Quality More Than Quantity: The Use of Carbohydrates in High-Fat Diets to Tackle Obesity in Growing Rats
Bibliographic record
Abstract
Childhood obesity prevention is important to avoid obesity and its comorbidities into adulthood. Although the energy density of food has been considered a main obesogenic factor, a focus on food quality rather that the quantity of the different macronutrients is needed. Therefore, this study investigates the effects of changing the quality of carbohydrates from rapidly to slowly digestible carbohydrates on metabolic abnormalities and its impact on obesity in growing rats fed a high-fat diet (HFD). Growing rats were fed on HFD containing carbohydrates with different digestion rates: a HFD containing rapid-digesting carbohydrates (OBE group) or slow-digesting carbohydrates (ISR group), for 4 weeks and the effect on the metabolism and signaling pathways were analyzed in different tissues. Animals from OBE group presented an overweight/obese phenotype with a higher body weight gain and greater accumulation of fat in adipose tissue and liver. This state was associated with an increase of HOMA index, serum diacylglycerols and triacylglycerides, insulin, leptin, and pro-inflammatory cytokines. In contrast, the change of carbohydrate profile in the diet to one based on slow digestible prevented the obesity-related adverse effects. In adipose tissue, GLUT4 was increased and UCPs and PPARγ were decreased in ISR group respect to OBE group. In liver, GLUT2, FAS, and SRBP1 were lower in ISR group than OBE group. In muscle, an increase of glycogen, GLUT4, AMPK, and Akt were observed in comparison to OBE group. In conclusion, this study demonstrates that the replacement of rapidly digestible carbohydrates for slowly digestible carbohydrates within a high-fat diet promoted a protective effect against the development of obesity and its associated comorbidities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".