Xuan Chen1, Alison H Affinati2, Yungchun Lee1, Adina F Turcu2, Norah Lynn Henry3, Elena Schiopu3, Angel Qin3, Megan Othus4, Dan Clauw5, Nithya Ramnath3,6, Lili Zhao1. 1. Department of Biostatistics, University of Michigan, Ann Arbor, MI. 2. Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI. 3. Department of Internal Medicine, University of Michigan, Ann Arbor, MI. 4. Biostatistics and Biomathematics Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Ann Arbor, MI. 5. Department of Anesthesiology, University of Michigan, Ann Arbor, MI. 6. Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI.
Abstract
OBJECTIVE: Type 1 diabetes mellitus (T1DM) is a rare, irreversible immune-related adverse event reported in patients receiving treatment with immune checkpoint inhibitors (ICI). However, clinical risk factors for ICI-induced T1DM (ICI-T1DM) and its impact on survival in patients remain unknown. RESEARCH DESIGN AND METHODS: We used Optum's Clinformatics Data Mart database for assessment of the incidence and characteristics of T1DM in a large de-identified cohort of patients treated with ICI between 2017 and 2020. We applied Fine-Gray and cause-specific hazard models to study associations between patient/treatment characteristics and ICI-T1DM and applied the Cox model with ICI-T1DM as a time-varying covariate to assess the impact of ICI-T1DM on survival. RESULTS: ICI-T1DM was observed in 261 of 30,337 (0.86%) patients. Dual use of antibodies to cytotoxic T lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1) or programmed cell death ligand 1 (PD-L1) was associated with increasing risk of ICI-T1DM (hazard ratio [HR] 1.62; 95% CI 1.15-2.26) vs. anti-PD-L1 or anti-PD-1 alone. Younger age (HR 1.19 for every 5-year decrease; 95% CI 1.13-1.25) and preexisting non-T1DM diabetes (HR 4.48; 95% CI 3.45-5.83) were also associated with higher risk of ICI-T1DM. Conversely, prior use of immunosuppressive medications (HR 0.57; 95% CI 0.34-0.95) was associated with lower incidence of ICI-T1DM, but part of its protective effect may be due to the increased mortality rate. Development of ICI-T1DM does not seem to significantly impact patient survival. CONCLUSIONS: The risk of ICI-T1DM is associated with the type of ICI therapy, patient age, and preexisting non-T1DM diabetes. These data may help guide risk assessment and screening practices for patients during ICI therapy.
OBJECTIVE: Type 1 diabetes mellitus (T1DM) is a rare, irreversible immune-related adverse event reported in patients receiving treatment with immune checkpoint inhibitors (ICI). However, clinical risk factors for ICI-induced T1DM (ICI-T1DM) and its impact on survival in patients remain unknown. RESEARCH DESIGN AND METHODS: We used Optum's Clinformatics Data Mart database for assessment of the incidence and characteristics of T1DM in a large de-identified cohort of patients treated with ICI between 2017 and 2020. We applied Fine-Gray and cause-specific hazard models to study associations between patient/treatment characteristics and ICI-T1DM and applied the Cox model with ICI-T1DM as a time-varying covariate to assess the impact of ICI-T1DM on survival. RESULTS: ICI-T1DM was observed in 261 of 30,337 (0.86%) patients. Dual use of antibodies to cytotoxic T lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1) or programmed cell death ligand 1 (PD-L1) was associated with increasing risk of ICI-T1DM (hazard ratio [HR] 1.62; 95% CI 1.15-2.26) vs. anti-PD-L1 or anti-PD-1 alone. Younger age (HR 1.19 for every 5-year decrease; 95% CI 1.13-1.25) and preexisting non-T1DM diabetes (HR 4.48; 95% CI 3.45-5.83) were also associated with higher risk of ICI-T1DM. Conversely, prior use of immunosuppressive medications (HR 0.57; 95% CI 0.34-0.95) was associated with lower incidence of ICI-T1DM, but part of its protective effect may be due to the increased mortality rate. Development of ICI-T1DM does not seem to significantly impact patient survival. CONCLUSIONS: The risk of ICI-T1DM is associated with the type of ICI therapy, patient age, and preexisting non-T1DM diabetes. These data may help guide risk assessment and screening practices for patients during ICI therapy.
Authors: Venessa H M Tsang; Rachel T McGrath; Roderick J Clifton-Bligh; Richard A Scolyer; Valerie Jakrot; Alexander D Guminski; Georgina V Long; Alexander M Menzies Journal: J Clin Endocrinol Metab Date: 2019-11-01 Impact factor: 5.958
Authors: Mario Sznol; Michael A Postow; Marianne J Davies; Anna C Pavlick; Elizabeth R Plimack; Montaser Shaheen; Colleen Veloski; Caroline Robert Journal: Cancer Treat Rev Date: 2017-06-22 Impact factor: 12.111