Ileal Transposition in Rats Reduces Energy Intake, Body Weight, and Body Fat Most Efficaciously When Ingesting a High-Protein Diet
Bibliographic record
Abstract
PURPOSE: Ileal transposition (IT) allows exploration of hindgut effects of bariatric procedures in inducing weight loss and reducing adiposity. Here we investigated the role of dietary macronutrient content on IT effects in rats. METHODS: Male Lewis rats consuming one of three isocaloric liquid diets enriched with fat (HF), carbohydrates (HC), or protein (HP) underwent IT or sham surgery. Body weight, energy intake, energy efficiency, body composition, and (meal-induced) changes in plasma GIP, GLP-1, PYY, neurotensin, and insulin levels were measured. RESULTS: Following IT, HC intake remained highest leading to smallest weight loss among dietary groups. IT in HF rats caused high initial weight loss and profound hypophagia, but the rats caught up later, and finally had the highest body fat content among IT rats. HP diet most efficaciously supported IT-induced reduction in body weight and adiposity, but (as opposed to other diet groups) lean mass was also reduced. Energy efficiency decreased immediately after IT irrespective of diet, but normalized later. Energy intake alone explained variation in post-operative weight change by 80%. GLP-1, neurotensin, and PYY were upregulated by IT, particularly during (0-60 min) and following 17-h post-ingestive intake, with marginal diet effects. Thirty-day post-operative cumulative energy intake was negatively correlated to 17-h post-ingestive PYY levels, explaining 47% of its variation. CONCLUSION: Reduction in energy intake underlies IT-induced weight loss, with highest efficacy of the HP diet. PYY, GLP-1, and neurotensin levels are upregulated by IT, of which PYY may be most specifically related to reduced intake and weight loss after IT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".