Adverse Events of Immune Checkpoint Inhibitors in Cancer Patients with Comorbid Diabetes: A Real-World Pharmacovigilance Analysis of the FDA Adverse Event Reporting System Database (2011

Abstract

Introduction

Immune checkpoint inhibitors (ICIs) have redefined cancer therapeutics. However, they may provoke immune-related adverse events (irAEs), with diabetes potentially altering their patterns. We aimed to investigate whether diabetic cancer patients exhibit a distinctive or intensified irAE pattern.

Methods

We performed a real-world, retrospective pharmacovigilance study of ICIs using the FDA Adverse Event Reporting System from 2011 to 2025. Reports listing anti-PD-1 (Nivolumab, Pembrolizumab, Cemiplimab), anti-PD-L1 (Atezolizumab, Avelumab, Durvalumab), and anti-CTLA-4 (Ipilimumab, Tremelimumab) agents as suspected drugs were extracted. Disproportionality signals were identified with 4 algorithms: Bayesian Confidence Propagation Neural Network, Reporting Odds Ratio, Proportional Reporting Ratio, and Multi-item Gamma Poisson Shrinker. Time-to-onset was calculated from therapy start to event date, modelled with Weibull distributions, and compared across subgroups with non-parametric tests.

Results

Of 22,775,812 FAERS reports, 1886 involved ICIs used in cancer patients with comorbid diabetes. 423 (22.4 %) were fatal and 1463 (77.6 %) non-fatal. Men predominated (71.5 %), and 63.0 % of patients were aged 65-85 years. Combination therapy (anti-CTLA-4 plus PD-1 or PD-L1) accounted for the highest death proportion (29.6 %). Disproportionality analysis revealed the strongest preferred-term signals for pneumonitis/interstitial lung disease, hypothyroidism, and colitis among all diabetic cancer patients receiving ICI therapy. At the system-organ-class level, endocrine, hepatobiliary, and blood/lymphatic disorders showed the most consistent risk across agents. Weibull modelling demonstrated an early-failure pattern (shape β < 1) with a median time-to-onset of 126.6 days overall, shortening to 90.9 days with combination therapy. Fatal subgroup occurred sooner than non-fatal subgroup (median 106.7 vs 132.5 days; P = 0.004).

Conclusion

Diabetic cancer patients experienced the full spectrum of ICI-associated toxicities, with combination treatments linked to greater lethality. Multidisciplinary surveillance during the first 3-4 months of therapy, glycemic control, and long-term follow-up may be essential to optimize benefit and minimize harm in this expanding population.

Plain language summary

Why was the study done?

Immune checkpoint inhibitors (ICIs) are effective cancer treatments but can trigger serious immune-related side effects. Diabetes is very common among cancer patients and may increase vulnerability to these side effects, but little is known about how ICIs affect this group. We wanted to understand whether patients with both cancer and diabetes experience different or more severe side effects when treated with ICIs.

What did the researchers do?

We analyzed more than 22 million reports of drug side effects in the U.S. Food and Drug Administration’s database between 2011 and 2025. From these, we identified nearly 1900 reports involving cancer patients with diabetes who were treated with ICIs. We studied the types of side effects, how quickly they occurred, and how they differed between patients receiving single drugs vs combination treatments.

What did the researchers find?

The most common serious side effects in diabetic patients included lung inflammation (pneumonitis), thyroid problems, and bowel inflammation (colitis). Patients receiving 2 ICIs in combination developed side effects earlier and were more likely to die from them than those receiving single drugs. More than half of patients needed hospitalization, and about 1 in 5 cases reported in the database were fatal. Overall, diabetic patients experienced the same range of side effects as other patients, but serious events occurred sooner.

What do the findings mean?

These results suggest that cancer patients with diabetes need especially close monitoring when treated with ICIs, particularly in the first 3-4 months of therapy and when receiving combination immunotherapy. Regular checks of blood sugar, thyroid and liver function, and attention to lung and digestive symptoms are essential. Involving diabetes specialists early may help prevent complications. As more people worldwide are living with both cancer and diabetes, our findings highlight the importance of tailored care strategies to ensure patients benefit from immunotherapy while staying safe.

Keywords

immune checkpoint inhibitors immune-related adverse events food and drug administration adverse event reporting system diabetes disproportionality analysis

Introduction

Immune checkpoint inhibitors (ICIs), including antibodies targeting cytotoxic T-lymphocyte antigen-4 (CTLA-4) and the programmed cell death-1 (PD-1)/PD-L1 axis, have revolutionized oncology by producing durable remissions in many malignancies.^1-3 These therapies have rapidly expanded treatment options and reshaped standards of care across cancer types, bringing new hope to patients who previously had limited options.^2,4 Unlike conventional chemotherapy toxicities, irAEs mimic autoimmune disorders and can affect virtually any organ system, ranging from mild dermatologic and endocrine disturbances to severe pneumonitis, colitis, hepatitis, or myocarditis.^5-7 Approximately one-quarter of patients experience some form of irAE, and about 10% suffer grade ≥3 severe irAEs during therapy.⁸ In extremely rare cases, it may also trigger fulminant autoimmune diabetes, a sudden-onset type 1 diabetes that can present as diabetic ketoacidosis.^9,10 Such observations underscore the complex immune dysregulation caused by ICIs.

In parallel with the rise of cancer immunotherapy, the global burden of diabetes has reached a concerning level. The International Diabetes Federation estimated that 536.6 million adults had diabetes in 2021, a number projected to climb to 783.2 million by 2045.¹¹ In the United States (US), 24.4% of cancer patients were diagnosed with type 2 diabetes and 25.8% with prediabetes.¹² This trend in the oncology population is partly attributable to shared risk factors like aging, obesity, and sedentary lifestyle, as well as the diabetogenic effects of certain cancer treatments. Diabetes itself adversely influences cancer outcomes, with diabetic patients often facing higher mortality and poorer treatment response, potentially due to comorbidities and immune dysfunction.^13-15 As the co-occurrence of these diseases becomes more common, there is a growing recognition that diabetes may not merely be an incidental comorbidity in oncology. Chronic hyperglycemia and insulin resistance drive systemic metabolic disturbances that can alter the tumor microenvironment and host immunity.¹⁶ This brings up the question of how diabetes might modulate not only cancer risk and outcomes, but also the patient’s immune response to therapies like ICIs.

Despite this plausibility, the clinical impact of diabetes on ICI safety remains inadequately understood. Patients with diabetes have largely been underrepresented or not specifically analyzed in major immunotherapy trials,¹⁷ and most evidence on irAE risk factors has focused on demographic or tumor-related features rather than metabolic health. Only recently have studies begun to explore this intersection. A single-center retrospective study in China suggested that baseline metabolic comorbidities may indeed heighten the risk of irAEs: in a cohort of 3489 ICI-treated patients, those with type 2 diabetes had a roughly 40% higher odds of developing irAEs compared to non-diabetics.¹⁸ That analysis was among the first to confirm an association between diabetes and increased irAEs, alongside similar trends for hypertension and hyperlipidemia. It also hinted at organ-specific patterns, noting that diabetes was linked particularly with higher irAE incidence in lung cancer patients and that metabolic comorbidities correlated with more frequent cardiovascular irAEs. These findings are insightful. But there is still a lack of large-scale, real-world analyses focusing on cancer patients with diabetes who receive ICIs. This knowledge gap leaves clinicians uncertain whether diabetic patients face a distinct safety profile on immunotherapy and how best to anticipate and manage potential toxicities in this growing population.

Here we presented a comprehensive pharmacovigilance investigation to bridge this gap. We analyzed the FDA Adverse Event Reporting System (FAERS) database to determine whether patients with diabetes exhibit any distinctive patterns or heightened frequencies of irAEs across all FDA-approved ICIs. The findings provide empiric evidence on ICI-related AEs in cancer patients with diabetes, thereby aiding personalized risk assessment and management strategies for immunotherapy in the context of metabolic comorbidity.

Methods

Data Sources

This retrospective pharmacovigilance study utilized data from the FAERS database, a voluntary reporting system that collects adverse event reports, medication errors, and product quality complaints for drugs and therapeutic biologics. FAERS is a cornerstone of post-marketing surveillance, capturing real-world data from diverse sources, including healthcare professionals, consumers, and manufacturers, which is particularly valuable for studying patient populations with comorbidities such as diabetes mellitus.^19,20

Data Processing Procedure

From the FAERS database, we extracted data on cancer patients with diabetes treated with ICIs from the first quarter of 2011 (when the first ICI was approved) through the first quarter of 2025. The FAERS database does not differentiate between Type 1 and Type 2 diabetes. Therefore, our analysis encompasses all diabetes cases without subtype stratification. ICIs of interest included the anti-PD-1 agents (Nivolumab, Pembrolizumab, Cemiplimab), anti-PD-L1 agents (Atezolizumab, Avelumab, Durvalumab), and anti-CTLA-4 agents (Ipilimumab, Tremelimumab) (Table S1). ICI combination therapy (anti-CTLA4 plus anti-PD1/PD-L1) was incorporated to investigate differences, reflecting the common utilization of ICI-based combination therapies in clinical practice. Only reports in which 1 of these ICIs was identified as a suspected drug were included, with a focus on those where the ICI was the primary suspect agent. We standardized drug names (generic and brand names) and merged data from relevant FAERS files (DEMO, DRUG, REAC, OUTC, RPSR, THER, and INDI) to construct the analysis dataset.

