Clinical Translation of Cardiovascular Outcome Trials in Type 2 Diabetes: Is There More or Is There Less Than Meets the Eye?

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.

Bridging the gap in cardiovascular care in diabetic patients: are cardioprotective antihyperglycemic agents underutilized?

INTRODUCTION

Atherosclerotic cardiovascular disease (ASCVD) and heart failure (HF) are two major complications of type 2 diabetes (T2DM). Cardiovascular protection is a key objective, yet not fully reached in clinical practice.

AREAS COVERED

Both glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is) have proven their efficacy in reducing major cardiovascular events in high-risk patients with T2DM and SGLT2is in reducing hospitalization for HF in placebo-controlled randomized trials. However, real-life studies worldwide revealed that only a minority of patients with T2DM receive either a GLP-1RA or an SGLT2i and surprisingly even less patients with established ASCVD or HF are treated with these cardioprotective antihyperglycemic agents.

EXPERT OPINION

Bridging the gap between evidence-based cardiovascular protection with GLP-1RAs and SGLT2is and their underuse in daily clinical practice in patients with T2DM at high risk is crucial from a public health viewpoint. However, the task appears hazardous and the goal not attained considering the current failure. Education of specialists/primary care physicians and patients is critical. Multifaceted and coordinated interventions involving all actors (physicians, patients and broadly health-care system) must be implemented to stimulate the adoption of these cardioprotective antihyperglycemic medications as part of routine cardiovascular care among patients with T2DM.

Incidence of diabetes following COVID-19 vaccination and SARS-CoV-2 infection in Hong Kong: A population-based cohort study

BACKGROUND

The risk of incident diabetes following Coronavirus Disease 2019 (COVID-19) vaccination remains to be elucidated. Also, it is unclear whether the risk of incident diabetes after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is modified by vaccination status or differs by SARS-CoV-2 variants. We evaluated the incidence of diabetes following mRNA (BNT162b2), inactivated (CoronaVac) COVID-19 vaccines, and after SARS-CoV-2 infection.

METHODS AND FINDINGS

In this population-based cohort study, individuals without known diabetes were identified from an electronic health database in Hong Kong. The first cohort included people who received ≥1 dose of COVID-19 vaccine and those who did not receive any COVID-19 vaccines up to September 2021. The second cohort consisted of confirmed COVID-19 patients and people who were never infected up to March 2022. Both cohorts were followed until August 15, 2022. A total of 325,715 COVID-19 vaccine recipients (CoronaVac: 167,337; BNT162b2: 158,378) and 145,199 COVID-19 patients were 1:1 matched to their respective controls using propensity score for various baseline characteristics. We also adjusted for previous SARS-CoV-2 infection when estimating the conditional probability of receiving vaccinations, and vaccination status when estimating the conditional probability of contracting SARS-CoV-2 infection. Hazard ratios (HRs) and 95% confidence intervals (CIs) for incident diabetes were estimated using Cox regression models. In the first cohort, we identified 5,760 and 4,411 diabetes cases after receiving CoronaVac and BNT162b2 vaccines, respectively. Upon a median follow-up of 384 to 386 days, there was no evidence of increased risks of incident diabetes following CoronaVac or BNT162b2 vaccination (CoronaVac: 9.08 versus 9.10 per 100,000 person-days, HR = 0.998 [95% CI 0.962 to 1.035]; BNT162b2: 7.41 versus 8.58, HR = 0.862 [0.828 to 0.897]), regardless of diabetes type. In the second cohort, we observed 2,109 cases of diabetes following SARS-CoV-2 infection. Upon a median follow-up of 164 days, SARS-CoV-2 infection was associated with significantly higher risk of incident diabetes (9.04 versus 7.38, HR = 1.225 [1.150 to 1.305])-mainly type 2 diabetes-regardless of predominant circulating variants, albeit lower with Omicron variants (p for interaction = 0.009). The number needed to harm at 6 months was 406 for 1 additional diabetes case. Subgroup analysis revealed no evidence of increased risk of incident diabetes among fully vaccinated COVID-19 survivors. Main limitations of our study included possible misclassification bias as type 1 diabetes was identified through diagnostic coding and possible residual confounders due to its observational nature.

