We obtained data from the National Health Insurance Service-National Sample Cohort (NHIS-NSC), which is a representative population-based cohort database in South Korea [11]. As the National Health Insurance Service (NHIS) covers virtually the entire South Korean population [12], NHIS-NSC was constructed by stratified random sampling for an accurate representation of the population. It contains socioeconomic data, health examination data, and medical treatment data for 2.2% of the total eligible Korean population [11]. We used data collected from 2002 to 2013 (11 years).

This study was approved by the Ajou University Hospital Institutional Review Board (AJIRB-MED-EXP-20-427), which waived the requirement for informed consent.

We conducted the study in three steps: selection of target diseases, selection of cases and controls for each disease, and statistical analysis. Fig. 1 demonstrates these steps and the overall scheme of this study.

In the first step, we selected target diseases. Complications of T2DM were selected as target diseases based on the temporal trajectories of accompanying comorbidities of T2DM constructed by Jeong et al. [10]. We describe the details of these trajectories in the “Temporal trajectories of accompanying comorbidities” sub-section. Second, we extracted the cases and controls for each target disease from patients with T2DM to conduct a nested case-control study for each disease-drug pair. Finally, conditional logistic regressions were conducted for each pair, which comprised all target diseases and prescribed drugs, except for topical drugs, fluids, and drugs prescribed for less than 30 days in patients. A candidate repositioning drug was selected when the P<0.05 and the upper limit of the 95% confidence interval (CI) for the odds ratio (OR) was less than 0.99.

We extracted the patients with T2DM aged over 40 years, who had first developed the condition after 2003 (Fig. 2). Type T2DM was defined as any T2DM-related diagnostic codes (E11-E14) with any prescription of anti-diabetic medications within 1 year of diagnosis. The anti-diabetic medications included metformin, insulin, glimepiride, glibenclamide, gliclazide, glipizide, chlorpropamide, gliquidone, acarbose, voglibose, miglitol, rosiglitazone, pioglitazone, nateglinide, repaglinide, mitiglinide, vildagliptin, gemigliptin, sitagliptin, saxagliptin, linagliptin, alogliptin, and exenatide. To determine the exact diagnosis date for T2DM among the extracted patients, we excluded patients who were diagnosed with T2DM before 2003, who entered the cohort after 2003, or who were initially diagnosed with T2DM coded E14.9. Of those who were initially diagnosed as E14.9, we observed a tendency toward the E14.9 code being assigned to patients whose diagnosis of T2DM mellitus was not confirmed at the first visit. In addition, those who were less than 40 years of age or who had died during the cohort period were also excluded.

To select target diseases, we used the temporal trajectories of accompanying comorbidities constructed by Jeong et al. [10]. These trajectories were constructed using NHIS-NSC, which was also used in this study, to determine diabetic complications, the pattern of their development, and differences in the pattern by gender and age group. These trajectories provided progression patterns (temporal trajectories) and relative risk for comorbidities of T2DM, which helped us delineate candidate diabetic complications according to their relative risk. Several diseases were determined to be significant but commonly not considered diabetic complications, such as epilepsy and mental and behavioral disorders due to use of alcohol. Therefore, the term “comorbidities” was adopted rather than “complications” in our previous study [10], but these could be candidate complications. In this context, we used the term “complications” generally.

Among three temporal trajectories, namely (Type 2 diabetes → First complication), (First complication → Second complication), and (Second complication → Third complication), we only considered the (Type 2 diabetes → First complication) stage. For this stage, we selected the top 30 diabetic complications (trajectories) with the highest relative risk. Among those, we excluded the (Type 2 diabetes → Type 1 diabetes) trajectory, as it was not a diabetic complication. In this case, patients might have had latent autoimmune diabetes in adults, which is often initially misdiagnosed as T2DM [13], and then correctly diagnosed as type 1 diabetes mellitus. Therefore, we selected 29 diabetic complications as the target diseases. Diagnoses are listed in descending order by their relative risks in Supplemental Table S1.

To conduct a nested case-control study for each disease-drug pair, cases who had developed a complication at least a year after T2DM were obtained. Next, controls who did not develop a complication throughout the observational period (2002 to 2013) were matched in a ratio of 1:2 to the cases in each trajectory. Matching was conducted in two steps (Fig. 1). First, to reduce the error related to the duration of diabetes, controls with the same diagnosis date of T2DM (year and month) were selected. Then, a disease risk score was used to finally match the most similar controls to cases in a ratio of 1:2 [14]. Disease risk score showed statistically better values than exact matching for multiple factors in a nested case-control study [15].

To match variables by disease risk score, we considered gender, age group, the number of days each class 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) [16]. The classes of anti-diabetic medications were defined as insulin, biguanides, others (sulfonylureas, thiazolidinediones, meglitinides, alpha-glucosidase inhibitors, dipeptidyl peptidase-4 inhibitors, and glucagon-like peptide-1 receptor agonists), and a combination of two or more of the classes (insulin, biguanides, sulfonylureas, thiazolidinediones, meglitinides, alpha-glucosidase inhibitors, dipeptidyl peptidase-4 inhibitors, and glucagon-like peptide-1 receptor agonists). For the comorbidities, we selected macrovascular and microvascular-associated diseases, as the development of diabetic complications is strongly associated with macrovascular and microvascular disorders [17]. Therefore, we selected hypertension, diabetes with complications, arrhythmia, acute myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, and renal disease as covariates. After the matching, the index dates of controls were assigned as the index dates of the matched cases. We tried to exclude trajectories for which the number of the cases and controls matched was fewer than 100, to ensure statistical power. However, no trajectory was excluded as the number of cases and controls matched was greater than 100 in all 29 trajectories.

