1. Introduction
Currently, a staggering 9 million people worldwide are living with type 1 diabetes mellitus (T1DM) [ 1 ]. The slow destruction of pancreatic cells and biological and environmental factors characterize this condition. However, symptoms do not reveal themselves until 80–90% of the beta cells of the pancreas have been destroyed [ 2 ]. Nearly 500,000 individuals are diagnosed with T1DM annually, yet definitive underlying risk factors and prevention strategies remain elusive [ 3 ]. Cancer care therapies introduced in 2011 have now led to the development of a subtype of T1DM, coined ICI-DM.
ICI-DM is an emerging phenomenon in the adult oncology population. Its classification and presentation often differ because it has an abrupt onset, and this population does not uniformly have pancreatic autoantibodies. Although this phenomenon has been described in the literature since the dual use of immunotherapy in the oncology population began in 2011, definitive risk factors associated with the development of ICI-DM require further study [ 4 ]. This review aimed to synthesize the published literature on ICI-DM to clarify what is known about the risk factors associated with the development of ICI-DM among adult oncology patients and to identify areas needing further investigation. Synthesis of current literature can lead to a better understanding of the factors associated with the development of ICI-DM and thus increase the measures to proactively identify those at risk.
2. Background and Significance
ICI-DM is characterized by abrupt destruction of the beta cells of the pancreas, leading to the development of insulin-dependent diabetes [ 5 ]. This condition results from the use of immunotherapy for various malignancies and was first reported among patients with malignant melanoma [ 6 ]. Immunotherapy is categorized into various classifications; the classifications associated with the development of ICI-DM include programmed cell death protein-1 (PD-1) and programmed death ligand (PD-L1) [ 7 ].
Immunotherapy aids in the destruction of cancer cells by upregulating the immune system to attack cancer cells [ 8 ]. For some, this enhanced immune response has adverse effects on non-targeted organs, resulting in immune checkpoint-related complications, such as ICI-DM [ 4 ]. The development of ICI-DM requires providers to act promptly to identify this life-threatening condition, which requires lifelong treatment with insulin. Because current evidence demonstrates a weak association with risk factors for ICI-DM, no preventive strategy is currently in place to protect against the development of this condition.
The incidence of ICI-DM has steadily risen. Earlier estimates suggested that approximately 1% of the oncology population developed this life-threatening condition [ 4 ]; however, recent studies report that the incidence has increased to 3.5% with expanded use of immunotherapy [ 9 ]. The development of ICI-DM and concurrent cancer can lead to a decline in the overall quality of life in this population, resulting in higher rates of psychological and emotional distress and a lack of confidence [ 10 ]. Furthermore, the cost associated with a diagnosis of T1DM is staggering. Persons with newly diagnosed diabetes account for one of every four healthcare dollars and a total estimated financial cost of USD 412.9 billion annually, of which USD 106.3 billion is associated with lost productivity from diabetes-related complications resulting in unemployment in the adult population [ 11 ].
Some risk factors that have been associated with ICI-DM include exposure to immunotherapy, solid tumor diagnosis, T-cell expression in the pancreas, and human leukocyte antigen (HLA) genotyping, which is measured by evaluating the DR3, DR4, and DR9 haplotypes, with A2 being the dominant HLA serotype [ 5 , 8 , 12 , 13 ]. However, these findings are not conclusive, and there is a lack of consensus regarding the demographic, biological, genetic, and environmental factors associated with ICI-DM development [ 12 ]. Identifying these factors can help preemptively recognize at-risk individuals and streamline active surveillance strategies for this population, thereby mitigating the adverse events associated with ICI-DM development.
Theoretical Framework
This review was guided by Snieszko’s Epidemiology Triad [ 14 ], which was described by Van Seventer and Hochberg [ 15 ]. This classic theoretical framework describes infectious disease causation by displaying the relationship between the agent (pathogen), host, and environmental factors. Although originally developed for infectious diseases, this framework was modified to illustrate the potential factors associated with ICI-DM and was used to guide the data extraction for this review ( Figure 1 ).
