Folic acid improves endothelial dysfunction in type 2 diabetes - an effect independent of homocysteine-lowering
Bibliographic record
Abstract
Diabetes is associated with endothelial dysfunction, which in part may be related to uncoupling of the endothelial nitric oxide (NO) synthase enzyme, thus reducing the availability of NO. As folates may potentially reverse the uncoupling of NO synthase, we wanted to determine whether folic acid supplementation could modulate endothelial function and markers of inflammation in patients with type 2 diabetes without vascular disease. Nineteen patients with type 2 diabetes were treated with folic acid (10mg/day for 2 weeks) versus placebo in a randomized, placebo-controlled, cross-over study with an 8-week washout period between treatments. Fasting endothelium-dependent flow-mediated dilatation (FMD) of the brachial artery, endothelium-independent nitroglycerin-mediated dilatation (NMD), plasma homocysteine, serum lipids, folate, and inflammatory markers (high-sensitivity C-reactive protein, soluble intercellular adhesion molecule-1 and vascular cell adhesion molecule-1, interleukin-18, tumor necrosis factor-alpha) were assessed after each 2-week treatment period. Folic acid supplementation significantly increased folate levels and lowered plasma homocysteine levels. Folic acid significantly improved FMD compared to placebo (5.8 +/- 4.8% vs 3.2 +/- 2.7%, p = 0.02). There were no significant effects of folic acid supplementation on lipids, NMD, or the inflammatory markers. There was no relationship between the change in homocysteine and the improvement in FMD. Thus, 2 weeks of folic acid supplementation can improve endothelial dysfunction in type 2 diabetics independent of homocysteine-lowering, but does not modulate markers of inflammation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".