A Metabolic Enhancer Protects against Diet-Induced Obesity and Liver Steatosis and Corrects a Pro-Atherogenic Serum Profile in Mice
Bibliographic record
Abstract
OBJECTIVE: Metabolic Syndrome (MetS) affects hundreds of millions of individuals and constitutes a major cause of morbidity and mortality worldwide. Obesity is believed to be at the core of metabolic abnormalities associated with MetS, including dyslipidemia, insulin resistance, fatty liver disease and vascular dysfunction. Although previous studies demonstrate a diverse array of naturally occurring antioxidants that attenuate several manifestations of MetS, little is known about the (i) combined effect of these compounds on hepatic health and (ii) molecular mechanisms responsible for their effect. METHODS: We explored the impact of a metabolic enhancer (ME), consisting of 7 naturally occurring antioxidants and mitochondrial enhancing agents, on diet-induced obesity, hepatic steatosis and atherogenic serum profile in mice. RESULTS: Here we show that a diet-based ME supplementation and exercise have similar beneficial effects on adiposity and hepatic steatosis in mice. Mechanistically, ME reduced hepatic ER stress, fibrosis, apoptosis, and inflammation, thereby improving overall liver health. Furthermore, we demonstrated that ME improved HFD-induced pro-atherogenic serum profile in mice, similar to exercise. The protective effects of ME were reduced in proprotein convertase subtilisin/kexin 9 (PCSK9) knock out mice, suggesting that ME exerts it protective effect partly in a PCSK9-dependent manner. CONCLUSIONS: Our findings suggest that components of the ME have a positive, protective effect on obesity, hepatic steatosis and cardiovascular risk and that they show similar effects as exercise training.
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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 | 0.001 | 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) | 0.002 | 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".