Data deduplication was carried out following FDA-recommended guidelines. These guidelines specify that if CASEIDs match, the most recent FDA_DT should be selected. When both CASEIDs and FDA_DT are identical, the higher PRIMARYID should be prioritized. Subsequently, AEs in each report were coded using the Medical Dictionary for Regulatory Activities (MedDRA) terminology. We mapped each reported event to its MedDRA Preferred Term (PT) and further categorized events by System Organ Class (SOC) as defined in MedDRA (version 27.1).²¹ The study design and data processing framework were comprehensively depicted in Figure 1.

Figure 1.

The Selection Process of ICI-Related Adverse Events Among Diabetic Cancer Patients in the FAERS Database

Signal Mining

Disproportionality analysis is the predominant analytical tool in pharmacovigilance studies for identifying potential connections between specific AEs and pharmacotherapeutic agents, with individual cases subsequently examined for clinical relevance. In this study, Bayesian Confidence Propagation Neural Network (BCPNN) was primarily utilized to investigate the likelihood of previously reported suspect drugs causing associated irAEs.^22,23 It was selected for its robust handling of sparse data and its ability to provide probabilistic estimates of signal strength. In the sensitivity analysis, we re-performed the analysis using Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Multi-item Gamma Poisson Shrinker (MGPS), primarily to compare results and detect inconsistencies.^22-25 Positive results from all 4 algorithms were required to classify the signal as a suspected AE signal. The detailed formulas and criteria for signal detection were outlined in Table S2.

Time-To-Onset (TTO) and Weibull Distribution Analysis

We examined the time-to-onset (TTO) of adverse events to characterize the latency of ICI-associated AEs. TTO was defined as the interval from the initiation of ICI therapy to the date of onset of the AE. For each case report, we calculated TTO using the therapy start date (START_DT) and the event onset date (EVENT_DT) recorded in FAERS. Reports with implausible or non-positive intervals (onset date on or before the start date) or missing dates were excluded from TTO analyses to ensure data quality.

We analyzed the distribution of TTO for various AEs and across ICI classes by fitting a Weibull distribution to the onset time data.^26-28 The Weibull model’s shape parameter (β) was used to assess the temporal pattern of AE occurrence. We interpreted the β parameter as an indicator of how hazard of the event changes over time in the absence of a reference population. If the estimated β was significantly less than 1 (with the entire 95% CI for β below 1), the TTO pattern was classified as “early failure,” indicating the risk of that AE is highest soon after ICI initiation and decreases over time (suggesting an acute onset). If β was approximately 1 (95% CI including 1), it suggested a constant hazard or random occurrence of the AE over the treatment course (no strong time dependence). If β was greater than 1 (with the 95% CI for β entirely above 1), the pattern was “wear-out failure,” indicating an increasing hazard with longer ICI exposure (late-onset or cumulative risk). These classifications helped describe whether particular AEs tended to occur early after therapy start or only after prolonged treatment. For each AE category of interest, we reported the median TTO and interquartile range (IQR), and we used cumulative incidence curves to visualize the fraction of events over time on treatment, stratified by gender, severity of outcomes, and therapeutic strategies.

Statistical Analysis

Descriptive analysis was employed to characterize the clinical features of cancer patients with diabetes treated with ICIs. Continuous data such as TTO were not normally distributed. Therefore, we used non-parametric tests to compare TTO between different groups. In particular, the Mann-Whitney U test (two-tailed Wilcoxon rank-sum test) was applied for comparisons of 2 independent groups, and the Kruskal–Wallis test was used for comparisons across more than 2 groups (different classes of ICIs).^19,21 For categorical variables, results were summarized as counts and percentages, and for continuous variables as medians with IQRs.

All statistical analyses were performed using R (version 4.3.1; R Foundation, Vienna, Austria), and data visualization was conducted using the ggplot2 R package. All tests were two-sided, and a P-value of less than 0.05 was considered statistically significant.

Ethics Statement

Ethical approval and informed consent were not required for this study, as the FAERS database is publicly accessible and patient records are anonymized.

Result

Descriptive Analysis

From Q1 2011 to Q1 2025, the FAERS database recorded 22,775,812 drug-related reports. After data cleaning, 1886 reports were identified as related to ICIs in patients with cancer and comorbid diabetes (Figure 1). Among these, 423 cases were classified as fatal (22.4%), and 1463 as non-fatal (77.6%) (Table 1). Males predominated across both non-fatal and fatal groups (69.7% vs 77.8%), with similar male predominance observed across treatment strategies, particularly in anti-CTLA-4 users (76.3%). Female representation was lower overall (25.6%), with the proportion of missing sex data limited (3.0%). Age distribution indicated that 63.0% of patients were aged 65-85 years, with this age group being more prominent in fatal cases (68.3%). Only 1.3% of patients were younger than 18 years. Clinical outcomes demonstrated that hospitalization was the most frequent outcome (53.7%), whereas life-threatening events (7.1%) and disabilities (0.6%) were infrequent. Combination therapy was associated with the highest proportion of death (29.6%), compared to monotherapy with anti-CTLA-4 (13.5%), anti-PD-L1 (21.1%), or anti-PD-1 (21.4%). Reporters were primarily physicians (66.0%), with variation across agents. Physician-reported events were highest for anti-PD-L1 (92.3%) and lowest for anti-CTLA-4 (35.1%). Geographically, Japan (27.8%) and the US (16.9%) were the leading sources of reports, followed by France (11.4%) and Germany (5.2%). Temporal trends showed that over 95% of reports were submitted after 2015, with 49.7% between 2016-2020 and 45.9% from 2021 to early 2025. Anti-CTLA-4 reports were concentrated before 2021 (75.6%), suggesting evolving usage patterns. Lung was the most common indication organ (36.0%), especially in fatal events (39.5%) (Figure 2, Table S3). Melanoma (12.9%) and kidney cancer (10.5%) were also frequent. Interestingly, anti-CTLA-4 use was predominantly reported for melanoma (81.1%), whereas anti-PD-L1 was disproportionately used in liver (15.0%) and bladder (10.4%). Combination therapy was more common in pancreatic (10.9%) and kidney (18.4%), suggesting possible off-label or advanced-stage usage.

Table 1.

Clinical Characteristics of Diabetic Cancer Patients Treated With Immune Checkpoint Inhibitors in the FAERS Database

Clinical characteristics	Non-fatal (N = 1463)	Fatal (N = 423)	Overall (N = 1886)	Anti-PD-1 (N = 843)	Anti-PD-L1 (N = 702)	Anti-CTLA4 (N = 37)	Combination therapy (N = 304)
Gender
Male	1019 (69.7%)	329 (77.8%)	1348 (71.5%)	589 (69.9%)	520 (74.1%)	29 (76.3%)	211 (69.4%)
Female	396 (27.1%)	86 (20.3%)	482 (25.6%)	219 (26.0%)	171 (24.4%)	9 (24.3%)	83 (27.3%)
Missing	48 (3.3%)	8 (1.9%)	56 (3.0%)	35 (4.2%)	11 (1.6%)		10 (3.3%)
Age group
<18	23 (1.6%)	1 (0.2%)	24 (1.3%)	16 (1.9%)	5 (0.7%)	NA	3 (1.0%)
18∼64.9	413 (28.2%)	109 (25.8%)	522 (27.7%)	210 (24.9%)	202 (28.8%)	14 (37.8%)	96 (31.6%)
65∼85	899 (61.4%)	289 (68.3%)	1188 (63.0%)	517 (61.3%)	469 (66.8%)	22 (59.5%)	180 (59.2%)
>85	22 (1.5%)	5 (1.2%)	27 (1.4%)	16 (1.9%)	6 (0.9%)	NA	5 (1.6%)
Missing	106 (7.2%)	19 (4.5%)	125 (6.6%)	84 (10.0%)	20 (2.8%)	1 (2.7%)	20 (6.6%)
Outcome
Death	0 (0%)	423 (100%)	423 (22.4%)	180 (21.4%)	148 (21.1%)	5 (13.5%)	90 (29.6%)
Disability	12 (0.8%)	0 (0%)	12 (0.6%)	7 (0.8%)	2 (0.3%)	1 (2.7%)	2 (0.7%)
Hospitalization	1013 (69.2%)	0 (0%)	1013 (53.7%)	425 (50.4%)	415 (59.1%)	15 (40.5%)	158 (52.0%)
Life-threatening	134 (9.2%)	0 (0%)	134 (7.1%)	58 (6.9%)	48 (6.8%)	2 (5.4%)	26 (8.6%)
Other	304 (20.8%)	0 (0%)	304 (16.1%)	173 (20.5%)	89 (12.7%)	14 (37.8%)	28 (9.2%)
Reporter type
Consumer	143 (9.8%)	53 (12.5%)	196 (10.4%)	162 (19.2%)	9 (1.3%)	12 (32.4%)	13 (4.3%)
Health professional	158 (10.8%)	30 (7.1%)	188 (10.0%)	135 (16.0%)	9 (1.3%)	1 (2.7%)	43 (14.1%)
Pharmacist	61 (4.2%)	26 (6.1%)	87 (4.6%)	63 (7.5%)	14 (2.0%)	2 (5.4%)	8 (2.6%)
Physician	971 (66.4%)	273 (64.5%)	1244 (66.0%)	375 (44.5%)	648 (92.3%)	13 (35.1%)	208 (68.4%)
Missing	130 (8.9%)	41 (9.7%)	171 (9.1%)	108 (12.8%)	22 (3.1%)	9 (24.3%)	32 (10.5%)
Reporter country
Top 5	Japan 371 (25.4%)	Japan 153 (36.2%)	Japan 524 (27.8%)	Japan 293 (34.8%)	Japan 149 (21.2%)	United States 12 (32.4%)	Japan 79 (26.0%)
	United States 264 (18.0%)	United States 54 (12.8%)	United States 318 (16.9%)	United States 157 (18.6%)	United States 106 (15.1%)	United Kingdom 6 (16.2%)	United States 43 (14.1%)
	France 174 (11.9%)	France 41 (9.7%)	France 215 (11.4%)	France 125 (14.8%)	Spain 63 (9.0%)	Germany 5 (13.5%)	Canada 42 (13.8%)
	Germany 82 (5.6%)	Canada 19 (4.5%)	Germany 99 (5.2%)	Germany 37 (4.4%)	France 59 (8.4%)	Japan 3 (8.1%)	France 29 (9.5%)
	Spain 72 (4.9%)	Spain、Germany 17 (4.0%)	Spain 89 (4.7%)	Italy 28 (3.3%)	Korea, south 45 (6.4%)	Canada、France、India 2 (5.4%)	Germany 21 (6.9%)
Other country	501 (34.2%)	122 (28.8%)	624(33.1%)	203(24.1%)	280 (39.9%)	6(15.8%)	90(29.1%)
Reporting year
2011-2015	68 (4.6%)	15 (3.5%)	83 (4.4%)	56 (6.7%)	0 (0%)	17 (45.9%)	5 (1.6%)
2016-2020	701 (47.9%)	236 (55.8%)	937 (49.7%)	419 (50.2%)	349 (49.7%)	11 (29.7%)	156 (51.3%)
2021-2025Q1	694 (47.4%)	172 (40.7%)	866 (45.9%)	368 (43.7%)	353 (50.3%)	2 (5.4%)	143 (47.0%)