According to this framework, the risk of developing ICI-DM depends, in part, on the characteristics of the host , making them more susceptible to ICI-DM. Existing literature suggests that the susceptible host is female with a history of melanoma, non-small cell lung cancer (NSCLC), or renal cell carcinoma (RCC) [ 7 , 12 ]. Additional host factors include younger age, a median age of 66 years +/− 5 years, and pre-existing diabetes [ 6 ].
Agent factors: The original framework describes exposure to an agent that leads to the development of a specified condition. For this review, the agent is immunotherapy, which leads to the development of ICI-DM. The development of ICI-DM is associated with the introduction of immunotherapy, most notably PD-1 and PD-L1 agents, and is seen after the first three cycles of treatment or within 9 weeks of immunotherapy exposure [ 4 , 5 , 6 , 7 , 12 ]. Once a susceptible host is exposed to one of these agents, the risk of developing ICI-DM increases by up to 1%.
Micro-Environment: In the original framework, environmental factors were outlined to depict the relationship between constructs and disease development. The micro-environment was described as external environments, such as social, behavioral, and economic factors [ 15 ]. For this review, the micro-environment was adapted to reflect the internal factors associated with ICI-DM development. When a susceptible person is exposed to certain agents, small changes in their body’s environment can trigger the immune system to attack the pancreas. This includes the appearance of autoantibodies like glutamic acid decarboxylase 65 (GAD65), destruction of islet cells, and the loss of insulin antibodies, C-peptide, zinc transporters, and insulin-associated protein [ 3 , 4 , 5 , 13 , 16 ]. Additional micro-environmental elements were identified and thought to play a role in developing this condition, including HLA expression and T-cell upregulation, rendering the pancreas more susceptible to destruction from the immunotherapy agent [ 16 ]. The concepts associated with the micro-environment describe the impact of immunotherapy on the host’s internal immune regulatory system, rendering the host more vulnerable to ICI-DM.
Disease: ICI-DM is at the center of the framework and is the outcome of interest. The triangle represents the relational constructs of host, agent, and micro-environmental factors identified as potential risk factors for ICI-DM ( Figure 1 ).
3. Methods
3.1. Data Sources and Searches
With the assistance of a medical librarian, we conducted a search of the literature published from 2014 to October 2024, reflecting the timing of immunotherapy toxicity publications. To ensure that a full scope of literature related to ICI-DM risk factors was captured, we searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and PubMed databases. The initial literature search was limited to full-text articles available in English. Given that published manuscripts are not uniform in the identification of ICI-DM, a combination of Medical Subject Headings (MeSH) and Boolean operators were employed to capture this phenomenon in CINAHL and PubMed using the following search terms: “immunotherapy induced diabetes AND risk factors,” “immune checkpoint inhibitors AND diabetes,” (TI (diabetes OR diabetic)) AND (TI (immunotherap* OR checkpoint inhibitor*)), “diabetes mellitus type 1,” “diabetic ketoacidosis,” “immunotherapy,” “checkpoint inhibitor,” “ICI” “nivolumab,” “pembrolizumab,” “PD-1,” “PD-L1,” and “risk factors”.
3.2. Inclusion and Exclusion Criteria
Articles were included in the review if they (a) included adults ≥ 18 years old with a primary aim of oncology population receiving immunotherapy, (b) were published in English, (c) reported an intervention that included checkpoint inhibitors, (d) measured ICI-DM as an outcome, and (e) were published within the past 10 years. Reports were excluded if they (a) included a pediatric population, (b) reported an intervention administering CAR-T cells, T-cell infusions, or any vaccine, (c) measured outcomes, including type 2 diabetes mellitus (T2DM), juvenile diabetes mellitus, COVID-19, and HIV, or (d) were a systematic or narrative review or case report. To evaluate the quality of the studies included, the Critical Appraisal Skills Programme for case-control, cohort, and observational studies was used [ 17 , 18 , 19 , 20 , 21 ]. Because this review aimed to understand the current risk factors associated with ICI-DM, no studies were excluded because of a lack of rigor. A two-person rater agreement was performed by V.C. and V.B. The variables extracted for this review included an analysis of the study design, population characteristics, and an assessment of outcome criteria. To minimize selection bias, each reviewer performed an independent evaluation of the identified studies. Any discrepancies were resolved by the primary author. An online tool, Covidence, which is a web-based collaboration software platform that streamlines the production of systematic and other literature reviews, was used to screen the titles/abstracts and full-text articles (2023).
