Caloric restriction-induced weight loss with a high-fat diet does not fully recover visceral adipose tissue inflammation in previously obese C57BL/6 mice
Bibliographic record
Abstract
Caloric restriction (CR) reduces body weight and systemic inflammation, but the effects on adipose tissue under dietary lipid overload are controversial. We evaluated the effects of CR-induced weight loss with a high-fat diet on adipose tissue inflammation of obese mice. Male mice were assigned into low-fat diet (LF) and high-fat diet (HF) groups. After 8 weeks, the mice in the HF group were reassigned for another 7 weeks into the following 3 conditions: (i) kept in the HF condition; (ii) changed to low-fat diet ad libitum (LFAL); and (iii) changed to high-fat calorie-restricted (RHF) diet to reach LFAL body weight. Serum markers, adipocytokines, morphology, and inflammatory infiltrates in retroperitoneal adipose tissue (RAT) were accessed. The body weights of the LFAL and RHF groups were reduced, equaling the body weights of the LF group. The LFAL mice had restored almost all inflammatory markers as the LF mice, except tumor necrosis factor-alpha (TNF-α), monocyte chemoattractant protein-1 (MCP-1), and adiponectin. Compared with the HF group, the RHF group had lowered visceral adiposity, retroperitoneal adipocyte sizes, and RAT inflammatory cell infiltration, as well as TNF-α, interleukin-6, and hepatic and serum C-reactive protein, which were higher than that of the LFAL group; adiponectin and MCP-1 did not change. CR with high-fat diet reduced body weight and attenuated visceral adiposity but did not fully recover visceral tissue inflammation. Novelty Caloric restriction in a high-fat diet ameliorated visceral adiposity. Caloric restriction in a high-fat diet did not recover visceral adipose tissue inflammation.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".