Effects of physical form of <i>β</i>-lactoglobulin and calcium ingestion on GLP-1 secretion, gastric emptying and energy intake in humans: a randomised crossover trial
Bibliographic record
Abstract
Abstract The aim of this study was to assess whether adding Ca2+ to aggregate or native forms of β-lactoglobulin alters gut hormone secretion, gastric emptying rates and energy intake in healthy men and women. Fifteen healthy adults (mean ± sd: 9M/6F, age: 24 ± 5 years) completed four trials in a randomised, double-blind, crossover design. Participants consumed test drinks consisting of 30 g of β-lactoglobulin in a native form with (NATIVE + MINERALS) and without (NATIVE) a Ca2+-rich mineral supplement and in an aggregated form both with (AGGREG + MINERALS) and without the mineral supplement (AGGREG). Arterialised blood was sampled for 120 min postprandially to determine gut hormone concentrations. Gastric emptying was determined using 13C-acetate and 13C-octanoate, and energy intake was assessed with an ad libitum meal at 120 min. A protein × mineral interaction effect was observed for total glucagon-like peptide-1 (GLP-1TOTAL) incremental AUC (iAUC; P < 0·01), whereby MINERALS + AGGREG increased GLP-1TOTAL iAUC to a greater extent than AGGREG (1882 ± 603 v. 1550 ± 456 pmol·l−1·120 min, P < 0·01), but MINERALS + NATIVE did not meaningfully alter the GLP-1 iAUC compared with NATIVE (1669 ± 547 v. 1844 ± 550 pmol·l−1·120 min, P = 0·09). A protein × minerals interaction effect was also observed for gastric emptying half-life (P < 0·01) whereby MINERALS + NATIVE increased gastric emptying half-life compared with NATIVE (83 ± 14 v. 71 ± 8 min, P < 0·01), whereas no meaningful differences were observed between MINERALS + AGGREG v. AGGREG (P = 0·70). These did not result in any meaningful changes in energy intake (protein × minerals interaction, P = 0·06). These data suggest that the potential for Ca2+ to stimulate GLP-1 secretion at moderate protein doses may depend on protein form. This study was registered at clinicaltrials.gov (NCT04659902).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".