Effect of Macronutrient Composition on Short-Term Food Intake and Weight Loss
Bibliographic record
Abstract
The purpose of this review is to describe the role of macronutrient composition on the suppression of short-term food intake (FI) and weight loss. The effects of macronutrient composition on short-term FI will be reviewed first, followed by a brief examination of longer-term clinical trials that vary in effects of dietary macronutrient composition on weight loss. The objectives were: 1) to examine the effect of macronutrient composition on the suppression of short-term FI, 2) to determine whether some macronutrient sources suppress FI beyond their provision of energy, 3) to assess the combined effects of macronutrients on FI and glycemic response, and 4) to determine whether knowledge of the effect of macronutrients on short-term FI has led to greater success in spontaneous weight loss, adherence to energy-restricted diets, and better weight maintenance after weight loss. Although knowledge of macronutrient composition on short-term FI regulation has advanced our understanding of the role of diet composition on energy balance, it has yet to lead to greater success in long-term weight loss and weight maintenance. It is clear from this review that many approaches based on manipulating dietary macronutrient composition can help people lose weight as long as they follow the diets. However, only by evaluating the interaction between the physiologic systems that govern FI and body weight may the benefits of dietary macronutrient composition be fully realized.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".