Effects of Daily Raspberry Consumption on Immune-Metabolic Health in Subjects at Risk of Metabolic Syndrome: A Randomized Controlled Trial
Bibliographic record
Abstract
Consumption of red raspberries has been reported to exert acute beneficial effects on postprandial glycemia, insulinemia, triglyceridemia, and cytokine levels in metabolically disturbed subjects. In a two-arm parallel-group, randomized, controlled trial, 59 subjects with overweight or abdominal obesity and with slight hyperinsulinemia or hypertriglyceridemia were randomized to consume 280 g/day of frozen raspberries or to maintain their usual diet for 8 weeks. Primary analyses measured metabolic differences between the groups. Secondary analyses performed with omics tools in the intervention group assessed blood gene expression and plasma metabolomic changes following the raspberry supplementation. The intervention did not significantly affect plasma insulin, glucose, inflammatory marker concentrations, nor blood pressure. Following the supplementation, 43 genes were differentially expressed, and several functional pathways were enriched, a major portion of which were involved in the regulation of cytotoxicity, immune cell trafficking, protein signal transduction, and interleukin production. In addition, 10 serum metabolites were found significantly altered, among which β-alanine, trimethylamine N-oxide, and bioactive lipids. Although the supplementation had no meaningful metabolic effects, these results highlight the impact of a diet rich in raspberry on the immune function and phospholipid metabolism, thus providing novel insights into potential immune-metabolic pathways influenced by regular raspberry consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".