4. Results
A total of 2030 studies met the search criteria. After removing 693 duplicates, we screened 1337 studies by their titles and abstracts. Thereafter, 988 studies were deemed irrelevant, leaving 349 studies for full-text review. Ultimately, 23 articles met the inclusion criteria and were included in our review ( Figure 2 ). These studies were imported in Covidence.
4.1. Study Characteristics
A total of 23 articles were included in the review ( Table 1 ). The studies were an accumulation of case-control series [ 22 , 23 , 24 , 25 ] and prospective analyses [ 26 , 27 , 28 , 29 , 30 , 31 ], with the majority being retrospective analyses [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. The studies were conducted in diverse countries, including China, Japan, Hong Kong, Australia, Korea, the United Kingdom, and the United States. Each study addressed at least one aspect of the conceptual framework ( Table 2 ); however, seven studies addressed all constructs of the framework [ 25 , 28 , 30 , 33 , 34 , 35 , 39 , 42 ], providing a complete analysis of the factors influencing the risk of ICI-DM. Of the studies reporting ICI-DM incidence, 11 reported less than 1% in their cohort [ 6 , 13 , 22 , 23 , 26 , 28 , 29 , 34 , 35 , 36 , 37 , 40 ]. However, 4 studies reported an incidence higher, averaging 1.2% [ 33 , 39 , 41 , 42 ]. Most studies reported ICI-DM presentation 3–4 months after onset. However, some studies reported a more delayed onset of up to 2 years [ 22 , 23 , 25 , 34 ].
4.1.1. Host Characteristics
The mean age at the onset of ICI-DM was 63 years [ 22 , 23 , 24 , 27 , 28 , 30 , 31 , 32 , 33 , 35 , 36 , 37 , 38 , 39 , 42 ]. However, a few studies reported the average age to be nearer in the early ’70s [ 6 , 25 , 26 , 29 ]. Gender analysis revealed that males had a higher risk of ICI-DM, whereas other studies did not report this association [ 23 , 27 ]. A history of diabetes was reported in 11 studies [ 22 , 26 , 28 , 32 , 33 , 36 , 37 , 38 , 39 , 41 , 42 ], of which three identified pre-existing T1DM or T2DM as positively correlated with the development of ICI-DM [ 6 , 13 , 33 , 34 ]. The solid tumor diagnoses in the study were diverse and included metastatic melanoma, NSCLC, RCC, urethral carcinoma, and cancers of the genitourinary system, breast, gastrointestinal tract, and head and neck [ 40 ].
4.1.2. Agent Characteristics
The results regarding which agents were positively correlated with ICI-DM development were conflicting. Three studies reported that the combination of CTLA-4 and PD-1/PD-L1 was positively correlated with the development of ICI-DM [ 6 , 13 , 36 ]. However, three other studies found that PD-1/PD-L1 agents were strongly correlated with ICI-DM development [ 27 , 39 , 41 ]. Two other studies did not report a significant difference between the immunotherapy agents and the risk for the development of ICI-DM, but did mention that the onset of ICI-DM was earlier in those receiving combination treatment than in those receiving single-agent PD-1/PD-L1 [ 32 , 35 ].
4.1.3. Micro-Environment Characteristics
HLA-Expression and T-Cell Upregulation
HLA haplotypes are typically seen in the development of T1DM. In the studies in which HLA protective and susceptible alleles were assessed, class II HLA-DRB1 expression was associated with an increased risk of total insulin loss (T1DM) (20–70%) [ 24 , 28 , 30 , 33 , 34 , 39 , 42 ]. One study found that class I HLA alleles were also associated with an increased risk of total insulin loss (T1DM) [ 35 ]. Other studies found that HLA-DRB1 haplotypes were protective against insulin deficiency, but at a much lower frequency than the susceptible allele [ 30 , 39 , 42 ]. Another study reported the detection of an allele that protected against the development of concurrent ICI-DM and immune checkpoint inhibitor-isolated adrenocorticotropic hormone deficiency [ 31 ]. No studies have analyzed the relationship between T-cell lymphocytes and the development of ICI-DM.
