# The Integrated Stress Response in Human Islets During Early T1D

> **NIH NIH U01** · UNIVERSITY OF CHICAGO · 2022 · $401,273

## Abstract

ABSTRACT
The project, Integrated Stress Response in Human Islets During Early Type 1 Diabetes (T1D), hypothesizes that
the activation of the integrated stress response and formation of stress granules is an early cellular response
initiating β cell stress in T1D that determines cell survival and can be monitored in pre- and early-T1D individuals
with minimal invasiveness. A multidisciplinary Team science approach is being taken to test this hypothesis,
collecting a large suite of heterogenous data, such as mRNA, lipidomics, proteomics and immunologic
measurements. Machine learning is being used to extract a multi-biomarker panel to aid in stratifying stress in
human islets and translating these findings to individuals at-risk for T1D and new-onset T1D. Although we are
formatting the multi-omics data for this specific machine learning task within the parent grant, the data being
generated, as well as our data collected from prior collaborations, are not generally AI/ML-ready for general
application of methods. They are however excellent candidates to be used as “flagship” datasets for AI/ML
readiness, both to test novel AI/ML approaches to tackle data pre-processing challenges and to extract molecular
signatures of T1D. These two gaps in analyses are the central themes of two aims. The first aim focuses on the
generation of AI/ML ready omics datasets that are properly annotated to address challenges in sparsity and bias,
such as imputation and batch correction. The second aim focuses AI/ML ready multi-omic datasets to enable
new studies in using machine learning to elicit biomarkers and pathway-level molecular signatures from the data
focused on standard AI/ML methods, as well as those specialized for small sample size. Dataset machine
learning model cards will be utilized to better enable to AI/ML research communities to utilize these datasets in
an efficient manner. For both aims there is a key focus on generating reusable software approaches to generate
data packages that can be directly imported into the most common AI/ML packages and released to the AI/ML
community through a variety of resources that enable feedback to continually improve and refine the AI/ML
readiness software development plan.

## Key facts

- **NIH application ID:** 10592566
- **Project number:** 3U01DK127786-03S1
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Thomas O Metz
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $401,273
- **Award type:** 3
- **Project period:** 2020-09-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10592566

## Citation

> US National Institutes of Health, RePORTER application 10592566, The Integrated Stress Response in Human Islets During Early T1D (3U01DK127786-03S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10592566. Licensed CC0.

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