# Multimodal analysis of the "honeymoon period" in autoimmune diabetes

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2024 · $495,495

## Abstract

The ultimate goal of this proposal is to define composite biomarkers that can be used to improve outcomes
in future type 1 diabetes (T1D) clinical trials. T1D is the major cause of diabetes in youth. It is characterized
by life-long insulin insufficiency due to autoimmune mediated ß cell destruction. Despite considerable efforts
over the past 30+ years, effective therapies are still lacking and there is an urgent need for a cure. Natural
history studies indicate that the rate of T1D progression varies greatly between individuals, both before, and
after onset. Indeed, the current paucity of validated mechanistic biomarkers that can accurately predict
“slow” or “fast” progression is a major impediment to finding a cure.
At least 40-60% of patients experience a period of partial remission (PRM) in the first 6 mo after they begin
taking insulin. This “honeymoon period” is highly variable, ranging from a few weeks to several years. Like
T1D, the factors that govern the onset and duration of PRM are not fully understood. Initially it was believed
that PRM is solely a metabolic phenomenon, but there is increasing evidence that the immune system also
plays an active part. This leads to the primary hypothesis that underpins our proposal: identification of
immunological, metabolic, and demographic features that associate with PRM duration will enable
the development of improved clinically actionable composite biomarkers for T1D.
Our study has a single specific aim, namely, to define and validate one or more classifiers that can
accurately predict fast or slow progression of T1D in the first 2y post-onset from baseline data. This will be
achieved through an in depth multimodal analysis of peripheral blood drawn from a cohort of 100 subjects
with a recent diagnosis of T1D. A single draw will be made at 3-6 months post diagnosis, and a range of
assays performed with DNA, RNA, protein and functional readouts, and ranging in complexity from single
analytes to single cell transcriptomes. PRM duration will be determined from clinical data collected over the
following 1.5-2y. Subjects will be randomized to training and validation cohorts matched for age, gender,
and content of “fast” and “slow” progressors. Features from the analytical data will be used to generate
models that predict PRM duration using DIFAcTO, a machine learning algorithm that combines univariate
filtering, hierarchical clustering, and LASSO regression, to select non-redundant features that result in an
optimal model. Performance of the final models will be evaluated by applying them to the independent
validation cohort.
The features retained in the resulting models will be prime candidates as composite biomarkers to improve
subject stratification at recruitment, and aid identification of responders and non-responders, in future
clinical trials. Thus, if successful, our study should have significant impact on the field.

## Key facts

- **NIH application ID:** 10794412
- **Project number:** 5R01DK129310-03
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** HOWARD W DAVIDSON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $495,495
- **Award type:** 5
- **Project period:** 2022-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10794412

## Citation

> US National Institutes of Health, RePORTER application 10794412, Multimodal analysis of the "honeymoon period" in autoimmune diabetes (5R01DK129310-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10794412. Licensed CC0.

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