Figure 2.

The Pie Charts Illustrate the Composition of Primary Cancer Sites in Patients Undergoing Immune Checkpoint Inhibitor Therapy. (A). The Overall Group of Diabetic Cancer Patients Receiving Immune Checkpoint Inhibitors. (B). Subgroup of Diabetic Cancer Patients With Non-fatal Outcomes. (C). Subgroup of Diabetic Cancer Patients With Fatal Outcomes. (D). Subgroup of Diabetic Cancer Patients Receiving anti-PD-1 Monotherapy. (E). Subgroup of Diabetic Cancer Patients Receiving anti-PD-L1 Monotherapy. (F). Subgroup of Diabetic Cancer Patients Receiving anti-CTLA-4 Monotherapy. (G). Subgroup of Diabetic Cancer Patients Receiving Combination Therapy (anti-PD-1/anti-PD-L1 Plus anti-CTLA-4)

Disproportionality Analysis

At the preferred term (PT) level

Based on disproportionality analysis of the FAERS database, the top 20 AEs with the highest signal detection percentages were identified among diabetic cancer patients treated with ICIs (Table 2, Figure 3).

Table 2.

The 20 Adverse Events With the Highest Signal Detection Percentages Among Diabetic Cancer Patients Receiving Immune Checkpoint Inhibitors

