Glucagon responses and regulation in people with or without Type 2 Diabetes (T2DM)
Bibliographic record
Abstract
Whether the increased glucagon release is deleterious or beneficial is still in debate. Our primary and novel finding is an accelerated and excessive glucagon secretion upon intake of high protein mixed meals in patients with type 2 diabetes compared to controls which is not evident with the single components. We compared the glucagon responses to mixed meal with different sugars (SAC or ISO) in Study 1 and pure proteins (whey and casein) with different dosage alone in Study 2, which revealed modestly impaired suppression of glucagon after SAC or ISO and modest glucagon increases in responses to the proteins. The rather excessive responses in the MMTs markedly exceed the responses to the single components and therefore indicate a potentiated response to the combined components. We further addressed the role of endogenous GIP and GLP-1 by using the 1,2- and 1,6-linked glucose-fructose dimers SAC and ISO in the mixed meals, which induce opposite profiles of the incretins but did not affect glucagon responses. On the other hand, previous studies had shown that diabetes remission was achieved in over 80% of participants by dietary, hypocaloric intervention. Thus, in Study 3 we investigated glucagon change in patients with T2DM by 3-month VLCD to achieve 15kg weight loss and we found that diabetes remission is associated with a highly significant reduction in fasting and postprandial glucagon release which has not been addressed before.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".