Antihyperglycemic Effects of Short Term Resveratrol Supplementation in Type 2 Diabetic Patients
Bibliographic record
Abstract
The objective of this study was to examine the effectiveness of resveratrol in lowering blood glucose in the presence of standard antidiabetic treatment in patients with type 2 diabetes, in a randomized placebo-controlled double-blinded parallel clinical trial. A total of 66 subjects with type 2 diabetes were enrolled in this study and randomly assigned to intervention group which was supplemented with resveratrol at a dose 1 g/day for 45 days and control group which received placebo tablets. Body weight, blood pressure, fasting blood glucose, haemoglobin A1c, insulin, homeostatic assessments for insulin resistance, triglycerides, total cholesterol, low density lipoprotein, high density lipoprotein, and markers of liver and kidney damage were measured at baseline and after 45 days of resveratrol or placebo supplementation. Resveratrol treatment significantly decreased systolic blood pressure, fasting blood glucose, haemoglobin A1c, insulin, and insulin resistance, while HDL was significantly increased, when compared to their baseline levels. On the other hand, the placebo group had slightly increased fasting glucose and LDL when compared to their baseline levels. Liver and kidney function markers were unchanged in the intervention group. Overall, this study showed that resveratrol supplementation exerted strong antidiabetic effects in patients with type 2 diabetes.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".