Antihyperglycaemic medication modifies factors of postprandial satiety in type 2 diabetes
Bibliographic record
Abstract
AIM: Type 2 diabetes is characterized by hyperglycaemia, delayed gastric emptying and a blunted response of gut hormones during feeding that may modulate satiety. We hypothesized that it is associated with more hunger when treated by medication. METHODS: We studied nine type 2 diabetic men (A1C: 6.7+/-0.3%, waist circumference: 104+/-4 cm) after an overnight fast, during 5 h in response to a 2.88 MJ breakfast, twice, in a crossover design, with or without antihyperglycaemic agents. Satiety ratings, thermic effect of meal, gastric emptying, plasma concentrations of gut peptides, leptin, insulin and substrates and intake from a subsequent buffet were determined. RESULTS: With medication, fasting and postprandial plasma glucose levels were lower but area under the curve (AUC) did not vary vs. without medication. Gastric emptying was shortened, branched chain amino acids (BCAA) AUC and thermic effect were lower, and postprandial glucagon-like peptide-1 (GLP-1) and peptide tyrosine tyrosine (PYY3-36) were maintained at higher levels beyond 4 h. Correlations were significant between duration of diabetes and fasting ghrelin (r=0.779, p=0.013) and peak insulin (r=-0.769, p=0.016), 5-h postmeal ghrelin and peak glucose (r=0.822, p=0.007), 5-h glucose and GLP-1 (r=-0.788, p=0.012), and 5-h hunger scores and energy intake at buffet (r=0.828, p=0.006). Without medication, fullness scores correlated with BCAA levels. Visual analogue scale scores, ghrelin and leptin levels did not differ between studies. CONCLUSIONS: The decrease in factors associated with postprandial satiety with treatment is counterbalanced by higher GLP-1 and PYY3-36. Medication may normalize the link between perception of hunger and subsequent food intake.
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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 | 0.000 |
| 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) | 0.002 | 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".