Maternal Dietary Fat Determines Metabolic Profile and the Magnitude of Endocannabinoid Inhibition of the Stress Response in Neonatal Rat Offspring
Bibliographic record
Abstract
Endocannabinoids (eCBs) are products of phospholipid (PL)-derived arachidonic acid (AA) that regulate hypothalamus-pituitary-adrenal axis activity. We hypothesized that differences in the quality and quantity of maternal dietary fat would modulate the PL AA content in the neonatal brain affecting stress responsiveness via differences in eCB production and activity in stress-activated brain areas. Pregnant rats were fed a 5% [control (C)] or 30% fat [high fat (HF)] diet rich in either n-6 (HF-n-6) or n-3 (HF-n-3) fat during the last week of gestation and lactation. Postnatal d 10 offspring were tested for metabolic hormones, AA (n-6) and eCB brain content, and hormonal effects of eCB receptor antagonism (AM251, 1 or 3 mg/kg ip) on stress responses. Like maternal diet, milk from HF-n-3 mothers had a reduced n-6/n-3 fat ratio compared with that of C and HF-n-6 mothers. Hypothalamic and hippocampal levels of PL AA were diet specific, reflecting the maternal milk and dietary n-6/n-3 ratio, with HF-n-3 offspring displaying reduced AA content relative to C and HF-n-6 offspring. Plasma corticosterone and insulin were elevated in HF-fed pups, whereas leptin was increased only in HF-n-6 pups. Basal eCB concentrations were also diet and brain region specific. In C pups, eCB receptor antagonist pretreatment increased stress-induced ACTH secretion, but not in the HF groups. Stress-induced corticosterone secretion was not sensitive to AM251 treatment in HF-n-3 pups. Thus, the nature of preweaning dietary fat differentially influences neonatal metabolic hormones, brain PL AA levels, and eCB, with functional consequences on hypothalamus-pituitary-adrenal axis modulation in developing rat pups.
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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 | 0.001 |
| 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".