Viscosity rather than quantity of dietary fibre predicts cholesterol-lowering effect in healthy individuals
Bibliographic record
Abstract
The well-documented lipid-lowering effects of fibre may be related to its viscosity, a phenomenon that has been understudied, especially when fibre is given against the background of a typical North American (NA) diet. In this three-arm experiment, we compared the lipid-lowering effect of low-viscosity wheat bran (WB), medium-viscosity psyllium (PSY) and a high-viscosity viscous fibre blend (VFB), as part of a fibre intervention aimed at increasing fibre intake to recommended levels within the context of a NA diet in apparently healthy individuals. Using a randomised cross-over design, twenty-three participants (twelve males and eleven females; age 35 (SD 12) years; LDL-cholesterol (C) 2.9 (SEM 0.6) mmol/l) consuming a typical NA diet received a standard, fibre-enriched cereal, where approximately one-third of the fibre was either a low-viscosity (570 centipoise (cP)) WB, medium-viscosity (14,300 cP) PSY or a high-viscosity (136,300 cP) novel VFB, for 3 weeks separated by washout periods of ≥ 2 weeks. There were no differences among the treatments in the amount of food consumed, total dietary fibre intake, reported physical activity and body weight. Final intake of the WB, PSY and VFB was 10.8, 9.0 and 5.1 g, respectively. Reduction in LDL-C was greater with the VFB compared with the medium-viscosity PSY (-12.6 (SEM 3.5) %, P = 0.002) and low-viscosity WB (-14.6 (SEM 4.2) %, P = 0.003). The magnitude of LDL-C reduction showed a positive association with fibre apparent viscosity (r - 0.41, P = 0.001). Despite the smaller quantity consumed, the high-viscosity fibre lowered LDL-C to a greater extent than lower-viscosity fibres. These data support the inclusion of high-viscosity fibre in the diet to reduce plasma lipids among apparently healthy individuals consuming a typical NA diet.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".