On the Potential Effect of Increased Dietary Intake of Fruits and Vegetables on Biomarkers of Lipid Peroxidation in Type Diabetes Patients
Bibliographic record
Abstract
The present study was designed to test the hypothesis that an increased consumption of fruits and vegetables up to 600 g per day in type 2 diabetes patients can reduce oxidative damages to lipids. After a clinical examination, 29 type 2 diabetes patients were divided into two groups: the first one did not receive any special instructions about the diet while the second one received fruits and vegetables consumption advices thanks to a picture catalogue describing types of fruits and vegetables as well as quantities to be eaten every day. After two months of intervention, plasma concentrations in vitamin C, b-carotene and polyphenols remained unchanged in both groups when compared to baseline values. No significant decrease in lipid peroxidation as evidenced by nine biomarkers (malonaldehyde as TBAR’s, lipid peroxides, oxidized LDL, antibodies against oxidized LDL, isoprostanes, 7-keto-cholesterol, 7b-hydroxycholesterol, 4-hydroxynonenal metabolite, LDL size) was also noted. In conclusion, our findings confirmed the fact that the effect of high intake of fruits and vegetables on reducing oxidative damage to lipids remains largely controversial even in patients having an oxidative stress profile. Moreover, the present leaves open the question of the most appropriate markers of lipid peroxidation since only small correlations were evidenced between the large battery of tests investigated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".