Pancreatic Autoantibodies
The total number of patients across all studies who developed ICI-DM or unexplained new-onset diabetes was n = 1185 out of a sample population of N = 61,003. Of this sample population, pancreatic autoantibodies were detected in 0.07% of cases, 90 of which were GAD65 [ 24 , 28 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Islet antigen antibodies were detected in 25 individuals [ 22 , 24 , 28 , 30 , 31 , 36 , 38 , 41 ]. In one study, insulin antibodies were detected in 11 individuals, and zinc transporters were detected in 2 individuals [ 36 ]. None of the other studies detected zinc transporters within their populations. Of the studies evaluating C-peptide production after new onset ICI-DM, all reported individuals had either an inappropriately low C-peptide level in the setting of hyperglycemia or undetectable levels [ 13 , 22 , 24 , 25 , 28 , 30 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 42 ].
5. Discussion
The included studies elucidated the various risk factors associated with ICI-DM development. Studies that included people with a medical history of diabetes noted that this condition was positively correlated with the development of ICI-DM [ 13 , 28 , 33 , 40 ]. However, this correlation was not established in all studies, as many excluded people with pre-existing T2DM and T1DM. Hence, future studies should focus on this association to determine whether pre-existing diabetes is a risk factor for ICI-DM development or whether those with pre-existing diabetes progress to ICI-DM faster. Two studies identified male gender as a factor; however, this finding could reflect selection basis, given that one study included only males [ 23 ], and in another study, over 65% of the sample were males [ 27 ]. The underlying malignancies were widely representative of many solid tumor diagnoses, reflecting the type of malignancies for which immunotherapy was first approved; therefore, higher rates of ICI-DM would be expected in this population, particularly those with melanoma, RCC, and NSCLC. Some studies included liquid tumor diagnoses such as lymphoma [ 24 , 30 , 41 ], but the small sample sizes made drawing conclusions difficult.
The relationship between the development of ICI-DM and immunotherapy agents was clear: most studies reported the frequent association of PD-1 and PD-L1 agents with the development of ICI-DM, and combination treatment was associated with higher risk. Therefore, given the known risk associated with PD-1 and PD-L1 agents, oncology providers should be sure to educate recipients of this treatment and its potential risks.
Micro-environment characteristics are an emerging area that could assist in the proactive identification of individuals with traits associated with an increased risk for ICI-DM. Recent studies have identified HLA haplotypes for susceptible and protective alleles akin to T1DM, identified in persons who develop ICI-DM [ 24 , 28 , 30 , 33 , 34 , 39 , 42 ]. Additionally, micro-environment characteristics, such as GAD65, insulin antibodies, zinc transporters, and islet cell antibodies, are infrequently detected in persons with ICI-DM and, therefore, did not appear useful in proactively identifying persons at high risk. C-peptide is consistently low or undetectable in those with ICI-DM; therefore, exploring timed testing to assess the rapidity of decline could be beneficial for detecting early ICI-DM, but it is not a marker that can be used to identify high-risk individuals.
Strengths and Weakness
This review highlights future directions for the assessment of patients receiving immunotherapy to proactively identify those at increased risk for ICI-DM. Although not consistently included in each study, factors associated with an increased risk of ICI-DM include a medical history of diabetes and the presence of HLA haplotype alleles susceptible to T1DM. The identification of these potential risk factors is paramount for this population and provides guidance for the development of screening algorithms for individuals identified as being at risk.
Twelve of the studies were retrospective analyses, thus limiting the researchers’ ability to control for confounding factors such as steroid administration and the ability to proactively assess for micro-environment characteristics used to identify risk factors for ICI-DM. Furthermore, 10 studies evaluated HLA characteristics, which have shown promise in identifying high-risk individuals, but additional studies are needed to confirm this relationship. Much of the cohort was unequally representative of the general population, and many of the studies were performed at single institutions. This review was limited to studies in English, which may have introduced language bias and affected our findings. This decision was based on the feasibility constraints of the authors. This limitation should be considered when interpreting the generalizability of our results. Several studies based in Asia comprised only various Asian demographics, and the U.S. studies had larger cohorts of White individuals. Also, inadequate sample size was a limitation in many of the studies that assessed only persons who developed ICI-DM rather than analyzing an entire cohort receiving immunotherapy. Potential confounders were not consistently addressed; for example, glucocorticoids were not consistently addressed in the included studies. We did not conduct a formal sensitivity analysis based on study quality. However, we acknowledge that variability in methodological rigor may have impacted the overall conclusions. We also recognize the potential for publication bias, as ICI-DM may be underreported, skewing the actual incidence risk patterns associated with immune checkpoint inhibitors.
