Comparative risk of severe hypoglycemia among concomitant users of thiazolidinedione antidiabetic agents and antihyperlipidemics

Combination of Glucagon-Like Peptide 1 Receptor Agonist and Thiazolidinedione for Mortality and Cardiovascular Outcomes in Patients With Type 2 Diabetes

Importance

Combination therapy has emerged as a critical area of interest in managing diabetes; however, the association of combination therapy with a glucagon-like peptide 1 receptor agonist (GLP-1RA) and thiazolidinedione and the risk of diabetes-related complications remains incompletely understood.

Objective

To compare the hazards of cardiovascular-related morbidities and mortality among patients with type 2 diabetes receiving combination therapy with a GLP-1RA plus thiazolidinedione, those receiving monotherapy with a GLP-1RA or thiazolidinedione, and nonusers.

Design, Setting, and Participants

This retrospective cohort study used nationwide data obtained from Taiwan's National Health Insurance Research Database. Patients older than 20 years with type 2 diabetes who received a GLP-1RA or thiazolidinedione between January 1, 2011, and December 31, 2020, were enrolled. The data analysis was performed from May 1 to May 22, 2024.

Main Outcomes and Measures

This study investigated the hazards of all-cause mortality, major adverse cardiovascular events, cardiovascular mortality, cardiovascular complications, and hypoglycemia in GLP-1RA and thiazolidinedione combination or monotherapy users compared with nonusers.

Results

A total of 110 411 patients were enrolled (mean [SD] age, 58.3 [11.9] years; 45.5% female; 47 526 GLP-1RA users, 32 203 thiazolidinedione users, and 30 682 GLP-1RA plus thiazolidinedione users), along with a propensity score-matched group of patients who did not use a GLP-1RA or thiazolidinedione, for a total cohort size of 220 822. Patients receiving GLP-1RA and thiazolidinedione dual therapy had significantly lower risk of all-cause mortality (adjusted hazard ratio [AHR], 0.20; 95% CI, 0.19-0.21; P < .001), major adverse cardiovascular events (AHR, 0.85; 95% CI, 0.82-0.89; P < .001), and cardiovascular mortality (AHR, 0.20; 95% CI, 0.18-0.23; P < .001) than those who did not receive a GLP-1RA or thiazolidinedione. However, a higher risk of hypoglycemia was seen in those receiving combination therapy (AHR, 1.61; 95% CI, 1.43-1.82; P < .001) and those receiving thiazolidinedione monotherapy (AHR, 1.69; 95% CI, 1.51-1.90; P < .001) compared with nonuse. This risk was mitigated with prolonged use. Thiazolidinedione monotherapy users had a significantly higher risk of all-cause mortality (AHR, 1.29; 95% CI, 1.24-1.34; P < .001) and cardiovascular mortality (AHR, 1.28; 95% CI, 1.13-1.45; P < .001) than GLP-1RA monotherapy users. Several sensitivity analyses further supported the robustness of these findings.

Conclusions and Relevance

In this cohort study of patients with type 2 diabetes, combination therapy with a GLP-1RA plus thiazolidinedione was associated with significantly lower hazards of mortality and cardiovascular complications compared with nonuse. The findings suggest that GLP-1RAs may mitigate the adverse cardiovascular effects of thiazolidinedione.

Drug-Drug Interaction Surveillance Study: Comparing Self-Controlled Designs in Five Empirical Examples in Real-World Data

Self-controlled designs, specifically the case-crossover (CCO) and the self-controlled case series (SCCS), are increasingly utilized to generate real-world evidence (RWE) on drug-drug interactions (DDIs). Although these designs share the advantages and limitations of within-individual comparison, they also have design-specific assumptions. It is not known to what extent the differences in assumptions lead to different results in RWE DDI analyses. Using a nationwide US commercial healthcare insurance database (2006-2016), we compared the CCO and SCCS designs, as they are implemented in DDI studies, within five DDI-outcome examples: (1) simvastatin + clarithromycin and muscle-related toxicity; (2) atorvastatin + valsartan, and muscle-related toxicity; and (3-5) dabigatran + P-glycoprotein inhibitor (clarithromycin, amiodarone, and verapamil) and bleeding. Analyses were conducted within person-time exposed to the object drug (statins and dabigatran) and adjusted for bias associated with the inhibiting drugs via control groups of individuals unexposed to the object drug. The designs yielded similar estimates in most examples, with SCCS displaying better statistical efficiency. With both designs, results varied across sensitivity analyses, particularly in CCO analyses with small number of exposed individuals. Analyses in controls revealed substantial bias that may be differential across DDI-exposed and control individuals. Thus, both designs showed no association between amiodarone or verapamil and bleeding in dabigatran-exposed but revealed strong positive associations in controls. Overall, bias adjustment via a control group had a larger impact on results than the choice of a design, highlighting the importance and challenges of appropriate control group selection for adequate bias control in self-controlled analyses of DDIs.

