Long-term dysbiosis promotes insulin resistance during obesity despite rapid diet-induced changes in the gut microbiome of mice
Bibliographic record
Abstract
Abstract The intestinal microbiota and insulin sensitivity are rapidly altered in response to a high fat diet (HFD). It is unclear if gut dysbiosis precedes insulin resistance or vice versa. The initial triggers of diet-induced insulin resistance can differ from mechanisms underlying chronic dysglycemia during prolonged obesity. It is not clear if intestinal dysbiosis contributes to insulin resistance during short-term or long-term HFD-feeding. We found that diet-induced changes in the composition of the fecal microbiome preceded changes in glucose and insulin tolerance at both the onset and removal of a HFD in mice. Dysbiosis occurred after 1-3 days of HFD-feeding, whereas insulin and glucose intolerance manifested by 3-4 days. Antibiotic treatment did not alter glucose tolerance during this short-term HFD period. Conversely, antibiotics improved glucose tolerance in mice with protracted obesity caused by long-term HFD feeding for over 2 months. We also found that microbiota transmissible glucose intolerance only occurred after prolonged diet-induced dysbiosis. Germ-free mice had impaired glucose tolerance when reconstituted with the microbiota from long-term, but not short-term HFD-fed animals. Our results are consistent with intestinal microbiota contributing to chronic insulin resistance and dysglycemia during prolonged obesity, despite rapid diet-induced changes in the taxonomic composition of the fecal microbiota.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".