As the number of diabetes medications continues to grow, clinicians are encouraged to consider individual factors when making treatment decisions. However, recommendations for specific patient groups rely on the use of meta-level subgroup analyses – and these are often limited by clinical heterogeneity across individual trials and the credibility of pooling data.
In a study published in Diabetes Care, Thomas Karagiannis and colleagues now offer a step-by-step approach to performing and interpreting subgroup meta-analyses. By focusing on the prevention of major cardiovascular events (MACE) in CVOTs with GLP-1 receptor agonists or SGLT2 inhibitors, the authors highlight the clinical importance of considering absolute (in addition to relative) treatment effects and of additional credibility assessments.
Balancing perspectives
GLP-1 receptor agonists and SGLT2 inhibitors have broadened the scope of diabetes management beyond glycaemic control. Among other benefits, both classes of drugs are known to improve the cardiovascular health of patients with type 2 diabetes. Karagiannis et al. pooled results from placebo-controlled cardiovascular outcome trials and specifically compared two subgroups: patients with established cardiovascular disease vs patients at high cardiovascular risk but without manifest disease.
By applying a comprehensive methodological framework, the study showed that by focusing solely on relative treatment effects, you may neglect clinically relevant absolute effects. This was illustrated by the finding that the absolute reduction in MACE was approximately two times greater with both drug classes in patients with established cardiovascular disease as compared with patients at high cardiovascular risk but without manifest disease while the relative risk reduction did not differ between subgroups.
Clinical implications
“The clinical interpretation of our findings is that it is reasonable to support a strong recommendation for using these medications to reduce MACE in people with type 2 diabetes and established cardiovascular disease, while they may be considered for patients at high cardiovascular risk but without manifest cardiovascular disease, given the lower absolute benefits in the latter subpopulation,” the authors argue in line with the 2022 EASD/ADA consensus statement on the management of hyperglycaemia in type 2 diabetes.
In addition, their findings highlight the importance of tailoring therapies to individual factors, such as a patient’s baseline cardiovascular risk, and therefore underscore the need for robust subgroup meta-analyses. The authors present a stepwise approach that can generally help clinicians address treatment differences between subgroups, regardless of the variable of interest.
Methodological precision: a step-by-step approach
Subgroup meta-analyses can provide valuable information but are also known to have limitations. “They should be approached and implemented with caution to prevent potential misinterpretation or unwarranted generalizations,” the authors write.
Based on these considerations, the study introduces a methodological framework that can be applied to any subgroup meta-analysis (see Figure 1). Essentially, each of the four steps is designed to systematically answer one important question about the comparability of the studies considered:
- How do the definitions of the subpopulation vary between individual studies?
- Is the baseline risk for each of these subpopulations sufficiently consistent across different studies to justify pooling the data in a meta-analysis?
- Does the relative treatment effect differ between the subpopulations?
- What are the absolute treatment effects for each subpopulation?
