Type 1 Diabetes Consortium


The Problem

By 2050, the number of individuals diagnosed with T1D in the U.S. alone is projected to more than triple. Those living with T1D are challenged to manage a complex disease and may face life-altering complications. Furthermore, because T1D starts well before symptoms are experienced, someone can progress silently for years before a diabetes diagnosis that, when it occurs, comes as a surprise, and can be accompanied by serious complications. Finally, despite many recent advances, T1D remains a challenge for affected individuals and their families to manage.

There remains significant unmet need for therapies to prevent, delay, treat and cure T1D; the T1DC is focused on opportunities to accelerate the development of such therapies.

The Solution

The ability to screen for risk and stage of T1D prior to the appearance of symptoms presents a valuable opportunity to delay and prevent symptomatic T1D. By employing the resources of all its members and through active engagement with regulatory agencies at each step of the process, C-Path’s T1D Consortium will build actionable models of the disease to enable more efficient and effective clinical trials. Enrichment strategies for clinical trial populations can dramatically increase the power of a trial to identify effective therapies and may also facilitate smaller, shorter, and more efficient trials accelerating the development of innovative solutions for T1D. Finally, when appropriate, the T1DC can work to qualify new biomarkers and endpoints supported from the analysis of aggregated data.

The Impact

After its founding in 2017, T1DC’s first effort was to achieve the regulatory qualification of islet autoimmunity antibodies as prognostic biomarkers to advance treatments and prevention of Type 1 Diabetes (T1D). In March 2022, the European Medicines Agency adopted a positive Qualification Opinion for the T1DC’s model-based submission supporting the use of islet autoantibodies (AAs) as enrichment biomarkers for T1D prevention clinical trials. This opinion was further commented on by the FDA in a publication entitled “Utility of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies: a viewpoint from the FDA” (Diabetologia. 2023 Mar;66(3):603-604.).

Building on this success, the goal of the T1DC looking ahead is to achieve regulatory endorsement from both the U.S. Food and Drug Administration and the European Medicines Agency of clinical trial simulation tools (CTST) for both “new-onset” T1D, as well as for prevention or delay of progression of the disease in its presymptomatic stages (Stage 1 and Stage 2). Such tools will enable more efficient clinical trial design that can accelerate the development of effective new therapies. Finally, our analysis and modeling of the T1DC’s comprehensive clinical trial and natural history study data (graciously shared with us by numerous academic groups and pharmaceutical companies) can provide unique insights into the disease. These insights will help both our consortium members and the broader T1D community in their efforts even before achieving the Consortium’s regulatory objectives.

Projects & Tools


The term prevention in this case refers to the prevention of symptomatic disease, as distinct from “primary prevention” that refers to interfering with the process by which the disease initially starts. The T1DC will focus on Stage 1 and Stage 2 T1D, seeking to refine the modeling of these stages. The T1D consortium is assembling a comprehensive dataset of clinical and observational studies that have been conducted in stage 1 and/or stage 2 T1D, building on the foundation built to enable the Type 1 Diabetes Biomarker Initiative that led to EMA qualification of islet autoantibodies as an enrichment tool for clinical trials in T1D prevention.

New-Onset (including TOMI-T1D)

The T1DC’s New-Onset Initiative began with the Trial Outcome Markers Initiative (TOMI)-T1D project that aimed to aggregate and analyze clinical data on patient demographics, diabetes baseline data (HLA genotype, autoantibodies, family history, duration), and available measures of beta cell function and glycemic control (C-peptide, glucose, HbA1c, insulin dose, etc). The initial results of this analysis have now been published (Lancet Diabetes Endocrinol. 2023 Dec;11(12):915-925). Working with the comprehensive new-onset T1D clinical dataset collected for TOMI, T1DC is now building a CTST for new-onset T1D that can be endorsed by regulators and be used in confidence by the biopharmaceutical industry to optimize clinical trial designs. By combining patient level data across studies, the power and interpretability of individual studies are increased. The CTST will include data from studies where treatment effect may not have been demonstrated, but which are valuable in characterizing disease progression and heterogeneity in T1D. We will also seek to incorporate additional clinical trial datasets as they become available, focusing on those trials that gathered data on glucose control using continuous glucose monitors. Our goal is to seek regulatory agency (FDA and EMA) endorsement of this CTS Tool which may help facilitate 1) identification of appropriate clinical endpoints, and 2) a more efficient path for drug development in new-onset T1D.

