Glucagon-like peptide-1 (GLP-1) raises blood-brain glucose transfer capacity and hexokinase activity in human brain
Bibliographic record
Abstract
In hyperglycemia, glucagon-like peptide-1 (GLP-1) lowers brain glucose concentration together with increased net blood-brain clearance and brain metabolism, but it is not known whether this effect depends on the prevailing plasma glucose (PG) concentration. In hypoglycemia, glucose depletion potentially impairs brain function. Here, we test the hypothesis that GLP-1 exacerbates the effect of hypoglycemia. To test the hypothesis, we determined glucose transport and consumption rates in seven healthy men in a randomized, double-blinded placebo-controlled cross-over experimental design. The acute effect of GLP-1 on glucose transfer in the brain was measured by positron emission tomography (PET) during a hypoglycemic clamp (3 mM plasma glucose) with (18)F-fluoro-2-deoxy-glucose (FDG) as tracer of glucose. In addition, we jointly analyzed cerebrometabolic effects of GLP-1 from the present hypoglycemia study and our previous hyperglycemia study to estimate the Michaelis-Menten constants of glucose transport and metabolism. The GLP-1 treatment lowered the vascular volume of brain tissue. Loading data from hypo- to hyperglycemia into the Michaelis-Menten equation, we found increased maximum phosphorylation velocity (V max) in the gray matter regions of cerebral cortex, thalamus, and cerebellum, as well as increased blood-brain glucose transport capacity (T max) in gray matter, white matter, cortex, thalamus, and cerebellum. In hypoglycemia, GLP-1 had no effects on net glucose metabolism, brain glucose concentration, or blood-brain glucose transport. Neither hexokinase nor transporter affinities varied significantly with treatment in any region. We conclude that GLP-1 changes blood-brain glucose transfer and brain glucose metabolic rates in a PG concentration-dependent manner. One consequence is that hypoglycemia eliminates these effects of GLP-1 on brain glucose homeostasis.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".