# GEneration and assessment of Multi-omic informed Subtypes of Type 2 Diabetes in Diverse Populations (GEMS-T2D)

> **NIH NIH U01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $784,636

## Abstract

Type 2 diabetes (T2D) represents one of the biggest global health problems of the 21st century. Defined by the
presence of hyperglycemia without another attributable cause, T2D is essentially a diagnosis of exclusion and
thus both clinically and etiologically heterogeneous. Several efforts have been made to identify T2D subtypes
using a variety of clustering approaches based on either clinical or genetic factors.
Current T2D classification approaches have limitations, preventing their implementation into patient care. T2D
subtypes based on clinical variables currently rely on measuring these traits at the time of diagnosis, which is
not practical for standard clinical care, as these measures may change over time and in relationship to medication
use. Genetic-based subtyping approaches have the advantage that they do not change over time but also do
not capture the present metabolic state of the individual. Therefore, there is an urgent need to develop a universal
and standardized T2D subtype classification to improve understanding of disease and provide a path forward for
applying personalized medicine to people with T2D.
To develop new T2D subtypes and understand their clinical utility, we have assembled a large and diverse
collection of cohorts with phenotypic and multi-omic data, including some of the largest T2D clinical trials in the
United States. These studies include Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness
Study (GRADE), Look AHEAD (Action for Health in Diabetes), Treatment Options for Type 2 Diabetes in
Adolescents and Youth (TODAY), and the Diabetes Prevention Program. Additionally, we have assembled
multiple large, diverse cohorts and biobanks with longitudinal data, including Search for Diabetes in Youth
(SEARCH), All of Us, Mass General Brigham, and Kaiser Permanente.
In Aim 1, we will use state of the art machine-learning clustering approaches to integrate multiple data types,
including clinical variables, genetics and metabolomics, to define new T2D subtypes and uncover disease
mechanisms. In Aim 2, we will evaluate the clinical relevance of existing and newly developed T2D subtypes
using data from clinical trials involving glucose-lowering medications and lifestyle interventions, while considering
ancestry, environmental factors and social determinants of health. In Aim 3, we will develop simplified versions
of the diabetes subtypes to assess the clinical utility of these subtypes in a “real world” setting.
This proposal will provide a unified and standard classification of T2D and identify subgroups of patients more
likely to respond to particular T2D medications and/or develop disease complications, providing a path forward
for precision medicine for all.

## Key facts

- **NIH application ID:** 10974663
- **Project number:** 1U01DK140757-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Josep Maria Mercader
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $784,636
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10974663

## Citation

> US National Institutes of Health, RePORTER application 10974663, GEneration and assessment of Multi-omic informed Subtypes of Type 2 Diabetes in Diverse Populations (GEMS-T2D) (1U01DK140757-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10974663. Licensed CC0.

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