Target drug	PT	N^**	IC (IC025)^***	ROR (95%CI)	PRR (χ2)	EBGM (EBGM05)
Overall	Pneumonitis^*	45	5.49 (4.06)	52.36 (38.17 - 71.81)	51.96 (1936.07)	44.86 (32.71)
	Interstitial lung disease^*	89	4.44 (3.82)	23.48 (18.90 - 29.15)	23.13 (1759.21)	21.64 (17.43)
	Colitis^*	44	4.33 (3.37)	21.55 (15.87 - 29.28)	21.4 (802.47)	20.12 (14.82)
	Hypothyroidism^*	51	4.14 (3.32)	18.74 (14.11 - 24.88)	18.58 (802.41)	17.62 (13.27)
	Febrile neutropenia^*	41	4.08 (3.15)	17.86 (13.02 - 24.48)	17.74 (613.88)	16.86 (12.30)
	Hepatic function^*	39	3.83 (2.95)	14.91 (10.80 - 20.57)	14.82 (480.49)	14.21 (10.29)
	Pyrexia^*	89	2.35 (1.98)	5.24 (4.24 - 6.47)	5.17 (295.55)	5.10 (4.13)
	Anemia^*	58	1.90 (1.46)	3.80 (2.93 - 4.93)	3.77 (117.08)	3.74 (2.88)
	Pneumonia^*	78	1.71 (1.34)	3.33 (2.66 - 4.16)	3.30 (124.00)	3.27 (2.61)
	Sepsis^*	44	1.82 (1.31)	3.59 (2.66 - 4.83)	3.57 (80.62)	3.54 (2.63)
	Dyspnea	67	0.86 (0.49)	1.83 (1.43 - 2.33)	1.82 (24.63)	1.81 (1.42)
	Diarrhea	108	0.69 (0.40)	1.63 (1.35 - 1.97)	1.62 (25.77)	1.62 (1.34)
	Hypertension	38	0.61 (0.12)	1.53 (1.11 - 2.11)	1.53 (6.92)	1.53 (1.11)
	Fatigue	57	0.43 (0.04)	1.35 (1.04 - 1.76)	1.35 (5.21)	1.35 (1.04)
	Asthenia	46	0.42 (−0.01)	1.35 (1.01 - 1.80)	1.34 (4.03)	1.34 (1.00)
	Decreased appetite	44	0.34 (−0.10)	1.27 (0.94 - 1.71)	1.27 (2.48)	1.27 (0.94)
	Death	38	0.24 (−0.23)	1.19 (0.86 - 1.63)	1.19 (1.11)	1.18 (0.86)
	Acute kidney injury	59	0.04 (−0.33)	1.03 (0.80 - 1.33)	1.03 (0.06)	1.03 (0.80)
	Vomiting	47	−0.28 (−0.69)	0.82 (0.62 - 1.09)	0.82 (1.81)	0.82 (0.62)
	Nausea	43	−1.04 (−1.46)	0.48 (0.36 - 0.65)	0.49 (23.59)	0.49 (0.36)
Anti-PD-1	Interstitial lung disease^*	53	4.71 (3.75)	27.61 (20.94 - 36.41)	27.14 (1286.41)	26.18 (19.85)
	Hypothyroidism^*	29	4.36 (3.10)	21.35 (14.73 - 30.94)	21.15 (540.95)	20.57 (14.19)
	Pemphigoid^*	28	3.74 (2.68)	13.71 (9.42 - 19.96)	13.59 (320.70)	13.35 (9.17)
	Pyrexia^*	33	1.88 (1.27)	3.73 (2.64 - 5.26)	3.70 (64.74)	3.68 (2.61)
	Pneumonia	33	1.44 (0.87)	2.73 (1.94 - 3.85)	2.71 (35.72)	2.71 (1.92)
	Anemia	23	1.54 (0.83)	2.92 (1.94 - 4.41)	2.91 (28.76)	2.90 (1.92)
	Dyspnea	40	1.03 (0.54)	2.05 (1.50 - 2.81)	2.04 (21.30)	2.04 (1.49)
	Rash	23	1.02 (0.36)	2.03 (1.35 - 3.07)	2.02 (11.94)	2.02 (1.34)
	Diarrhea	51	0.59 (0.17)	1.51 (1.15 - 20.00)	1.50 (8.72)	1.50 (1.14)
	Arthralgia	19	0.86 (0.16)	1.82 (1.16 - 2.87)	1.82 (7.02)	1.82 (1.16)
	Pruritus	20	0.68 (0.01)	1.61 (1.04 - 2.50)	1.61 (4.61)	1.61 (1.03)
	Fatigue	30	0.47 (−0.07)	1.39 (0.97 – 2.00)	1.39 (3.31)	1.39 (0.97)
	Asthenia	25	0.48 (−0.11)	1.40 (0.94 - 2.07)	1.39 (2.77)	1.39 (0.94)
	Death	20	0.32 (−0.33)	1.25 (0.80 - 1.94)	1.25 (0.97)	1.24 (0.80)
	Malaise	24	0.12 (−0.47)	1.08 (0.73 - 1.62)	1.08 (0.16)	1.08 (0.73)
	Decreased appetite	18	−0.14 (−0.80)	0.91 (0.57 - 1.44)	0.91 (0.16)	0.91 (0.57)
	Vomiting	24	−0.32 (−0.89)	0.80 (0.54 - 1.20)	0.80 (1.18)	0.80 (0.54)
	Acute kidney injury	19	−0.55 (−1.18)	0.68 (0.43 - 1.07)	0.68 (2.83)	0.68 (0.43)
	Weight decreased	20	−0.58 (−1.19)	0.66 (0.43 - 1.03)	0.67 (3.35)	0.67 (0.43)
	Nausea	23	−1.08 (−1.64)	0.47 (0.31 - 0.70)	0.47 (13.88)	0.47 (0.31)
Anti-PD-L1	Radiation pneumonitis^*	20	9.57 (3.55)	2054.96 (1003.01 - 4210.18)	2031.40 (15 220.7)	762.40 (372.12)
	Pneumonitis^*	20	6.13 (3.38)	74.94 (47.60 - 117.99)	74.09 (1359.72)	69.90 (44.40)
	Febrile neutropenia^*	20	4.90 (3.01)	30.85 (19.75 - 48.20)	30.51 (557.11)	29.79 (19.07)
	Interstitial lung disease^*	24	4.33 (2.93)	20.74 (13.82 - 31.14)	20.47 (437.42)	20.15 (13.42)
	Proteinuria^*	17	5.05 (2.88)	34.41 (21.20 - 55.84)	34.08 (531.16)	33.18 (20.44)
	Hepatic function abnormal^*	21	4.47 (2.87)	22.89 (14.83 - 35.34)	22.63 (426.49)	22.24 (14.40)
	Platelet count decreased^*	15	3.38 (1.98)	10.57 (6.34 - 17.60)	10.48 (127.69)	10.40 (6.24)
	Pyrexia^*	32	2.60 (1.89)	6.19 (4.36 - 8.79)	6.09 (135.94)	6.07 (4.27)
	Pneumonia^*	36	2.33 (1.70)	5.12 (3.68 - 7.13)	5.03 (116.40)	5.02 (3.60)
	Anemia^*	25	2.42 (1.63)	5.44 (3.66 - 8.08)	5.38 (88.92)	5.36 (3.61)
	Sepsis^*	19	2.43 (1.49)	5.45 (3.46 - 8.57)	5.40 (67.99)	5.38 (3.42)
	Hypertension	19	1.33 (0.57)	2.53 (1.61 - 3.98)	2.51 (17.37)	2.51 (1.60)
	Acute kidney injury	27	0.72 (0.14)	1.66 (1.14 - 2.43)	1.65 (6.97)	1.65 (1.13)
	Dyspnea	20	0.79 (0.11)	1.74 (1.12 - 2.71)	1.73 (6.22)	1.73 (1.11)
	Diarrhea	26	0.38 (−0.20)	1.31 (0.89 - 1.93)	1.30 (1.85)	1.30 (0.88)
	Death	14	0.57 (−0.23)	1.48 (0.88 - 2.51)	1.48 (2.20)	1.48 (0.87)
	Asthenia	14	0.40 (−0.38)	1.33 (0.78 - 2.25)	1.32 (1.12)	1.32 (0.78)
	Decreased appetite	15	0.36 (−0.39)	1.29 (0.78 - 2.15)	1.29 (0.97)	1.29 (0.77)
	Fatigue	15	0.24 (−0.50)	1.18 (0.71 - 1.97)	1.18 (0.42)	1.18 (0.71)
	Nausea	13	−1.14 (−1.86)	0.45 (0.26 - 0.77)	0.45 (8.75)	0.45 (0.26)
Anti-CTLA4	Colitis^*	9	7.28 (2.29)	166.74 (84.71 - 328.23)	157.12 (1378.81)	155.12 (78.80)
	Pyrexia^*	5	3.43 (0.83)	11.14 (4.57 - 27.17)	10.82 (44.63)	10.81 (4.43)
	Hypophysitis^*	3	10.90 (0.40)	2319.85 (669.09 - 8043.37)	2274.97 (5742.29)	1915.92 (552.59)
	Dehydration^*	4	2.78 (0.34)	7.02 (2.60 - 18.95)	6.86 (20.11)	6.86 (2.54)
	Diarrhea	6	1.76 (0.22)	3.48 (1.54 - 7.87)	3.38 (10.20)	3.38 (1.50)
	Headache	4	2.05 (0.03)	4.24 (1.57 - 11.43)	4.15 (9.63)	4.15 (1.54)
	Fatigue	4	1.84 (−0.08)	3.64 (1.35 - 9.82)	3.57 (7.45)	3.57 (1.32)
	Prerenal failure	2	7.41 (−0.13)	174.34 (42.79 - 710.30)	172.10 (335.48)	169.71 (41.65)
	Adrenal insufficiency	2	6.78 (−0.13)	112.24 (27.64 - 455.72)	110.81 (215.68)	109.81 (27.05)
	Hypokalemia	2	4.06 (−0.26)	16.85 (4.17 - 68.04)	16.64 (29.39)	16.62 (4.12)
	Neuropathy peripheral	2	2.73 (−0.48)	6.72 (1.67 - 27.14)	6.65 (9.61)	6.65 (1.65)
	Rash	2	1.78 (−0.76)	3.47 (0.86 – 14.00)	3.44 (3.47)	3.44 (0.85)
	Pruritus	2	1.64 (−0.82)	3.14 (0.78 - 12.66)	3.11 (2.87)	3.11 (0.77)
	Asthenia	2	1.14 (−1.04)	2.21 (0.55 - 8.93)	2.20 (1.31)	2.20 (0.54)
	Dysuria	1	3.13 (−1.21)	8.80 (1.23 - 62.95)	8.75 (6.87)	8.75 (1.22)
	Malaise	2	0.78 (−1.22)	1.72 (0.43 - 6.96)	1.72 (0.60)	1.71 (0.42)
	Acute kidney injury	2	0.40 (−1.43)	1.32 (0.33 - 5.33)	1.32 (0.15)	1.32 (0.33)
	Anemia	1	1.28 (−1.55)	2.44 (0.34 - 17.43)	2.43 (0.84)	2.43 (0.34)
	Nausea	2	−0.23 (−1.84)	0.85 (0.21 - 3.44)	0.85 (0.05)	0.85 (0.21)
	Dyspnea	1	0.03 (−2.04)	1.02 (0.14 - 7.28)	1.02 (0.00)	1.02 (0.14)
Combination therapy	Immune-mediated enterocolitis^*	18	10.08 (3.40)	2206.83 (1144.74 - 4254.33)	2166.36 (19 479.54)	1083.68 (562.13)
	Myocarditis^*	9	7.24 (2.29)	163.97 (83.05 - 323.76)	162.48 (1343.66)	151.21 (76.58)
	Hypophysitis^*	9	10.00 (2.19)	1967.76 (797.84 - 4853.25)	1949.72 (9226.13)	1026.64 (416.26)
	Pneumonitis^*	9	5.80 (2.18)	57.87 (29.76 - 112.51)	57.34 (485.49)	55.89 (28.75)
	Interstitial lung disease^*	12	4.16 (2.15)	18.25 (10.30 - 32.33)	18.04 (191.69)	17.90 (10.11)
	Febrile neutropenia^*	10	4.73 (2.12)	27.04 (14.45 - 50.61)	26.78 (245.22)	26.46 (14.14)
	Hypothyroidism^*	10	4.42 (2.03)	21.83 (11.67 - 40.83)	21.62 (194.82)	21.42 (11.45)
	Colitis^*	9	4.65 (1.96)	25.68 (13.27 - 49.69)	25.45 (209.05)	25.17 (13.01)
	Pyrexia^*	19	2.68 (1.68)	6.52 (4.14 - 10.28)	6.42 (86.88)	6.40 (4.06)
	Sepsis^*	14	2.82 (1.58)	7.15 (4.22 - 12.14)	7.07 (72.82)	7.05 (4.15)
	Hyponatremia^*	8	3.04 (1.22)	8.34 (4.15 - 16.75)	8.28 (51.06)	8.25 (4.11)
	Dehydration^*	16	2.13 (1.16)	4.44 (2.71 - 7.28)	4.38 (41.88)	4.38 (2.67)
	Anemia	9	1.78 (0.54)	3.45 (1.79 - 6.66)	3.43 (15.52)	3.43 (1.78)
	Diarrhea	25	1.15 (0.51)	2.26 (1.52 - 3.36)	2.23 (17.08)	2.23 (1.50)
	Abdominal pain	10	1.34 (0.27)	2.54 (1.36 - 4.74)	2.53 (9.24)	2.52 (1.35)
	Pneumonia	9	1.16 (0.07)	2.24 (1.16 - 4.32)	2.23 (6.13)	2.23 (1.16)
	Vomiting	15	0.60 (−0.18)	1.52 (0.91 - 2.54)	1.51 (2.64)	1.51 (0.91)
	Decreased appetite	10	0.61 (−0.33)	1.53 (0.82 - 2.86)	1.53 (1.82)	1.53 (0.82)
	Acute kidney injury	11	0.26 (−0.61)	1.20 (0.66 - 2.17)	1.19 (0.35)	1.19 (0.66)
	Fatigue	8	0.16 (−0.82)	1.12 (0.56 - 2.25)	1.12 (0.10)	1.12 (0.56)

PT, Preferred terminology; IC, information component; IC025, the lower limit of 95%CI of the IC; ROR, reporting odds ratio; CI, confidence interval; PRR, proportional reporting ratio; χ², chi-squared; EBGM, empirical Bayesian geometric mean; EBGM05, the lower 95% one-sided CI of EBGM. *: A positive signal identified by all 4 algorithms. **: The number of reports of specific adverse events for specific drugs in the FAERS database. ***: The PTs are sorted in descending order based on their IC values.

Figure 3.

