Endocrinol Metab.  2022 Feb;37(1):65-73. 10.3803/EnM.2021.1275.

Drug Repositioning Using Temporal Trajectories of Accompanying Comorbidities in Diabetes Mellitus

• Park N
• Jeon JY
• Jeong E
• Kim S
• Yoon D

• KMID: 2527138
• DOI: http://doi.org/10.3803/EnM.2021.1275

Abstract

Background
Most studies of systematic drug repositioning have used drug-oriented data such as chemical structures, gene expression patterns, and adverse effect profiles. As it is often difficult to prove repositioning candidates’ effectiveness in real-world clinical settings, we used patient-centered real-world data for screening repositioning candidate drugs for multiple diseases simultaneously, especially for diabetic complications.
Methods
Using the National Health Insurance Service-National Sample Cohort (2002 to 2013), we analyzed claims data of 43,048 patients with type 2 diabetes mellitus (age ≥40 years). To find repositioning candidate disease-drug pairs, a nested case-control study was used for 29 pairs of diabetic complications and the drugs that met our criteria. To validate this study design, we conducted an external validation for a selected candidate pair using electronic health records.
Results
We found 24 repositioning candidate disease-drug pairs. In the external validation study for the candidate pair cerebral infarction and glycopyrrolate, we found that glycopyrrolate was associated with decreased risk of cerebral infarction (hazard ratio, 0.10; 95% confidence interval, 0.02 to 0.44).
Conclusion
To reduce risks of diabetic complications, it would be possible to consider these candidate drugs instead of other drugs, given the same indications. Moreover, this methodology could be applied to diseases other than diabetes to discover their repositioning candidates, thereby offering a new approach to drug repositioning.

Keyword

Diabetes mellitus; type 2; Drug repositioning; Retrospective studies

Figure

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  Fig. 1 The overall scheme of the methodology. It consisted of three steps: selection of target diseases; selection of cases and controls for each disease; and statistical analysis. NHIS-NSC, National Health Insurance Service-National Sample Cohort; T2DM, type 2 diabetes mellitus; Comp, complication. aMatching variables: gender, age group, days classes of anti-diabetic medications were prescribed as a proportion of the assessment period (from the day first diagnosed with T2DM to a year thereafter), comorbidities, and Charlson comorbidity index (CCI).

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  Fig. 2 The flowchart of selecting the study population and cases/controls. aAnti-diabetic medications: insulin, metformin, others (sulfonylureas, alpha-glucosidase inhibitors, thiazolidinediones, meglitinides, dipeptidyl peptidase-4 inhibitors, and glucagon-like peptide-1 receptor agonist), and combinations; bComorbidities: hypertension, diabetes with complications, arrhythmia, acute myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, and renal disease.

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  Fig. 3 Kaplan-Meier curves of the time to the first event (cerebral infarction) in the external validation study. The glycopyrrolate group (blue line) and the comparator drugs group (red line) showed different patterns; the P value of the log-rank test (P in the graph) was less than 0.0001. This indicates that the incidence of cerebral infarction was significantly different between the groups.

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