The effect of low-fat diets on appetite: a systematic review of randomized clinical trials
Bibliographic record
Abstract
BACKGROUND: Adherence to low-fat (LF) diets may be inversely associated with appetite; however, findings from available randomized controlled trials (RCTs) are conflicting. The present study aimed to systematically review RCTs assessing the effects of LF diets on appetite status in adult participants. METHODS: We searched PubMed, Scopus, Web of Science, and the Cochrane Library from the inception of the database to June 2, 2024, for RCTs that evaluated the effects of LF diets (≤ 30% total energy from fat), versus high-fat (HF, > 30% total energy from fat) diets on appetite status. No language restrictions were applied. RESULTS: Initially, 2471 articles were identified, of which nine studies met the inclusion criteria. Seven studies examined the effect of LF diets on hunger response, three of which reported a significantly lower hunger response. LF diets did not exert an effect on satiety, desire to eat, and palatability. Only one study showed that the LF diet, compared to the HF diet, had greater decreases in their total appetite score over a 6-month period. CONCLUSIONS: We found that there were little or no additional benefits in changes to appetite status following LF diets in adults. However, due to methodological factors, shortcomings among studies and small number of studies, the current evidence on the effect of LF diets on appetite regulation is poor. Further long-term trials are needed to investigate the effect of LF diets on appetite and appetite-regulating hormones.
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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 | 0.023 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".