American Diabetes Association and JDRF Research Symposium: Diabetes and the Microbiome
Bibliographic record
Abstract
From 27-29 October 2014, more than 100 people gathered in Chicago, IL, to participate in a research symposium titled "Diabetes and the Microbiome," jointly sponsored by the American Diabetes Association and JDRF. The conference brought together international scholars and trainees from multiple disciplines, including microbiology, bioinformatics, endocrinology, metabolism, and immunology, to share the current understanding of host-microbe interactions and their influences on diabetes and metabolism. Notably, this gathering was the first to assemble specialists with distinct expertise in type 1 diabetes, type 2 diabetes, immunology, and microbiology with the goal of discussing and defining potential pathophysiologies linking the microbiome and diabetes. In addition to reviewing existing evidence in the field, speakers presented their own original research to provide a comprehensive view of the current understanding of the topics under discussion.Presentations and discussions throughout the conference reflected a number of important concepts. The microbiota in any host represent a complex ecosystem with a high degree of interindividual variability. Different microbial communities, comprising bacteria, archaea, viruses, and fungi, occupy separate niches in and on the human body. Individually and collectively, these microbes provide benefits to the host-including nutrient harvest from food and protection against pathogens. They are dynamically regulated by both host genes and the environment, and they critically influence both physiology and lifelong health. The objective of the symposium was to discuss the relationship between the host and the microbiome-the combination of microbiota and their biomolecular environment and ecology-specifically with regard to metabolic and immunological systems and to define the critical research needed to understand and potentially target the microbiome in the prevention and treatment of diabetes. In this report, we present meeting highlights in the following areas: 1) relationships between diabetes and the microbiome, 2) bioinformatic tools, resources, and study design considerations, 3) microbial programming of the immune system, 4) the microbiome and energy balance, 5) interventions, and 6) limitations, unanswered questions, and resource and policy needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".