Effect of Substituting Starchy Noodles with Konjac Fiber Noodles on Satiety, Palatability and Subsequent Food Intake in Healthy Individuals: A Dose Response Study
Bibliographic record
Abstract
Background Foods that provides satiety with little calories are in high demand. We evaluated the effect of substituting regular noodles with noodles made from Konjac fiber (KF) on signals of appetite and consecutive food intake in healthy individuals. Methods Following an overnight fast, sixteen healthy volunteers (12F/4M; Age: 26±12 years; BMI: 23±3kg/m 2 ) were randomly assigned to receive one of three‐isovolumetric pre‐load meals on three separate occasions. Meals differed by increments of ~250 kcal due to partial or full replacement of starchy noodles by KF Shirataki noodles (zero% KF:442kcal; 50% KF:259kcal and 100% KF:77kcal), followed 90min later by an ad libitum wafer cookies post‐load. Measures of satiety using a visual analogue scale were collected over 90 min and ad libitum food intake was assessed. Results Hunger was significantly higher after 100%KF compared to zero%KF (control)(p=0.04). Fullness was lower and prospective consumption was higher after100%KF compared to 50% KF (p=0.03). Post‐load energy intake after all preloads were not significantly different (p=0.71), resulting in a net caloric deficit of 201 and 421 kcal in cumulative energy intake compared to control after 50% KF(p<0.001) and 100% KF (p<0.001), respectively. Cumulative energy intake was also significantly lower after the 100%KF meal compared to the 50% KF meal (p<0.05). There were no differences in palatability ratings between the meals. Conclusion Replacement of a high carbohydrate preload with a low energy konjac fiber food did not increase subsequent food intake in healthy individuals. The caloric deficit incurred from the volume‐matched preloads may have relevance in weight loss regimes however further studies should evaluate whether the effects of konjac fiber foods extend over the long term and its mechanisms of action.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".