Effects of dietary fibre on subjective appetite, energy intake and body weight: a systematic review of randomized controlled trials
Bibliographic record
Abstract
Dietary fibres are believed to reduce subjective appetite, energy intake and body weight. However, different types of dietary fibre may affect these outcomes differently. The aim of this review was to systematically investigate the available literature on the relationship between dietary fibre types, appetite, acute and long-term energy intake, and body weight. Fibres were grouped according to chemical structure and physicochemical properties (viscosity, solubility and fermentability). Effect rates were calculated as the proportion of all fibre-control comparisons that reduced appetite (n = 58 comparisons), acute energy intake (n = 26), long-term energy intake (n = 38) or body weight (n = 66). For appetite, acute energy intake, long-term energy intake and body weight, there were clear differences in effect rates depending on chemical structure. Interestingly, fibres characterized as being more viscous (e.g. pectins, β-glucans and guar gum) reduced appetite more often than those less viscous fibres (59% vs. 14%), which also applied to acute energy intake (69% vs. 30%). Overall, effects on energy intake and body weight were relatively small, and distinct dose-response relationships were not observed. Short- and long-term effects of dietary fibres appear to differ and multiple mechanisms relating to their different physicochemical properties seem to interplay. This warrants further exploration.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".