High-fat diet leads to adiposity and adipose tissue inflammation: the effect of whey protein supplementation and aerobic exercise training
Bibliographic record
Abstract
There is little understanding about dietary proteins and their potential contribution to obesity-induced inflammation. This study investigates the effect of 10 weeks of aerobic training and whey protein (WP) supplementation on visceral adipose tissue inflammation in rats fed a high-fat diet (HFD). In the first phase, which lasted 9 weeks, 40 male Wistar rats were randomly divided into 2 groups: (1) normal diet (n = 8), and (2) HFD (n = 32). In the second phase, rats fed an HFD were randomly assigned into 4 groups (n = 8/group): (1) sedentary, (2) WP, (3) aerobic training, and (4) WP + aerobic training. The aerobic training was performed for 10 weeks, 5 days/week at 21 m/min, 15% incline, for 60 min/day. HFD significantly increased body weight, adiposity index, fat pads weight, glucose levels, and insulin resistance index compared with the normal diet. Also, levels of tumor necrosis factor alpha (TNF-α), monocyte chemoattractant protein-1 (MCP-1), hypoxia inducible factor 1 alpha (HIF-1α), and vascular endothelial growth factor A (VEGF-A) in adipose tissue and serum levels of TNF-α were increased in the HFD group. Glucose levels, insulin resistance index, and triglycerides were reduced only by WP, independently of aerobic training. Both the aerobic training and WP reduced the fat pads weight and levels of TNF-α, HIF-1α, and VEGF-A in adipose tissue. Nevertheless, the levels of MCP-1 in adipose tissue and serum levels of TNF-α and MCP-1 were not reduced significantly by WP or aerobic training. These findings suggest that both aerobic training and WP supplementation lead to a reduction in adiposity and ameliorate obesity-induced inflammation in visceral adipose tissue.
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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 | 0.001 |
| 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".