Top 20 Preferred Terms (PTs) of Adverse Events Associated With ICI and Their Corresponding System Organ Classes (SOCs). (A). The Overall Group of Diabetic Cancer Patients Receiving Immune Checkpoint Inhibitors. (B). Subgroup of Diabetic Cancer Patients Receiving anti-PD-1 Monotherapy. (C). Subgroup of Diabetic Cancer Patients Receiving anti-PD-L1 Monotherapy. (D). Subgroup of Diabetic Cancer Patients Receiving anti-CTLA-4 Monotherapy. (E). Subgroup of Diabetic Cancer Patients Receiving Combination Therapy (anti-PD-1/anti-PD-L1 Plus anti-CTLA-4). The Asterisk (*) Denotes a Positive Signal Identified by all Four Disproportionality Analysis Methods

Across the entire ICI-treated cohort, immune-related pneumonitis (IC025 = 4.06), interstitial lung disease (IC025 = 3.82), and colitis (IC025 = 3.37) showed the strongest signals, with RORs exceeding 20 (Table 2, Figure S1). Notably, hypothyroidism, febrile neutropenia, hepatic function abnormal, pyrexia, anemia, pneumonia, and sepsis also demonstrated significant signals. Common symptoms such as diarrhea, dyspnea, vomiting, and nausea showed no significant disproportionality. In the anti-PD-1 monotherapy subgroup, interstitial lung disease exhibited the highest disproportionality signal (IC025 = 3.75), indicating a strong association (Table 2, Figure S2). Hypothyroidism, pemphigoid, and pyrexia also presented significant signals. Conversely, nausea, vomiting, weight decreased, and acute kidney injury did not show significant disproportionality. In the anti-PD-L1 agent group, radiation pneumonitis exhibited the most pronounced disproportionality signal (IC025 = 3.55), followed by pneumonitis (IC025 = 3.38) and febrile neutropenia (IC025 = 3.01) (Table 2, Figure S3). Interstitial lung disease, proteinuria, hepatic function abnormal, platelet count decreased, pyrexia, pneumonia, anemia, and sepsis also showed significant signals. In contrast, nausea showed a negative signal, suggesting no increased reporting. In the anti-CTLA-4 monotherapy cohort, colitis showed an exceptionally strong disproportionality signal (IC025 = 2.29), consistent with its known immune-mediated toxicity (Table 2, Figure S4). Pyrexia, hypophysitis, and dehydration also demonstrated a markedly elevated signal, although with fewer reports. Several rare but severe toxicities such as adrenal insufficiency and prerenal failure exhibited high RORs despite limited case numbers. In the combination therapy group, immune-mediated enterocolitis exhibited the most pronounced disproportionality signal (IC025 = 3.40) (Table 2, Figure S5). Myocarditis and hypophysitis also demonstrated markedly elevated signals. Other significant adverse events included pneumonitis, interstitial lung disease, febrile neutropenia, hypothyroidism, colitis, pyrexia, sepsis, hyponatremia, and dehydration, all with IC025 values above 1.0, indicating prominent immune-related toxicities. Common symptoms such as diarrhea and vomiting showed lower or non-significant disproportionality.

As depicted in Figure 4, among all diabetic cancer patients receiving ICI therapy, 185 PTs (14.1%) exhibited positivity in all 4 algorithms (Figure 4A). Subgroup analyses showed varying degrees of convergence: anti-PD-1 monotherapy (Figure 4B) yielded 103 PTs (10.8%) with consistent signals; anti-PD-L1 monotherapy (Figure 4C) reported 81 PTs (13.3%); anti-CTLA-4 monotherapy (Figure 4D) had only 6 PTs (5.6%), indicating limited overlap in this subgroup. In contrast, the combination therapy group (Figure 4E) demonstrated 54 PTs (13.8%) detected by all 4 algorithms.

Figure 4.

The Venn Diagrams Depict the Overlap of Significant PTs Detected by all Four algorithms. (A). The Overall Group of Diabetic Cancer Patients Receiving Immune Checkpoint Inhibitors. (B). Subgroup of Diabetic Cancer Patients Receiving anti-PD-1 Monotherapy. (C). Subgroup of Diabetic Cancer Patients Receiving anti-PD-L1 Monotherapy. (D). Subgroup of Diabetic Cancer Patients Receiving anti-CTLA-4 Monotherapy. E. Subgroup of Diabetic Cancer Patients Receiving Combination Therapy (anti-PD-1/anti-PD-L1 Plus anti-CTLA-4)

At the system organ class (SOC) level

At the SOC level, gastrointestinal disorders and general disorders and administration site conditions were the most frequently reported SOCs across all treatment groups. Overall, gastrointestinal disorders accounted for 616 cases (10.54%), followed by general disorders and administration site conditions (521 cases; 8.91%), and metabolism and nutrition disorders (486 cases; 8.31%) (Figure S6). Anti-PD-1 therapy reported high incidences in general disorders (284 cases; 9.59%) and gastrointestinal disorders (281 cases; 9.49%) (Figure S7). Anti-PD-L1 therapy was notable for infections and infestations (182 cases; 10.44%) (Figure S8). Anti-CTLA-4 therapy exhibited fewer events, primarily gastrointestinal disorders (26 cases; 16.15%) and general disorders and administration site conditions (21 cases; 13.04%) (Figure S9). Combination therapy highlighted gastrointestinal disorders (137 cases; 13.97%) and metabolism and nutrition disorders (113 cases; 11.52%) (Figure S10). This distribution reflected the varied adverse event profile across different immune checkpoint inhibitor treatments among diabetic cancer patients.

Furthermore, the strength of association varied significantly across SOCs when evaluated using disproportionality metrics. Across all treatment groups, endocrine disorders consistently exhibited the strongest safety signals (IC025 ranging from 2.08 to 4.05), with the highest observed in the combination therapy group (IC025 = 4.05) (Table S4). Hepatobiliary disorders and blood and lymphatic system disorders also demonstrated robust signals in the overall, anti-PD-1, anti-PD-L1, and combination therapy groups (IC025 > 1.4), suggesting class-wide organ-specific irAEs (Tables S4-8). Respiratory disorders showed notable associations in the overall and both monotherapy subgroups (anti-PD-1 and anti-PD-L1), though the signal was less pronounced (Tables S4-6). Notably, neoplasms benign, malignant and unspecified (incl cysts and polyps) emerged as a distinct signal in the anti-PD-1 groups (IC025 = 1.32) (Table S5). The anti-CTLA-4 group showed a limited number of -marked SOCs, with endocrine disorders presenting the only strong signal (IC025 = 2.08) (Table S7).

Time-To-Onset and Weibull Distribution Analysis

Weibull distribution modelling was employed to characterize the temporal profile of ICI-related AEs (Table 3). The overall median TTO was 126.6 days (IQR 20.0 - 152.8), with a scale parameter (α) of 105.23 days (95% CI 97.57 - 112.90) and a shape parameter (β) of 0.76 (95% CI 0.73 - 0.79), indicating an early failure pattern (Table 3, Figure 5A). Subgroup analysis revealed a significantly shorter time to onset in fatal subgroup (median 106.7 days, IQR 18.0 - 108.0) compared with non-fatal subgroup (median 132.5 days, IQR 21.0 - 158.0; P = 0.004) (Table 3, Figure 5B). Correspondingly, fatal cases had a smaller α (85.89 vs 111.39) and β (0.74 vs 0.77), underscoring a more rapid AE onset and accentuated early failure kinetics in fatal outcomes. When stratified by sex, female patients demonstrated a longer TTO (median 134.9 days, IQR 21.0 - 165.5) than males (median 123.6 days, IQR 19.0 - 145.0), although the difference did not reach statistical significance (P = 0.091) (Table 3, Figure 5C). The Weibull shape parameter in both sexes remained <1 (male: β = 0.75; female: β = 0.79), reinforcing the early failure tendency across genders. Regarding treatment strategies, the time to AE onset varied significantly (P = 0.006) (Table 3, Figure 5D). Anti-PD-L1 therapy exhibited the longest median onset (142.2 days, IQR 20.8 - 169.2), followed by anti-PD-1 (124.5 days), while anti-CTLA-4 monotherapy (86.5 days, IQR 20.8 - 63.0) and combination therapy (90.9 days, IQR 20.0 - 90.0) were associated with more rapid AE emergence. Notably, treatment groups demonstrated early failure dynamics (β < 1), with combination therapy yielding the highest β (0.82), potentially reflecting a steeper initial hazard.

Table 3.

Weibull Distribution Analysis for the Time to Onset of ICI-Associated Adverse Events

Group	Case reports	Scale parameter: α (95%Cl)	Shape parameter: β (95%CI)	Type
Overall	1418	105.23 (97.57 - 112.90)	0.76 (0.73 - 0.79)	Early failure
Non-fatal	1097	111.39 (102.27 -120.50)	0.77 (0.73 - 0.80)	Early failure
Fatal	321	85.89 (72.36 - 99.42)	0.74 (0.68 - 0.80)	Early failure
Gender
Male	1035	101.46 (92.70 - 110.22)	0.75 (0.71 - 0.78)	Early failure
Female	354	116.26 (100.00 - 132.51)	0.79 (0.73 - 0.85)	Early failure
Treatment strategy
Anti-PD-1	558	105.86 (93.80 - 117.91)	0.77 (0.72 - 0.82)	Early failure
Anti-PD-L1	624	116.76 (103.74- 129.79)	0.74 (0.70-0.79)	Early failure
Anti-CTLA4	25	56.66 (20.95 - 92.37)	0.68 (0.50 - 0.85)	Early failure
Combination therapy	212	79.14 (65.23 - 93.06)	0.82 (0.73 - 0.89)	Early failure

IQR, interquartile range; CI, confidence interval.

Figure 5.

