Effect of wild blueberry (Vaccinium angustifolium) juice consumption on cardiovascular and inflammatory markers in male participants
Bibliographic record
Abstract
Wild blueberries are rich in antioxidants and may offer a novel approach to treat heart disease due to anti‐inflammatory properties. Thus, this study examined the effect of human consumption of wild blueberry juice on markers of cardiovascular disease. Fourteen middle‐aged men with cardiovascular risk factors consumed wild blueberry juice for 3 weeks in a single‐blind, randomized, placebo‐controlled, crossover intervention trial (with a 2‐week washout). Exclusion criteria included use of lipid‐altering medications or history of inflammatory disease. Compliance was monitored using 3‐day food records. Fasting blood samples were taken at the beginning and end of each treatment period, and serum analyzed. In general, results were not statistically significant. However there were trends toward an increase in adiponectin, and a decrease in serum glucose (p=0.075) and insulin concentrations in the treatment group. Insulin resistance, estimated using the homeostasis model assessment, decreased in the treatment group whereas it increased in the placebo group. Also, blueberry lowered levels of inflammatory cytokines (IL‐6, CRP, TNFα (p=0.081)). These results suggest that dietary blueberry may exhibit cardio‐protective properties in men. However, more studies with a greater sample size and longer treatment time are needed to further define efficacy and dose. [Funded by PEI WBGA and AIF]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.003 | 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".