A High-Fat High-Sucrose Diet Rapidly Alters Muscle Integrity, Inflammation and Gut Microbiota in Male Rats
Bibliographic record
Abstract
The chronic low-level inflammation associated with obesity is known to deleteriously affect muscle composition. However, the manner in which obesity leads to muscle loss has not been explored in detail or in an integrated manner following a short-term metabolic challenge. In this paper, we evaluated the relationships between compromised muscle integrity, diet, systemic inflammatory mediators, adipose tissue, and gut microbiota in male Sprague-Dawley rats. We show that intramuscular fat, fibrosis, and the number of pro-inflammatory cells increased by 3-days and was sustained across 28-days of high-fat high-sugar feeding compared to control-diet animals. To understand systemic contributors to muscle damage, dynamic changes in gut microbiota and serum inflammatory markers were evaluated. Data from this study links metabolic challenge to persistent compromise in muscle integrity after just 3-days, a finding associated with altered gut microbiota and systemic inflammatory changes. These data contribute to our understanding of early consequences of metabolic challenge on multiple host systems, which are important to understand as obesity treatment options are developed. Therefore, intervention within this early period of metabolic challenge may be critical to mitigate these sustained alterations in muscle integrity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".