The Cumulative Distribution Curves Reveal the Onset Times of ICI - Related AEs in Diabetic Cancer Patients Following ICI Therapy. (A). The Overall Group of Diabetic Cancer Patients Receiving Immune Checkpoint Inhibitors. (B). Subgroups Divided by Outcomes (Non-fatal and Fatal). (C). Subgroups Divided by Gender (Male and Female). (D). Subgroups Divided by ICI Treatment Strategies. Statistical Analysis was Performed Using the Nonparametric Tests (the Mann-Whitney Test and the Kruskal-Wallis Test)

Discussion

In this real-world pharmacovigilance analysis of the FAERS database, we identified 1886 AE reports in cancer patients with comorbid diabetes who were treated with ICIs. The cohort was predominantly older and male. Over one-fifth of reported cases were fatal. Three observations were of paramount significance and should be highlighted. First, immune-related toxicities affecting the lungs (pneumonitis, interstitial lung disease), endocrine organs (hypothyroidism, hypophysitis), gastrointestinal tract (colitis, enterocolitis), and hematologic system (febrile neutropenia, anemia) emerged as the most disproportionally reported AEs. Second, combination ICI therapy was associated with the highest death proportion and showed a trend toward earlier onset of AEs. Monotherapy with anti-CTLA-4 (ipilimumab) was uniquely associated with high reporting of colitis and pituitary inflammation, whereas anti-PD-1 and anti-PD-L1 agents more frequently elicited pulmonary reactions (pneumonitis) and thyroid dysfunction. Third, Weibull modelling demonstrated an early-failure pattern across all subgroups, with the hazard of toxicity decreasing over time yet remaining non-negligible beyond 6-12 months. These findings indicated that cancer patients with diabetes may be susceptible to the full spectrum of serious ICI-induced irAEs, thus highlighting the potential need for closer monitoring of this vulnerable population.

Our results align with the general safety profile of ICIs observed in broader populations, while offering new insights specific to patients with diabetes comorbidity. It is well-recognized that the organ systems affected by ICIs vary by drug class: CTLA-4 blockade often causes colitis, hypophysitis and dermatologic toxicities, whereas PD-1/PD-L1 inhibitors more commonly lead to pneumonitis and thyroid dysfunction.^29-33 Combination therapy amplifies toxicity frequency, notably cutaneous and endocrine events.^34,35 These patterns were mirrored in our diabetic cohort. For example, ipilimumab monotherapy showed a striking disproportional signal for colitis (IC025 = 2.3; ROR = 166.7) and hypophysitis (IC025 = 0.4; ROR = 2319.9), consistent with its known predilection for gastrointestinal and pituitary irAEs. Likewise, anti-PD-1 agents in our study had strong signals for interstitial lung disease and hypothyroidism, reflecting the well-documented tendency of PD-1 blockade to precipitate pneumonitis and thyroiditis. Anti-PD-L1 agents showed prominent signals for pneumonitis (including radiation pneumonitis) and renal effects (proteinuria), again aligning with clinical trial data where PD-L1 inhibitors have been associated with pulmonary and renal irAEs.^36,37 Importantly, the high reporting frequency of endocrine disorders we observed, such as hypothyroidism being a primary signal across agents, corroborates the fact that endocrine irAEs are among the most common irAEs in ICI therapy.³⁸ Our finding that 185 PTs (14%) met signal criteria across all 4 disproportionality algorithms in the overall ICI group suggests a robust core of recurrent toxicities, similar to recent global pharmacovigilance data showing skin reactions, pneumonitis, colitis, and thyroiditis as the most frequently reported ICI toxicities. Indeed, Lebrun-Vignes and colleagues’ worldwide analysis of 50,000 + ICI irAE reports noted pneumonitis (18%), enterocolitis (14%), and thyroiditis (12%) among the leading events, which resonates with our observations in diabetic patients, albeit with some differences in rank order. We found gastrointestinal and general disorders slightly more frequent at the SOC level, possibly reflecting the older age and comorbidities of our cohort.

Despite these similarities, our investigation targeted the previously under-described subgroup of cancer patients with diabetes, revealing that they underwent a wide range of irAEs comparable to those in the typical ICI-treated population. Historically, patients with significant comorbidities (especially autoimmune diseases like type 1 diabetes) were excluded from immunotherapy trials due to concerns about heightened toxicity.³⁹ However, emerging evidence indicates that ICIs can be used cautiously in such patients. For instance, a recent multi-center study by Hilder et al. followed 11 patients with pre-existing type 1 diabetes on anti-PD-1/PD-L1 therapy and found no significant increase in overall irAE incidence or severity compared to matched controls without diabetes. In that series, the odds of experiencing any-grade or severe irAEs were statistically comparable between type 1 diabetics and non-diabetics (OR = 0.8 for any irAE; OR = 1.7 for grade ≥3, P > 0.5). Our analysis added to these findings, demonstrating that ICIs preserve their standard safety features in diabetic patients. No new toxicity categories were observed, but the frequency and impact of certain irAEs might be increased. In fact, a contemporary observational study by Li et al,¹⁸ involving over 3000 patients treated with ICIs, provided epidemiological evidence that type 2 diabetes and associated metabolic comorbidities significantly increase the risk of irAEs. In that study, the presence of type 2 diabetes was associated with a 40% higher odds of developing any immune-related toxicity (OR = 1.40, 95%CI: 1.12-1.74), and particularly, diabetics had higher likelihood of irAEs among lung cancer patients. Indeed, ICIs are known to sometimes trigger fulminant autoimmune new-onset diabetes, a rare (<1%) but life-threatening irAE characterized by rapid beta-cell destruction and presentation in diabetic ketoacidosis.^40-42 Furthermore, the frequent reporting of metabolic and nutrition disorders, which were the third most common in SOC, suggested that glucose instability and associated metabolic issues held clinical significance in this group. In practice, oncologists should remain alert to glucose fluctuations and other endocrine changes in diabetic patients receiving ICIs.^43-45 Prompt intervention, such as insulin administration for hyperglycemia or hormone replacement for thyroiditis or adrenalitis, is of critical importance.^3,46-48

A striking finding in our study was the high proportion of serious outcomes (hospitalization in 54%, and death in 22% of reports), especially with combination immunotherapy. This is congruent with clinical trial data showing that combining CTLA-4 and PD-1 blockade markedly increases toxicity.⁴⁹ For example, the landmark trials of ipilimumab plus nivolumab in melanoma reported grade ≥3 irAEs in over 50% of patients, about double the rate of PD-1 monotherapy, necessitating intensive supportive care in many cases.²⁹ Our real-world data indicated that this risk might be even more pronounced in patients with co-existing metabolic illness. We observed that combination-treated diabetic patients not only had a higher fatality rate but also tended to experience AEs earlier compared to those on single-agent ICIs.

This early-onset toxicity pattern in combinations suggested clinicians should exercise particular caution during the initial cycles of therapy. Therefore, for diabetic patients initiating combination ICI therapy, a thorough baseline assessment is recommended prior to treatment. During the initial 2-3 months, intensive follow-up should include weekly monitoring of liver enzymes, creatinine, thyroid function, and blood glucose. Ultrasound or computed tomography imaging should be performed as clinically indicated to ensure comprehensive monitoring. Therefore, in diabetic patients starting combination ICI therapy, a thorough baseline evaluation and intensive follow-up during the first 2-3 months are advised, including monitoring pulmonary symptoms, hepatic enzymes, thyroid function, and glycemic control. Multidisciplinary management is essential. For instance, endocrinologists should be involved at the outset to help distinguish ICI-induced endocrinopathies from diabetes-related complications and to guide hormone replacement without exacerbating glycemic instability.^50,51 Similarly, any sign of pneumonitis or colitis in these patients must prompt early intervention, as delayed therapy can lead to worse outcomes. Notably, pneumonitis and severe infections, such as sepsis, were dominant in the signals, especially in our analysis of anti-PD-L1 therapy. Given that diabetes is a recognized risk factor for infections in general, the intersection of ICI-induced pneumonitis with underlying diabetic susceptibility to infection could explain the observed co-occurrence of pneumonia and sepsis signals.¹⁸ This underscores the necessity for supportive care in such cases to include vigilant monitoring and management of infections, particularly to rule out opportunistic infections that may be mistaken for or occur alongside immune - related pneumonitis.

This study has several limitations that should be acknowledged. First, the FAERS database, like other spontaneous reporting systems, has inherent biases. Underreporting is a primary concern, as minor or expected adverse effects such as low-grade rash or isolated Thyroid-stimulating hormone elevation may not be reported. Conversely, the novelty or severity of an event may boost the likelihood of its being reported, which may consequently result in an overestimation of the frequency of rare but serious events. Our disproportionality approach mitigated some bias, but it could not adjust for all confounders. For instance, diabetic patients might be more closely monitored or more frequently hospitalized, which could lead to increased detection and reporting of certain AEs, such as infections, compared with non - diabetic patients. Moreover, the FAERS database, headquartered in the US, primarily comprises reports from the US. Additionally, some countries, such as Japan, which have pharmacovigilance agreements with the FDA, contribute a substantial number of reports, whereas other regions are underrepresented. This may affect the generalizability of the findings, and we suggest that further studies or data from other countries would be valuable to confirm our findings in different settings. Second, the identification of “diabetic cancer patients” in FAERS relied on the reporting of diabetes as a comorbidity or concurrent condition; this could be incomplete. It’s possible that some patients with diabetes were not captured due to missing data, while others might have been misclassified. Notably, we could not distinguish type 1 vs type 2 diabetes in the reports, an important clinical distinction, as the interaction between autoimmune diabetes and ICI treatment may differ from that of metabolic syndrome. However, considering the age distribution, which included only 1.3% of individuals under 18 years old and a predominance of elderly patients, type 2 diabetes was probably the most common form. Third, we lacked denominators. We did not know how many total diabetic patients received ICIs over this period, so we could not calculate absolute incidence or risk. Our findings reflected reporting rates and signal strength rather than true population incidence. Thus, caution is needed in interpreting the percentages. For example, the 22.4% fatal proportion in reports does not mean 22.4% of diabetic patients on ICIs will die from toxicity; it means 22.4% of the reported cases were fatal outcomes. Fourth, clinical detail in FAERS was limited. We lacked detailed data on cancer stage, diabetes duration or control (HbA1c levels), and concomitant medications that could influence irAE risk. For example, the use of corticosteroids could dampen irAEs.^3,49 Such factors were not accounted for in our analysis. The TTO calculation also assumed the accuracy of the reported start date and event date, with any errors or omissions potentially affecting the Weibull model. Fifth, we did not differentiate between ICI monotherapy and combination therapies that include other agents, such as chemotherapy and Tyrosine Kinase Inhibitors. Although the FAERS database lists suspected drugs, it does not explicitly attribute each AE to a specific drug. For instance, AEs like febrile neutropenia, pneumonia, or sepsis may be related to cytotoxic therapies rather than solely to ICIs, especially in patients with lung or colorectal cancer. This could potentially distort the results. Finally, despite using 4 algorithms to strengthen signal detection, disproportionality analysis cannot prove causality. These limitations underscore the need for further prospective studies with more granular clinical information to validate our pharmacovigilance findings and better inform clinical decision-making in diabetic populations undergoing immunotherapy.

