A double blind, randomized, placebo‐controlled study of Salacia Chinensis, with alpha‐glucosidase inhibitor properties, on post‐prandial glycemia.
Bibliographic record
Abstract
Alpha‐glucosidase inhibitors are used as antidiabetic agents to slow carbohydrate absorption. We examined a medicinal plant, Salacia, with known α‐glucosidase inhibitor properties. In this clinical trial, we measured glycemic indices after a mixed meal tolerance test in healthy overweight/obese individuals. We randomized 51 subjects (BMI: 28.7 ± 3.5 kg/m 2 ; 58% female) to Salacia Chinensis (SC) extract (300 mg and 500 mg dose) and placebo in random order using a double‐blind, placebo‐controlled, 3‐way cross‐over design. After an overnight fast, participants consumed a dose of SC or placebo along with a fixed breakfast meal (275 kcal; 50% carbohydrate; 30% fat; 20% protein). Serum was collected, and satiety and taste perception were measured using a visual analog scale before and after the meal (0, 30, 60, 90, 120, 180 minutes). Forty‐eight individuals with (age 33±12 y) completed the trial. After the meal, the peak serum glucose levels were lower, at either dose, compared to placebo (p < 0.05). In addition, the positive incremental area under the curve (iAUC+) of glucose was reduced by 32% at the 300mg dose compared to placebo (p < 0.05). One hour after the meal, the concentrations of amylin were lower (p < 0.05) and glucose‐like peptide‐1 tended to be higher compared to placebo (p < 0.06). Thus far, serum insulin was measured in a subset (n=29) and treatment suppressed iAUC+ compared to placebo (p < 0.05). This study shows that SC lowers postprandial serum glycemic indices in overweight/obese participants. Support or Funding Information OmniActive Health Technologies Ltd, Canada
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".