Tricaprylin Alone Increases Plasma Ketone Response More Than Coconut Oil or Other Medium-Chain Triglycerides: An Acute Crossover Study in Healthy Adults
Bibliographic record
Abstract
Ketones are the brain’s main alternative fuel to glucose. Dietary medium-chain triglyceride (MCT) supplements increase plasma ketones, but their ketogenic efficacy relative to coconut oil (CO) is not clear. The aim was to compare the acute ketogenic effects of the following test oils in healthy adults: coconut oil [CO; 3% tricaprylin (C8), 5% tricaprin (C10)], classical MCT oil (C8-C10; 55% C8, 35% C10), C8 (>95% C8), C10 (>95% C10), or CO mixed 50:50 with C8-C10 or C8. In a crossover design, 9 participants with mean ± SD ages 34 ± 12 y received two 20-mL doses of the test oils prepared as an emulsion in 250 mL lactose-free skim milk. During the control (CTL) test, participants received only the milk vehicle. The first test dose was taken with breakfast and the second was taken at noon but without lunch. Blood was sampled every 30 min over 8 h for plasma acetoacetate and β-hydroxybutyrate (β-HB) analysis. C8 was the most ketogenic test oil with a day-long mean ± SEM of +295 ± 155 µmol/L above the CTL. C8 alone induced the highest plasma ketones expressed as the areas under the curve (AUCs) for 0–4 and 4–8 h (780 ± 426 µmol ⋅ h/L and 1876 ± 772 µmol ⋅ h/L, respectively); these values were 813% and 870% higher than CTL values (P < 0.01). CO plasma ketones peaked at +200 µmol/L, or 25% of the C8 ketone peak. The acetoacetate-to-β-HB ratio increased 56% more after CO than after C8 after both doses. In healthy adults, C8 alone had the highest net ketogenic effect over 8 h, but induced only half the increase in the acetoacetate-to-β-HB ratio compared with CO. Optimizing the type of MCT may help in developing ketogenic supplements designed to counteract deteriorating brain glucose uptake associated with aging. This trial was registered at clinicaltrials.gov as NCT 02679222.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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".