Are all fibres created equal with respect to lipid lowering? Comparing the effect of viscous dietary fibre to non-viscous fibre from cereal sources: a systematic review and meta-analysis of randomised controlled trials
Bibliographic record
Abstract
Abstract Although compelling evidence from observational studies supports a positive association between consumption of cereal fibre and CVD risk reduction, randomised controlled trials (RCT) often target viscous fibre type as the prospective contributor to lipid lowering to reduce CVD risk. The objective of our study is to compare the lipids-lowering effects of viscous dietary fibre to non-viscous, cereal-type fibre in clinical studies. RCT that evaluated the effect of viscous dietary fibre compared with non-viscous, cereal fibre on LDL cholesterol and alternative lipid markers, with a duration of ≥ 3 weeks, in adults with or without hypercholesterolaemia were included. Medline, EMBASE, CINAHL and the Cochrane Central Register were searched through October 19, 2021. Data were extracted and assessed by two independent reviewers. The generic inverse variance method with random effects model was utilised to pool the data which were expressed as mean differences (MD) with 95 % CI. Eighty-nine trials met eligibility criteria (n 4755). MD for the effect of viscous dietary fibre compared with non-viscous cereal fibre were LDL cholesterol (MD = –0·26 mmol/l; 95 % CI: –0·30, −0·22 mmol/l; P < 0·01), non-HDL cholesterol (MD = –0·33 mmol/l; 95 % CI: –0·39, −0·28 mmol/l; P < 0·01) and Apo-B (MD = –0·04 g/l; 95 % CI: –0·06, −0·03 g/l; P < 0·01). Viscous dietary fibre reduces LDL cholesterol and alternative lipid markers relative to the fibre from cereal sources, hence may be a preferred type of fibre-based dietary intervention targeting CVD risk reduction.
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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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".