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Associated Trial(s):- NCT02065791 - A Randomized, Double-blind, Event-driven, Placebo-controlled, Multicenter Study of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Subjects With Type 2 Diabetes Mellitus and Diabetic Nephropathy
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- NCT01032629 - A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of JNJ-28431754 on Cardiovascular Outcomes in Adult Subjects With Type 2 Diabetes Mellitus
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Status: OngoingResearch Proposal
Project Title: Meta-analysis on the regional differences of cardiovascular outcomes for patients treated with Sodium Glucose cotransporter 2 (SGLT-2) inhibitors
Scientific Abstract:
Background: SGLT-2 inhibitors are effective in reducing major cardiovascular events (MACE), slowing renal disease progression in diabetes mellitus type 2, and improving outcomes in heart failure with both preserved and reduced ejection fraction. However, some trials suggest regional variation in treatment efficacy.
Objective: To investigate the causes of differences in the efficacy of SGLT-2 inhibitors across continents.
Study Design: One-stage individual patient data (IPD) meta-analysis of cardiovascular outcome trials evaluating SGLT-2 inhibitors. A mixed-effects Cox proportional hazards model will be used.
Participants: Patients with type 2 diabetes, heart failure (any ejection fraction), or renal impairment enrolled in major SGLT-2 inhibitor trials.
Outcomes: We will first quantify regional differences in treatment effect for major outcomes including MACE (CV death, non-fatal MI, non-fatal stroke), renal failure, and all-cause mortality. Next, we will assess whether baseline characteristics explain regional variation using interaction terms in the Cox model.
Secondary Analyses: Region will be modeled as a random effect to account for geographic clustering. Additional models will explore treatment interactions with individual covariates (e.g., LDL, age). Cause-specific hazard models will evaluate differences across CV outcomes.
Sensitivity Analyses: We will compare models stratified by trial (fixed effects) and perform a two-stage meta-analysis for robustness.
Brief Project Background and Statement of Project Significance: Several trial on SGLT-2 inhibitors have provided a subgroup analysis in which patients are subdivided based on the continent where they live. Some of these analyses suggest a difference in efficacy. We aim to quantify this difference and examen the cause of this discrepancy. This information can help to better predict which patient will have the most advantage of taking a SGLT-2 inhibitor and which don't. This information can be valuable for clinicians and the authors of guidelines to make optimal use of this class of drugs. By doing this research, we hope to make sure that people everywhere can benefit as much as possible from these medicines, no matter where they live.
Specific Aims of the Project:
To examen wheter there are geographical differences in the efficacy of SGLT-2 inhibitors. If this hypothesis is true, the next answer which can be answered through our research is: why is there a difference in efficacy between regions of the world.
The answer to the first question will also give us a point estimate and a confidence interval on the effect of this class of drugs in different areas of the world. If a difference in effectiveness is found, the question which needs to be answered is: why does this class of drugs work differently in these different populations? Can this be explained by patient characteristics being different in the included participants (age, sex, comorbidities) or in the concurrent treatment of different cardiovascular risk factors?
The goal is to provide this information and to contribute to better healthcare for patients around the globe.
Study Design: Meta-analysis (analysis of multiple trials together)
What is the purpose of the analysis being proposed? Please select all that apply.: New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Software Used: R
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study:
Inclusion criteria: outcome trails of SGLT2-inhibitors. Outcomes of interest are cardiovascular and/or renal outcomes.
Exclusion criteria: none
Other trials we aim to include: EMPA-REG OUTCOME, CANVAS(-R), VERTIS CV, DAPA-HF, DELIVER, CREDENCE, DAPA-CKD, EMPA-KIDNEY, SCORED, EMPEROR-Perserved, EMPEROR-Reduced.
We are contacting the corresponding authors and in many cases the sponsors. We have a additional submission with Vivli on: EMPEROR-Perserved, EMPEROR-Reduced and Vertis-CV.
We are planning to use microsoft Excel to unify the IPD and use R as the statistical software. We will be conducting these analysis in the secured environment provided by the Yoda platform.
Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study:
Primary outcome: assess the differences in the efficacy of SGLT-2 inhibitors between continents. Measure of efficacy will be MACE and its individual components and renal outcomes. We will plot these values in forrest plots.
Next we will perform a adjusted interaction model to see if differences in baseline risk factor (treatment) can explain these differences.
