Spirulina liquid extract regulates gene expression related to glucose and lipid metabolisms of soleus muscle during exercise training in young male Wistar rats fed a high-fat diet
Bibliographic record
Abstract
Metabolic disorders induced by high-fat diets (HFD) contribute to obesity, diabetes, and cardiovascular diseases. While Spirulina liquid extract (SLE) has shown promise in improving lipid accumulation and insulin resistance, in vivo evidence remains limited, particularly in combination with exercise. Muscle activity is a key regulator of metabolism, but the potential combined effect of SLE and physical training under HFD conditions has not been established. In this study, young male rats were fed a HFD (60 % energy from lipids) and assigned to four groups: HFD with 10% fructose (HF f ), HF f with SLE (HF f SP), HF f with exercise (HF f T), and HF f with both interventions (HF f SPT). Bodyweight (BW), lipid profiles, glycemia regulation, and gene expression in soleus muscle (SOL) of lipid and glucose metabolism were assessed. SLE reduced fasting glycemia compared to the HF f group (1.19-fold) and upregulated Gys1 (1.78-fold) and CPT1A expression (4.13-fold) in SOL. Training improved glucose tolerance, as reflected by reduced AUC (p = 0.01), and upregulated PGC1⍺ and CPT1A expression. The combined intervention (HF f SPT) decreased BW, increased HDL-cholesterol (1.62-fold), reduced the atherogenic index (AIP) (1.39-fold). During training conditions, PGC1⍺ expression was downregulated by SLE (3.03-fold), suggesting a possible interference with exercise-induced muscle adaptation. p38 MAPK, elevated by HFD, was downregulated by SLE, exercise, and their combination (3.20-, 5.14-, and 2.72-fold, respectively). Overall, these findings support the potential of SLE as a complementary strategy to exercise in attenuating HFD-induced metabolic dysfunctions, while also raising concerns about possible interference with training adaptations.
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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".