Influence of prenatal nutrition and obesity on tissue specific fat mass and obesity-associated ( <i>FTO</i> ) gene expression
Bibliographic record
Abstract
The recent discovery of an association between body composition, energy intake and the fat mass and obesity-associated (FTO) gene represents a promising new therapeutic target in obesity prevention. In a well, pre-established large animal model, we investigated the regulation of FTO gene expression under conditions either leading to obesity or increased risk of obesity related disorders: i) a sedentary 'Western' lifestyle and ii) prenatal exposure to nutrient restriction. Pregnant sheep were either fed to fully meet their nutritional requirements throughout gestation or 50% of this amount from early-to-mid gestation. Following weaning, offspring were either made obese through exposure to a sedentary obesogenic environment or remained lean. A significant positive relationship between placental FTO gene expression and fetal weight was found at 110 days gestation. In both the newborn and adult offspring, the hypothalamus was the major site of FTO gene expression. Hypothalamic FTO gene expression was upregulated by obesity and was further increased by prenatal nutrient restriction. Importantly, we found a strong negative relationship between the hypothalamic FTO gene expression and food intake in lean animals only that may imply FTO as a novel controller of energy intake. In contrast, FTO gene expression in the heart was downregulated in obese offspring born to nutrient restricted mothers. In addition, FTO gene expression was unaffected by obesity or prenatal diet in insulin-dependent tissues, where it changed with age possibly reflecting adaptations in cellular energetic activity. These findings extend information gained from human epidemiology and provide new insights into the regulation of in vivo energy metabolism to prevent obesity.
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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".