Sodium alginate addition to chocolate milk reduces appetite and glycemic responses in healthy young men
Bibliographic record
Abstract
Sodium alginate reduced appetite and glycemia, when consumed in water‐based drinks. But, its effects when added to usual beverages were not reported. Because chocolate milk (CM) is criticized for its elevated glycemic property, we hypothesized that alginate additions to CM promote satiety and attenuate glycemia. In a randomized crossover design, 24 men (22.9±0.4 years; 22.5±0.3 kg/m 2 ) were provided isovolumetric preloads (325 ml) of CM, 1.25% alginate CM, 2.5% alginate CM or 2.5% alginate solution. All treatments were standardized for lactose and sucrose content and provided 120 min prior to an ad libitum pizza meal. Pizza intake and total caloric intake were not different among treatments. Glucose, insulin and appetite were measured at baseline and at intervals pre‐ and post‐meal. Pre‐meal appetite was attenuated dose‐dependently by alginate; CM with 2.5% alginate resulted in the lowest appetite (P<0.0001). Glucose area under the curve was reduced by 36% after 2.5% alginate CM compared to CM (P=0.004), with no differences with 1.25% alginate CM and 2.5% alginate solution. Yet, glucose peaks at 30 min were lower after 2.5% alginate CM compared to 1.25% alginate CM and CM (P<0.0001). Insulin peaks at 30 min were also lower after 2.5% alginate CM relative to CM (P<0.0001). In conclusion, 2.5% alginate in CM exerted an additive effect on satiety, and improved glycemia in a synergistic manner while reducing insulin demand. Supported by Natural Sciences and Engineering Research Council of Canada‐Collaborative Research and Development Garant, Dairy Farmers of Ontario and Kraft Canada Inc.
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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 | 0.001 |
| 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 | 0.001 | 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".