Alcohol Consumption and Hematological Malignancies: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
Introduction: Alcohol-use disorders (AUDs) affect an estimated 283 million people globally, with highest prevalence in the European region (14.8% for men and 3.5% for women) and the Americas (11.5% for men and 5.1% for women), and more common in high-income countries. Hematological malignancies, cancers affecting blood, bone marrow, and lymph nodes, also pose a significant global health burden. While alcohol use is linked to various health risks, its relationship with hematological malignancies is unclear. This systematic review aims to synthesize evidence on the risk of hematological malignancies associated with alcohol use, providing a comprehensive understanding of this relationship and addressing current literature gaps. Methods: Following the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we systematically searched 6 databases (PubMed, Cochrane Library, Scopus, Web of Science, and EMBASE) on 01/14/24. We searched for studies that met our inclusion criteria to assess the relative risk (RR) of developing hematologic malignancies associated with alcohol consumption. To enable a comprehensive analysis, we categorized hematologic malignancies into five major groups: Lymphoid Neoplasms, Myeloid Neoplasms, Hodgkin Lymphoma (HL), Non-Hodgkin Lymphoma (NHL), and Multiple Myeloma (MM). We registered our study in PROSPERO with the reference CRD42024507139. Meta-analysis was performed using the R Software to calculate the effect size, presented as logRR with 95% confidence intervals (CI). The random-effects model was used for pooling analysis to compensate for the heterogeneity of the studies. We conducted a risk of bias assessment using the Newcastle-Ottawa Scale to appraise case-control and cohort studies. Results: A total of 4,659 article records were screened, and 39 studies comprising 13,372,717 participants with hematological malignancies and RR of alcohol consumption were included for analysis. The included studies were distributed across the continents: America (28%), Europe (33%), Asia (31%), and Australia (8%). The overall RR= 0.90 (95% CI: 0.81 to 1.0, I²= 78.6%, p=0.05). The prediction interval extended from 0.50 to 1.63, indicating substantial variability in the study outcomes. In a subgroup analysis, we observed differences based on the type of malignancy. For non-Hodgkin lymphoma (NHL), 17 studies were analyzed, resulting in a relative risk (RR) of 0.85 (95% CI: 0.77 to 0.93, I²= 70.6%). A leave-one-out analysis identified three studies that significantly increased heterogeneity. When these outliers were excluded and the remaining 14 studies were analyzed using a random effects model, the heterogeneity decreased, and the RR narrowed to 0.90 (95% CI: 0.86 to 0.94, I²= 0%). For Myeloid Malignancy, 12 studies were analyzed, indicating an RR= 1.0225 (95% CI: 0.73 to 1.41, p= 0.89, I²= 81.1%). The leave-one-out analysis revealed one study to significantly increase the heterogeneity, an analysis excluding this outlier under the random effects model for the remaining 11 studies showed an RR of 0.92 (95% CI: 0.69 to 1.22, p= 0.59, I²= 75.1%). In the case of Lymphoid Neoplasm, 4 studies estimated an R= 1.17 (95% CI: 0.65 to 2.12, p= 0.58, I²= 77.4%). Regarding MM, 4 studies showed a RR= 0.98, 95% CI: 0.85 to 1.12, p= 0.79, I²= 91.9%). Since there was only one study on HL, it was only included in the overall analysis. Conclusions: The meta-analysis showed an overall RR= 0.90 (95% CI 0.81-1.00) for hematological malignancies related to alcohol use, suggesting a slight non-statistical significative protective effect. Significant heterogeneity was found in subgroup analyses, indicating variability in effect sizes among different hematological malignancies. Despite the overall high heterogeneity, the NHL subgroup showed a significant protective effect with no observed heterogeneity. However, this result should be interpreted with caution due to methodological limitations and the known health risks of alcohol.
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 | 0,011 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,018 | 0,036 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 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) | 0,005 | 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 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 ».