The long‐term effect of cereal fiber on weight, fasting glucose, insulin, cholesterol, triglycerides and free fatty acids in hyperinsulinemic humans
Bibliographic record
Abstract
Canada Epidemiological studies have shown that a high intake of cereal fiber is associated with reduced plasma insulin and reduced weight gain. However, long‐term clinical studies establishing causation have not been conducted. We hypothesized that a long‐term increase in cereal fiber intake in hyperinsulinemic humans would result in reduced plasma glucose, insulin, FFA, and weight. Twenty seven hyperinsulinemic subjects were randomized to receive a control (C) (n=13) or fiber (F) (24g) (n=14) cereal daily for 1 year. Body weight was measured monthly and fasting blood samples collected at baseline, 3, 6, 9, and 12 months. There was no significant difference between the two treatments in change in weight or fasting cholesterol, triglycerides (TG) or FFA. There was a significant treatment X time interaction for change in fasting glucose (p=0.03) and insulin (p=0.05). Fasting glucose increased in the C group and decreased in the F group. Conversely, fasting insulin decreased in the C group and increased in the F group. In conclusion, a daily increase in cereal fiber intake may improve beta cell function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".