user. DISCUSSION: Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings. CONCLUSION: EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.

Interplay between particle size and microbial ecology in the gut microbiome.

Physical particles can serve as critical abiotic factors that structure the ecology of microbial communities. For non-human vertebrate gut microbiomes, fecal particle size (FPS) has been known to be shaped by chewing efficiency and diet. However, little is known about what drives FPS in the human gut. Here, we analyzed FPS by laser diffraction across a total of 76 individuals and found FPS to be strongly individualized. Surprisingly, a behavioral intervention with 41 volunteers designed to increase chewing efficiency did not impact FPS. Dietary patterns could also not be associated with FPS. Instead, we found evidence that mammalian and human gut microbiomes shaped FPS. Fecal samples from germ-free and antibiotic-treated mice exhibited increased FPS relative to colonized mice. In humans, markers of longer transit time were correlated with smaller FPS. Gut microbiota diversity and composition were also associated with FPS. Finally, ex vivo culture experiments using human fecal microbiota from distinct donors showed that differences in microbiota community composition can drive variation in particle size. Together, our results support an ecological model in which the human gut microbiome plays a key role in reducing the size of food particles during digestion, and that the microbiomes of individuals vary in this capacity. These new insights also suggest FPS in humans to be governed by processes beyond those found in other mammals and emphasize the importance of gut microbiota in shaping their own abiotic environment.