Bibliographic record
Abstract
Nutrients interact with the human genome to modulate molecular pathways that may become disrupted, resulting in an increased risk of developing various chronic diseases. Genetic polymorphisms affect the metabolism of dietary factors, which in turn affects the expression of genes involved in a number of important metabolic processes. Genetic polymorphisms affecting nutrient metabolism may explain some of the inconsistencies among epidemiological studies relating diet to chronic diseases such as cancer, diabetes, rheumatoid arthritis, osteoporosis and cardiovascular disease. Understanding how genetic variations influence nutrient digestion, absorption, transport, biotransformation, uptake and elimination will provide a more accurate measure of exposure to the bioactive food ingredients ingested. Furthermore, genetic polymorphisms in the targets of nutrient action such as receptors, enzymes or transporters could alter molecular pathways that influence the physiological response to dietary interventions. Among the candidate genes with functional variants that affect nutrient metabolism are those that code for xenobiotic-metabolizing enzymes (also called drug-metabolizing enzymes). These enzymes are involved in the phase I and II biotransformation reactions that produce metabolites with either increased or decreased biological activity compared to the parent compound. A number of dietary factors are known to alter the expression of these genes that, in turn, metabolize a vast array of foreign chemicals including dietary factors such as antioxidants, vitamins, phytochemicals, caffeine, sterols, fatty acids and alcohol. Knowledge of the genetic basis for the variability in response to these dietary factors should result in a more accurate measure of exposure of target tissues of interest to these compounds and their metabolites. Examples of how 'slow' and 'fast' metabolizers respond differently to the same dietary exposures will be discussed. Identifying relevant diet-gene interactions will benefit individuals seeking personalized dietary advice as well as improve public health recommendations by providing sound scientific evidence linking diet and health.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.291 | 0.126 |
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".