The effect of neonatal total parenteral nutrition on glucose metabolism in neonates and adult Yucatan miniature pigs
Bibliographic record
Abstract
Total parenteral nutrition (TPN) is used when oral nutrition is not possible, but may cause metabolic disturbances, increasing the risk of Type 2 diabetes mellitus (T2DM). We hypothesize that TPN feeding early in life can alter glucose metabolism in a way that persists into adulthood, leading to the development of biomarkers associated with T2D. Additionally, we hypothesized that supplementing TPN with betaine and creatine could potentially correct these changes, and that intrauterine growth-restriction (IUGR) could exacerbate TPN-induced changes. We assigned 32 female Yucatan miniature piglets to four groups: normal birth weight receiving TPN (TPN); sow-fed (SF); normal birthweight TPN supplemented with betaine and creatine (TPN-B+C); and IUGR piglets fed TPN (TPN�IUGR). After 2 weeks on TPN (or SF), glucose metabolism and insulin sensitivity was assessed. All pigs were then fed an oral diet for ~10 mo, and glucose metabolism tests were repeated. TPN feeding showed significantly more sensitive glucose metabolism, which were more pronounced immediately after TPN but remained significant 10 mo later. TPN also increased insulin sensitivity, which was corrected by adding betaine and creatine. IUGR did not exacerbate TPN effects. This study suggests that TPN in early life can permanently impact glucose metabolism into adulthood, but these changes do not align with T2DM.
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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".