Putative hepatoprotective effects of coffee: authors' reply
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.026 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".