Dietary supplement combination reduces inflammation and improves vigor in stressed subjects
Bibliographic record
Abstract
Objective The purpose of this study was to evaluate the combined effects of two commercially available dietary supplements on measures of Metabolic Balance, Inflammation, and Psychological Mood State. The supplements are (1) a whole Mangosteen fruit/rind juice (Xango ™ ), and (2) an herbal blend containing Citrus sinensis , Eurycoma longifolia , Camelia sinensis , and L‐theanine (Eleviv ™ ). Previous studies have shown benefits on measures of inflammation (Xango) and hormone balance and mood (Eleviv). Methods We recruited 30 moderately stressed subjects (22 women/8 men) for this 4‐week placebo‐controlled, double‐blind study. Each participant was randomly assigned to consume 6oz/d of the Juice/Placebo (3oz AM/3oz PM) and 2 capsules/d of the Herbal/Placebo (AM). Results There were no significant differences on measures of female reproductive hormones (Progesterone, Estradiol, Estriol, Estrone, 2‐hydroxyestrone, 16‐alpha‐hydroxysterone, and Estrone Sulfate). Significant differences between groups were found for measures of Inflammation (−59% C‐Reactive Protein); Metabolic Balance (+421% ratio between Free Androgen Index/24h Cortisol exposure), Vigor (+27%), and Tension (−34%). Conclusion These data provide clear evidence that consuming the supplements together for 4 weeks provides a range of metabolic benefits. This study was conducted by SupplementWatch and funded by Xango, LLC.
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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) | 0.001 | 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 | 0.000 |
| 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".