Gut microbiota impairs insulin clearance in obese mice
Bibliographic record
Abstract
OBJECTIVE: Hyperinsulinemia can be both a cause and consequence of obesity and insulin resistance. Hyperinsulinemia can result from increased insulin secretion and/or reduced insulin clearance. While many studies have focused on mechanisms triggering insulin secretion during obesity, the triggers for changes in insulin clearance during obesity are less defined. In this study, we investigated the role of the microbiota in regulating insulin clearance during diet-induced obesity. METHODS: Blood glucose and insulin clearance were tested in conventional male mice treated with antibiotics and germ-free mice colonized with microbes from mice that were fed a control (chow) diet or an obesogenic high-fat diet (HFD). The composition of the fecal microbiota was analyzed using 16S rRNA sequencing. RESULTS: Short-term HFD feeding and aging did not alter insulin clearance in the mice. Oral antibiotics mitigated impaired blood insulin clearance in the mice fed an HFD for 12 weeks or longer. Germ-free mice colonized with microbes from HFD-fed donor mice had impaired insulin but not C-peptide clearance. Microbe-transmissible insulin clearance impairment was only observed in germ-free mice after more than 6 weeks post-colonization upon HFD feeding. Five bacterial taxa predicted >90% of the variance in insulin clearance. Mechanistically, impaired insulin clearance was associated with lower levels of hepatic Ceacam-1 but increased liver and skeletal muscle insulin-degrading enzyme (IDE) activity. CONCLUSIONS: Gut microbes regulate insulin clearance during diet-induced obesity. A small cluster of microbes or their metabolites may be targeted for mitigating defects in insulin clearance and hyperinsulinemia during the progression of obesity and type 2 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".