Secondary outcomes: Include region as a random effect to account for clustering by geography. Explore treatment interaction with individual covariates (e.g., treatment x LDL, treatment x age). Perform cause-specific hazard models if different CV outcomes are of interest (e.g., HF hospitalization vs stroke)
Main Predictor/Independent Variable and how it will be categorized/defined for your study:
The main predictor is the geographic location of the patient. We will subdivide patients based on the continent where they live and examen its effect on the primary outcome reported in the trials.
For the first part of the analysis concerned with the question: "is there a difference in treatment effect of SGLT2 inhibitors across the globe?" the continent where the patient is living is the main predictor.
For the second part of the analysis concerned with the question "what variables explain the geographical difference in SGLT2 inhibitor effect?" the continent where the patient is living is a variable of interest as we wll be using a individual patient data meta-regression analysis, assessing how patient-level covariates influence treatment effect across regions.
Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study:
1. Geographical Region: Patient's country or region of residence,
2. Demographic Information: Age, sex, and relevant socioeconomic factors when available.
3. Baseline Clinical Characteristics: Details on comorbid conditions (i.e. history of atherosclerotic vascular disease of which vascular bed and heart failure), baseline cardiovascular and metabolic risk factors, including hypertension, diabetes, smoking and cholesterol levels.
4. Risk Factor Management Data: Information on medication use at baseline and during the study. Measurements of the abovementioned risk factors (e.g. blood pressure, cholesterol levels, glycated hemoglobin (HbA1c)).
5. Clinical Endpoints: Data on primary and secondary clinical outcomes as defined in the trial, including cardiovascular events, renal outcomes, and all-cause mortality.
6. Follow-Up Details: Duration of follow-up and time after enrolment when first endpoint of interest was met, if applicable.
Statistical Analysis Plan:
We will use multiple imputation for missing values and a weighted analysis for handling loss to follow-up.
Type of analysis: One-stage individual participant data meta-analysis
Outcome: Time to first major cardiovascular event (e.g., MACE: cardiovascular (CV) death, non-fatal myocardial infarction (MI), non-fatal stroke) and additional relevant
outcomes (renal failure, all cause mortality)
Key moderators:
Region/Continent (categorical: Europe, North America, Latin America, Asia, Africa, Oceania)
Risk factors: LDL-cholesterol, systolic blood pressure (SBP), age, sex, smoking, body mass index (BMI)
Comorbidities: type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), heart failure (HF), prior MI/stroke
Primary One-Stage Model: Mixed-effects Cox proportional hazards model
Main interaction model to assess regional variation in treatment effect
Adjusted Interaction Model: add baseline risk factors to assess if regional variation is explained by covariates:
LDL cholesterol
Systolic blood pressure
Age
Sex
BMI
Smoking status
History of cardiovascular disease (CVD), CKD, heart failure (HF)
Diabetes duration, HbA1c (if available)
Socio-economic status (income by country)
Quantify interaction terms using a Cox model and exponentiate this term to acquire hazard ratio per 1-unit increase
Secondary analysis:
Include region as a random effect to account for clustering by geography.
Explore treatment interaction with individual covariates (e.g., treatment x LDL, treatment x age).
Perform cause-specific hazard models if different CV outcomes are of interest (e.g., HF hospitalization vs stroke).
Sensitivity analysis
Stratify by trial instead of using random intercept (fixed effects per trial)
Compare with two-stage meta-analysis estimates
Narrative Summary:
What are SGLT-2 inhibitors and how do they work?
SGLT-2 inhibitors are medicines that help people with diabetes by lowering their blood sugar. They work by making the kidneys get rid of extra sugar through the urine. Scientists have also found that these medicines can help protect the heart and kidneys, which is really important for people who are at risk of heart or kidney problems.
Why is this research important?
Doctors have noticed that these medicines might not work the same way for everyone, depending on where they live. For example, people in one part of the world might see better results than people in another. Understanding why this happens is important because it helps doctors mak
Project Timeline:
The project can start when the first data is received. We expect this proces in Yoda and Vivli to take several months. We hope to be able to begin combining the data in august of 2025.
In October of 2025 we want to end the statistical analysis and to have the first draft of the article in February 2026. A publication is expected in the following months.
Dissemination Plan:
We aim to publish our paper in a peer reviewed journal. The target audience mainly consists of internist and cardiologists.
Suitable journals would be JAMA, the European Heart Journal or the journal of the American Heart Association
Bibliography: