Inulin‐type fructans and whey protein both modulate appetite but only fructans alter gut microbiota in adults with overweight/obesity: A randomized controlled trial
Bibliographic record
Abstract
SCOPE: Independently, prebiotics and dietary protein have been shown to improve weight loss and/or alter appetite. Our objective was to determine the effect of combined prebiotic and whey protein on appetite, body composition and gut microbiota in adults with overweight/obesity. METHODS AND RESULTS: In a 12 week, placebo-controlled, double-blind study, 125 adults with overweight/obesity were randomly assigned to receive isocaloric snack bars of: (1) Control; (2) Inulin-type fructans (ITF); (3) Whey protein; (4) ITF + Whey protein. Appetite, body composition and gut microbiota composition/genetic potential were assessed. Compared to Control, body fat was significantly reduced in the Whey protein group at 12 wks. Hunger, desire to eat and prospective food consumption were all lower with ITF, Whey protein and ITF + Whey protein compared to Control at 12 wks. Microbial community structure differed from 0 to 12 wks in the ITF and ITF +Whey Protein groups (i.e. increased Bifidobacterium) but not Whey Protein or Control. Changes in microbial genetic potential were seen between Control and ITF-containing treatments. CONCLUSION: Adding ITF, whey protein or both to snack bars improved several aspects of appetite control. Changes in gut microbiota may explain in part the effects of ITF but likely not whey protein.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".