A Case of Nivolumab-Induced Acute-Onset Type 1 Diabetes Mellitus in Melanoma
Bibliographic record
Abstract
Nivolumab, an anti-PD-1 antibody, is now considered an important therapeutic agent in several advanced malignancies. However, immune-related adverse events such as endocrinopathies have been reported with its use. Thyroid disorder and isolated adrenocorticotropic hormone deficiency have frequently been reported as nivolumab-induced immune-related adverse events. Another endocrinopathy is nivolumab-induced type 1 diabetes mellitus (t1dm), described as diabetes mellitus with rapid onset and complete insulin insufficiency, at times leading to fulminant t1dm. We report the case of a 68-year-old woman who developed pancreatic islet-related autoantibody-negative t1dm, possibly induced by nivolumab, under continuous glucocorticoid administration. She was treated with nivolumab for advanced malignant melanoma, concomitant with 10 mg prednisolone daily for thrombophlebitis tapered to 5 mg after 13 courses of nivolumab therapy. At approximately the 27th course of nivolumab therapy, she showed elevated plasma glucose levels despite preserved insulin secretion. A month later, she developed diabetic ketoacidosis. Her insulin secretion decreased and finally was exhausted. She was diagnosed with acute-onset rather than fulminant t1dm because of a rapidly progressive course to diabetic ketoacidosis during just more than 1 week. She is currently receiving insulin replacement. There has been no recurrence of the melanoma. Thus, nivolumab might induce autoimmune diabetes mellitus, with patients having t1dm-sensitive human leucocyte antigen being more susceptible even when receiving glucocorticoids. Physicians should be aware that nivolumab could potentially induce t1dm as a critical immune-related adverse event.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".