Manipulation of Dietary Amino Acids Prevents and Reverses Obesity in Mice Through Multiple Mechanisms That Modulate Energy Homeostasis
Bibliographic record
Abstract
Reduced activation of energy metabolism increases adiposity in humans and other mammals. Thus, exploring dietary and molecular mechanisms able to improve energy metabolism is of paramount medical importance because such mechanisms can be leveraged as a therapy for obesity and related disorders. Here, we show that a designer protein-deprived diet enriched in free essential amino acids can 1) promote the brown fat thermogenic program and fatty acid oxidation, 2) stimulate uncoupling protein 1 (UCP1)-independent respiration in subcutaneous white fat, 3) change the gut microbiota composition, and 4) prevent and reverse obesity and dysregulated glucose homeostasis in multiple mouse models, prolonging the healthy life span. These effects are independent of unbalanced amino acid ratio, energy consumption, and intestinal calorie absorption. A brown fat-specific activation of the mechanistic target of rapamycin complex 1 seems involved in the diet-induced beneficial effects, as also strengthened by in vitro experiments. Hence, our results suggest that brown and white fat may be targets of specific amino acids to control UCP1-dependent and -independent thermogenesis, thereby contributing to the improvement of metabolic health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".