General Information
Conflict of Interest
Request Clinical Trials
Associated Trial(s): What type of data are you looking for?: Individual Participant-Level Data, which includes Full CSR and all supporting documentationRequest Clinical Trials
Data Request Status
Status: OngoingResearch Proposal
Project Title: Comparing Beta Cell Preservation Across Clinical Trials in Recent-Onset Type 1 Diabetes
Scientific Abstract:
Background: Several immunotherapies have demonstrated endogenous insulin preservation in recent-onset type 1 diabetes (T1D). We previously considered the primary results of studies using rituximab, abatacept, teplizumab, alefacept, high-dose ATG, low-dose ATG, and low-dose ATG +/- GCSF in new onset T1D in an attempt to rank the effectiveness of the agents studied. We now seek to broaden our analysis to include therapeutics that have subsequently demonstrated benefit in T1D. Namely, we seek to add data from trials using imatinib, golimumab, ustekinumab, verapamil, and baracitinib.
Objective: To determine the relative ability of different therapeutics to preserve C-peptide in new onset T1D.
Study Design: Retrospective comparative analysis across trials demonstrating preservation of C-peptide in new onset T1D. Participants: All subjects from the new onset T1D studies that have demonstrated preservation of C-peptide will be utilized.
Primary outcome measure: Rank ordering of therapies capable of preserving beta cell function in new onset.
Statistical Analysis: Individual subject data from baseline, 12 months, and 24 months will be used, C-peptide 2-h area under the curve means will be modeled using analysis of covariance. The experimental treatment group effect for each study, compared with its internal control, will be estimated after adjusting for baseline C-peptide and age. Percentage increase in C-peptide over placebo and the absolute difference within study will be calculated to compare and contrast effect size among interventions.
Brief Project Background and Statement of Project Significance: At least 9 therapies have now been identified with the capacity for preserving beta cell function in new onset T1D. However, none of these therapies have been directly studied in head-to-head clinical trials. As such, it is challenging to describe which therapies provide the greatest preservation of C-peptide without performing cross-trial analyses of adjusted C-peptide data. This study will help to further our understanding of which drugs provide the greatest benefit by expanding on our prior analyses to include therapeutics that have seen success since the publication of our initial manuscript. These additional therapies include imatinib, golimumab, ustekinumab, verapamil, and baracitinib. These data will guide decision making in the design of future studies seeking to combine therapies and optimize C-peptide preservation in T1D.
Specific Aims of the Project: Determine the relative, study adjusted, AUC C-Peptide preservation across all successful T1D immune intervention studies in new onset Stage 3 T1D
Study Design: Meta-analysis (analysis of multiple trials together)
What is the purpose of the analysis being proposed? Please select all that apply.: Participant-level data meta-analysis Meta-analysis using data from the YODA Project and other data sources
Software Used: R
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study:
Inclusion Criteria - All individual subjects in the aforementioned clinical trials
Exclusion Criteria - None
This analysis will pool data from recently published clinical trials of imatinib, golimumab, ustekinumab, verapamil, and baracitinib in T1D. Investigators and study teams from each of these efforts have agreed to share their data for the purposes of this analysis, with the exception of the golimumab data which are held in YODA.
Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study:
Primary Outcome Measures: Difference in AUC C-peptide at baseline, 1 year, and 2 years when comparing age adjusted means for each study. This will be reported exactly as was done in our DTT publication from 2020.
Secondary: A1c, Insulin dose (units/kg/day), IDAA1c, Percent subjects with A1c <6.5%, Percent subjects with Insulin dose <0.25units/kg/day)
Main Predictor/Independent Variable and how it will be categorized/defined for your study: Change in Adjusted AUC C-peptide by Study
Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study:
Age will be used to adjust for differences in the individuals and populations used in the different studies
AUC C-peptide will be used from each individual subject at baseline, 12 months, and 24 months (where available by study) to determine the change in adjusted AUC-C-peptide by study.
Secondary Endpoints: A1c, Insulin dose (units/kg/day), IDAA1c, Percent subjects with A1c <6.5%, Percent subjects with Insulin dose <0.25units/kg/day) at 12 and 24 months
Statistical Analysis Plan: An analysis of covariance (ANCOVA) model will be fitted to 1-year (and separately 2-year) C-peptide AUC means from the 2-h MMTT using publicly available data from each study. The C-peptide AUC mean will be calculated by first determining the AUC using the trapezoidal rule then dividing by 120 min. The inclusion of study as a covariate will assure the treatment effect estimate is relative to internal control. Each treatment group mean will be determined using the fitted fixed-effect model and substituting the mean age and the mean baseline C-peptide of the entire-study cohort (i.e., age and baseline C-peptide adjusted) while retaining the coefficient specific to each study. The absolute treatment effect difference will then be the model-based means of the experimental minus the placebo group. To compare across interventions, the percentage treatment effect, the absolute difference divided by the placebo group mean ( x 100), will be calculated for each immunotherapy. A similarly structured model was previously applied by Bundy et al and provides details on the valid use of the ANCOVA model when fitted to C-peptide. The transformation ln[Cp +1] to both C-peptide variables satisfies the requirement of normally distributed residuals. We will assure there is no violation of homogeneity of variance among the dichotomous variables of treatment group using Bartlett's test. In addition, we will use the bootstrap technique to estimate the 95% confidence intervals for each group mean and treatment effect by study. A permutation test (utilizing repeated random assignment of the experimental treatments to subject values) will be computed on the ANCOVA residuals after adjusting for age, baseline C-peptide, and study for year 1 and separately for year 2. To assess the evidence that there is a differential treatment effect by baseline C-peptide, an interaction term between baseline C-peptide and each experimental treatment will be tested within the ANCOVA model.
Narrative Summary:
Several immunotherapies have demonstrated endogenous insulin preservation in recent-onset type 1 diabetes (T1D). We considered the primary results of rituximab, abatacept, teplizumab, alefacept, high-dose antithymocyte globulin (ATG), low-dose ATG, and low-dose ATG +/- granulocyte-colony--stimulating factor trials in an attempt to rank the effectiveness of the agents studied and published those findings in Diabetes Technology and Therapeutics (Vol 22, Number 12, 2020).
We now seek to expand that analysis to include all other agents that have demonstrated benefit in Stage 3 T1D including verapamil, imatinib, baricitinib, ustekinumab, and golimumab.
Project Timeline: Within 3 months we hope to have all data sets acquired and to have performed the primary analysis. Within 6 months we hope to have the manuscript written and submitted to Diabetes Care.
Dissemination Plan: Submit to Diabetes Care.
Bibliography:
Jacobsen et al, Comparing Beta Cell Preservation Across Clinical Trials in Recent-Onset Type 1 Diabetes. Diabetes Technology and Therapeutics. Vol 22 No 12, 2020
Supplementary Material: Cross-Trial-Comparison-DTT-2020-Published-PDF.pdf