Blueberry diets improve glucose tolerance and decrease oxidative stress in spontaneously hypertensive stroke‐prone rats
Bibliographic record
Abstract
Metabolic syndrome, characterized by insulin resistance, hyperglycemia and dyslipidemia, is a risk factor for cardiovascular disease. Eating fruits and vegetables benefits the symptoms of metabolic syndrome, and so we tested the effects of blueberries on insulin resistance in spontaneously hypertensive stroke‐prone rats (SHRSP). Control rats (WISTAR) and SHRSP were fed either a control AIN93G diet (CON) or a diet containing 1% or 3% freeze‐dried blueberry extract (BB) for 6 wks. All rats drank 2% NaCl. Plasma glucose and insulin were measured prior to and 15, 30, and 60 min following oral gavage of 40% glucose. The overall insulin response (AUC) to oral glucose was lower in SHRSP compared to WISTAR on CON diet (p<0.05), resulting in glucose being elevated in SHRSP at 15 minutes (p<0.001). In contrast, SHRSP fed 1% and 3% BB had insulin responses similar to WISTAR and there were no significant differences in plasma glucose levels between the groups. SHRSP showed signs of oxidative stress, with elevated levels of F2‐isoprostanes/creatinine in the urine (p<0.05). Feeding 3% BB lowered F2‐isoprostanes to normal values. There was no evidence of dyslipidemia in SHRSP. Our data suggest that SHRSP fed BB have less oxidative stress, and improved overall health. As well, feeding blueberries made animals better able to withstand a glucose challenge by increasing the insulin response, thereby decreasing blood glucose levels. Including blueberries in the diet may benefit metabolic syndrome. Funded by NSERC & Atlantic Innovation Fund.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".