To discover candidate drugs for drug repositioning that could prevent a complication in each trajectory, the number of days each drug was prescribed was calculated from the diagnosis date of T2DM to the index date as a proportion of that period. We excluded topical drugs, fluids, and drugs prescribed for fewer than 30 days for the patients.

We conducted a conditional logistic regression using the calculated proportion of the prescription days for each drug as an independent variable. As covariates, we used the prescription days for classes of anti-diabetic medications as a proportion of the assessment period (from the first day of T2DM diagnosis to a year thereafter), comorbidities, and CCI. The classes of anti-diabetic medications and the comorbidities were defined as above (see “Matching the cases and controls in each trajectory” sub-section). Finally, a disease-drug pair with P<0.05 and upper limit of the 95% CI for the OR <0.99 was selected as a repositioning candidate.

To validate this methodology, an external validation study was conducted using an electronic health record (EHR) dataset from Ajou University Hospital. Among the disease-drug pairs, a candidate pair was selected with two criteria: (1) a drug that could be taken in the relatively long term and (2) among those, a drug that was prescribed most in the EHR dataset. In accordance with these two criteria, we selected the (cerebral infarction-glycopyrrolate [Anatomical Therapeutic Chemical (ATC) Classification System code “A03AB02” or “R03BB06”]) candidate pair. Next, we chose comparable drugs to glycopyrrolate, which were used for the same indications as glycopyrrolate. The other anticholinergics for peptic ulcer or obstructive airway diseases (drugs with an ATC code that started with “A03AB” or “R03BB”) were selected as comparator drugs.

As the study population, we selected the patients with T2DM who had first developed the condition after 1999. Then, we extracted the patients who had taken glycopyrrolate and one of the comparator drugs from the patients with T2DM. Next, three exclusion criteria were applied: (1) patients who had taken both glycopyrrolate and one of the comparator drugs; (2) patients whose index date, which was the first prescription date of the drug (glycopyrrolate or one of the comparator drugs), was within 180 days of the diagnosis date of T2DM; and (3) patients whose index date was after the date cerebral infarction was diagnosed. After applying exclusion criteria, extracted patients who had taken glycopyrrolate or one of the comparator drugs were considered as treated or non-treated, respectively. To adjust for confounding factors, treated and non-treated cases were then matched in a ratio of 1:1 using propensity score matching. Matching variables used were gender, age group, and comorbidities.

A Cox proportional hazards model was used to test whether the treated and non-treated groups differed in the development of cerebral infarction. Covariates consisted of hypertension, diabetes with complications, acute myocardial infarction, cardiac arrhythmia, peripheral vascular disease, cerebrovascular disease, and renal disease.

From the NHIS-NSC dataset, 43,048 patients were extracted as the study population. After excluding topical drugs, fluids, and drugs prescribed for fewer than 30 days in the study population, 29,270 disease-drug pairs with 29 trajectories were analyzed.

Of these, 24 pairs with 13 trajectories were identified as candidates for drug repositioning (Table 1). Among these, the top five pairs with the lowest ORs were: mental and behavioral disorders due to use of alcohol-pyridostigmine (OR, 0.51; 95% CI, 0.31 to 0.85); hypertensive renal disease-ondansetron (OR, 0.61; 95% CI, 0.37 to 0.99); sequelae of cerebrovascular disease-lysozyme (OR, 0.66; 95% CI, 0.46 to 0.95); heart failure-pyridostigmine (OR, 0.69; 95% CI, 0.57 to 0.84); and sequelae of cerebrovascular disease-kanamycin (OR, 0.81; 95% CI, 0.69 to 0.95).

To validate this methodology, we selected a candidate pair using two criteria described in the Methods section. By applying them, we selected the (cerebral infarction-glycopyrrolate [ATC code “A03AB02” or “R03BB06,” which is anticholinergics for peptic ulcer or obstructive airway diseases]) candidate pair with 10,428 prescription records in the EHR dataset.

After the matching, the number of treated and non-treated patients was 596 in each case. Comparison of their baseline characteristics (Table 2) revealed that the treated group was younger and had a higher proportion of females. In terms of comorbidities, the treated group had a higher proportion of hypertension and diabetes with complications. However, the former had a lower proportion of acute myocardial infarction, cardiac arrhythmia, peripheral vascular disease, cerebrovascular disease, and renal disease.

The Kaplan-Meier curve showed a significant difference between the glycopyrrolate-treated and comparator drugs-treated group (Fig. 3). In the Cox proportional hazards model, glycopyrrolate was associated with decreased risk of cerebral infarction compared to the comparator drugs (hazard ratio, 0.10; 95% CI, 0.02 to 0.44), consistent with our study results.