Oxidative Stress And Redox-Based Interventions In Chronic Diseases: A Systematic Review
Bibliographic record
Abstract
Background:Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) production and antioxidant defense systems, has emerged as a critical factor in the pathogenesis of various chronic diseases. Understanding its mechanisms and therapeutic implications is essential for developing effective interventions. Objective:This review aims to evaluate the role of oxidative stress in chronic diseases and to analyze the efficacy of pharmacological interventions with antioxidant or pro-oxidant properties across cardiovascular, metabolic, neurodegenerative, and oncological disorders. Methods:A systematic search was conducted across PubMed, Scopus, and Web of Science for articles published between January 2000 and December 2024. Inclusion criteria encompassed original studies (RCTs, observational, and preclinical) examining oxidative stress biomarkers and therapeutic outcomes. Quality assessment employed validated tools including the Cochrane Risk of Bias Tool, Newcastle-Ottawa Scale, and AMSTAR-2. A narrative synthesis approach was used due to heterogeneity in study designs. Results:Malondialdehyde (MDA) and 8-hydroxy-2'-deoxyguanosine (8-OHdG) were the most consistently elevated markers across diseases such as type 2 diabetes mellitus, cardiovascular disease, Parkinson’s disease, COPD, psoriasis, and chronic kidney disease. Antioxidant enzymes—superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH)—were frequently reduced, indicating compromised redox defense. MDA and SOD levels were responsive to lifestyle or pharmacological interventions, highlighting their potential for therapeutic monitoring. Heterogeneity in biomarker assessment methods limited cross-study comparisons. Conclusion:Oxidative stress biomarkers demonstrate consistent alterations across chronic diseases, suggesting their utility in disease monitoring and risk stratification. MDA, 8-OHdG, SOD, and CAT are among the most clinically informative markers. Standardized measurement protocols are essential to improve their translational value in redox-based diagnostics and therapeutic evaluation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".