TOMI-T1D refers to a JDRF- and Diabetes UK-funded international partnership between researchers from academic institutions, the pharmaceutical industry, and independent non-profit organizations. TOMI’s mission was to accelerate drug development and optimize immune intervention trials in T1D through the development of a composite outcome measure which 1) improves clinical interpretability and patient acceptability, 2) shortens the time to primary outcome, and 3) minimizes the number of participants required in trials. A worldwide inventory and dataset of completed clinical trials and observational studies has been assembled, the results of the initial modeling published, and additional analyses are ongoing under the New-Onset program.

Islet Autoantibody Clinical Trial Enrichment Tool

What the tool is:

A quantitative clinical trial enrichment tool to help optimize clinical trial design for therapies to prevent or delay diagnosis of T1D, using islet AAs with other relevant clinical features. Access the tool and read more about it by clicking the button below.

New-Onset Clinical Trial Simulation Tool (In Development)

A model-based clinical trial simulation platform describing the progression of new-onset T1D comprising four individual drug-disease-trial models that measure: C-peptide area under the time-concentration curve (AUC) during the 2-hour mixed-meal tolerance test (MMTT), glucose concentrations measured by AUC during 2-hour MMTT, insulin use over time, and HbA1c %. Models of each measure will incorporate relevant sources of variability, including age at diagnosis, length of time since diagnosis, sex, body mass index (BMI), insulin use, and baseline values for C-peptide and glucose as measured through 2-hour MMTT and HbA1c. This tool can be used to perform clinical trial simulations to inform trial design, including inclusion or exclusion criteria, enrichment, and stratification approaches for individuals within 100 days of T1D diagnosis.

Prevention Clinical Trial Simulation Tool (In Development)

A model-based simulation platform describing the progression to T1D comprising of joint models using disease progression models that measure HbA1c (%), glucose area under the curve and C-peptide area under the curve during the 2-hour oral glucose tolerance test, and a time-to-T1D diagnosis model. Models will incorporate relevant sources of variability, including sex, race, BMI, age at study entry, HLA genotype, and the presence, order of detection, and time-course for the appearance of islet autoantibodies (AAs). This tool can be used to perform clinical trial simulations to inform trial design-including trial duration, inclusion or exclusion criteria, enrichment, and stratification approaches for individuals at risk of T1D, defined as being a first degree relative of a T1D patient, or having a specific HLA subtype of risk (HLA-DR3/3, DR4/4, DR3/4, DR3/X [X≠3], DR4/X [X≠4]).

Regulatory Successes

C-Path Receives Qualification Opinion from EMA on Type 1 Diabetes Biomarker Initiative

C-Path’s Type 1 Diabetes Consortium published an article titled “Consortium-based approach to receiving an EMA qualification opinion on the use of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies” in Diabetologia. This paper was accompanied by an article authored by FDA titled “Utility of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies: a viewpoint from the FDA.” Read FDA article here.

C-Path Provided Comments to FDA on Draft Guidance

C-Path’s Type 1 Diabetes Consortium provided comments on Draft Guidance provided by FDA Division of Diabetes, Lipid Disorders and Obesity regarding “Diabetes Mellitus: Efficacy Endpoints for Clinical Trials Investigating Antidiabetic Drugs and Biological Products.”

Read C-Path’s comments here.

C-Path Receives Letter of Support from EMA on Type 1 Diabetes Biomarker Initiative

On April 28, 2020, C-Path announced that its Type 1 Diabetes (T1D) Consortium received a letter of support from the European Medicines Agency (EMA) to facilitate the development and validation of the proposed regulatory qualification of pancreatic islet autoantibodies commonly used in clinical practice to diagnose T1D: insulin autoantibodies, glutamic acid decarboxylase 65, and insulinoma antigen-2 autoantibodies as enrichment biomarkers for T1D clinical trials. For more information, see the document linked below:



Joseph (Joe) Hedrick, PhD
Executive Director, T1DC

Paul Belmonte, PhD
Scientific Director, T1DC

Brianna Greeno, PMP, DASM
Senior Project Manager, T1DC

Erin Beach, MPA
Project Manager and Senior Project Coordinator, T1DC

Sarah David, MPH
Associate Director, Pediatrics Program

Data and Modeling Team

Alex Lozano
Data Manager II, DCC

Frank Walker
Data Manager III, DCC

Kimberly Collins, PhD
Senior Quantitative Medicine Scientist, QuantMed

Zihan Cui, PhD
Quantitative Medicine Developer II, QuantMed

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