CONCLUSIONS

There was no evidence of increased risks of incident diabetes following COVID-19 vaccination. The risk of incident diabetes increased following SARS-CoV-2 infection, mainly type 2 diabetes. The excess risk was lower, but still statistically significant, for Omicron variants. Fully vaccinated individuals might be protected from risks of incident diabetes following SARS-CoV-2 infection.

Restricted Mean Survival Time Analysis to Estimate SGLT2i-Associated Heterogeneous Treatment Effects on Primary and Secondary Prevention of Cardiorenal Outcomes in Patients With Type 2 Diabetes in Taiwan

IMPORTANCE

Increasing numbers of post hoc analyses have applied restricted mean survival time (RMST) analysis on the aggregated-level data from clinical trials to report treatment effects, but studies that use individual-level claims data are needed to determine the feasibility of RMST analysis for quantifying treatment effects among patients with type 2 diabetes in routine clinical settings.

OBJECTIVES

To apply RMST analysis for assessing sodium-glucose cotransporter-2 inhibitor (SGLT2i)-associated cardiovascular (CV) events and estimating heterogenous treatment effects (HTEs) on CV and kidney outcomes in routine clinical settings.

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness study of Taiwan's National Health Insurance Research Database examined 21 144 propensity score (PS)-matched pairs of patients with type 2 diabetes with SGLT2i and dipeptidyl peptidase-4 inhibitor (DPP4i) treatment for assessing CV outcomes, and 19 951 PS-matched pairs of patients with type 2 diabetes with SGLT2i and DPP4i treatment for assessing kidney outcomes. Patients were followed until December 31, 2018. Statistical analysis was performed from August 2021 to April 2022.

EXPOSURES

Newly stable SGLT2i or DPP4i use in 2017.

MAIN OUTCOMES AND MEASURES

Study outcomes were CV events including hospitalization for heart failure (HHF), 3-point major adverse CV events (3P-MACE: nonfatal myocardial infarction [MI], nonfatal stroke, and CV death), 4-point MACE (4P-MACE: HHF and 3P-MACE), and all-cause death, and chronic kidney disease (CKD). RMST and Cox modeling analyses were applied to estimate treatment effects on study outcomes.

RESULTS

After PS matching, the baseline patient characteristics were comparable between 21 144 patients with stable SGLT2i use (eg, mean [SD] age: 58.3 [10.7] years; 11 990 [56.7%] male) and 21 144 patients with stable DPP4i use (eg, mean [SD] age: 58.1 [11.6] years; 12 163 [57.5%] male) for assessing CV outcomes, and those were also comparable between 19 951 patients with stable SGLT2i use (eg, mean [SD] age: 58.1 [10.7] years; 11 231 [56.2%] male) and 19 951 patients with stable DPP4i use (eg, mean [SD] age: 57.9 [11.5] years; 11 340 [56.8%] male) for assessing kidney outcome. The 2-year difference in RMST between patients with SGLT2i use and patients with DPP4i use was 4.99 (95% CI, 3.56-6.42) days for HHF, 4.12 (95% CI, 2.72-5.52) days for 3P-MACE, 7.72 (95% CI, 5.83-9.61) days for 4P-MACE, 1.26 (95% CI, 0.47-2.04) days for MI, 2.70 (95% CI, 1.57-3.82) days for stroke, 0.69 (95% CI, 0.28-1.11) days for CV death, 6.05 (95% CI, 4.89-7.20) days for all-cause death, and 14.75 (95% CI, 12.99-16.52) days for CKD. Directions of hazard ratios from Cox modeling analyses were consistent with RMST estimates. No association was found between study treatment and the negative control outcome (dental visits for tooth care). Consistent results across sensitivity analyses using high-dimensional PS-matched and PS-weighting approaches supported the validity of primary analysis results. Largest difference in RMST of SGLT2i vs DPP4i use for HHF and CKD was found among patients with established heart failure (30.80 [95% CI, 5.08-56.51] days) and retinopathy (40.43 [95% CI, 31.74-49.13] days), respectively.

