Bibliographic record
Abstract
Modulation of the gut microbiota is an area of growing interest, particularly for its link to improving and maintaining the systemic health of the host. It has been suggested to have potential to reduce risk factors associated with chronic diseases, such as elevated cholesterol levels in coronary heart disease (CHD). Diets of our evolutionary ancestors were largely based on plant foods, high in dietary fiber and fermentable substrate, and our gut microbiota has evolved against a background of such diets. Therapeutic diets that mimic plant-based diets from the early phases of human evolution may result in drug-like cholesterol reductions. In contrast, typical Western diets low in dietary fiber and fermentable substrate, and high in saturated and trans fatty acids, are likely contributors to the increased need for pharmacological agents for cholesterol reduction. The gut microbiota of those consuming a Western diet are likely underutilized and depleted of metabolic fuels, resulting in a less than optimal gut microbial profile. As a result, this diet is mismatched to our archaic gut microbiota and, therefore, to our genome, which has changed relatively little since humans first appeared. While the exact mechanism by which the gut microbiota may modulate cholesterol levels still remains uncertain, end products of bacterial fermentation, particularly the short chain fatty acids (i.e., propionate), have been suggested as potential candidates. While more research is required to clarify the potential link between gut microbiota and CHD risk reduction, consuming a therapeutic diet rich in plant foods, dietary fiber, and fermentable substrate would be a useful strategy for improving systemic health, possibly by altering the gut microbiota.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".