Type 2 Diabetes Mellitus Increases the Risk for Developing Monoclonal Gammopathy of Undetermined Significance in Asian Populations
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".