6. Conclusions
Immunotherapy has changed the prognosis for previously terminal malignancies by improving overall life expectancy, but it is not without risk. Immunotherapy has been associated with developing ICI-DM, a subtype of T1DM, given its hallmark of absolute insulin deficiency. To date, no identifiable factors have helped identify high-risk individuals and prevent this life-altering condition. This review aimed to elucidate what is currently known about people who develop this condition and summarize potential factors associated with high-risk individuals. The findings of this review suggest that HLA haplotypes for susceptible traits, medical history of diabetes, and exposure to PD-1 or PD-L1 place individuals at higher risk. Future studies should explore how data mining, machine learning, and artificial intelligence-based predictive models could enhance the identification and risk stratification of ICI-DM.
Acknowledgments
Markeda Wade for editorial assistance.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Author Contributions
V.C.: conceptualization, methodology, writing, and editing. V.B.: writing, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript: NR NR-Not Reported CA Cancer ICI Immune Checkpoint Inhibitor PD-L1 Programmed Death-Ligand 1 PD-1 Programmed Cell Death Protein 1 CTLA-4 Cytotoxic T-Lymphocyte-Associated Protein 4 MM Multiple Myeloma SCLS Small Cell Lung Cancer GI Gastrointestinal SCC Squamous Cell Carcinoma HCC Hepatocellular Carcinoma ATC Anaplastic Thyroid Carcinoma GBM Glioblastoma H&N Head and Neck NSCLC Non-Small Cell Lung Cancer Chemo Chemotherapy RCC Renal Cell Carcinoma IAA Insulin Autoantibodies GAD65 Glutamic Acid Decarboxylase 65 IA-2 Islet Antigen-2 ZnT8 Zinc Transporter 8 Pre-DM Pre-Diabetes Mellitus T2DM Type 2 Diabetes Mellitus PMH Past Medical History DM Diabetes Mellitus ICI-DM Immune-Checkpoint Inhibitor diabetes mellitus RA Retrospective Analysis PA Prospective Analysis W Weeks M Months D Days C Cycles T1DM Type 1 Diabetes Mellitus NR Not Reported CC Conventional Chemotherapy PTS Patients irAE Immune-Mediated Adverse Effects HLA Human Leukocyte Antigen ICI-IAD- Immune Checkpoint Inhibitor Isolated Adrenocorticotropic Hormone Deficiency CINAHL Cumulative Index to Nursing and Allied Health Literature MESH Medical Subject Headings
References
1. World Health Organization Diabetes. World Health Organization Web Site. Updated 2023 Available online: https://www.who.int/news-room/fact-sheets/detail/diabetes (accessed on 31 August 2024)
2. Zorena K. Michalska M. Kurpas M. Jaskulak M. Murawska A. Rostami S. Environmental Factors and the Risk of Developing Type 1 Diabetes—Old Disease and New Data Biology 2022 11 608 10.3390/biology11040608 35453807 PMC9027552
3. Ziegler N.G. Nepom G.T. Prediction and Pathogenesis in Type 1 Diabetes Immunity 2011 32 468 10.1016/j.immuni.2010.03.018 20412757 PMC2861716
4. Cho Y.K. Jung C.H. Immune-Checkpoint Inhibitors-Induced Type 1 Diabetes Mellitus: From Its Molecular Mechanisms to Clinical Practice Diabetes Metab. J. 2023 47 757 10.4093/dmj.2023.0072 37482654 PMC10695719
