Impact of High-Fat Diet-induced Metabolic Dysfunction-associated Steatotic Liver Disease on Heart, Kidney, and Skeletal Muscle Metabolomes in Wild-Type Mice
Bibliographic record
Abstract
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) can be recapitulated in mice fed a high-fat diet. The development of MASLD and the diet per se can both perturb metabolism in key extrahepatic tissues such as the heart, kidney, and skeletal muscle. To date, these alterations have not been well described in this animal model of diet-induced MASLD. Methodology: Male C57BL/6J mice were fed either standard (SC, n = 12) or high-fat chow (HF, n = 11) for 18 weeks. Metabolites were extracted from the heart, kidney, and skeletal muscle and analyzed by 1 H nuclear magnetic resonance (NMR) spectroscopy, along with multivariate and univariate statistical analyses. Results: Kidney metabolite profiles exhibited the largest differences between HF and SC diets, followed by those of skeletal muscle and then the heart. Some alterations were common across all tissues, namely decreased trimethylamine and elevated levels of linoleic acid and polyunsaturated fatty acids in HF compared to SC ( p < 0.05 for all three metabolites). Overall, the metabolite variations were consistent with shifts in carbohydrate and lipid substrate selection for oxidation, increased tissue stress in the heart and kidneys, and altered choline metabolism. These findings may serve as additional important descriptors of MASLD onset and progression.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".