A Novel Multi-Ingredient Supplement Activates a Browning Program in White Adipose Tissue and Mitigates Weight Gain in High-Fat Diet-Fed Mice
Bibliographic record
Abstract
We investigated the effects of a novel multi-ingredient supplement comprised of polyphenol antioxidants and compounds known to facilitate mitochondrial function and metabolic enhancement (ME) in a mouse model of obesity. In this study, 6-week-old male C57/BL6J mice were placed on a high-fat diet (HFD; ~60% fat) for 6 weeks, with subsequent allocation into experimentalgroups for 4 weeks: HFD control, HFD + ME10 (10 components), HFD + ME7 (7 components), HFD + ME10 + EX, HFD + EX (where '+EX' animals exercised 3 days/week), and chow-fed control. After the intervention, HFD control animals had significantly greater body weight and fat mass. Despite the continuation of HFD, animals supplemented with multi-ingredient ME or who performed exercise training showed an attenuation of fat mass and preservation of lean body mass, which was further enhanced when combined (ME+EX). ME supplementation stimulated the upregulation of white and brown adipose tissue mRNA transcripts associated with mitochondrial biogenesis, browning, fatty acid transport, and fat metabolism. In WAT depots, this was mirrored by mitochodrial oxidative phosphorylation (OXPHOS) protein expression, and increased in vivo fat oxidation measured via CLAMS. ME supplementation also decreased systemic and local inflammation markers. Herein, we demonstrated that novel multi-ingredient nutritional supplements induced significant fat loss independent of physical activity while preserving muscle mass in obese mice. Mechanistically, these MEs appear to act by inducing a browning program in white adipose tissue and decreasing other pathophysiological impairments associated with obesity, including mitochondrial respiration alterations induced by HFD.
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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".