5. De Filette J.M.K. Pen J.J. Decoster L. Vissers T. Bravenboer B. Van der Auwera B.J. Gorus F.K. O Roep B. Aspeslagh S. Neyns B. Immune checkpoint inhibitors and type 1 diabetes mellitus: A case report and systematic review Eur. J. Endocrinol. 2019 181 363 10.1530/EJE-19-0291 31330498 PMC6709545
6. Chen X. Affinati A.H. Lee Y. Turcu A.F. Henry N.L. Schiopu E. Qin A. Othus M. Clauw D. Ramnath N. Immune Checkpoint Inhibitors and Risk of Type 1 Diabetes Diabetes Care 2022 45 1170 1176 10.2337/dc21-2213 35238899 PMC9174959
7. Takada S. Hirokazu H. Yamagishi K. Hideki S. Masayuki E. Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors Asian Pac. J. Cancer Prev. 2020 21 1697 10.31557/APJCP.2020.21.6.1697 32592366 PMC7568867
8. Wu L. Tsang V.H.M. Sasson S.C. Menzies A.M. Carlino M.S. Brown D.A. Clifton-Bligh R. Gunton J.E. Unravelling Checkpoint Inhibitor Associated Autoimmune Diabetes: From Bench to Bedside Front. Endocrinol. 2021 12 764138 10.3389/fendo.2021.764138 PMC8603930 34803927
9. Vergès B. Diabetes Induced by Immune Checkpoint Inhibitors (ICIs) Ann. D’endocrinologie 2023 84 351 10.1016/j.ando.2023.03.012 36965854
10. Vanstone M. Rewegan A. Brundisini F. Dejean D. Giacomini M. Patient perspectives on quality of life with uncontrolled type 1 diabetes mellitus: A systematic review and qualitative meta-synthesis Ont. Health Technol. Assess. Ser. 2015 15 1 PMC4664939 26649106
11. American Diabetes Association New American Diabetes Association Report Finds Annual Costs of Diabetes to be $412.9 Billion. 1 November 2023 Available online: https://diabetes.org/newsroom/press-releases/new-american-diabetes-association-report-finds-annual-costs-diabetes-be (accessed on 12 September 2024)
12. Zhu J. Luo M. Liang D. Gao S. Zheng Y. He Z. Zhao W. Yu X. Qiu K. Wu J. Type 1 diabetes with immune checkpoint inhibitors: A systematic analysis of clinical trials and a pharmacovigilance study of postmarketing data Int. Immunopharmacol. 2022 110 109053 10.1016/j.intimp.2022.109053 35978507
13. Ruiz-Esteves K.N. Shank K.R. Deutsch A.J. Gunturi A. Chamorro-Pareja N. Colling C.A. Zubiri L. Perlman K. Ouyang T. Villani A.-C. Identification of Immune Checkpoint Inhibitor–Induced Diabetes JAMA Oncol. 2024 10 1409 1416 10.1001/jamaoncol.2024.3104 39207773 PMC11362970
14. Snieszko S.F. The effects of environmental stress on outbreaks of infectious diseases of fishes J. Fish Biol. 1974 6 197 208 10.1111/j.1095-8649.1974.tb04537.x
15. Van Seventer J.M. Hochberg N.S. Principles of infectious diseases: Transmission, diagnosis, prevention, and control Int. Encycl. Public Health 2017 22-39 22 39 10.1016/B978-0-12-803678-5.00516-6
16. Wu L. Carlino M.S. Brown D.A. Long G.V. Clifton-Bligh R. Mellor R. Moore K. Sasson S.C. Menzies A.M. Tsang V. Checkpoint Inhibitor-Associated Autoimmune Diabetes Mellitus Is Characterized by C-peptide Loss and Pancreatic Atrophy J. Clin. Endocrinol. Metab. 2023 109 1301 10.1210/clinem/dgad685 PMC11031227 37997380
17. Critical Appraisal Skills Programme CASP Checklist: CASP Cohort Study Checklist. Web Site. Updated 2023 Available online: https://casp-uk.net/casp-tools-checklists/ (accessed on 27 September 2024)
18. Critical Appraisal Skills Programme CASP Checklist: CASP Qualitative Studies Checklist. Web Site. Updated 2023 Available online: https://casp-uk.net/casp-tools-checklists/qualitative-studies-checklist/ (accessed on 27 September 2024)
