Early overnutrition in male mice negates metabolic benefits of a diet high in monounsaturated and omega-3 fats
Bibliographic record
Abstract
Overconsumption of saturated fats promotes obesity and type 2 diabetes. Excess weight gain in early life may be particularly detrimental by promoting earlier diabetes onset and potentially by adversely affecting normal development. In the present study we investigated the effects of dietary fat composition on early overnutrition-induced body weight and glucose regulation in Swiss Webster mice, which show susceptibility to high-fat diet-induced diabetes. We compared glucose homeostasis between a high-fat lard-based (HFL) diet, high in saturated fats, and a high-fat olive oil/fish oil-based (HFO) diet, high in monounsaturated and omega-3 fats. We hypothesized that the healthier fat profile of the latter diet would improve early overnutrition-induced glucose dysregulation. However, early overnutrition HFO pups gained more weight and adiposity and had higher diabetes incidence compared to HFL. In contrast, control pups had less weight gain, adiposity, and lower diabetes incidence. Plasma metabolomics revealed reductions in various phosphatidylcholine species in early overnutrition HFO mice as well as with diabetes. These findings suggest that early overnutrition may negate any beneficial effects of a high-fat diet that favours monounsaturated and omega-3 fats over saturated fats. Thus, quantity, quality, and timing of fat intake throughout life should be considered with respect to metabolic health outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".