# Dynamic prediction of type 1 diabetes risk and autoantibody status by a joint model of longitudinal and multistate models

> **NIH NIH R03** · UNIVERSITY OF SOUTH FLORIDA · 2024 · $150,000

## Abstract

Project Summary/Abstract
 Type 1 diabetes is a chronic autoimmune disease that features the destruction of pancreatic beta-cells
resulting in insulin deficiency and daily insulin injections for survival. Early identification of type 1 diabetes can
be achieved by continuously monitoring islet autoantibody status and longitudinal markers that measure the
immunological and metabolic functions. The goal of this proposal is to develop a statistical model that can
give dynamic predictions about type 1 diabetes risk based on autoantibody status and the historical data of an
individual. A longitudinal model for characterizing time-varying risk factors, a multistate model for predicting
autoantibody status, and a survival model for predicting disease progression will be combined in a joint model
to achieve the goal. The model will be applied to a dataset derived from The Environmental Determinants of
Diabetes in the Young (TEDDY) study. It may be challenging to develop a model with such a complex structure.
However, the advances in statistical methodology and computational technology have opened up opportunities
to resolve the problems. In Aim 1, we will formulate the proposed joint model and apply it to the TEDDY data.
Statistical inferences can be made to investigate how the changes in diabetes-related antoantibodies and other
longitudinal risk factors are associated with the risk for type 1 diabetes diagnosis. In Aim 2, based on the
proposed joint model, a dynamic prediction algorithm will be derived that predicts autoantibody development
and the subsequent risk of type 1 diabetes given the historical data of an individual. Lastly, in Aim 3, we will
evaluate the accuracy of the proposed dynamic prediction algorithm using a variety of diagnostic measures.
We expect that the proposed joint model will demonstrate better performance than the conventional static
survival models that use baseline characteristics or last available measurements. The proposed research can
answer critical research questions about the natural history of type 1 diabetes and the relationship between
longitudinal risk factors.

## Key facts

- **NIH application ID:** 10903719
- **Project number:** 5R03DK135437-02
- **Recipient organization:** UNIVERSITY OF SOUTH FLORIDA
- **Principal Investigator:** Lu You
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $150,000
- **Award type:** 5
- **Project period:** 2023-08-10 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10903719

## Citation

> US National Institutes of Health, RePORTER application 10903719, Dynamic prediction of type 1 diabetes risk and autoantibody status by a joint model of longitudinal and multistate models (5R03DK135437-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10903719. Licensed CC0.

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