Comment on “Modulation of Metabolic Detoxification Pathways Using Foods and Food-Derived Components: A Scientific Review with Clinical Application”
Notice bibliographique
Résumé
The article “Modulation of Metabolic Detoxification Pathways Using Foods and Food-Derived Components: A Scientific Review with Clinical Application” is misleading in its title and the discussion does not fit this title. This narrative review article presents a comprehensive review of food and food components on specific metabolic detoxification pathways (phase I cytochrome enzymes, phase II conjugation enzymes, antioxidant support systems, and metallothionein) for naturopathic therapies in 11 extensive tables. This paper has many strengths; the strongest of these is the authors' discussion of the important limitations of this science. While their emphasis was about finding evidence to support clinical recommendations to use foods and food-based constituents to reduce toxins, they recognized the following. “It is best to take precaution in firmly advocating foods or food-based nutrients that only have cell or animal data as support. It is best to rely on the clinical [human] studies”; “science has not fully demonstrated the individual impacts of these [smoking, physical activity, or stress] factors, along with all of them together to be able to understand the effects of altering the function of some detoxifying enzymes”; “in several instances, certain foods exhibited a particular activity on an enzyme, while, at higher doses, they had another, opposite effect”; “for patients who are taking multiple pharmaceuticals, it is important to know which detoxification systems will be influenced by nutrients and foods so that side effects are minimized or avoided”; “without a full understanding of a patient's SNPs [single nucleotide polymorphisms], it becomes difficult to make accurate assessments about nutrients and dosing”; “in some of the research presented here, effects on detoxification enzymes were seen after several days of food intake or supplementation, while, in other cases, induction of an enzyme might be fairly rapid, followed by efficient adaptability” [1]. Given all of the limitations to current knowledge and the large effects seen in a few sparse studies of food-based compounds altering detoxification enzymes and the metabolism of medications [2–5] and other compounds such as steroid hormones [6] and selected carcinogens [7], the paper's title, which states that the authors have produced “clinical applications,” is misleading. The paper does not provide clinical applications. The tables in the paper do not indicate the direction of effects of the tested foods/food-based components and which outcomes the test substance was assessed for. We agree with the authors' conclusion about the state of knowledge: “the resulting clinical takeaway might be to encourage patients to follow a mixed, varied diet, full of different plant-based, whole foods” [1]. More research is needed before knowledge is sufficient to be able to provide advice to individuals on how to raise their detoxifying abilities. This science is in its infancy, as this review capably pointed out.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,066 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,008 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,027 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,015 |
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 source (Gemma direct ou Codex distillé), 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 ».