Concurrent Prebiotic Intake Reverses Insulin Resistance Induced by Early-Life Pulsed Antibiotic in Rats
Bibliographic record
Abstract
Pulsed antibiotic treatment (PAT) early in life increases risk of obesity. Prebiotics can reduce fat mass and improve metabolic health. We examined if co-administering prebiotic with PAT reduces obesity risk in rat pups weaned onto a high fat/sucrose diet. Pups were randomized to (1) control [CTR], (2) antibiotic [ABT] (azithromycin), (3) prebiotic [PRE] (10% oligofructose (OFS)), (4) antibiotic + prebiotic [ABT + PRE]. Pulses of antibiotics/prebiotics were administered at d19–21, d28–30 and d37–39. Male and female rats given antibiotics (ABT) had higher body weight than all other groups at 10 wk of age. The PAT phenotype was stronger in ABT males than females, where increased fat mass, hyperinsulinemia and insulin resistance were present and all reversible with prebiotics. Reduced hypothalamic and hepatic expression of insulin receptor substrates and ileal tight junction proteins was seen in males only, explaining their greater insulin resistance. In females, insulin resistance was improved with prebiotics and normalized to lean control. ABT reduced Lactobacillaceae and increased Bacteroidaceae in both sexes. Using a therapeutic dose of an antibiotic commonly used for acute infection in children, PAT increased body weight and impaired insulin production and insulin sensitivity. The effects were reversed with prebiotic co-administration in a sex-specific manner.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".