Cathrine Lind1,2, Anders Stockmarr2, Rune Viig Overgaard1
1Novo Nordisk A/S, 2Technical University of Denmark
Introduction: Cardiovascular diseases (CVDs) are of the most common complications for individuals with diabetes, reflecting a complex interplay between risk factors and pathogenesis [1]. The efforts needed to identify cardiovascular (CV) benefits of antidiabetic therapies are massive with historical CV outcome trials (CVOTs) enrolling 3.183-17.160 participants for a duration of over 2 years [2,3]. So, any model-based tools that can support early efficacy assessments and trial design are highly valuable. Current meta-analyses have investigated their effects on glycated haemoglobin (HbA1c) and body weight in relation to major adverse cardiovascular events (MACE), while the effect on inflammatory biomarkers such as low-lipodensity cholesterol (LDL-C), triglycerides, and c-reactive protein (CRP), has not been investigated [4-6]. Objectives: This model-based meta-analysis (MBMA) aimed to assess and evaluate the efficacy and safety of GLP-1 and GIP RAs, SGLT2 inhibitors, and insulins. Through model-based techniques the drug effects on LDL-C, triglycerides, and CRP levels, and their impact on CVD risk in patients with type 2 diabetes (T2D) were explored and investigated. This analysis aimed to quantify the effect of the effective dose of various anti-diabetic drugs on the CVD risk (MACE-3) to provide valuable insights to this nuanced relationship. Methods: A systematic literature review was performed to collect publicly available summary-level data from eligible key phase 3 studies of GLP-1 and GIP RA- and SGLT2 inhibitor-based therapies in T2D populations. The studies were identified through searches in the PubMed, Web of Science, and Scopus databases. Randomized clinical studies were selected if they compared GLP-1 and GIP RAs, SGLT2 inhibitors, or insulins with placebos in terms of measurements of LDL-C, triglycerides, CRP, or hazard ratio (HR) for MACE. Studies were only included if response values were accompanied with measures of precision. A MBMA was performed to investigate multiple responses described as the differences between the placebo and drug effects. A biomarker and CVD risk relationship were investigated through linear univariate meta-regression models for GLP-1 and GIP RAs, and SGLT2 inhibitors separately. Biomarkers examined were placebo-adjusted percentage change from baseline of HbA1c, body weight, LDL-C, triglycerides, and CRP. Based on these biomarkers the effective dose was identified within each drug class except insulins. Drug specific potency (ED50) parameters were estimated and used to adjust doses to the relative potency of semaglutide or dapagliflozin respectively. Adjusted dose-response relationships were investigated using model-based techniques to assess the CVD risk for GLP-1 and GIP RAs, and SGLT2 inhibitors. Results: A total of 45 studies were included in the meta-analysis, of which 15 were CVOTs. Across all three drug classes, random-effects meta-analysis models showed that the change from baseline of LDL-C were decreased with 0.63 mg/dL (95% CI -1.86 to 0.6), and that triglyceride were decrease with -5.15 mg/dL (95% CI -8.09 to -2.22). Across GLP-1 and GIP RAs, the change from baseline of CRP were -0.48 mg/L (95% CI -0.87 to -0.09). An association between drug treatment and CVD risk was observed, (HR 0.91, 95% CI 0.87 to 0.96), where a subgroup analysis showed similar responses between GLP-1 and GIP RAs, and SGLT2 inhibitors. Random-effects meta-regression analyses showed, for GLP-1 and GIP RAs, that HbA1c (slope = 0.234, p = 0.033) and body weight (slope = 0.078, p = 0.027) placebo-adjusted percentage change from baseline were the best descriptors of MACE-3. This significant relationship was not observed for LDL-C and triglycerides, as well as for SGLT2 inhibitors. The most predictive marker for separating effects for GLP-1 and GIP RAs was the effective dose. The same behaviour was not observed with SGLT2 inhibitors with dapagliflozin potency-equivalent adjusted doses. Conclusion: This MBMA demonstrated that for GLP-1 and GIP RAs, HbA1c and body weight reductions led to a significant benefit for CVD risk. The effective dose based on clinical endpoints appeared as the best predictor, superior to each individual biomarker for CV effects of GLP-1 and GIP RAs, offering a potential powerful tool to understand effects in future CVOTs. Whereas for SGLT2 inhibitors, more work is needed to identify biomarkers relevant for the CV effects.
[1] Wong, N.D., Sattar, N. Cardiovascular risk in diabetes mellitus: epidemiology, assessment and prevention. Nature Reviews Cardiology 20, 685–695 (2023). [2] M. Hasebe et al, Efficacy of antihyperglycemic therapies on cardiovascular and heart failure out-comes: an updated meta-analysis and meta-regression analysis of 35 randomized cardiovascular out-come trials, Cardiovascular Diabetology, vol. 22, 2023 [3] U.S. Food and Drug Administration. Guidance for Industry: Diabetes Mellitus – Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes, dec. 2008. [4] M. Maiorino et al, Improvement of glycemic control and reduction of major cardiovascular events in 18 cardiovascular outcome trials: an updated meta-regression, Cardiovascular Diabetology, vol. 20, no. 210, p. 2238–2245, 2021. [5] M. Hasebe et al, Efficacy of antihyperglycemic therapies on cardiovascular and heart failure out-comes: an updated meta-analysis and meta-regression analysis of 35 randomized cardiovascular out-come trials, Cardiovascular Diabetology, vol. 22, 2023. [6] M. Maiorino et al, Improvement of glycemic control and reduction of major cardiovascular events in 18 cardiovascular outcome trials: an updated meta-regression, Cardiovascular Diabetology, vol. 20, no. 210, p. 2238–2245, 2021.
Reference: PAGE 33 (2025) Abstr 11541 [www.page-meeting.org/?abstract=11541]
Poster: Drug/Disease Modelling - Safety