Effects of a paternal diet high in animal protein (casein) versus plant protein (pea protein with added methionine) on offspring metabolic and gut microbiota outcomes in rats
Bibliographic record
Abstract
Evidence suggests that paternal diet can influence offspring metabolic health intergenerationally but whether dietary animal and plant proteins differ in their impact on fathers and their offspring is not known. Our objective was to examine the effects of a paternal diet high in casein versus pea protein on fathers and their offspring. Five-week-old male rats were fed: (1) control, (2) high animal protein (AP, 36.1% of energy as casein), or (3) high plant protein (PP, 36.1% of energy as pea protein with added methionine) diets for 8-11 weeks before being mated. Offspring were challenged with a high fat/sucrose diet (HFD) from 10 to 16 weeks of age. Metabolic and microbial outcomes were assessed in both generations. In fathers fed PP diet, enhanced insulin sensitivity and lower liver triglycerides were seen alongside altered hepatic microRNA expression and gut microbial profiles. Few changes were seen in their offspring. In contrast, the paternal AP diet influenced adult offspring hepatic microRNA expression and programmed a latent increase in adiposity, dysregulated satiety hormones, and modified gut microbial composition in their adult offspring that occurred following the HFD. Overall, a diet high in pea protein with added methionine demonstrated protective effects on biomarkers of metabolic health in the fathers but led to minimal effects on the offspring while a paternal diet high in casein led to evidence of an increase in characteristics of metabolic dysfunction in their adult offspring when unmasked by exposure to a HFD for 6 weeks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".