The Effect of Exogenous Ketone Monoester Ingestion on Plasma BDNF During an Oral Glucose Tolerance Test
Bibliographic record
Abstract
Brain-derived neurotrophic factor (BDNF) is important for brain and metabolic function. Ingestion of a ketone monoester (KME) drink containing beta-hydroxybutyrate (β-OHB) attenuates hyperglycemia in humans and increases neuronal BDNF in rodents. Whether KME affects BDNF in humans is currently unknown. This study examined the effect of KME ingestion before an oral glucose tolerance test (OGTT) on plasma BDNF in normal-weight adults (NW) and adults with obesity (OB). Methods: Exploratory, secondary analyses of two studies were performed. Study 1 included NW (n=18; Age=25.3±4.3 years; BMI=22.2±2.3 kg/m2) and Study 2 included OB (n=12; Age=48.8±9.5 years; BMI=33.7±5.0 kg/m2). Participants ingested 0.45 mL/kg-1 body weight KME or Placebo 30-minutes prior to completing a 75 g OGTT. β-OHB and BDNF were measured via blood samples at fasting baseline (pre-OGTT) and 120 min post-OGTT. Results: Study 1: KME significantly increased β-OHB by 800±454% (p<0.001). BDNF significantly decreased post-OGTT compared to pre-OGTT in Placebo (718.6±830.8 pg/mL vs. 389.3±595.8 pg/mL; p=0.018), whereas BDNF was unchanged in KME (560.2±689.6 pg/mL vs. 469.2±791.8 pg/mL; p=0.28). Study 2: KME significantly increased β-OHB by 1586±602% (p<0.001). BDNF was significantly higher post-OGTT in the KME condition in OB (time x condition interaction; p=0.037). There was a moderate relationship between β-OHB and ∆ %BDNF (r=0.616; p<0.001). Fasting plasma BDNF was significantly lower in OB compared to NW (132.8±142.8 pg/mL vs. 639.4±756.8 pg/mL; g=0.845; p=0.002). Conclusions: Plasma BDNF appears differentially impacted by KME ingestion with OGTT in OB compared to NW. Raising β-OHB via KME may be a strategy for increasing/protecting BDNF during hyperglycemia.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".