Korean Red Ginseng Rootlets Decrease Acute Postprandial Glycemia: Results from Sequential Preparation- and Dose-Finding Studies
Bibliographic record
Abstract
BACKGROUND: Fractionation of a ginseng source to produce differences in the ginsenoside profile might influence its effect on postprandial glycemia. To explore this possibility and identify an efficacious ginseng for a longterm study, we conducted a preparation-finding study of different Korean red ginseng (KRG) root fractions followed by a dose-finding study of the most efficacious fraction. METHODS: A double-blind, randomized, within-subject design was used in both studies. In the preparation-finding study, 7 healthy subjects (sex: 3m:4f, age: 32 +/- 4 y, BMI: 24 +/- 2 kg/m2) received 6 g placebo and KRG-rootlets, -body, and -H2O extract 40 min before a 50 g-OGTT with finger-prick blood samples at -40-, 0-, 15-, 30-, 45-, 60-, 90-, 120-min. In the dose-finding study, 12 healthy subjects (sex: 9M,3F, age: 29 +/- 3 y, BMI: 22.5 +/- 1 kg/m2) received 0 g (placebo), 2 g, 4 g, and 6 g of the most efficacious root fraction following the same protocol. Ginsenosides were analyzed using HPLC-UV. RESULTS: In the preparation-finding study, a wide variation in the ginsenoside profiles was achieved across the 3 KRG fractions. This variation coincided with differential effects. The main effects of KRG-rootlets (p = 0.050) and time (p < 0.001) and their interaction (p < 0.1) were significant. This was reflected in a 29% reduction in area under the curve (AUC) by KRG-rootlets compared with placebo (p = 0.052). Conversely, neither KRG-H2O extract nor KRG-body affected glycemia. Stepwise-multiple regression models identified Rg1 as the sole predictor of mean- and AUC postprandial blood glucose. In the dose-finding study, KRG-rootlets were tested as the most efficacious fraction. A significant effect of KRG-rootlets treatment (mean of 3 doses) but not dose was found. The mean of 3 doses decreased AUC by 17% compared with placebo (p = 0.057). CONCLUSIONS: Together the studies indicate 2 g KRG-rootlets is sufficient to achieve reproducible reductions in postprandial glycemia. But the longterm sustainability of KRG selected using this approach remains to be tested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".