Nutrigenetics and Modulation of Oxidative Stress
Bibliographic record
Abstract
Oxidative stress develops as a result of an imbalance between the production and accumulation of reactive species and the body's ability to manage them using exogenous and endogenous antioxidants. Exogenous antioxidants obtained from the diet, including vitamin C, vitamin E, and carotenoids, have important roles in preventing and reducing oxidative stress. Individual genetic variation affecting proteins involved in the uptake, utilization and metabolism of these antioxidants may alter their serum levels, exposure to target cells and subsequent contribution to the extent of oxidative stress. Endogenous antioxidants include the antioxidant enzymes superoxide dismutase, catalase, glutathione peroxidase, paraoxanase, and glutathione S-transferase. These enzymes metabolize reactive species and their by-products, reducing oxidative stress. Variation in the genes coding these enzymes may impact their enzymatic antioxidant activity and, thus, the levels of reactive species, oxidative stress, and risk of disease development. Oxidative stress may contribute to the development of chronic disease, including osteoporosis, type 2 diabetes, neurodegenerative diseases, cardiovascular disease, and cancer. Indeed, polymorphisms in most of the genes that code for antioxidant enzymes have been associated with several types of cancer, although inconsistent findings between studies have been reported. These inconsistencies may, in part, be explained by interactions with the environment, such as modification by diet. In this review, we highlight some of the recent studies in the field of nutrigenetics, which have examined interactions between diet, genetic variation in antioxidant enzymes, and oxidative stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".