Inter-individual Variability in Response to Plant Sterol and Stanol Consumption
Bibliographic record
Abstract
Despite the abundance of clinical trial data demonstrating the cholesterol-lowering action of plant sterol supplementation, substantial variability in efficacy exists in responsiveness across individuals. The goal of this review is to examine factors responsible for this heterogeneity in responsiveness of blood cholesterol levels to dietary plant sterols. Although initially thought to be due to random noise in the data, demonstrated consistency in degree of responsiveness in the context of controlled feeding designs from person to person suggests that other systematic drivers are responsible. Genetic explanations explaining this phenomenon appear to be gaining momentum. Particularly, single nucleotide polymorphisms within the genes coding for CYP7A1 and ApoE, as well as possibly other genes including ABCG5 and ABCG8, exist as predictors of whether LDL-C levels will decrease or even increase subsequent to plant sterol administration. In summary, nutrigenetic differences across genes associated with cholesterol trafficking pathways may be important in predicting how well any given individual will respond to dietary interventions. It is anticipated that eventually genetic tests will be developed that can guide health care professionals to optimize dietary strategies for health optimization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".