19. Critical Appraisal Skills Programme CASP Checklist: CASP Systematic Review Checklist. Web Site. Updated 2023 Available online: https://casp-uk.net/casp-tools-checklists/ (accessed on 27 September 2024)
20. Critical Appraisal Skills Programme CASP Checklist: Systematic Reviews with Meta-Analysis of Observational Studies Web Site. Updated 2023. Available online: https://casp-uk.net/casp-tools-checklists/systematic-review-checklist/ (accessed on 27 September 2024)
21. Grima L. Hammerbeck U. Quality appraisal results using the Critical Appraisal Skills Programme Cohort Studies checklist Zenodo 2023 10.5281/zenodo.10057510
22. Basak E.A. De Joode K. Uyl T.J.J. van der Wal R. Schreurs M.W. Berg S.A.v.D. Hoop E.O.-D. van der Leest C.H. Chaker L. Feelders R.A. The course of C-peptide levels in patients developing diabetes during anti-PD-1 therapy Biomed. Pharmacother. 2022 156 113839 10.1016/j.biopha.2022.113839 36228374
23. Lee M. Jeong K. Park Y.R. Rhee Y. Increased risk of incident diabetes after therapy with immune checkpoint inhibitor compared with conventional chemotherapy: A longitudinal trajectory analysis using a tertiary care hospital database Metabolism 2022 138 155311 10.1016/j.metabol.2022.155311 36122764
24. Kawata S. Kozawa J. Yoneda S. Fujita Y. Kashiwagi-Takayama R. Kimura T. Hosokawa Y. Baden M.Y. Uno S. Uenaka R. Inflammatory Cell Infiltration into Islets Without PD-L1 Expression Is Associated with the Development of Immune Checkpoint Inhibitor–Related Type 1 Diabetes in Genetically Susceptible Patients Diabetes 2023 72 511 519 10.2337/db22-0557 36657987 PMC10033247
25. Inaba H. Morita S. Kosugi D. Asai Y. Kaido Y. Ito S. Hirobata T. Inoue G. Yamamoto Y. Jinnin M. Amino acid polymorphisms in human histocompatibility leukocyte antigen class II and proinsulin epitope have impacts on type 1 diabetes mellitus induced by immune-checkpoint inhibitors Front. Immunol. 2023 14 1165004 10.3389/fimmu.2023.1165004 37114039 PMC10128036
26. Inaba H. Kaido Y. Ito S. Hirobata T. Inoue G. Sugita T. Yamamoto Y. Jinnin M. Kimura H. Kobayashi T. Human Leukocyte Antigens and Biomarkers in Type 1 Diabetes Mellitus Induced by Immune-Checkpoint Inhibitors Endocrinol. Metab. 2022 37 84 10.3803/EnM.2021.1282 PMC8901959 35255603
27. Chan J.S.K. Lee S. Kong D. Lakhani I. Ng K. Dee E.C. Tang P. Lee Y.H.A. Satti D.I. Wong W.T. Risk of diabetes mellitus among users of immune checkpoint inhibitors: A population-based cohort study Cancer Med. 2023 12 8144 10.1002/cam4.5616 36647331 PMC10134274
28. Stamatouli A.M. Quandt Z. Perdigoto A.L. Clark P.L. Kluger H. Weiss S.A. Gettinger S. Sznol M. Young A. Rushakoff R. Collateral Damage: Insulin-Dependent Diabetes Induced with Checkpoint Inhibitors Diabetes 2018 67 1471 1480 10.2337/dbi18-0002 29937434 PMC6054443
29. Knight T. Cooksley T. Emergency Presentations of Immune Checkpoint Inhibitor-Related Endocrinopathies J. Emerg. Med. 2021 61 140 10.1016/j.jemermed.2021.02.020 33795170
30. Marchand L. Thivolet A. Dalle S. Chikh K. Reffet S. Vouillarmet J. Fabien N. Cugnet-Anceau C. Thivolet C. Diabetes mellitus induced by PD-1 and PD-L1 inhibitors: Description of pancreatic endocrine and exocrine phenotype Acta Diabetol. 2018 56 441 10.1007/s00592-018-1234-8 30284618
