Antioxidant Effects of Tocotrienol-rich Fractions Supplementation on Obesity-induced Oxidative Stress in Female Reproductive System: A Systematic Review
Bibliographic record
Abstract
Introduction: This review summarizes the antioxidant effects of tocotrienol-rich fractions (TRF) supplementation on obesity-induced oxidative stress (OS) in female reproductive system. Design: Systematic review. Data sources: PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar. Materials and methods: The authors conducted an electronic search of articles published from 2014 to 2024 on the effects of TRF supplementation on obesity-induced OS in the female reproductive system via several databases. Data were gathered from indexed articles, research papers, case studies, and experimental studies. Risk of bias was performed using standardised tools such as the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. Results: 25 studies investigated the benefit of TRF on the maternal obesity-induced OS in human studies and animal model. From the 25 studies, 9 studies demonstrated the benefits of TRF supplementation in reducing OS biomarkers in maternal obese models. Likewise, 16 of the 25 studies reported the benefits of TRF supplementation in improving reproductive health, whereas 15 of the 25 studies demonstrated the mechanism of action of TRF supplementation in scavenging the radicals and preventing oxidative damage and lipid peroxidation in maternal obese models. Conclusion: TRF supplementation significantly reduced the OS biomarkers by scavenging the radicals, which inhibit lipid peroxidation and oxidative damage mechanisms, thus improving female reproductive health outcomes in maternal obese models. Hence, this systematic review provides significant evidence supporting the benefit of TRF supplementation in obesity-induced OS in female reproductive system.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".