Probiotics Do Not Enhance Anti‐hypertensive Effect of Blueberry Diets in Hypertensive Rats
Bibliographic record
Abstract
Previously we showed that feeding polyphenol‐rich wild blueberries (BB) to hypertensive rats lowered systolic blood pressure (BP). Since probiotic bacteria produce secondary metabolites from berry polyphenols which augment the health benefits of berry consumption, we hypothesized that adding probiotics to a BB‐enriched diet would augment the anti‐hypertensive effects of BB. Male spontaneously hypertensive rats were fed different AIN93G‐based diets for 8 weeks (n=8 rats each diet): Control; 3% freeze‐dried wild BB; 1% probiotic bacteria (PRO); or 3% BB + 1% PRO. BP was measured biweekly by the tail‐cuff method, and urine was collected twice to determine markers of oxidative stress [F2‐isoprostanes], nitric oxide synthesis [nitrites] and polyphenol metabolism [hippuric acid]. Systolic BP at week 8, but not sooner, was lower in the BB and PRO groups (163 + 5.9 mmHg and 168 + 6.2 mmHg, respectively) compared to the control diet (188 + 7.6 mmHg; p=0.04). Diet had main effects on diastolic BP at week 8 as well (p<0.05). In contrast, the combined BB + PRO diet produced no significant effects on BP. Hippuric acid excretion, a marker of polyphenol metabolism, was 91% higher after 4 weeks and 74% higher after 8 weeks in BB‐fed rats compared to CON (p=0.0006). PRO by itself had no effect on urinary hippuric acid, and BB+PRO diet did not reduce the magnitude of the BB effect. Excretion of isoprostanes (p=0.989) and nitrite (p=0.373) did not differ across diet groups. Thus adding probiotics to a blueberry‐enriched diet does not enhance and actually may impair the antihypertensive effect of BB consumption. However, probiotic bacteria are not interfering with metabolism of BB polyphenols into hippuric acid.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".