The effects of gelled konjac glucomannan fibre on appetite and energy intake in healthy individuals: a randomised cross-over trial
Bibliographic record
Abstract
Konjac glucomannan (KGM) is a viscous dietary fibre that can form a solid, low-energy gel when hydrated and is commonly consumed in a noodle form (KGM-gel). Increased meal viscosity from gel-forming fibres have been associated with prolonged satiety, but no studies to date have evaluated this effect with KGM-gel. Thus, our objective was to evaluate subsequent food intake and satiety of KGM-gel noodles when replacing a high-carbohydrate preload, in a dose-response manner. Utilising a randomised, controlled, cross-over design, sixteen healthy individuals (twelve females/four males; age: 26·0 (sd 11·8) years; BMI: 23·1 (sd 3·2) kg/m2) received 325 ml volume-matched preloads of three KGM-gel noodle substitution levels: (i) all pasta with no KGM-gel (1849 kJ (442 kcal), control), half pasta and half KGM-gel (1084 kJ (259 kcal), 50-KGM) or no pasta and all KGM-gel (322 kJ (77 kcal), 100-KGM). Satiety was assessed over 90 min followed by an ad libitum dessert. Compared with control, cumulative energy intake was 47 % (-1761 kJ (-421 kcal)) and 23 % (-841 kJ (-201 kcal)) lower for 100-KGM and 50-KGM, respectively (both P<0·001), but no differences in subsequent energy intake was observed. Ratings of hunger were 31 % higher (P=0·03) for 100-KGM when compared with control, and were 19 % lower (P=0·04) for fullness and 28 % higher (P=0·04) for prospective consumption when comparing 100-KGM to 50-KGM. Palatability was similar across all treatments. Replacement of a high-carbohydrate preload with low-energy KGM-gel noodles did not promote additional food intake despite large differences in energy. The energy deficit incurred from partial KGM-gel substitution may have relevance in weight loss regimens, and should be further evaluated beyond the healthy population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".