Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».