Biomedical Informatics Approaches to Identifying Drug-Drug Interactions: Application to Insulin Secretagogues

BACKGROUND

Drug-drug interactions with insulin secretagogues are associated with increased risk of serious hypoglycemia in patients with type 2 diabetes. We aimed to systematically screen for drugs that interact with the five most commonly used secretagogues-glipizide, glyburide, glimepiride, repaglinide, and nateglinide-to cause serious hypoglycemia.

METHODS

We screened 400 drugs frequently coprescribed with the secretagogues as candidate interacting precipitants. We first predicted the drug-drug interaction potential based on the pharmacokinetics of each secretagogue-precipitant pair. We then performed pharmacoepidemiologic screening for each secretagogue of interest, and for metformin as a negative control, using an administrative claims database and the self-controlled case series design. The overall rate ratios (RRs) and those for four predefined risk periods were estimated using Poisson regression. The RRs were adjusted for multiple estimation using semi-Bayes method, and then adjusted for metformin results to distinguish native effects of the precipitant from a drug-drug interaction.

RESULTS

We predicted 34 pharmacokinetic drug-drug interactions with the secretagogues, nine moderate and 25 weak. There were 140 and 61 secretagogue-precipitant pairs associated with increased rates of serious hypoglycemia before and after the metformin adjustment, respectively. The results from pharmacokinetic prediction correlated poorly with those from pharmacoepidemiologic screening.

CONCLUSIONS

The self-controlled case series design has the potential to be widely applicable to screening for drug-drug interactions that lead to adverse outcomes identifiable in healthcare databases. Coupling pharmacokinetic prediction with pharmacoepidemiologic screening did not notably improve the ability to identify drug-drug interactions in this case.

Comparative risk of serious hypoglycemia with oral antidiabetic monotherapy: A retrospective cohort study

PURPOSE

To examine and compare risks of serious hypoglycemia among antidiabetic monotherapy-treated adults receiving metformin, a sulfonylurea, a meglitinide, or a thiazolidinedione.

METHODS

We performed a retrospective cohort study of apparently new users of monotherapy with metformin, glimepiride, glipizide, glyburide, pioglitazone, rosiglitazone, nateglinide, or repaglinide within a dataset of Medicaid beneficiaries from California, Florida, New York, Ohio, and Pennsylvania. We did not include users of dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 agonists, or sodium-glucose co-transporter 2 inhibitors. We identified serious hypoglycemia outcomes within 180 days following new use using a validated, diagnosis-based algorithm. We calculated age- and sex-standardized outcome occurrence rates for each drug and generated propensity score-adjusted hazard ratios vs metformin using Cox proportional hazards regression.

RESULTS

The ranking of standardized occurrence rates of serious hypoglycemia was glyburide > glimepiride > glipizide > repaglinide > nateglinide > rosiglitazone > pioglitazone > metformin. Rates were increased for all study drugs at higher average daily doses. Adjusted hazard ratios (95% confidence intervals) vs metformin were 3.95 (3.66-4.26) for glyburide, 3.28 (2.98-3.62) for glimepiride, 2.57 (2.38-2.78) for glipizide, 2.03 (1.64-2.52) for repaglinide, 1.21 (0.89-1.66) for nateglinide, 0.90 (0.75-1.07) for rosiglitazone, and 0.80 (0.68-0.93) for pioglitazone.

CONCLUSIONS

Sulfonylureas were associated with the highest rates of serious hypoglycemia. Among all study drugs, the highest rate was seen with glyburide. Pioglitazone was associated with a lower adjusted hazard for serious hypoglycemia vs metformin, while rosiglitazone and nateglinide had hazards similar to that of metformin.