Re: "Reduced Risk of Lung Cancer With Metformin Therapy in Diabetic Patients: A Systematic Review and Meta-Analysis"
Notice bibliographique
Résumé
We read with interest the recently published meta-analysis by Zhang et al. (1), in which the authors reported a 29% reduced risk of lung cancer and a 15% reduced risk of respiratory cancer in diabetic patients treated with metformin. The authors concluded that metformin therapy appears to be associated with a lower risk of lung cancer and that this association is more prominent than associations for other cancers of the respiratory system. As the authors briefly mentioned, time-related biases were present in some of the observational studies included in their meta-analysis (1). Such conclusion-altering biases have been shown to greatly exaggerate the benefit of metformin in cancer incidence (2). As such, we are concerned that Zhang et al. failed to differentiate such studies in their meta-analysis. We believe that time-related biases affected 3 of the 6 studies included (3–8). In their case-control study, Mazzone et al. (3) reported that metformin was associated with a 52% reduction in risk of lung cancer. However, their methods suggest that there was differential assessment of metformin exposure between cases and controls. Specifically, for the lung cancer cases, exposure to metformin was correctly assessed prior to the date of diagnosis, whereas for controls, exposure was assessed at any time, which included periods before and after the time of diagnosis of the matched case. As a result, controls had a substantially higher probability of being exposed in comparison with cases, which led to a time-window bias (2). In their cohort study, Hsieh et al. (4) found a 30% increased risk of lung cancer for type 2 diabetes patients being treated with sulfonylurea as compared with metformin monotherapy. However, aside from a very opaque description of their methods, Hsieh et al. did not account for several important time-related factors. To be eligible for analysis, patients had to receive continuous drug coverage for at least 1 year at any time during follow-up, and patients with a diagnosis of cancer before initiation of antidiabetic therapy were excluded. First, since patients in any stage of type 2 diabetes at baseline were included, it is likely that users of sulfonylureas as second-line therapy were at a later stage of disease than those on metformin. This might have resulted in confounding by disease duration (2), since patients in advanced stages of type 2 diabetes may be at higher risk for cancer. In addition, patients who were on sulfonylurea monotherapy might have been on metformin before cohort entry. However, these patients were selectively excluded if they had a cancer diagnosis, and the impact of prior metformin therapy on cancer incidence in those on sulfonylurea monotherapy was disregarded, because no latency time window between start of therapy and the cancer event was considered. We assume that the benefit of metformin observed in this study was triggered by these biases due to time lag and latency. The same applies to the study by Libby et al. (5), which has been discussed in detail elsewhere (2).
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 | 0,008 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,009 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
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 ».