Glucose Intolerance and Resistin Expression in Rat Offspring Exposed to Ethanol in Utero: Modulation by Postnatal High-Fat Diet
Bibliographic record
Abstract
High-fat diet and intrauterine growth retardation may predispose to obesity, insulin resistance, and type 2 diabetes. Because prenatal ethanol (ETOH) exposure causes intrauterine growth retardation, we investigated its interactions with postnatal high-fat diet on glucose tolerance and adipocyte-derived hormones in the rat offspring. High-fat-fed offspring had increased adiposity, serum leptin, and muscle uncoupling protein-3, but decreased adiponectin mRNA, compared with corresponding chow-fed groups. ETOH-exposed offspring had normal adiponectin, but increased resistin mRNA and protein, compared with controls, regardless of postnatal diet. Skeletal muscle glucose transporter-4 content was decreased after both ETOH exposure and high-fat feeding. Glycemic and insulin responses to an ip glucose challenge were equally increased in non-ETOH-exposed high-fat-fed offspring and in ETOH-exposed chow-fed offspring, with additive effects of ETOH and high-fat diet. Pancreatic insulin content was elevated only in non-ETOH-exposed high-fat-fed offspring. The data suggest that high-fat diet worsens glucose intolerance in offspring of rats exposed to ETOH. Prenatal ETOH exposure and postnatal high-fat diet might cause insulin resistance through separate mechanisms, involving resistin and adiponectin, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".