A Pilot Crossover Study of Berberine and its Short-Term Effects on Blood Glucose Levels in Healthy Volunteers
Bibliographic record
Abstract
Abstract INTRODUCTION: Different berberine preparations were evaluated for their effectiveness on lowering blood glucose in a pilot, open-label, crossover study conducted in healthy adults. METHODS: Fourteen healthy volunteers of both sexes were recruited, and seven completed all treatments in the study. Study participants ingested one of three berberine preparations containing 500mg of berberine, respectively. A one week wash out period was included between treatments. A control group was included to measure the participants’ baseline response to a 75g glucose solution without any treatment, and the following interventions were administered: berberine powder in hard gelatin capsules, berberine in an oil matrix encapsulated in soft-gelatin capsules, and berberine in LipoMicel® matrix encapsulated in soft-gelatin capsules. Blood glucose concentrations in each participant were monitored from pre-dose baseline, before the capsules were ingested, until up to 3 hours after the capsules were ingested. RESULTS: LipoMicel Berberine treatment led to reduced blood glucose concentrations Area Under Curve (AUC, mean difference: 1.57; 95% CI: 0.021 - 3.12; P = 0.046; Cohen’s d = 1.53) and reduced maximum glucose concentration (Gmax, mean difference: 1.07; 95% CI 0.004 - 2.14; P = 0.049; Cohen’s d = 1.52) compared to the control group when no-treatment was given. No adverse events related to berberine treatments were reported by participants throughout the study period. CONCLUSIONS: LipoMicel Berberine was effective in lowering blood glucose AUC by 12% after two 500mg doses in 7 participants. Berberine in other formulations may require a longer dosing regimen before blood glucose lowering effects can be demonstrated.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".