Effects of folinic acid on forearm blood flow in patients with end-stage renal disease
Bibliographic record
Abstract
BACKGROUND: Abnormalities of endothelial function are likely to contribute to the accelerated atherosclerotic risk in subjects with end-stage renal disease (ESRD). While folates can improve endothelial function, their role in ESRD has not been fully studied. The objective was to determine the acute and 12 week-effect of folinic acid on endothelium-dependent vasodilation in subjects with ESRD. METHODS: Forearm blood flow (FBF) was assessed by strain gauge plethysmography at baseline and after 12 weeks in 34 ESRD patients (57 +/- 14 years). Vascular function was assessed with acetylcholine (ACh), and sodium nitroprusside (SNP). Patients were randomized to receive folinic acid (50 mg i.v. once weekly) or a matching placebo. A subset of 25 subjects also received folinic acid (500 microg/min intra-arterially) or placebo to determine the acute effect on ACh and SNP mediated dilation at the time of the baseline vascular study. RESULTS: Folinic acid acutely improved the maximum change in ACh mediated FBF (10.0 +/- 2.4 to 12.8 +/- 2.2 ml/min/100 ml, P = 0.017), but did not change SNP responses. Chronic active therapy did not change ACh or SNP-mediated increases in FBF. Folinic acid resulted in a non-significant decrease in homocysteine (21 +/- 6 vs 28 +/- 18 micromol/l, P = 0.16) and diastolic blood pressure was significantly reduced (P = 0.05). CONCLUSIONS: The present study demonstrated that folinic acid acutely improved endothelium-dependent vasodilatation in patients with ESRD suggesting a direct vascular effect. Chronic treatment with folinic acid did not show benefit in endothelial function, but did lower diastolic blood pressure. Further work is required to determine the optimal regime to protect vascular health in subjects with ESRD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".