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Korean Red Ginseng Rootlets Decrease Acute Postprandial Glycemia: Results from Sequential Preparation- and Dose-Finding Studies

2006· article· en· W2066685724 on OpenAlex

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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American College of Nutrition · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsUniversity of OttawaUniversity of Toronto
FundersAmerican Diabetes Association
KeywordsGinsengPostprandialPlaceboGinsenosideMedicinePhytotherapyTraditional medicineInternal medicinePharmacologyInsulin

Abstract

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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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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.292
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it