31. Ono M. Nagao M. Takeuchi H. Fukunaga E. Nagamine T. Inagaki K. Fukuda I. Iwabu M. HLA investigation in ICI-induced T1D and isolated ACTH deficiency including meta-analysis Eur. J. Endocrinol. 2024 191 9 16 10.1093/ejendo/lvae081 38917237
32. Muniz T.P. Araujo D.V. Savage K.J. Cheng T. Saha M. Song X. Gill S. Monzon J.G. Grenier D. Genta S. CANDIED: A Pan-Canadian Cohort of Immune Checkpoint Inhibitor-Induced Insulin-Dependent Diabetes Mellitus Cancers 2021 14 89 10.3390/cancers14010089 35008256 PMC8750429
33. Iwamoto Y. Kimura T. Iwamoto H. Sanada J. Fushimi Y. Katakura Y. Tatsumi F. Shimoda M. Nakanishi S. Mune T. Incidence of endocrine-related immune-related adverse events in Japanese subjects with various types of cancer Front. Endocrinol. 2023 14 1079074 10.3389/fendo.2023.1079074 PMC9899881 36755909
34. Akturk H.K. Michel K. Couts K. Karakus K.E. Robinson W. Michels A. Routine Blood Glucose Monitoring Does Not Predict Onset of Immune Checkpoint Inhibitor–Induced Type 1 Diabetes Diabetes Care 2024 47 e29 e30 10.2337/dc23-1964 38232313 PMC10909677
35. Byun D.J. Braunstein R. Flynn J. Zheng J. Lefkowitz R.A. Kanbour S. Girotra M. Immune Checkpoint Inhibitor–Associated Diabetes: A Single-Institution Experience Diabetes Care 2020 43 3106 10.2337/dc20-0609 33051330 PMC7770268
36. Jeun R. Iyer P.C. Best C. Lavis V. Varghese J.M. Yedururi S. Brady V. Oliva I.C.G. Dadu R. Milton D.R. Clinical Outcomes of Immune Checkpoint Inhibitor Diabetes Mellitus at a Comprehensive Cancer Center Immunotherapy 2023 15 417 10.2217/imt-2021-0316 37013834 PMC10088048
37. Leiter A. Carroll E. Brooks D. Ben Shimol J. Eisenberg E. Wisnivesky J.P. Galsky M.D. Gallagher E.J. Characterization of hyperglycemia in patients receiving immune checkpoint inhibitors: Beyond autoimmune insulin-dependent diabetes Diabetes Res. Clin. Pract. 2020 172 108633 10.1016/j.diabres.2020.108633 33347896 PMC7940559
38. Zhang Z. Sharma R. Hamad L. Riebandt G. Attwood K. Incidence of diabetes mellitus in patients treated with immune checkpoint inhibitors (ICI) therapy—A comprehensive cancer center experience Diabetes Res. Clin. Pract. 2023 202 110776 10.1016/j.diabres.2023.110776 37311494
39. Liu Y. Liu H. Zhao S. Chen K. Jin P. Clinical and HLA genotype analysis of immune checkpoint inhibitor-associated diabetes mellitus: A single-center case series from China Front. Immunol. 2023 14 1164120 10.3389/fimmu.2023.1164120 37359544 PMC10288983
40. Elshafie O. Khalil A.B. Salman B. Atabani A. Al-sayegh H. Immune Checkpoint Inhibitors-Induced Endocrinopathies: Assessment, Management and Monitoring in a Comprehensive Cancer Centre Endocrinol. Diabetes Metab. 2024 7 e00505 10.1002/edm2.505 38932429 PMC11208163
41. Kotwal A. Haddox C. Block M. Kudva Y.C. Immune checkpoint inhibitors: An emerging cause of insulin-dependent diabetes BMJ Open Diabetes Res. Care 2019 7 e000591 10.1136/bmjdrc-2018-000591 PMC6398813 30899528
42. Tsang V.H.M. Mcgrath R.T. Clifton-Bligh R.J. A Scolyer R. Jakrot V. Guminski A.D. Long G.V. Menzies A.M. Checkpoint Inhibitor–Associated Autoimmune Diabetes Is Distinct from Type 1 Diabetes J. Clin. Endocrinol. Metab. 2024 104 5499 5506 10.1210/jc.2019-00423 31265074