Bioactive oat β-glucan reduces LDL cholesterol in Caucasians and non-Caucasians
Bibliographic record
Abstract
BACKGROUND: There is increasing global acceptance that viscous soluble fibers lower serum LDL cholesterol (LDL-C), but most evidence for this comes from studies in Caucasians. To see if oat β-glucan lowers LDL-C in Caucasians and non-Caucasians we conducted a post-hoc analysis of the results of a randomized, controlled, double-blind, multi-center clinical trial whose primary aim was to determine if molecular-weight (MW) influenced the LDL-C-lowering effect of oat β-glucan. RESULTS: Caucasian and non-Caucasian subjects with LDL-C-C ≥ 3.0 and ≤ 5.0 mmol/L (n = 786 screened, n = 400 ineligible, n = 19 refused, n = 367 randomized, n = 345 completed, n = 1 excluded for missing ethnicity) were randomly assigned to consume cereal containing wheat-fiber (Control, n = 74:13 Caucasian:non-Caucasian) or 3 g high-MW (3H, 2,250,000 g/mol, n = 67:19), 4 g medium-MW (4 M, 850,000 g/mol, n = 50:17), 3 g medium-MW (3M, 530,000 g/mol, n = 54:9) or 4 g low-MW (4 L, 210,000 g/mol, n = 51:12) oat β-glucan daily for 4 weeks. LDL-C after 4 weeks was influenced by baseline LDL-C (p < 0.001) and treatment (p = 0.003), but not ethnicity (p = 0.74). In all subjects, compared to control, 3 H, 4 M and 3 M reduced LDL-C significantly by 4.8 to 6.5%, but 4 L had no effect. Compared to control, the bioactive oat β-glucan treatments (3H, 4M and 3M) reduced LDL-C by a combined mean (95% CI) of 0.18 (0.06, 0.31) mmol/L (4.8%, n = 171, p = 0.004) in Caucasians, a value not significantly different from the 0.37 (0.09, 0.65) mmol/L (10.3%, n = 45, p = 0.008) reduction in non-Caucasians. CONCLUSION: We conclude that oat β-glucan reduces LDL-C in both Caucasians and non-Caucasians; there was insufficient power to determine if the magnitude of LDL-C-lowering differed by ethnicity. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00981981.
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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 | 0.001 |
| 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 | 0.001 |
| 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".