Antioxidants in the Treatment of Diabetes
Bibliographic record
Abstract
Diabetes is a chronic metabolic disorder that continues to present as a major health problem worldwide. It is characterized by absolute or relative deficiencies in insulin secretion and/or insulin action and is associated with chronic hyperglycemia and disturbances of carbohydrate, lipid, and protein metabolism. Many studies suggest a central role for oxidative stress in the pathogenesis of this multi-faceted metabolic disorder. This has prompted investigations in the use of antioxidants as a complementary therapeutic approach. In this review we briefly summarize oxidative mechanisms implicated in diabetic complications and then focus on the findings resulting from human clinical trials where antioxidants were studied as an adjuvant to standard diabetes treatment during the last ten years. A literature search using PubMed (last ten years) was performed using the following terms: vitamin E, vitamin C, coenzyme Q10, alpha lipoic acid, L-carnitine, ruboxistaurin or LY 333531 and diabetes. This search was limited to human clinical trials. We conclude there is not any established benefit for antioxidant use in the management of diabetic complications. Therefore, routine vitamin or mineral supplementation is not generally recommended in human diabetes. Keywords: Anti-Oxidants, Diabetes, Oxidative Stress, Chronic Metabolic Disorder, Insulin, Vitamin E, Vitamin C, Coenzyme Q10, Alpha Lipoic Acid, L-Carnitine, Reactive Oxygen Species, Antioxidants in the Treatment of Diabetes, Antioxidants
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".