A diabetologists’ guide to subgroup meta-analyses
Determining which treatment is suited best for specific patient groups can be a complex challenge. A recent study proposes a simple roadmap to guide clinicians through the interpretation of subgroup meta-analyses. Using data from recent cardiovascular outcome trials (CVOTs) with GLP-1 receptor agonists or SGLT2 inhibitors in patients with type 2 diabetes, the authors define four essential steps to consider when dealing with subgroup data.
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?
Avoiding misinterpretation and navigating complexity
In addition to differences in study criteria (hypertension was the only cardiovascular risk factor that was included in every trial analysed), the authors identified several challenges when comparing trial data. They emphasise the importance of transparency and the reporting of both absolute and relative treatment effects. Also, they advocate in favour of credibility assessments, using tools such as the Instrument for assessing the Credibility of Effect Modification Analyses (ICEMAN) and Grading of Recommendations Assessment, Development and Evaluation (GRADEpro GDT), to ensure a nuanced understanding of subgroup effects.
“In addressing important considerations and challenges associated with the evaluation of clinical heterogeneity, power calculation, and credibility assessment of subgroup meta-analysis, and translation of relative estimates into clinically meaningful absolute estimates, this framework provides a stepwise guide allowing for a robust interpretation of treatment effects across different subpopulations,” the authors conclude.
- In absolute terms, GLP-1 receptor agonists and SGLT2 inhibitors show greater reductions in MACE in patients with type 2 diabetes and established cardiovascular disease compared with those at high cardiovascular risk without manifest disease, reflecting differences in baseline cardiovascular risk.
- Using the example of MACE reduction in CVOTs with GLP-1 receptor agonists or SGLT2 inhibitors, this study provides a comprehensive four-step approach for conducting and interpreting subgroup meta-analyses, including the consistency of subpopulation definitions, baseline risks, and relative and absolute treatment effects.
- The authors caution against making unfounded generalizations and emphasise the role of nuanced clinical decision-making based on absolute treatment effects.
Author: Hanna Gabriel, BA MSc. Any opinions expressed in this article are the responsibility of EASD e-Learning.
