Comparative Pharmacokinetic of Curcuminoids Formulations with an Omega-3 Fatty Acids Monoglyceride Carrier: A Randomized Cross-Over Triple-Blind Study
Bibliographic record
Abstract
There is a growing interest for curcuminoids in the general population and the scientific research community. Curcuminoids, derived from turmeric spice, are lipophiles and therefore have a low solubility in water which hence have a low bioavailability in the human plasma. To circumvent this issue, a natural product developed by Biodroga Nutraceuticals combined curcuminoids with omega-3 fatty acids (OM3) esterified in monoglycerides (MAG). The objective was to perform a 24 h pharmacokinetics in humans receiving a single dose of curcuminoid formulated by three different means, and to compare their plasma curcuminoids concentration. Sixteen males and fifteen females tested three formulations: 400 mg of curcuminoids powder extract, 400 mg of curcuminoids in rice oil and 400 mg of curcuminoids with 1 g MAG-OM3. Blood samples were collected at 0, 1, 2, 3, 4, 5, 6, 8, 10 and 24 h post dose intake. Plasma samples were analyzed by ultra high-performance liquid chromatography with a triple quadrupole mass spectrometer (UPLC-MS/MS). Twenty-four hours after a single dose intake, the total plasma curcuminoids area under the curve (AUC) reached 166.8 ± 17.8 ng/mL*h, 134.0 ± 12.7 ng/mL*h and 163.1 ± 15.3 ng/mL*h when curcuminoids were provided with MAG-OM3, with rice oil or in powder, respectively. The Cmax of total curcuminoids reached between 11.9–17.7 ng/mL at around 4 h (Tmax). One-hour post-dose, the curcuminoids plasma concentration was 40% higher in participants consuming the MAG-OM3 compared to the other formulations. Thus, in a young population, plasma curcuminoids 24 h pharmacokinetics and its increase shortly after the single dose intake were higher when provided with MAG-OM3 than rice oil.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".