Herbs and Spices- Biomarkers of Intake Based on Human Intervention Studies – A Systematic Review
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Notice bibliographique
Résumé
Culinary herbs and spices have been used as both food flavoring and food preservative agents for centuries. Moreover, due to their known and presumptive health benefits, herbs and spices have also been used in medical practices since ancient times. Some of the health effects attributed to herbs and spices include antioxidant, anti-microbial, and anti-inflammatory effects as well as potential protection against cardiovascular disease, neurodegeneration, type 2 diabetes, and cancer. While interest in herbs and spices as medicinal agents remains high and their use in foods continues to grow, there have been remarkably few studies that have attempted to track the dietary intake of herbs and spices and even fewer that have tried to find potential biomarkers of food intake (BFIs). The aim of the present review is to systematically survey the global literature on herbs and spices in an effort to identify and evaluate specific intake biomarkers for a representative set of common herbs and spices in humans. A total of 25 herbs and spices were initially chosen, including anise, basil, black pepper, caraway, chili pepper, cinnamon, clove, cumin, curcumin, dill, fennel, fenugreek, ginger, lemongrass, marjoram, nutmeg, oregano, parsley, peppermint and spearmint, rosemary, saffron, sage, tarragon, and thyme. However, only 17 of these herbs and spices had published, peer-reviewed studies describing potential biomarkers of intake. In many studies, the herb or spice of interest was administrated in the form of a capsule or extract and very few studies were performed with actual foods. A systematic assessment of the candidate biomarkers was also performed. Given the limitations in the experimental designs for many of the published studies, further work is needed to better evaluate the identified set of BFIs. Although the daily intake of herbs and spices is very low compared to most other foods, this important set of food seasoning agents should not be underestimated, especially given their potential benefits to human health.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| 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 découle