Absolute treatment effects for the primary outcome and all-cause mortality in the cardiovascular outcome trials of new antidiabetic drugs: a meta-analysis of digitalized individual patient data

The potential of precision diabetology for type 2 diabetes treatment-evidence from a meta-regression for all-cause mortality from large cardiovascular outcome trials

AIMS

Two prerequisites must be met for the precision treatment approach to be beneficial for treated individuals. First, there must be treatment heterogeneity; second, in case of treatment heterogeneity, clinical predictors to identify people who would benefit from one treatment more than from others must be available. There is an established meta-regression approach to assess these two prerequisites that relies on measuring the variability of a clinical outcome after treatment in placebo-controlled randomised trials. We recently applied this approach to the treatment of type 2 diabetes for the clinical outcomes of glycaemic control and body weight and repeat it for the clinical outcome of all-cause mortality.

METHODS

We performed a meta-regression analysis using digitalized individual participant information on time to death from 10 large cardiovascular outcome trials (7563 deaths from 99,746 participants) on DPP-4 inhibitors, GLP-1 receptor agonists, and SGLT-2 inhibitors with respect to the variability of all-cause mortality and its potential predictors after treatment.

RESULTS

The adjusted difference in log(SD) values of time to death between the verum and placebo arms was -0.036 (95%-CI: -0.059; -0.013), showing larger variability of time to death in the placebo arms. No clinical predictors were found to explain treatment heterogeneity.

CONCLUSIONS

This analysis suggests that the potential of the precision treatment approach in type 2 diabetes is low, at least with regard to improvement of all-cause mortality in population with high cardiovascular risk. This extends our previous findings for the clinical outcomes of glycaemic control and body weight.

Absolute treatment effects of novel antidiabetic drugs on a composite renal outcome: meta-analysis of digitalized individual patient data

BACKGROUND

Absolute treatment benefits-expressed as numbers needed to treat-of the glucose lowering and cardiovascular drugs, glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose transporter 2 (SGLT2) inhibitors on renal outcomes remain uncertain. With the present meta-analysis of digitalized individual patient data, we aimed to display and compare numbers needed to treat of both drugs on a composite renal outcome.

METHODS

From Kaplan-Meier plots of major cardiovascular outcome trials of GLP-1 receptor agonists and SGLT2 inhibitors vs. placebo, we digitalized individual patient time-to-event information on composite renal outcomes with WebPlotDigitizer 4.2; numbers needed to treat from individual cardiovascular outcome trials were estimated using parametric Weibull regression models and compared to original data. Random-effects meta-analysis generated meta-numbers needed to treat with 95% confidence intervals (CI).

RESULTS

Twelve cardiovascular outcome trials (three for GLP-1 receptor agonists, nine for SGLT2 inhibitors) comprising 90,865 participants were included. Eight trials were conducted in primary type 2 diabetes populations, two in a primary heart failure and two in a primary chronic kidney disease population. Mean estimated glomerular filtration rate at baseline ranged between 37.3 and 85.3 ml/min/1.73 m2. Meta-analyses estimated meta-numbers needed to treat of 85 (95% CI 60; 145) for GLP-1 receptor agonists and 104 (95% CI 81; 147) for SGLT2 inhibitors for the composite renal outcome at the overall median follow-up time of 36 months.

CONCLUSION

The present meta-analysis of digitalized individual patient data revealed moderate and similar absolute treatment benefits of GLP-1 receptor agonists and SGLT2 inhibitors compared to placebo for a composite renal outcome.

A Methodological Framework for Meta-analysis and Clinical Interpretation of Subgroup Data: The Case of Major Adverse Cardiovascular Events With GLP-1 Receptor Agonists and SGLT2 Inhibitors in Type 2 Diabetes

We present a methodological framework for conducting and interpreting subgroup meta-analyses. Methodological steps comprised evaluation of clinical heterogeneity regarding the definition of subpopulations, credibility assessment of subgroup meta-analysis, and translation of relative into absolute treatment effects. We used subgroup data from type 2 diabetes cardiovascular outcomes trials (CVOTs) with glucagon-like peptide 1 (GLP-1) receptor agonists and sodium-glucose cotransporter 2 (SGLT2) inhibitors for patients with established cardiovascular disease and those at high cardiovascular risk without manifest cardiovascular disease. First, we evaluated the variability in definitions of the subpopulations across CVOTs using major adverse cardiovascular events (MACE) incidence in the placebo arm as a proxy for baseline cardiovascular risk. As baseline risk did not differ considerably across CVOTs, we conducted subgroup meta-analyses of hazard ratios (HRs) for MACE and assessed the credibility of a potential effect modification. Results suggested using the same overall relative effect for each of the two subpopulations (HR 0.85, 95% CI 0.80-0.90, for GLP-1 receptor agonists and HR 0.91, 95% CI 0.85-0.97, for SGLT2 inhibitors). Finally, we calculated 5-year absolute treatment effects (number of fewer patients with event per 1,000 patients). Treatment with GLP-1 receptor agonists resulted in 30 fewer patients with event in the subpopulation with established cardiovascular disease and 14 fewer patients with event in patients without manifest cardiovascular disease. For SGLT2 inhibitors, the respective absolute effects were 18 and 8 fewer patients with event per 1,000 patients. This framework can be applied to subgroup meta-analyses regardless of outcomes or modification variables.