Association between type 2 diabetes mellitus and multiple myeloma: Fact or fiction
Bibliographic record
Abstract
Multiple myeloma is a plasma cell cancer causing bone and marrow damage, resulting in hypercalcemia, anemia, and renal insufficiency. Diabetes mellitus occurs in 6-24% of multiple myeloma cases, associated with immunosuppression, inflammation, and lymphocyte dysfunction, possibly contributing to multiple myeloma development. Insulin and insulin-like growth factor-1 also contribute to multiple myeloma pathogenesis. The incidence of both multiple myeloma and diabetes mellitus is expected to rise due to the aging population, lifestyle changes, genetic predisposition, and improved diagnostic methods. Although the link between diabetes mellitus and hematological malignancy risk is less conclusive, insulin resistance and growth factors may promote tumor cell proliferation. Genetic variants linked to type 2 diabetes mellitus (T2DM) influence multiple myeloma risks. The insulin like growth factor 1 (IGF1) gene triggers malignant plasma cell proliferation. Additionally, poorly managed T2DM-induced acidosis creates a favorable environment for cancer cell growth, including multiple myeloma. T2DM and metabolic syndrome (MetS) increase multiple myeloma risks through insulin resistance, hyperinsulinemia, inflammation, and dyslipidemia. Inflammatory cytokines [interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), and interleukin-1β (IL-1β)] contribute to insulin resistance, chronic inflammation, and multiple myeloma cell survival too. The coexistence of diabetes and multiple myeloma presents challenges in managing complications like neuropathy, nephropathy, and retinopathy. In conclusion, the association between T2DM and multiple myeloma has been established, with a discernible influence from distinct genetic variations. Notably, IL-6, TNF-alpha, and IL-1β exert significant influence on the development of insulin resistance and the proliferation of cancer cells, and also their viability. Consequently, the involvement of inflammatory cytokines, dyslipidemia, and IGF1 in the progression of MM among patients with T2DM and MetS is noteworthy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".