Perinatal Maternal Fat Intake Affects Metabolism and Hippocampal Function in the Offspring
Bibliographic record
Abstract
Both undernutrition and overnutrition of the mother during pregnancy and lactation produce a syndrome of altered energy balance in the offspring and has long-lasting consequences on CNS systems regulating food intake, metabolism, and food reward. Homeostatic circulating factors like insulin, glucocorticoids, and leptin that are generally increased by exposure to high fat/high caloric diets constitute important signals in these processes. They trigger functional activation of specific intracellular cascades mediating cellular sensitivity, survival, and synaptic plasticity. Using a model whereby the late fetal and neonatal rat is exposed to increased high fat (HF) via HF feeding of the mother, we investigated the proximal (neonatal) and distal (adult) consequences on metabolism and hippocampal function in the offspring. Adult offspring of HF-fed mothers displayed several of the physiological and behavioral changes susceptible to leading to metabolic complications. These include elevated circulating concentrations of leptin and corticosterone, increased body weight gain and food intake, modest preference for fat-containing food types, as well as the onset of hypothalamic leptin resistance. In the hippocampus, HF-fed offspring or neonates treated with leptin show similar increases in neurogenesis and survival of newborn neurons. We identified some of the direct effects of leptin to increase synaptic proteins, N-methyl-d-aspartate (NMDA), and glucocorticoid receptors, and to reduce long-term potentiation (LTP) prior to weaning. While these studies have documented effects in animal models, concepts can easily be translated to human nutrition in order to help design better perinatal diets and nutritional preventive measures for mothers in a coordinated effort to curb the obesity trend.
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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".