Contributions of upper gut hormones and motility to the energy intake-suppressant effects of intraduodenal nutrients in healthy, lean men - a pooled-data analysis
Bibliographic record
Abstract
We have previously identified pyloric pressures and plasma cholecystokinin (CCK) concentrations as independent determinants of energy intake following administration of intraduodenal lipid and intravenous CCK. We evaluated in healthy men whether these parameters also determine energy intake in response to intraduodenal protein, and whether, across the nutrients, any predominant gastrointestinal (GI) factors exist, or many factors make small contributions. Data from nine published studies, in which antropyloroduodenal pressures, GI hormones, and GI /appetite perceptions were measured during intraduodenal lipid or protein infusions, were pooled. In all studies energy intake was quantified immediately after the infusions. Specific variables for inclusion in a mixed-effects multivariable model for determination of independent predictors of energy intake were chosen following assessment for collinearity, and within-subject correlations between energy intake and these variables were determined using bivariate analyses adjusted for repeated measures. In models based on all studies, or lipid studies, there were significant effects for amplitude of antral pressure waves, premeal glucagon-like peptide-1 (GLP-1) and time-to-peak GLP-1 concentrations, GLP-1 AUC and bloating scores (P < 0.05), and trends for basal pyloric pressure (BPP), amplitude of duodenal pressure waves, peak CCK concentrations, and hunger and nausea scores (0.05 < P ≤ 0.094), to be independent determinants of subsequent energy intake. In the model including the protein studies, only BPP was identified as an independent determinant of energy intake (P < 0.05). No single parameter was identified across all models, and effects of the variables identified were relatively small. Taken together, while GI mechanisms contribute to the regulation of acute energy intake by lipid and protein, their contribution to the latter is much less. Moreover, the effects are likely to reflect small, cumulative contributions from a range of interrelated factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".