Effects of High-Fat Diet Induced Obesity and Fructooligosaccharide Supplementation on Cardiac Protein Expression
Bibliographic record
Abstract
The mechanism by which high fat-diet induced obesity affects cardiac protein expression is unclear, and the extent to which this is modulated by prebiotic treatment is not known. These outcomes were assessed in rats initially fed a high-fat diet, then the top 40% weight gain group were randomly allocated to control (CON), high-fat (HF) and HF supplemented with fructooligosaccharide (32 g; HF-FOS) treatments for 12 weeks (n = 10/group). At sacrifice, left ventricles were either frozen or preserved in formalin. Serum was stored for glucose and insulin measurements. Protein spectra was obtained using an Orbitrap analyzer, processed with Sequest and fold changes assessed with Scaffold Q +. Treatment effects for body weights, glucose and insulin were assessed using one-way ANOVA, and the differential protein expression was assessed by a Mann–Whitney U test. The Database for Annotation, Visualization and Integrated Discovery and the Kyoto Encyclopedia of Genes and Genomes identified pathways containing overrepresented proteins. Hematoxylin and eosin sections were graded for hypertrophy and also quantified; differences were identified using Chi-square analyses and Mann-Whitney U tests. HF diet fed rats were significantly (p < 0.05) heavier than CON, and 23 proteins involved in mitochondrial function and lipid metabolism were differentially expressed between HF and CON. Between HF-FOS and HF, 117 proteins involved in contractility, lipid and carbohydrate metabolism were differentially expressed. HF cardiomyocytes were significantly (p < 0.05) more hypertrophic than CON. We conclude that high-fat feeding and FOS are associated with subcellular deviations in cardiac metabolism and contractility, which may influence myocardial function and alter the risk of cardiovascular disease.
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 | 0.000 | 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".