Glutamate supplementation is associated with improved glucose metabolism following carbohydrate ingestion in healthy males
Bibliographic record
Abstract
Glutamate is linked to the glycolytic process, particularly when co-ingested with carbohydrate, but its effects on glucose metabolism are poorly characterised. The present study aimed to (1) specifically examine the effects of carbohydrate administration on circulating glutamate concentrations and (2) investigate the effect of increased glutamate availability, independent of carbohydrate ingestion, on glucose metabolism. A total of nine participants underwent four trials: (1) glutamate supplement+carbohydrate drink (GLU+CHO); (2) glutamate supplement+placebo drink (GLU); (3) placebo supplement+carbohydrate drink (CHO); (4) placebo supplement+placebo drink (CON). Following a fasting blood sample, participants ingested monosodium L-glutamate (MSG; 150 mg/kg body weight) or placebo capsules at each trial followed by a 75 g carbohydrate or a non-energy placebo drink 30 min later. Blood samples were taken at 0, 10, 20, 30, 40, 50, 60, 75, 90, 105 and 120 min. Plasma glutamate concentrations were significantly elevated relative to baseline during the GLU (approximately 10-fold) and GLU+CHO trials (approximately 6-fold). The glucose response to a carbohydrate load was blunted when glutamate was increased in the circulation (peak serum glucose: 5.50 (SE 0.54) mmol/l during the GLU+CHO trial v. 7.69 (SE 0.53) mmol/l during the CHO trial, P< 0.05). On average, c-peptide results revealed that insulin secretion did not differ between the GLU+CHO and CHO trials; however, four participants demonstrated increased insulin secretion during the GLU+CHO trial and five participants demonstrated decreased insulin secretion under the same conditions. In conclusion, when administration is staggered, MSG and carbohydrate supplementation can be used to manipulate plasma glutamate; however, future studies should control for this dichotomous insulin response.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".