Acute Post-Prandial Cognitive Effects of Brown Seaweed Extract in Humans
Bibliographic record
Abstract
(Poly)phenols and, specifically, phlorotannins present in brown seaweeds have previously been shown to inhibit α-amylase and α-glucosidase, key enzymes involved in the breakdown and intestinal absorption of carbohydrates. Related to this are observations of modulation of post-prandial glycemic response in mice and increased insulin sensitivity in humans when supplemented with seaweed extract. However, no studies to date have explored the effect of seaweed extract on cognition. The current randomized, placebo-controlled, double-blind, parallel groups study examined the impact of a brown seaweed extract on cognitive function post-prandially in 60 healthy adults (N = 30 per group). Computerized measures of episodic memory, attention and subjective state were completed at baseline and 5 times at 40 min intervals over a 3 h period following lunch, with either seaweed or placebo consumed 30 min prior to lunch. Analysis was conducted with linear mixed models controlling for baseline. Seaweed led to significant improvements to accuracy on digit vigilance (p = 0.035) and choice reaction time (p = 0.043) tasks. These findings provide the first evidence for modulation of cognition with seaweed extract. In order to explore the mechanism underlying these effects, future research should examine effects on cognition in parallel with blood glucose and insulin responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".