B-vitamin Supplementation Mitigates Effects of Fine Particles on Cardiac Autonomic Dysfunction and Inflammation: A Pilot Human Intervention Trial
Bibliographic record
Abstract
Abstract Ambient fine particle (PM2.5) pollution triggers acute cardiovascular events. Individual-level preventions are proposed to complement regulation in reducing the global burden of PM2.5–induced cardiovascular diseases. We determine whether B vitamin supplementation mitigates PM2.5 effects on cardiac autonomic dysfunction and inflammation in a single-blind placebo-controlled crossover pilot trial. Ten healthy adults received two-hour controlled-exposure-experiment to sham under placebo, PM2.5 (250 μg/m3) under placebo, and PM2.5 (250 μg/m3) under B-vitamin supplementation (2.5 mg/d folic acid, 50 mg/d vitamin B6, and 1 mg/d vitamin B12), respectively. At pre-, post-, 24 h-post-exposure, we measured resting heart rate (HR) and heart rate variability (HRV) with electrocardiogram, and white blood cell (WBC) counts with hematology analyzer. Compared to sham, PM2.5 exposure increased HR (3.8 bpm, 95% CI: 0.3, 7.4; P = 0.04), total WBC count (11.5%, 95% CI: 0.3%, 24.0%; P = 0.04), lymphocyte count (12.9%, 95% CI: 4.4%, 22.1%; P = 0.005), and reduced low-frequency power (57.5%, 95% CI: 2.5%, 81.5%; P = 0.04). B-vitamin supplementation attenuated PM2.5 effect on HR by 150% (P = 0.003), low-frequency power by 90% (P = 0.01), total WBC count by 139% (P = 0.006), and lymphocyte count by 106% (P = 0.02). In healthy adults, two-hour PM2.5 exposure substantially increases HR, reduces HRV, and increases WBC. These effects are reduced by B vitamin supplementation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".