Effect of Long-Term Homocysteine Reduction with B Vitamins on Arterial Wall Inflammation Assessed by Fluorodeoxyglucose Positron Emission Tomography: A Randomised Double-Blind, Placebo-Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Homocysteine may promote atherosclerosis by exacerbating inflammatory processes within the arterial wall. B-vitamin supplements reduce total plasma homocysteine concentrations (tHcy), but it is not known whether the treatment also reduces arterial wall inflammation. We used (18)F-fluorodeoxygluose positron emission tomography ((18)F-FDG PET) to investigate whether long-term homocysteine-lowering treatment alters arterial wall inflammation in patients with a history of ischemic stroke. METHODS: 30 stroke patients were randomly assigned to B-vitamin therapy (folic acid 2 mg, vitamin B(6) 25 mg and vitamin B(12) 0.5 mg) or placebo in a double-blind clinical trial. After a mean treatment period of 4.0 +/- 0.7 years, all subjects had tHcy, carotid intima-medial thickness (CIMT) and flow-mediated dilation (FMD) of the brachial artery measured and underwent an (18)F-FDG PET scan. Standardised uptake values (SUV) were measured at six sites in the carotid, femoral and aortic arteries. Areas of locally increased tracer uptake in the arterial wall ('hot spots') were also identified and counted. RESULTS: Long-term B-vitamin treatment significantly reduced tHcy compared with placebo (8.4 micromol/l, 95% confidence interval, CI, 7.2-9.6 vs. 11.6 micromol/l, 95% CI 10.0-13.4, p = 0.002). The treatment did not affect mean arterial SUV (2.0 +/- 0.3 vitamins vs. 2.1 +/- 0.3 placebo, p = 0.65) or the number of hot spots (n = 1.1 +/- 1.0 vitamins vs. n = 1.2 +/- 1.0 placebo, p = 0.65). There was no significant correlation between mean arterial SUV and CIMT or FMD. CONCLUSIONS: These results suggest that a long-term Hcy reduction with B vitamins does not affect arterial wall inflammation assessed by (18)F-FDG PET.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".