Evidence map›Paper›PMID 41049975›Full record

ArticleCureus2025

Preventing Life-Threatening Hyperglycemia in Immune Checkpoint Inhibitor-Induced Type 1 Diabetes: Insights From Two Cases and Literature Review.

Nako Matsumoto, Hitoshi Iwasaki, Yuhei Sasai, Nao Aono-Soma, Motohiro Sekiya

Abstract readCase Reports
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nako MatsumotoDepartment of Endocrinology and Metabolism, University of Tsukuba, Tsukuba, JPN.
Hitoshi IwasakiDepartment of Endocrinology and Metabolism, University of Tsukuba, Tsukuba, JPN.
Yuhei SasaiDepartment of Endocrinology and Metabolism, University of Tsukuba, Tsukuba, JPN.
Nao Aono-SomaDepartment of Endocrinology and Metabolism, University of Tsukuba, Tsukuba, JPN.
Motohiro SekiyaDepartment of Endocrinology and Metabolism, University of Tsukuba, Tsukuba, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the use of immune checkpoint inhibitors (ICIs) has expanded rapidly, accompanied by a marked increase in associated immune-related adverse events (irAEs). Among these, ICI-related type 1 diabetes (ICI-T1D) is frequently recognized only after the onset of life-threatening hyperglycemia, in part due to its rare prevalence and latent nature of disease progression. Herein we report two cases of ICI-T1D successfully treated before the development of marked hyperglycemia. In both cases, despite the complete depletion of insulin secretion, blood glucose control was achieved prior to the onset of diabetic ketoacidosis, and the clinical course could be monitored in detail, making them valuable cases for documentation. Taken together with the literature, these cases suggest that, compared with classic fulminant type 1 diabetes (FT1D), ICI-T1D tends to progress more gradually. Even in the presence of only mild hyperglycemia and partially preserved insulin secretion, its onset should be suspected and the clinical course monitored carefully. Since most patients undergo regular blood testing, careful monitoring may allow for the timely detection and optimal management of ICI-T1D. In addition, we found that not only glucose metrics but also transient increases in red cell distribution width (RDW) levels may be a new predictive marker for irAEs.

Indexed as

fulminant type 1 diabetesimmune checkpoint inhibitorsimmune checkpoint inhibitors-induced type 1 diabetesimmune-related adverse eventsred cell distribution width

Identifiers

PMID41049975
PMCPMC12495784

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.