A systematic review and meta‐analysis of RCTs on the effect of psyllium fiber on lipid targets for CVD risk reduction
Bibliographic record
Abstract
Background Psyllium fiber, while initially promoted for its digestive health benefits, is now known to moderately improve LDL cholesterol. However, it is less known whether psyllium fiber has an effect on two newer clinical lipid targets of cardiovascular disease, non‐HDL cholesterol and apolipoprotein B. Objective To conduct a systematic review and meta‐analysis of randomized controlled trials (RCTs) investigating the cholesterol‐lowering impact of psyllium fiber on LDL cholesterol, non‐HDL cholesterol and apolipoprotein B for cardiovascular disease (CVD) risk reduction. Design MEDLINE, Embase, CINAHL and the Cochrane CENTRAL were searched. We included RCTs of 3‐week duration assessing the effect of psyllium fiber on LDL cholesterol, non‐HDL cholesterol or apolipoprotein B. Two independent reviewers extracted relevant data and assessed study quality and risk of bias. Data were pooled using the generic inverse‐variance method with random effects models and expressed as mean differences (MD) with 95% confidence intervals (CI). Heterogeneity was assessed by the Cochran Q‐statistic and quantified by the I2 statistic. Results Sixteen studies (n = 1208) were included in the meta‐analysis. Psyllium fiber significantly reduced LDL cholesterol (MD = −0.26 [−0.33, −0.20] mmol/L), non‐HDL cholesterol (MD = −0.43 [−0.81, −0.05] mmol/L), and apolipoprotein B (MD = −0.06 [−0.10, −0.02] g/L). Conclusion Pooled analyses show that psyllium fiber significantly improves LDL cholesterol and non‐HDL cholesterol. Inclusion of psyllium fiber may be a strategy for achieving targets in 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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.035 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".