Conclusion

In summary, our study provided a comprehensive real-world overview of ICIs in cancer patients with diabetes receiving ICI therapy. The spectrum of irAEs in this comorbid population closely parallels that seen in the general population of ICI-treated patients, indicating no organ-specific immune-mediated toxicity. However, these patients frequently experienced serious outcomes involving the endocrine, gastrointestinal, pulmonary, and hematologic systems. Combination ICI regimens in particular demand caution in diabetics, given their association with higher and earlier toxicity. These findings highlighted the importance of tailored pharmacovigilance: clinicians should actively manage pre-existing diabetes before and during ICI treatment, educate patients about early symptoms of irAEs, and ensure the timely involvement of a multidisciplinary team, comprising oncology, endocrinology, gastroenterology, and other relevant specialties, at the first indication of an AE.^51-54 With the rising elderly population, ICIs are being used more frequently in older adults and those with diabetes. It is essential to understand the relationship between metabolic diseases and immune - related toxicities. Future research should prospectively examine the mechanisms behind the increased vulnerability to irAEs in diabetic patients. Examining the immunological characteristics or biomarkers of these patients may reveal strategies, such as preventive endocrine assessments or altered dosing schedules, to reduce risk while maintaining antitumor efficacy.^10,55 Ultimately, our real-world findings support the notion that ICIs can be effectively utilized in cancer patients with diabetes, provided that clinicians remain vigilant and responsive to the unique challenges of this population. Such a balanced strategy may facilitate the maximization of immunotherapy’s oncological benefits while protecting patients from preventable harm.

Supplemental Material

Supplemental material - Adverse Events of Immune Checkpoint Inhibitors in Cancer Patients With Comorbid Diabetes: A Real-World Pharmacovigilance Analysis of the FDA Adverse Event Reporting System Database (2011–2025)

Supplemental material for Adverse Events of Immune Checkpoint Inhibitors in Cancer Patients With Comorbid Diabetes: A Real-World Pharmacovigilance Analysis of the FDA Adverse Event Reporting System Database (2011-2025) by Minxia Yang, Di Qiu, Minguang Huang, Shengjian Yu, Feng Xuan in Cancer Control.

Footnotes

Acknowledgments

We sincerely appreciate the contributions of researchers, clinicians, patients, and regulatory agencies to the FAERS data.

ORCID iD

Feng Xuan

Author Contributions

Minxia Yang: contributed to conceptualization, investigation, formal analysis, methodology, data curation, software, visualization, validation, and writing original draft, review and editing.

Di Qiu: contributed to conceptualization, formal analysis, methodology, validation, and writing review and editing.

Minguang Huang: contributed to conceptualization, methodology, and writing original draft.

Shengjian Yu: contributed to conceptualization, methodology, and writing original draft.

Feng Xuan: contributed to conceptualization, formal analysis, methodology, project administration, supervision, software, visualization, validation, and writing review and editing.

All the authors have read and approved the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported in part by grants from Medical and Health Research Project of Zhejiang Province (grant number, 2023KY1236),Science and Technology Plan Basic Public Welfare Project of Shaoxing (grant number, 2022A14010).

Declaration of Conflicting Interests

The authors confirm the absence of any competing interests.

Data Availability Statement

The datasets utilized in the current study are accessible in the public domain: . Further information is available from the corresponding author upon request.

Supplemental Material

Supplemental material for this article is available online.

References

Tan

Day

Nicholls

Segelov

. Immune checkpoint inhibitor therapy in oncology: current uses and future directions: JACC: cardiooncology state-of-the-art review. JACC CardioOncol. 2022;4(5):579-597. doi:10.1016/j.jaccao.2022.09.004

Robert

. A decade of immune-checkpoint inhibitors in cancer therapy. Nat Commun. 2020;11(1):3801. doi:10.1038/s41467-020-17670-y

Martins

Sofiya

Sykiotis

, et al. Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nat Rev Clin Oncol. 2019;16(9):563-580. doi:10.1038/s41571-019-0218-0

Shiravand

Khodadadi

Kashani

SMA

, et al. Immune checkpoint inhibitors in cancer therapy. Curr Oncol. 2022;29(5):3044-3060. doi:10.3390/curroncol29050247

Baxi

Yang

Gennarelli

, et al. Immune-related adverse events for anti-PD-1 and anti-PD-L1 drugs: systematic review and meta-analysis. Br Med J. 2018;360:k793. doi:10.1136/bmj.k793

Chen

Liang

, et al. Cardiotoxicity induced by immune checkpoint inhibitors: a pharmacovigilance study from 2014 to 2019 based on FAERS. Front Pharmacol. 2021;12:616505. doi:10.3389/fphar.2021.616505

Peeraphatdit

Wang

Odenwald

Hart

Charlton

. Hepatotoxicity from immune checkpoint inhibitors: a systematic review and management recommendation. Hepatology. 2020;72(1):315-329. doi:10.1002/hep.31227

Yoshikawa

Imamura

Yamauchi

, et al. Prevalence of immune-related adverse events and anti-tumor efficacy following immune checkpoint inhibitor therapy in Japanese patients with various solid tumors. BMC Cancer. 2022;22(1):1232. doi:10.1186/s12885-022-10327-7

Wright

Powers

Johnson

. Endocrine toxicities of immune checkpoint inhibitors. Nat Rev Endocrinol. 2021;17(7):389-399. doi:10.1038/s41574-021-00484-3

10.

Iwama

Kobayashi

Arima

. Management, biomarkers and prognosis in people developing endocrinopathies associated with immune checkpoint inhibitors. Nat Rev Endocrinol. 2025;21(5):289-300. doi:10.1038/s41574-024-01077-6

11.

Sun

Saeedi

Karuranga

, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. doi:10.1016/j.diabres.2021.109119

12.

Parikh

Sawczuk

Turner

Hong

. Health care use among cancer patients with diabetes, national health and nutrition examination survey, 2017-2020. Prev Chronic Dis. 2024;21:E58. doi:10.5888/pcd21.240066

13.

Supabphol

Seubwai

Wongkham

Saengboonmee

. High glucose: an emerging association between diabetes mellitus and cancer progression. J Mol Med (Berl). 2021;99(9):1175-1193. doi:10.1007/s00109-021-02096-w

14.

Zhu

Pei

Zhang

Wei

. The relationship between diabetes and colorectal cancer prognosis: a meta-analysis based on the cohort studies. PLoS One. 2017;12(4):e0176068. doi:10.1371/journal.pone.0176068

15.

Giovannucci

Harlan

Archer

, et al. Diabetes and cancer: a consensus report. CA Cancer J Clin. 2010;60(4):207-221. doi:10.3322/caac.20078

16.

Chang

Yang

. Hyperglycemia, tumorigenesis, and chronic inflammation. Crit Rev Oncol Hematol. 2016;108:146-153. doi:10.1016/j.critrevonc.2016.11.003

17.

Cho

Shang

Zhou

. Comorbidities were associated with cancer clinical trial discussion and participation: findings from the health information national trends survey-surveillance, epidemiology, and end results program (2021). J Clin Epidemiol. 2023;163:62-69. doi:10.1016/j.jclinepi.2023.09.016

18.

Liu

Huang

Peng

. Association of type 2 diabetes, hypertension, and hyperlipidemia with immune-related adverse events in patients undergoing immune checkpoint inhibitors therapy. Front Immunol. 2025;16:1472197. doi:10.3389/fimmu.2025.1472197

19.

Shen

Lin

, et al. Characterization of second primary malignancies post CAR T-cell therapy: real-world insights from the two global pharmacovigilance databases of FAERS and VigiBase. eClinicalMedicine. 2024;73:102684. doi:10.1016/j.eclinm.2024.102684

20.

Hua

Xiong

Chong

, et al. Abemaciclib increases the risk of venous thromboembolism in breast cancer: integrate meta-analysis, pharmacovigilance database analysis, and in vitro validation. Cancer Treat Rev. 2024;130:102827. doi:10.1016/j.ctrv.2024.102827

21.

Zhou

Peng

Lin

, et al. Psychiatric disorders associated with immune checkpoint inhibitors: a pharmacovigilance analysis of the FDA adverse event reporting system (FAERS) database. eClinicalMedicine. 2023;59:101967. doi:10.1016/j.eclinm.2023.101967

22.

