Type 2 Diabetes Mellitus Increases the Risk for Developing Monoclonal Gammopathy of Undetermined Significance in Asian Populations
Notice bibliographique
Résumé
Background: Epidemiological studies have shown inconsistent evidence of the association between type 2 diabetes mellitus (T2DM) and lymphoproliferative disorders, including monoclonal gammopathy of undetermined significance (MGUS), the most common plasma cell disorder. MGUS is also a premalignant condition of multiple myeloma, Waldenström macroglobulinemia, light-chain amyloidosis, or related conditions. Studies have shown that the association between T2DM and MGUS may be explained by insulin resistance and chronic inflammation via inflammatory cytokines, tumor necrosis factor alpha, and interleukin-1β. However, this association may be attenuated by the use of T2DM medications. This association is further perplexed by well-established racial differences in the prevalence and incidence of T2DM and MGUS, with a higher percentage of Black populations inflicted with the two diseases than their White counterparts. It is however unclear whether the association holds in Asian populations, as these populations are understudied. The goal of this study was to examine whether T2DM increased the risk for MGUS in Asian populations. Methods: We used data from Taiwan National Health Insurance databases. Taiwan National Health Insurance is a national health insurance system introduced in 1995. As the enrollment is mandatory, 99% of the citizens (>95% are Asians) are enrolled. We identified patients diagnosed with T2DM from 2001-2021. The outcome was time from age 16 (baseline) to MGUS diagnosis, if any. The association between T2DM and MGUS was estimated by a multivariable-adjusted hazard ratio (aHR) using the Fine-Gray distribution hazard model with death as a competing event and incident T2DM as time-varying exposure. The covariates included age at incident T2DM, gender, and Charlson Comorbidity Index (CCI) at baseline. Results: This study included 3,195,750 individuals who had incident T2DM between 2001 and 2021. Among them, 53.7% were female, mean CCI was 0.01 (standard deviation, std, 0.14), 1,620 (0.05%) developed MGUS with a median follow-up of 51 (interquartile range, 41-60) years. The mean age at incident T2DM was 57 (std 14) years and the mean age at MGUS was 70 (std 12) years. In the multivariable analysis, T2DM increased the risk for MGUS (aHR: 2.33, 95% confidence interval, CI: 1.93-2.81, p <0.0001). In addition, male sex (aHR: 1.21, 95% CI: 1.09-1.33, p =0.0002) and higher CCI (aHR: 1.45, 95% CI: 1.06-1.99, p =0.0221) were independently associated with higher risk for MGUS. Older age at incident T2DM was negatively associated with the risk for MGUS (aHR: 0.990, 95% CI: 0.985-0.995, p <0.0001). Conclusions and Relevance: In Taiwan, with populations predominantly Asian, T2DM increases the risk for developing MGUS by 133% and the risk for MGUS decreases by age at incident T2DM. More studies in other Asian countries are needed to confirm the generalizability of a positive association between T2DM and MGUS in Asian populations.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».