Putative hepatoprotective effects of coffee: authors' reply
Notice bibliographique
Résumé
Sirs, We thank Mascitelli et al. for their observation regarding a possible relationship between coffee consumption, impaired iron absorption and reduced risk of liver injury. The last number of years has seen an explosion in the literature concerning coffee consumption and its effects on a variety of liver diseases. Despite the wealth of publications on the subject, little is known about the mechanisms underlying this effect. Whilst there is some evidence supporting the role of inhibition of iron absorption by polyphenols in coffee’s hepatoprotective effect, there are limitations to this hypothesis.1, 2 The study by Hurrell et al. reported that coffee is an inhibitor of iron absorption, however, a number of other beverages were also examined, including tea and cocoa, reporting that these were also associated with impairment of iron absorption.2 A number of investigators have examined the effects of the consumption of other beverages on liver disease (including a variety of teas). In contrast to that seen with coffee, no significant relationship between the consumption of these other polyphenol containing (and therefore potentially iron reducing) beverages and either risk of cirrhosis or hepatoma was demonstrable.3–5 There are many putative mechanisms explaining how coffee consumption may protect against liver damage with evidence both supporting and refuting a caffeine-mediated3, 6 and a diterpine-mediated7, 8 hypothesis. In addition, work by Glei et al. and Goya et al. have suggested that coffee may protect against oxidative stress-induced liver cell damage.9, 10 Unfortunately, the majority of the literature relating to this subject comes from population-based studies which can only determine the presence of an association between coffee consumption and liver disease rather than providing an explanation of any underlying mechanisms. Further studies are necessary focusing specifically on individual components of coffee and their effects on hepatic function and disease. Declaration of personal and funding interests: Dr I. Cadden has no competing interests to declare. Although not directly related to this article, Dr E. M. Yoshida has received honoraria for appearing at events sponsored by Hoffman LaRoche and Schering Plough. Dr Yoshida has also received clinical research funds from Hoffman LaRoche, Schering Plough, Vertex Inc., Human Genome Sciences, Indenix Inc., Novartis, Astellas, Pfizer, Jansen Ortho, Bristol Myers Squibb, Microgenix, Gilead Sciences and Wyeth Ayerst. Dr Yoshida has received honoraria for attending research meetings from Hoffman Laroche. However, Dr Yoshida has no financial interest in any pharmaceutical company and is not a member of any speaking bureau.
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 | 0,006 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,003 | 0,006 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,026 | 0,036 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,004 |
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 ».