The Effect of Wild Blueberry Juice Consumption in Women at Risk for Type 2 Diabetes
Bibliographic record
Abstract
Wild blueberries have a high content of polyphenols, but there is limited data evaluating their health benefits in people at risk for type 2 diabetes. The objective of the study was to investigate whether 100% wild blueberry juice (WBJ) consumption causes biomarker changes in glucoregulatory control or in those that reflect protection against oxidative stress, inflammation or vascular status, all of which are associated with diabetes risk. A single‐blind randomized cross‐over design study was conducted in which women (n = 19, ages 39‐64 y) at risk for type 2 diabetes consumed 240 ml WBJ or 240 ml of a control beverage as part of their free‐living diet for 7 days. Outcome variables included biomarkers of glucose regulation, oxidative stress, and inflammation. Vascular status, assessed by EndoPat, and blood pressure were also evaluated. WBJ consumption produced no significant changes in biomarkers of glucose regulation and surrogate markers of insulin resistance (glucose, insulin, HOMA‐IR and QUICKI); oxidative stress (total 8‐isoprostanes and LDL‐oxidation); inflammation (IL‐6, IL‐10, CRP, TNF‐alpha, ICAM, VCAM, SAA); and vascular status. However, WBJ consumption showed a trend for lowering systolic blood pressure when compared to the control beverage: 120.8 ± 2.2 mmHg in the placebo group vs 116.0 ± 2.2 mmHg in the WBJ group ( P = 0.088). Short term intake of WBJ consumption did not have harmful effects on biomarkers of glucose regulation, oxidative stress, inflammation and vascular status in women at risk for type 2 diabetes. In addition, WBJ may improve systolic blood pressure.
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) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".