Plasma glucagon-like peptide-1 responses to ingestion of protein with increasing doses of milk minerals rich in calcium
Bibliographic record
Abstract
Abstract A high dose of whey protein hydrolysate fed with milk minerals rich in calcium (Capolac®) results in enhanced glucagon-like peptide-1 (GLP-1) concentrations in lean individuals; however, the effect of different calcium doses ingested alongside protein is unknown. The present study assessed the dose response of calcium fed alongside 25 g whey protein hydrolysate on GLP-1 concentrations in individuals with overweight/obesity. Eighteen adults (mean ±sd: 8M/10F, 34 ± 18 years, 28·2 ± 2·9 kgm−2) completed four trials in a randomised, double-blind, crossover design. Participants consumed test solutions consisting of 25 g whey protein hydrolysate (CON), supplemented with 3179 mg (LOW), 6363 mg (MED) or 9547 mg (HIGH) Capolac®on different occasions, separated by at least 48 h. The calcium content of test solutions equated to 65, 892, 1719 and 2547 mg, respectively. Arterialised-venous blood was sampled over 180 min to determine plasma concentrations of GLP-1TOTAL, GLP-17–36amide, insulin, glucose, NEFA, and serum concentrations of calcium and albumin.Ad libitumenergy intake was measured at 180 min. Time–averaged incremental AUC (iAUC) for GLP-1TOTAL(pmol·l−1·min−1) did not differ between CON (23 ± 4), LOW (25 ± 6), MED (24 ± 5) and HIGH (24 ± 6). Energy intake (kcal) did not differ between CON (940 ± 387), LOW (884 ± 345), MED (920 ± 334) and HIGH (973 ± 390). Co-ingestion of whey protein hydrolysate with Capolac®does not potentiate GLP-1 release in comparison with whey protein hydrolysate alone. The study was registered at clinical trials (NCT03819972).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".