Maternal taurine supplementation in rats partially prevents the adverse effects of early-life protein deprivation on β-cell function and insulin sensitivity
Bibliographic record
Abstract
Dietary protein restriction during pregnancy and lactation in rats impairs β-cell function and mass in neonates and leads to glucose intolerance in adult offspring. Maternal taurine (Tau) supplementation during pregnancy in rats restores β-cell function and mass in neonates, but its long-term effects are unclear. The prevention of postnatal catch-up growth has been suggested to improve glucose tolerance in adult offspring of low-protein (LP)-fed mothers. The objective of this study was to examine the relative contribution of β-cell dysfunction and insulin resistance to impaired glucose tolerance in 130-day-old rat offspring of LP-fed mothers and the effects of maternal Tau supplementation on β-cell function and insulin resistance in these offspring. Pregnant rats were fed i) control, ii) LP, and iii) LP+Tau diets during gestation and lactation. Offspring were given a control diet following weaning. A fourth group consisting of offspring of LP-fed mothers, maintained on a LP diet following weaning, was also studied (LP-all life). Insulin sensitivity in the offspring of LP-fed mothers was reduced in females but not in males. In both genders, LP exposure decreased β-cell function. Tau supplementation improved insulin sensitivity in females and β-cell function in males. The LP-all life diet improved β-cell function in males. We conclude that i) maternal Tau supplementation has persistent effects on improving glucose metabolism (β-cell function and insulin sensitivity) in adult rat offspring of LP-fed mothers and ii) increasing the amount of protein in the diet of offspring adapted to a LP diet after weaning may impair glucose metabolism (β-cell function) in a gender-specific manner.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".