Effect of the perception of breakfast consumption on subsequent appetite and energy intake in healthy males
Bibliographic record
Abstract
PURPOSE: This study aimed to assess the effects of consuming a very-low-energy placebo breakfast on subsequent appetite and lunch energy intake. METHODS: Fourteen healthy males consumed water-only (WAT), very-low-energy, viscous placebo (containing water, low-calorie flavoured squash, and xanthan gum; ~ 16 kcal; PLA), and whole-food (~ 573 kcal; FOOD) breakfasts in a randomised order. Subjects were blinded to the energy content of PLA and specific study aims. Venous blood samples were collected pre-breakfast, 60- and 180-min post-breakfast to assess plasma acylated ghrelin and peptide tyrosine tyrosine concentrations. Subjective appetite was measured regularly, and energy intake was assessed at an ad libitum lunch meal 195-min post-breakfast. RESULTS: Lunch energy intake was lower during FOOD compared to WAT (P < 0.05), with no further differences between trials (P ≥ 0.132). Cumulative energy intake (breakfast plus lunch) was lower during PLA (1078 ± 274 kcal) and WAT (1093 ± 249 kcal), compared to FOOD (1554 ± 301 kcal; P < 0.001). Total area under the curve (AUC) for hunger, desire to eat and prospective food consumption were lower, and fullness was greater during PLA and FOOD compared to WAT (P < 0.05). AUC for hunger was lower during FOOD compared to PLA (P < 0.05). During FOOD, acylated ghrelin was suppressed compared to PLA and WAT at 60 min (P < 0.05), with no other hormonal differences between trials (P ≥ 0.071). CONCLUSION: Consuming a very-low-energy placebo breakfast does not alter energy intake at lunch but may reduce cumulative energy intake across breakfast and lunch and attenuate elevations in subjective appetite associated with breakfast omission. TRIAL REGISTRATION: NCT04735783, 2nd February 2021, retrospectively registered.
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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) | 0.003 | 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".