Almenoff

Pattishall

Gibbs

DuMouchel

Evans

Yuen

. Novel statistical tools for monitoring the safety of marketed drugs. Clin Pharmacol Ther. 2007;82(2):157-166. doi:10.1038/sj.clpt.6100258

23.

Liu

, et al. Real-world study of adverse events associated with triptan use in migraine treatment based on the U.S. food and drug administration (FDA) adverse event reporting system (FAERS) database. J Headache Pain. 2024;25(1):206. doi:10.1186/s10194-024-01913-0

24.

Rothman

Lanes

Sacks

. The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiol Drug Saf. 2004;13(8):519-523. doi:10.1002/pds.1001

25.

Evans

Waller

Davis

. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001;10(6):483-486. doi:10.1002/pds.677

26.

Sauzet

Carvajal

Escudero

Molokhia

Cornelius

. Illustration of the weibull shape parameter signal detection tool using electronic healthcare record data. Drug Saf. 2013;36(10):995-1006. doi:10.1007/s40264-013-0061-7

27.

Zhou

Zheng

, et al. Exploration of the potential association between GLP-1 receptor agonists and suicidal or self-injurious behaviors: a pharmacovigilance study based on the FDA adverse event reporting system database. BMC Med. 2024;22(1):65. doi:10.1186/s12916-024-03274-6

28.

Xiang

Luo

, et al. Risk of angioedema and thrombolytic therapy among stroke patients: an analysis of data from the FDA adverse event reporting system database. Neurotherapeutics. 2025;22(1):e00474. doi:10.1016/j.neurot.2024.e00474

29.

Yin

Han

, et al. Immune-related adverse events of immune checkpoint inhibitors: a review. Front Immunol. 2023;14:1167975. doi:10.3389/fimmu.2023.1167975

30.

Gupta

De Felice

Loftus

Jr. Khanna

. Systematic review: colitis associated with anti-CTLA-4 therapy. Aliment Pharmacol Ther. 2015;42(4):406-417. doi:10.1111/apt.13281

31.

Spain

Diem

Larkin

. Management of toxicities of immune checkpoint inhibitors. Cancer Treat Rev. 2016;44:51-60. doi:10.1016/j.ctrv.2016.02.001

32.

Tian

Liu

, et al. The risk of immune-related thyroid dysfunction induced by PD-1/PD-L1 inhibitors in cancer patients: an updated systematic review and meta-analysis. Front Oncol. 2021;11:667650. doi:10.3389/fonc.2021.667650

33.

Suresh

Voong

Shankar

, et al. Pneumonitis in non-small cell lung cancer patients receiving immune checkpoint immunotherapy: incidence and risk factors. J Thorac Oncol. 2018;13(12):1930-1939. doi:10.1016/j.jtho.2018.08.2035

34.

Cheng

Kang

Zhao

. Dual blockade immunotherapy targeting PD-1/PD-L1 and CTLA-4 in lung cancer. J Hematol Oncol. 2024;17(1):54. doi:10.1186/s13045-024-01581-2

35.

Willsmore

Coumbe

BGT

Crescioli

, et al. Combined anti-PD-1 and anti-CTLA-4 checkpoint blockade: treatment of melanoma and immune mechanisms of action. Eur J Immunol. 2021;51(3):544-556. doi:10.1002/eji.202048747

36.

Trisal

Low

Pathan

Gangadharan Komala

. Kidney adverse events associated with immune checkpoint inhibitor therapy: a systematic review and bayesian network meta-analysis. Clin J Am Soc Nephrol. 2023;18(7):843-849. doi:10.2215/cjn.0000000000000160

37.

Lima

Silva

Pípolo

Delfino

VDA

Bignardi

. Acute kidney injury associated with anti-PD-1 and anti-PD-L1 drugs: a meta-analysis of randomized clinical trials. Immunopharmacol Immunotoxicol. 2024;46(4):470-481. doi:10.1080/08923973.2024.2360071

38.

Gougis

Jochum

Abbar

, et al. Clinical spectrum and evolution of immune-checkpoint inhibitors toxicities over a decade-a worldwide perspective. eClinicalMedicine. 2024;70:102536. doi:10.1016/j.eclinm.2024.102536

39.

Hilder

Tsai

Quandt

, et al. Safety and efficacy of immune checkpoint inhibitor cancer therapy in patients with preexisting type 1 diabetes mellitus. Front Endocrinol. 2023;14:1242830. doi:10.3389/fendo.2023.1242830

40.

Akturk

Michels

. Adverse events associated with immune checkpoint inhibitors: a new era in autoimmune diabetes. Curr Opin Endocrinol Diabetes Obes. 2020;27(4):187-193. doi:10.1097/med.0000000000000546

41.

Liu

Zhou

Zhang

, et al. Reporting of immune checkpoint inhibitor therapy-associated diabetes, 2015-2019. Diabetes Care. 2020;43(7):e79-e80. doi:10.2337/dc20-0459

42.

Tachibana

Imagawa

. Type 1 diabetes related to immune checkpoint inhibitors. Best Pract Res Clin Endocrinol Metabol. 2022;36(3):101657. doi:10.1016/j.beem.2022.101657

43.

Kani

Karaviti

, et al. Pathophysiology, diagnosis, and management of immune checkpoint inhibitor-induced diabetes mellitus. Endocrine. 2025;87(3):875-890. doi:10.1007/s12020-024-04050-5

44.

Shen

Chen

Zhao

, et al. Real-world adherence to toxicity management guidelines for immune checkpoint inhibitor-induced diabetes mellitus. Front Endocrinol. 2023;14:1213225. doi:10.3389/fendo.2023.1213225

45.

Chieng

JHL

Htet

Zhao

, et al. Clinical presentation of immune-related endocrine adverse events during immune checkpoint inhibitor treatment. Cancers (Basel). 2022;14(11):2687. doi:10.3390/cancers14112687

46.

Dökmetaş

Muğlu

Özcan

, et al. Endocrine adverse events in patients treated with immune checkpoint inhibitors: a comprehensive analysis. Medicina (Coimbra). 2025;61(1):123. doi:10.3390/medicina61010123

47.

Schneider

Naidoo

Santomasso

, et al. Management of immune-related adverse events in patients treated with immune checkpoint inhibitor therapy: ASCO guideline update. J Clin Oncol. 2021;39(36):4073-4126. doi:10.1200/jco.21.01440

48.

Kotwal

Perlman

Goldner

Marr

Mammen

. Endocrine dysfunction from immune checkpoint inhibitors: pearls and pitfalls in evaluation and management. JCO Oncol Pract. 2023;19(7):395-402. doi:10.1200/op.23.00023

49.

Hassel

Heinzerling

Aberle

, et al. Combined immune checkpoint blockade (anti-PD-1/anti-CTLA-4): evaluation and management of adverse drug reactions. Cancer Treat Rev. 2017;57:36-49. doi:10.1016/j.ctrv.2017.05.003

50.

Arima

Iwama

Inaba

, et al. Management of immune-related adverse events in endocrine organs induced by immune checkpoint inhibitors: clinical guidelines of the Japan endocrine society. Endocr J. 2019;66(7):581-586. doi:10.1507/endocrj.EJ19-0163

51.

Elia

Ferrari

Galdiero

, et al. New insight in endocrine-related adverse events associated to immune checkpoint blockade. Best Pract Res Clin Endocrinol Metabol. 2020;34(1):101370. doi:10.1016/j.beem.2019.101370

52.

Paschou

Stefanaki

Psaltopoulou

, et al. How we treat endocrine complications of immune checkpoint inhibitors. ESMO Open. 2021;6(1):100011. doi:10.1016/j.esmoop.2020.100011

53.

Byun

Wolchok

Rosenberg

Girotra

. Cancer immunotherapy - immune checkpoint blockade and associated endocrinopathies. Nat Rev Endocrinol. 2017;13(4):195-207. doi:10.1038/nrendo.2016.205

54.

Sznol

Postow

Davies

, et al. Endocrine-related adverse events associated with immune checkpoint blockade and expert insights on their management. Cancer Treat Rev. 2017;58:70-76. doi:10.1016/j.ctrv.2017.06.002

55.

Shalit

Sarantis

Koustas

Trifylli

Matthaios

Karamouzis

. Predictive biomarkers for immune-related endocrinopathies following immune checkpoint inhibitors treatment. Cancers (Basel). 2023;15(2):375. doi:10.3390/cancers15020375

Supplementary Material

Please find the following supplemental material available below.

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Adverse Events of Immune Checkpoint Inhibitors in Cancer Patients with Comorbid Diabetes: A Real-World Pharmacovigilance Analysis of the FDA Adverse Event Reporting System Database (2011–2025)

Abstract

Introduction

Methods

Results

Conclusion

Plain language summary

Why was the study done?

What did the researchers do?

What did the researchers find?

What do the findings mean?

Keywords

Introduction

Methods

Data Sources

Data Processing Procedure

Signal Mining

Time-To-Onset (TTO) and Weibull Distribution Analysis

Statistical Analysis

Ethics Statement

Result

Descriptive Analysis

Disproportionality Analysis

At the preferred term (PT) level

At the system organ class (SOC) level

Time-To-Onset and Weibull Distribution Analysis

Discussion

Conclusion

Supplemental Material

Supplemental material - Adverse Events of Immune Checkpoint Inhibitors in Cancer Patients With Comorbid Diabetes: A Real-World Pharmacovigilance Analysis of the FDA Adverse Event Reporting System Database (2011–2025)

Footnotes

Acknowledgments

ORCID iD

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

Supplemental Material

References

Supplementary Material