CONCLUSIONS AND RELEVANCE

In this comparative effectiveness study, RMST analysis was feasible for translating treatment effects into more clinical intuitive estimates and valuable for quantifying HTEs among diverse patients in routine clinical settings.

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

AIMS

Treatment effects from the large cardiovascular outcome trials (CVOTs) of new antidiabetic drugs are almost exclusively communicated as hazard ratios, although reporting guidelines recommend to report treatment effects also on an absolute scale, e.g. as numbers needed to treat (NNT). We aimed to analyse NNTs in CVOTs comparing dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, or sodium-glucose cotransporter-2 (SGLT2) inhibitors to placebo.

METHODS

We digitalized individual time-to-event information for the primary outcome and all-cause mortality from 19 CVOTs that compared DPP-4 inhibitors, GLP-1 receptor agonists, or SGLT2 inhibitors to placebo. We estimated Weibull models for each trial and outcome and derived monthly NNTs. NNTs were summarized across all trials and within drug classes by random effects meta-analysis methods.

RESULTS

Treatment effects in the CVOTs appear smaller if they are reported as NNTs: Overall, 100 (95%-CI: 60, 303) patients have to be treated for 29 months (the median follow-up time across all trials) to avoid a single event of the primary outcome, and 128 (95%-CI: 85, 265) patients have to be treated for 39 months to avoid a single death. NNT time courses are very similar for GLP-1 receptor agonists and SGLT2 inhibitors, whereas treatment effects with DPP-4 inhibitors are smaller.

CONCLUSIONS

We found that the respective treatment effects look less impressive when communicated on an absolute scale, as numbers needed to treat. For a valid overall picture of the benefit of new antidiabetic drugs, trial authors should also report treatment effects on an absolute scale.

Non-inferiority and clinical superiority of glucagon-like peptide-1 receptor agonists and sodium-glucose co-transporter-2 inhibitors: Systematic analysis of cardiorenal outcome trials in type 2 diabetes

AIMS

Most trials leading to the approval of glucagon-like peptide receptor agonists (GLP-1RAs) and sodium-glucose co-transporter-2 inhibitors (SGLT2is) were primarily designed to confirm their non-inferiority to placebo (commonly using an upper 95% confidence limit threshold of 1.3) and, if confirmed, superiority (threshold 1): this asymmetry of margins (1 vs. 1.3) favours the active intervention. We aimed to quantify the probability of clinical superiority of the active treatment by applying the same threshold used to claim non-inferiority.

MATERIALS AND METHODS

We searched PubMed and Cochrane CENTRAL for cardiorenal outcome trials in subjects with type 2 diabetes published before 5 December 2021, to reconstruct from Kaplan-Meier plots individual-level data for the primary outcome or all-cause mortality. We calculated Bayesian posterior densities to obtain the probability for a treatment effect (hazard ratio) <0.769, which is symmetric to the 1.3 threshold (i.e. its reciprocal 1/1.3), emulating a scenario where the active treatment is placebo and placebo is the active treatment.

RESULTS

We extracted data from 27 Kaplan-Meier plots (18 for the primary outcome, nine for mortality). Probabilities of clinical superiority to placebo varied significantly: for GLP-1RAs, from a minimum of 0% to a maximum of 69% for the primary outcome and from 0% to 8% for mortality; corresponding estimates for SGLT2is were 0% to 96% and 0% to 93%. Probabilities were on average greater for SGLT2is, particularly in trials investigating kidney or heart failure outcomes.

