What is the clinical evidence from this study?

Study design: Cohort. Population: Prediabetes (n=5567). Intervention: Metabolome-based subgroups vs. Other prediabetes subgroups. Primary outcome: Development of type 2 diabetes (T2D) (p=0.03).

What does this research mean for the field?

Individuals with prediabetes can be clustered into distinct metabolome-based subgroups that exhibit different long-term risks of progressing to type 2 diabetes, despite having similar fasting glucose and insulin levels. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to investigate whether distinct metabolomic subgroups exist in prediabetes and their relationship to the risk of developing type 2 diabetes (T2D).

June 7, 2026

2335-P: Metabolomic Analysis Reveals That Prediabetes Subgroups Have Different Likelihoods of T2D Onset: A Definition, Etiology, and Function: Integration to Enhance T2D Treatment (DEFINE-T2D) Consortium Analysis

Key Result

Metabolomic profiling of individuals with prediabetes identified three distinct subgroups with significantly different likelihoods of developing type 2 diabetes over 20 years (P=0.03).

Key Points

This research aims to investigate whether distinct metabolomic subgroups exist in prediabetes and their relationship to the risk of developing type 2 diabetes (T2D).
Analyzed data from 5567 participants, including 2003 with prediabetes.
Used machine learning to select metabolites differentiating prediabetes from normoglycemia.
Employed Cox-proportional hazards models to assess T2D risk among identified subgroups over a 20-year follow-up.
31 metabolites were identified that discriminated prediabetes from normoglycemia (AUC=0.96).
Three distinct metabolomic subgroups were identified among individuals with prediabetes.
Subgroups showed different likelihoods of developing T2D (P=.03), despite no differences in fasting glucose and insulin levels (p<.001).

Study Design

Type

Cohort (n=5,567)

Multicenter

Yes

Structured PICO

Do metabolome-based subgroups of individuals with prediabetes have different likelihoods of developing Type 2 Diabetes?

Population

5,567 participants, including 2,003 with prediabetes (fasting glucose 100 to <126 mg/dL), followed for 20 years to assess type 2 diabetes risk.

Exposure

Metabolomic profiling and clustering into subgroups based on 31 prediabetes-related metabolites

Comparator

Comparison between the 3 identified metabolomic subgroups

Outcome

Development of Type 2 Diabetes (T2D) over 20 years follow-uphard clinical

Metabolomic profiling can identify distinct subgroups of individuals with prediabetes who have differing long-term risks of developing type 2 diabetes, independent of standard glycemic markers.

Main Result

p-value: p=0.03

Abstract

Introduction and Objective: Patients with T2D can be clustered into distinct etiologic subgroups, but it is not clear if the same is true for those with prediabetes (preDM). As metabolites mark the onset of T2D prior to clinical thresholds, we clustered individuals with preDM by preDM-related metabolites and examined subgroups for long-term T2D risk. Methods: Data were included from N=5567 participants, N=2003 of whom had preDM (100≤fasting glucose126 mg/dL). Machine learning was used to select baseline metabolites that distinguished preDM from normoglycemia. Those with preDM were clustered into subgroups by their values of the selected compounds, and chi-squared tests quantified baseline subgroup differences. Cox-proportional hazards models, controlling for demographics, and fasting glucose and insulin, examined subgroup differences in T2D risk over 20 years follow-up. Results: N=31 metabolites discriminated preDM from normoglycemia (AUC=0.96;fig1A). Clustering individuals by these compounds identified 3 subgroups (fig1B). Fasting glucose and insulin did not differ between subgroups (p.001;fig1C), but the likelihood of developing T2D did (P=.03;fig1C Current; Beef Checkoff. Consultant; Ended; Lundquist Institute for Biomedical Innovation. M.R. Rooney: None. I.R. Konigsberg: None. Z. Chen: None. A. Manning: None. R.E. Gerszten: None. L. Szczerbinski: Research Support; Current; Novo Nordisk. Consultant; Current; Eli Lilly and Company. M. Mi: None. M. Sevilla-Gonzalez: Research Support; Current; Novo Nordisk. Q. Qi: None. Q. Pan: None. S.K. Das: None. J.I. Rotter: None. Funding Veterans Affairs, United States Department of Agriculture (USDA-3092-10700-068-006-N), and National Institutes of Health (1U01DK140757; HL181500; DK127073; U01DK140761; U01DK140738; U01DK140778)

Bookmark