Association of Oxidative Stress Biomarkers with Polycystic Ovary Syndrome (PCOS) and Its Metabolic Outcomes, Including Insulin Resistance and Dyslipidemia: A Systematic Review
Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is a common endocrine metabolic disorder characterized by hyperandrogenism, ovulatory dysfunction, and insulin resistance. Increasing evidence suggests that oxidative stress (OS) contributes to its metabolic and reproductive complications. Objectives: To systematically review primary studies evaluating oxidative stress biomarkers in PCOS and their associations with metabolic outcomes. Methods: A comprehensive search was conducted in PubMed, Scopus, and Cochrane Library up to April 2024, following PRISMA 2020 guidelines. Eligible studies included case–control and cross-sectional designs reporting quantitative OS biomarker data in PCOS versus controls. Quality and risk of bias were assessed using the Newcastle Ottawa Scale (NOS). Results: Fifteen studies (2014–2024) involving over 1,000 participants were included. Malondialdehyde (MDA) was elevated in nearly all studies, indicating enhanced lipid peroxidation. Total antioxidant capacity (TAC/FRAP) and enzymatic antioxidants (SOD, CAT) were consistently reduced, while non-enzymatic antioxidants (GSH, vitamins A/C/E) were also lower. PON1 activity and sRAGE levels decreased, and 8-isoprostane in follicular fluid correlated with poorer oocyte quality. OS markers were positively associated with BMI, insulin resistance, and dyslipidemia. Five studies were rated low risk and ten moderate risk by NOS criteria. Conclusions: PCOS is characterized by increased oxidative stress and reduced antioxidant defense, closely linked to metabolic severity. Incorporating OS biomarkers into clinical evaluation and exploring phenotype-specific antioxidant interventions may improve metabolic and reproductive outcomes. Future longitudinal studies should standardize biomarker measurement to strengthen clinical applicability.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".