CONCLUSIONS

The probability of clinical superiority to placebo varies widely across trials previously reported as showing superiority of GLP-1RAs or SGLT2is compared with placebo. These results showed within- and between-class differences, highlight the drawbacks of a binary interpretation of the results, particularly in the context of the current designs of non-inferiority trials, and have implications for decision makers and future clinical recommendations.

Phenomapping-Derived Tool to Individualize the Effect of Canagliflozin on Cardiovascular Risk in Type 2 Diabetes

OBJECTIVE

Sodium-glucose cotransporter 2 (SGLT2) inhibitors have well-documented cardioprotective effects but are underused, partly because of high cost. We aimed to develop a machine learning-based decision support tool to individualize the atherosclerotic cardiovascular disease (ASCVD) benefit of canagliflozin in type 2 diabetes.

RESEARCH DESIGN AND METHODS

We constructed a topological representation of the Canagliflozin Cardiovascular Assessment Study (CANVAS) using 75 baseline variables collected from 4,327 patients with type 2 diabetes randomly assigned 1:1:1 to one of two canagliflozin doses (n = 2,886) or placebo (n = 1,441). Within each patient's 5% neighborhood, we calculated age- and sex-adjusted risk estimates for major adverse cardiovascular events (MACEs). An extreme gradient boosting algorithm was trained to predict the personalized ASCVD effect of canagliflozin using features most predictive of topological benefit. For validation, this algorithm was applied to the CANVAS-Renal (CANVAS-R) trial, comprising 5,808 patients with type 2 diabetes randomly assigned 1:1 to canagliflozin or placebo.

RESULTS

In CANVAS (mean age 60.9 ± 8.1 years; 33.9% women), 1,605 (37.1%) patients had a neighborhood hazard ratio (HR) more protective than the effect estimate of 0.86 reported for MACEs in the original trial. A 15-variable tool, INSIGHT, trained to predict the personalized ASCVD effects of canagliflozin in CANVAS, was tested in CANVAS-R (mean age 62.4 ± 8.4 years; 2,164 [37.3%] women), where it identified patient phenotypes with greater ASCVD canagliflozin effects (adjusted HR 0.60 [95% CI 0.41-0.89] vs. 0.99 [95% CI 0.76-1.29]; Pinteraction = 0.04).

CONCLUSIONS

We present an evidence-based, machine learning-guided algorithm to personalize the prescription of SGLT2 inhibitors for patients with type 2 diabetes for ASCVD effects.

Glycaemic variabilities: Key questions in pursuit of clarity

After years of intensive investigation, the definition of glycaemic variability remains unclear and the term variability in glucose homoeostasis might be more appropriate covering both short and long-term glycaemic variability. For the latter, we remain in the search of an accurate definition and related targets. Recent work leads us to consider that the within-subject variability of HbA1c calculated from consecutive determinations of HbA1c at regular time-intervals could be the most relevant index for assessing the long-term variability with a threshold value of 5% (%CV = SD of HbA1c/mean HbA1c) to separate stability from lability of HbA1c. Presently, no one can deny that short- and long-term glucose variability should be maintained within their lower ranges to limit the incidence of hypoglycaemia. Usually, therapeutic strategies aimed at reducing post-meal glucose excursions, i.e. the major contributor to daily glucose fluctuations, exert a beneficial effect on the short-term glucose variability. This explains the effectiveness of adjunct therapies with either GLP- receptor agonists or SGLT inhibitors in type 2 diabetes. In type 1 diabetes, the application of a CGM device alone reduces the short-term glycaemic variability. In contrast, sophisticated insulin delivery does not necessarily lead to such reductions despite marked downward shifts of 24-hour glycaemic profiles. Such contrasting observations raise the question as to whether the prolonged wear of CGM devices is or not the major causative factor for improvement in glucose variability among intensively insulin-treated persons with type 1 diabetes.