Dietary supplementation with blueberry partially restores T-cell-mediated function in high-fat-diet-induced obese mice
Bibliographic record
Abstract
Blueberry, rich in antioxidant and anti-inflammatory phytochemicals, has been demonstrated to lower inflammatory status in adipose induced by high-fat diet (HFD) and obesity. The effect of blueberry on systemic immune functions has not been examined. C57BL/6 mice were randomised to one of three diets - low-fat diet (LFD), HFD and HFD plus 4 % (w/w) blueberry (HFD+B) - for 8 or 12 weeks. Ex vivo T-cell mitogens (concanavalin A (Con A); phytohaemagglutinin), T-cell antibody (anti-CD3; anti-CD3/CD28)-stimulated T-cell proliferation and cytokine production were assessed. After 8 weeks, both HFD groups weighed more (>4 g) than the LFD group; after 12 weeks, HFD+B-fed mice weighed more (>6 g) and had 41 % more adipose tissue than HFD-fed mice (P<0·05). After 12 weeks, T-cell proliferation was less in both HFD groups, compared with the LFD group. HFD-associated decrements in T-cell proliferation were partially (10-50 %) prevented by blueberry supplementation. At 12 weeks, splenocytes from HFD mice, but not from HFD+B mice, produced 51 % less IL-4 (CD3/CD28) and 57 % less interferon-γ (Con A) compared with splenocytes from LFD mice (P<0·05). In response to lipopolysaccharide challenge, splenocytes from both HFD groups produced 24-30 % less IL-6 and 27-33 % less TNF-α compared with splenocytes from LFD mice (P<0·05), indicating impaired acute innate immune response. By demonstrating deleterious impacts of HFD feeding on T-cell proliferation and splenocyte immune responses, our results provide insights into how HFD/obesity can disrupt systemic immune function. The protective effects of blueberry suggest that dietary blueberry can buttress T-cell and systemic immune function against HFD-obesity-associated insults.
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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 | 0.000 |
| 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 | 0.001 |
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".