Homocysteine-Lowering Treatment With Folic Acid, Cobalamin, and Pyridoxine Does Not Reduce Blood Markers of Inflammation, Endothelial Dysfunction, or Hypercoagulability in Patients With Previous Transient Ischemic Attack or Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Epidemiological and laboratory studies suggest that increasing concentrations of plasma homocysteine (total homocysteine [tHcy]) accelerate cardiovascular disease by promoting vascular inflammation, endothelial dysfunction, and hypercoagulability. METHODS: We conducted a randomized controlled trial in 285 patients with recent transient ischemic attack or stroke to examine the effect of lowering tHcy with folic acid 2 mg, vitamin B12 0.5 mg, and vitamin B6 25 mg compared with placebo on laboratory markers of vascular inflammation, endothelial dysfunction, and hypercoagulability. RESULTS: At 6 months after randomization, there was no significant difference in blood concentrations of markers of vascular inflammation (high-sensitivity C-reactive protein [P=0.32]; soluble CD40L [P=0.33]; IL-6 [P=0.77]), endothelial dysfunction (vascular cell adhesion molecule-1 [P=0.27]; intercellular adhesion molecule-1 [P=0.08]; von Willebrand factor [P=0.92]), and hypercoagulability (P-selectin [P=0.33]; prothrombin fragment 1 and 2 [P=0.81]; D-dimer [P=0.88]) among patients assigned vitamin therapy compared with placebo despite a 3.7-micromol/L (95% CI, 2.7 to 4.7) reduction in total homocysteine (tHcy). CONCLUSIONS: Lowering tHcy by 3.7 micromol/L with folic acid-based multivitamin therapy does not significantly reduce blood concentrations of the biomarkers of inflammation, endothelial dysfunction, or hypercoagulability measured in our study. The possible explanations for our findings are: (1) these biomarkers are not sensitive to the effects of lowering tHcy (eg, multiple risk factor interventions may be required); (2) elevated tHcy causes cardiovascular disease by mechanisms other than the biomarkers measured; or (3) elevated tHcy is a noncausal marker of increased vascular risk.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".