# Longitudinal omics-based trajectories of type 2 diabetes subtypes: the T2D Heterogeneity Consortium

> **NIH NIH U01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $823,473

## Abstract

PROJECT SUMMARY/ABSTRACT
Millions of Americans are at risk of type 2 diabetes, but we lack a precise understanding of mechanisms
causing high blood glucose levels, life-altering complications, and premature death. Current criteria for type 2
diabetes use glucose and HbA1c diagnostic cutoffs based on a link to retinopathy and thus do not account for
risk of other microvascular and macrovascular complications. To improve approaches to defining prediabetes
and diabetes, risk stratification is critical. Recent studies from our group and others demonstrate the potential
to classify patients with early diabetes that may ultimately result in personalized treatment and better
outcomes. Despite the reproducibility within existing subtype definitions, including genetically determined
clusters and new-onset diabetes clusters, there is no unifying theory or strategy for type 2 diabetes
classification, which must integrate different data types. Type 2 diabetes needs to be re-defined so that precise
and accurate diagnosis can be provided to patients, and the appropriate mechanism-guided therapies applied,
to alleviate suffering and prevent complications. We plan to challenge the current paradigm that defines
diabetes and prediabetes based solely on glucose and HbA1c. Our overarching goal is to integrate clinical and
multi-omic data into prediabetes and type 2 diabetes classification and build prediction models of disease
trajectories over the lifespan. Our central hypothesis is that multi-omic data can refine definitions of type 2
diabetes, identify those patients most like to develop complications, and better identify people at risk for future
development of type 2 diabetes. The following three specific aims are proposed: Aim 1: We will use multi-omic
data to refine existing prediabetes and type 2 diabetes definitions and define new distinct subtypes of these
conditions. Aim 2: We will describe the associations of diabetes subtypes with microvascular and
macrovascular complications and identify environmental contexts that modify disease trajectories. Aim 3: We
will determine whether subtypes based on high dimensional clinical and multi-omic data in MESA, ARIC,
HCHS/SOL, All of Us and other cohorts from the consortium can be recapitulated using clinical biomarkers.
Our expected outcomes are (1) characterization of more homogenous subgroups of diabetes patients among
US populations of multi-ethnic groups, (2) identification of lifestyle and/or medical interventions that reduce
type 2 diabetes risk or risk of future complications for any given subgroup, and (3) new diagnostic tools that
can be translated to a clinical setting to more accurately classify type 2 diabetes.

## Key facts

- **NIH application ID:** 10974745
- **Project number:** 1U01DK140761-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Alisa Knodle Manning
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $823,473
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10974745

## Citation

> US National Institutes of Health, RePORTER application 10974745, Longitudinal omics-based trajectories of type 2 diabetes subtypes: the T2D Heterogeneity Consortium (1U01DK140761-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10974745. Licensed CC0.

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