Early overnutrition reduces Pdx1 expression and induces β cell failure in Swiss Webster mice
Bibliographic record
Abstract
Childhood obesity and early rapid growth increase the risk for type 2 diabetes. Such early overnutrition can be modeled in mice by reducing litter size. We investigated the effects of early overnutrition and increased dietary fat intake on β cell function in Swiss Webster mice. On a moderate-fat diet, early overnutrition accelerated weight gain and induced hyperinsulinemia in pups. Early overnutrition males exhibited higher β cell mass but reduced islet insulin content and Pdx1 expression. Males had a high diabetes incidence that was increased by early overnutrition, characterized by a progressive increase in insulin secretion as well as β cell death, indicated by histological analysis and increased circulating miR-375 levels. Females maintained normoglycemia throughout life. High-fat diet (HFD) increased diabetes incidence in males, whereas low-fat diet was completely protective. This protective effect was abolished in early overnutrition males transiently exposed to HFD in early life. Although Swiss Webster mice are not known to be diabetes-prone, the high diabetes incidence suggests an underlying genetic susceptibility that can be induced by overnutrition and increased dietary fat intake in early life. Thus, the nutritional environment in early life may impact long-term β cell function and increase diabetes risk, particularly in genetically susceptible individuals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".