Bibliographic record
Abstract
Introduction The etiology of complex chronic diseases involves both environmental and genetic factors, with environmental influences such as diet exerting a greater effect among individuals with certain genetic profiles. Nutrition is clearly one of the most important determinants of health. Too much or too little of a nutrient can result in metabolic disturbances that predispose individuals to various diseases such as osteoporosis, diabetes, rheumatoid arthritis, cardiovascular disease (CVD) and certain types of cancer. Non-nutritive food bioactives can also affect the risk of developing various chronic diseases. Functional foods that are enriched with certain food bioactives have been suggested to play an important role in combating CVD and other chronic illnesses. (1) However, inconsistencies among epidemiological studies have yielded conflicting advice on the optimal level of intake for nutrients and specific food bioactives. These inconsistencies may be due, in part, to genetic difference between populations that are studied. Nutrigenomics is the science that uses genomic information along with high-throughput 'omics' technologies to address issues important to nutrition and health. (2) Nutrigenomics is sometimes called nutritional genomics, which is increasingly being used as an umbrella term to refer to both the study of how diet affects genes and how genes affect diet. (3) One approach used to explore how dietary and genetic factors interact to influence various health outcomes is to examine how diet alters the function of genes or their protein products such as enzymes, receptors, transporters and ion channels that are known to regulate important biochemical pathways and cellular processes. (4) Another approach is to examine how variations in genes affect responsiveness to specific dietary factors, an area that is sometimes referred to as nutrigenetics. (5) Candidate genes that are studied tend be those that are the targets of a nutrient or food bioactive, or those that impact the metabolism of the bioactive compound, including its absorption, biotransformation, distribution or elimination. For example, how efficiently we absorb fat, how rapidly we digest starch, or how slowly we eliminate caffeine from our circulation all determine the levels of a food bioactive that a target cell would be exposed to. Knowledge of the genetic basis for the variability in response to food bioactives should result in a more accurate measure of exposure of target tissues of interest to these compounds and their metabolites. Human Genetic Variation and Response to Diet Genetic variation across the human genome is being recognized as increasingly complex. Single nucleotide polymorphisms (SNPs) are the most common form of sequence variation in the human genome with over 10 million SNPs reported in public databases. (6) Nucleotide repeats, insertions and deletions are also common types of variations. Genetic polymorphisms are normally found in at least 1% of the population, although common polymorphisms can be found in over 40-50% of the population. Genetic polymorphisms can appear to be 'silent' or have significant effects on physiological features and disease risk (i.e. phenotype). Copy number variants (CNV) represent another form of genetic variation that appears to be much more widespread than previously expected and have marked effects on gene expression. (7) The importance of how genetic variations influence the response to diet is best illustrated by studies involving inborn errors of metabolism. (8) Newborn screening for inborn errors of metabolism, such as phenylketonuria (PKU), provides a classic example of how nutrition can treat 'genetic' disorders. (9) Other examples include defects associated with long chain fatty acid oxidation (e.g. X-linked adrenoleukodystrophy--Lorenzo's oil) and iron absorption (e.g. haemochromatosis), which can be reasonably well managed with dietary restrictions. …
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".