A high-protein total diet replacement alters the regulation of food intake and energy homeostasis in healthy, normal-weight adults
Bibliographic record
Abstract
PURPOSE: Dietary intake can affect energy homeostasis and influence body weight control. The aim of this study was to compare the impact of high-protein total diet replacement (HP-TDR) versus a control (CON) diet in the regulation of food intake and energy homeostasis in healthy, normal-weight adults. METHODS: In this acute randomized controlled, cross-over study, participants completed two isocaloric arms: a) HP-TDR: 35% carbohydrate, 40% protein, and 25% fat; b) CON: 55% carbohydrate, 15% protein, and 30% fat. The diets were provided for 32 h while inside a whole-body calorimetry unit. Appetite sensations, appetite-related hormones, and energy metabolism were assessed. RESULTS: Forty-three healthy, normal-weight adults (19 females) participated. Appetite sensations did not differ between diets (all p > 0.05). Compared to the CON diet, the change in fasting blood markers during the HP-TDR intervention was smaller for peptide tyrosine-tyrosine (PYY; - 18.9 ± 7.9 pg/mL, p = 0.02) and greater for leptin (1859 ± 652 pg/mL, p = 0.007). Moreover, postprandial levels of glucagon-like peptide 1 (1.62 ± 0.36 pM, p < 0.001) and PYY (31.37 ± 8.05 pg/mL, p < 0.001) were higher in the HP-TDR. Significant correlations were observed between energy balance and satiety (r = - 0.41, p = 0.007), and energy balance and PFC (r = 0.33, p = 0.033) in the HP-TDR. CONCLUSION: Compared to the CON diet, the HP-TDR increased blood levels of anorexigenic hormones. Moreover, females and males responded differently to the intervention in terms of appetite sensations and appetite-related hormones. TRIAL REGISTRATION: NCT02811276 (retrospectively registered on 16 June 2016) and NCT03565510 (retrospectively registered on 11 June 2018).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".