Cisplatin Exposure Dysregulates Insulin Secretion in Male and Female Mice
Bibliographic record
Abstract
Cancer survivors have an increased risk of developing type 2 diabetes compared with the general population. Patients treated with cisplatin, a common chemotherapeutic agent, are more likely to develop metabolic syndrome and type 2 diabetes than age- and sex-matched control patients. Surprisingly, the impact of cisplatin on pancreatic islets has not been reported. Our study aimed to determine whether mouse islet function is adversely affected by systemic (in vivo) or direct (in vitro) exposure to cisplatin. In vivo cisplatin exposure led to deficits in glucose-stimulated plasma insulin levels in both male and female mice, despite no differences in glucose tolerance. In vitro cisplatin exposure to mouse islets dysregulated insulin release and reduced oxygen consumption in a non–sex-specific manner. When shifting our focus to male mouse islets, cisplatin altered the expression of genes related to insulin production, oxidative stress, and the Bcl-2 family as early as 6 h postexposure. Genome-wide expression analysis confirmed the pronounced downregulation of genes within the insulin secretion pathway in cisplatin-exposed mouse islets. Data from three human organ donors confirmed that the detrimental effects of cisplatin on insulin secretion and gene expression are reproduced in human islets. Our findings indicate that cisplatin exposure causes significant defects in insulin secretion and may have lasting effects on islet health. Article Highlights Cancer survivors who receive cisplatin chemotherapy have an increased risk of type 2 diabetes, but the underlying mechanisms remain unclear. The aim of this study was to investigate whether cisplatin impacts β-cell health and function, thereby contributing to increased type 2 diabetes risk in cancer survivors. In vivo and in vitro cisplatin exposure dysregulated insulin secretion in male and female mice. In vitro cisplatin exposure reduced oxygen consumption, impaired β-cell exocytotic capacity, and altered expression of genes within the insulin secretion pathway in mouse islets. Understanding how chemotherapeutic drugs cause β-cell injury is critical for designing targeted interventions to reduce the risk of cancer survivors developing type 2 diabetes after treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".