Modulation of type 1 and type 2 diabetes risk by the intestinal microbiome
Bibliographic record
Abstract
The prevalence of type 1 and type 2 diabetes have both risen dramatically over the last 50 years. Recent findings point towards the gut microbiota as a potential contributor to these trends. The hundred trillion bacteria residing in the mammalian gut have established a symbiotic relation with their host and influence many aspects of host metabolism, physiology, and immunity. In this review, we examine recent data linking gut microbiome composition and function to anti-pancreatic immunity, insulin-resistance, and obesity. Studies in rodents and human longitudinal studies suggest that an altered gut microbiome characterized by lower diversity and resilience is associated with type 1 and type 2 diabetes. Through its metabolites and enzymatic arsenal, the microbiota shape host metabolism, energy extracted from the diet and contribute to the normal development of the immune system and to tissue inflammation. Increasing evidence underscores the importance of the maternal microbiome, the gestational environment and the conditions of newborn delivery in establishing the gut microbiota of the offspring. Perturbations of the maternal microbiome during gestation, or that of the offspring during early infant development may promote a pro-inflammatory environment conducive to the development of autoimmunity and metabolic disturbance. Collectively the findings reviewed herein underscore the need for mechanistic investigations in rodent models and in human studies to better define the relationships between microbial and host inflammatory activity in diabetes, and to evaluate the potential of microbe-derived therapeutics in the prevention and treatment of both forms of diabetes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".