Viscosity rather than quantity determines lipid lowering effects of dietary fiber in individuals consuming typical North American diet
Bibliographic record
Abstract
Objective To investigate the role of low (wheat), medium (psyllium), and high (viscous fiber blend PGX TM ) viscosity fibers on serum lipid profile. Methods Using a randomized, single blind, crossover design, 22 healthy participants (12M:10F, 34±11 years) on a typical North American diet (50% carbohydrates, 35% fat and 15% protein) and dietary fiber intake 15±5 g/day received addition of either Wheat Bran (WB), Bran Buds with Psyllium (BBP), or PGX (4.7 g/day) cereals, or low‐fiber wheat flour cereal (control) for three weeks separated by one‐week washout periods. Results Comparison between PGX and the control showed that the addition of PGX to the diet significantly lowered serum levels of cholesterol (6.5%, p=0.008), triglycerides (24.0%, p=0.02), ApoB (3.8%, p=0.048), LDL (7.1%, p=0.047), cholesterol/HDL (9.3%, p=0.004), and LDL/HDL (8.4%, p=0.045). Compared to the baseline, PGX significantly decreased serum levels of cholesterol (10.4%, p<0.0001), ApoB (5.7%, p=0.0005), LDL (13.3%, p=0.0007), cholesterol/HDL (9.6%, p=0.001), and LDL/HDL (11.1%, p=0.002) at the end of the 3‐week study period. Other diets (WB, BBP) did not induce significant changes in lipid levels except elevation in ApoB and cholesterol at week 3 on the AB diet. Comparison between diets showed superior lipid‐lowering effect of PGX. Conclusion The addition of a high viscous fiber PGX, but not wheat bran or psyllium, to a metabolically controlled typical North American diet significantly improves blood lipid profile in healthy subjects. Viscosity rather than quantity determines lipid lowering effects of dietary fiber. Research Support: Inovobiologic Inc, Calgary
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".