Programmed Cell Death-1 Monoclonal Antibody Therapy and Type 1 Diabetes Mellitus: A Review of the Literature

2019 ◽  
pp. 089719001985092 ◽  
Author(s):  
Kyle A. Farina ◽  
Michael P. Kane

Two Food and Drug Administration-approved programmed cell death-1 (PD-1) inhibitors, nivolumab (Opdivo®), and pembrolizumab (Keytruda®), are indicated for treatment-resistant malignancies. Inhibition of PD-1 also inhibits T-cell peripheral tolerance, enhancing autoimmunity. Various autoimmune conditions have been reported with the use of these agents, including type 1 diabetes mellitus (T1DM). This article reviews literature regarding the development of T1DM in patients treated with PD-1 inhibitors and identifies strategies for the appropriate identification, monitoring, and follow-up of these patients. Published cases of T1DM related to PD-1 inhibitor therapy were identified using PubMed. Eighty-three identified publications were reviewed, of which 37 publications involving 42 cases of anti-PD-1 therapy-induced T1DM were identified. The average age of patients at presentation was 62 years and 59.5% were male. The mean number of PD-1 inhibitor doses received was 5, with a mean time to presentation of 11 weeks. Initial presentation of diabetic ketoacidosis was reported in 69% of cases, with an average blood glucose of 660 mg/dL and an average HbA1cof 8.7%. The exact mechanism PD-1 inhibitor therapy-induced T1DM is unknown. Blood glucose monitoring is recommended for all patients receiving anti-PD-1 therapy. Further research is needed to delineate the frequency of this adverse effect, as well as to evaluate potential risk factors and ideal management strategies.

2016 ◽  
Vol 7 (6) ◽  
pp. 915-918 ◽  
Author(s):  
Masahide Okamoto ◽  
Mitsuhiro Okamoto ◽  
Koro Gotoh ◽  
Takayuki Masaki ◽  
Yoshinori Ozeki ◽  
...  

2018 ◽  
Vol 10 (1) ◽  
pp. 58-66 ◽  
Author(s):  
Megu Yamaguchi Baden ◽  
◽  
Akihisa Imagawa ◽  
Norio Abiru ◽  
Takuya Awata ◽  
...  

Metabolism ◽  
2007 ◽  
Vol 56 (7) ◽  
pp. 905-909 ◽  
Author(s):  
Yoshihisa Hiromine ◽  
Hiroshi Ikegami ◽  
Tomomi Fujisawa ◽  
Koji Nojima ◽  
Yumiko Kawabata ◽  
...  

2018 ◽  
Vol 103 (9) ◽  
pp. 3144-3154 ◽  
Author(s):  
Katrien Clotman ◽  
Katleen Janssens ◽  
Pol Specenier ◽  
Ilse Weets ◽  
Christophe E M De Block

2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).


Sign in / Sign up

Export Citation Format

Share Document