The Risk of Lymphoproliferative Disorders In Patients with Diabetes Mellitus: A Meta-Analysis
Notice bibliographique
Résumé
Abstract Abstract 3108 Introduction: The incidence and prevalence of lymphoproliferative disorders (LPDs) has been increasing over the past few decades. The epidemiology and risk factors associated with these malignancies continue to be in need of further research. Diabetes mellitus type 2 (DM2) has emerged as a potential risk factor for the development of LPDs, including non-Hodgkin lymphoma (NHL), chronic lymphocytic leukemia (CLL) and plasma cell myeloma (PCM). However, the association of diabetes with specific histological subtypes of LPD is unclear. The present study evaluates the association of DM2 to common subtypes of LPD using a meta-analytical approach. Methods: MEDLINE and the Cochrane Database were searched from January 1, 1950 to December 31, 2009 for observational retrospective case-control and prospective cohort studies reporting outcomes with 95% confidence intervals (CI) on the association between DM2 and incidence of LPDs. The search keyword was “diabetes AND (chronic lymphocytic leukemia OR lymphoma OR myeloma)”. Data were gathered independently by two investigators. Disagreements were addressed by consensus. Relative risk (RR) and 95% CI were calculated using a random-effects model (REM). Heterogeneity among studies was assessed using the I2 and the Cochrane Q statistics, the quality of the studies was assessed with the Newcastle-Ottawa scale (NOS), and the dissemination bias was assessed by direct examination of a funnel plot and by the trim-and-fill analysis. Results: From a total of 1,009 returns, 12 case-control (average NOS 5.8) and 8 cohort studies (average NOS 7.6) were included in our final analysis. Sixteen studies were included for the association of NHL; four for follicular lymphoma (FL), four for diffuse large B-cell lymphoma (DLBCL), four for small lymphocytic lymphoma (SLL)/CLL, and three for peripheral T-cell lymphoma (PTCL). Seven studies were included for PCM. The overall RR for NHL in DM2 patients was 1.2 (95% CI 1.1 to 1.4; p=0.002). There was moderate heterogeneity among studies (I2=42%; Q=29.4, p=0.03). There was also minimal dissemination bias; the trim-and-fill analysis found one imputed study, which would not have affected our results. The RR for patients with FL was 0.8 (95% CI 0.6 to 1.1; p=0.15), 1.1 (0.8 to 1.4; p=0.19) for DLBCL, 1.2 (95% CI 0.9 to 1.5; p=0.33) for CLL/SLL, and 2.4 (95% CI 1.3 to 4.6; p=0.007) for PTCL. The RR for PCM in DM2 patients was 1.1 (95% CI 0.8 to 1.4; p=0.67). There was a high degree of heterogeneity among studies (I2=59%; Q=26.9, p=0.005) but there was no evidence of dissemination bias. Conclusion: The findings of this study corroborate the 20% increased risk of NHL associated with DM2. Among NHL subtypes, FL, DLBCL, nor CLL/SLL were associated with increased risk from DM2. Similarly, there was not an association between DM2 and PCM. Although a small subset, we found a 40% increased risk of PTCL in DM2 patients. Large population-based studies focusing specifically on common chronic conditions, such as DM2, and their relationship with specific LPD subtypes are needed to confirm the results of this study. Disclosures: No relevant conflicts of interest to declare.
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 | 0,015 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,013 | 0,058 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».