Maternal Nutritional Environment and the Development of the Melanocortin System
Bibliographic record
Abstract
The maternal nutritional and/or metabolic environment is crucial for future offspring health outcomes, and impairments during critical periods of development can alter the development of brain circuits that regulate energy balance, predisposing individuals to metabolic disorders throughout life. Epigenetic changes, changes in cell number and/or organ structure, and cellular metabolic differentiation could be some of the fetal adaptations leading to the development of metabolic disorders later in life. Here, we review animal models showing that the nutritional environment to which the offspring are exposed during their perinatal life can influence the development of the hypothalamic melanocortin system, promoting increased feeding and fat deposition. Following maternal undernutrition, the development of obesity in the offspring may be related to decreased POMC neuronal function since birth. Similarly, maternal diabetes and obesity also induce hypothalamic changes that result in an imbalance in AgRP/NPY and POMC expression during adulthood. Widespread impairments in brain development may also induce a global downregulation of the melanocortin system. Furthermore, animal models highlight that the time and type of exposure are key to the offspring outcomes, as are their sex and age. Possible sex-specific differences remain unclear, as most studies have evaluated only the male offspring, despite females having an increased risk of developing obesity and gestational diabetes during their pregnancy, which imposes a transgenerational effect of metabolic disorders. Studies aiming at evaluating the long-term effects of the maternal nutritional environment in both males and females could help delineate how the susceptibility to metabolic disorders development worsens over time.
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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 | 0.001 | 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".