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June 9, 2026Diabetes0 citations

1261-OR: Molecular Risk Scores Enhance Type 2 Diabetes (T2D) Prediction beyond Hemoglobin A1c (HbA1c) and Body Mass Index (BMI) across Diverse Clinical Contexts

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MSMAGDALENA SEVILLA-GONZALEZAMALAN M. MARTINEZ-MUÑOZPHPaul A. Hanson

Key Result

Each standard deviation increase in metabolomic risk score was associated with an increased risk of incident Type 2 Diabetes (fully adjusted HR 1.7; 95% CI 1.4-2.0).

Key Points

  • The research aims to evaluate the effectiveness of molecular risk scores in predicting type 2 diabetes beyond standard clinical factors like HbA1c and BMI.
  • Analyzed data from 36,352 adults free of diabetes in the Mass General Brigham Biobank followed for ~6 years.
  • Developed metabolomic risk scores using elastic net regression and a polygenic risk score from T2D GWAS data.
  • Utilized Cox models to analyze data while adjusting for various clinical risk factors and HbA1c levels.
  • MRS and PRS improved discrimination for incident T2D, with P < 10-10 and C-index improvements.
  • A one standard deviation increase in MRS indicated a threefold increased risk for T2D (HR 3.0, 95% CI 2.8-3.2) in fully adjusted models.
  • Individuals with high MRS and PRS displayed a 30% 10-year T2D risk by age 40, contrasting with <3% for lower risk profiles.

Study Design

Type

Cohort (n=36,352)

Multicenter

Yes

Structured PICO

Do metabolomic and polygenic risk scores improve the prediction of incident Type 2 Diabetes beyond standard clinical factors including HbA1c and BMI in adults?

P
Population
36,352 adults free of diabetes at metabolite assessment, followed for approximately 6 years.
E
Exposure
Metabolomic risk score (MRS) and polygenic risk score (PRS)
C
Comparator
Standard clinical risk factors including BMI, blood pressure, family history, lipids, random glucose, and HbA1c
O
Outcome
Incident Type 2 Diabetes (T2D)hard clinical

Metabolomic and polygenic risk scores significantly enhance the prediction of incident Type 2 Diabetes beyond standard clinical factors like HbA1c and BMI, enabling better risk stratification.

Main Result

Hazard Ratio: 1.7 (95% CI 1.4–2)

Abstract

Introduction and Objective: Translating genetic and molecular biomarkers into clinically actionable T2D risk prediction models is challenged by uncertain incremental value beyond standard clinical factors in real-world populations. We assessed metabolomic risk score (MRS) and a polygenic risk score (PRS) for incident T2D prediction in a diverse healthcare system. Methods: We studied 36,352 adults free of diabetes at metabolite assessment in the Mass General Brigham Biobank and followed for care for ~6 years. We derived the MRS using elastic net regression in UK Biobank (N=233K; 10,707 incident cases; follow-up: ~13 years) profiled on the Nightingale platform, and a PRS constructed from T2D GWAS summary statistics. Cox models to predict incident T2D were sequentially adjusted from demographics to established clinical risk scores (BMI, blood pressure, family history, and lipids), random glucose and HbA1c to mimic varying levels of clinical data availability. Results: Both MRS and PRS were associated with incident T2D and improved discrimination in all models including those adjusted for HbA1c (P 10-10, iC-index 0.01). Each standard deviation increase in MRS was associated with a threefold risk (age/sex adjusted model: HR 3.0, 95% CI 2.8-3.2, C-index 0.83; fully adjusted model: HR 1.7, 1.4-2.0, 0.84). Associations were stronger at higher BMI and among GLP-1 receptor agonist users (P interaction 10-4) adjusting for clinical factors including BMI. Absolute risk estimation showed marked stratification: individuals with high MRS and PRS had ~30% 10-year T2D risk by age 40 and 50% by age 60 whereas lower risk profiles were 3% in all BMI categories. Higher MRS was also associated with increased risk of incident chronic kidney disease among individuals with T2D. Conclusion: MRS and PRS provide complementary, clinically meaningful prediction of T2D beyond HbA1c and BMI supporting the integration of molecular profiling with standard clinical risk factors for targeted T2D prevention and risk reduction. Disclosure M. Sevilla-Gonzalez: Research Support; Current; Novo Nordisk. A.M. Martinez-Muñoz: None. P.A. Hanson: None. A. Huerta: None. M. Vora: None. E.W. Karlson: None. J.C. Florez: Research Support; Current; Novo Nordisk. Consultant; Current; Alveus Therapeutics. C.J. Patel: None. J. Mercader: None. A. Leong: Other - A close family member was an employee until August 2024.; Ended; Merck & Co., Inc. Funding 1K99DK139461-01A and R01DK137993 from NIH

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Cite This Study

SEVILLA-GONZALEZ et al. (2026) conducted a cohort in Type 2 Diabetes (T2D) (n=36,352). Metabolomic risk score (MRS) and polygenic risk score (PRS) vs. Lower risk profiles was evaluated on Incident Type 2 Diabetes (HR 1.7, 95% CI 1.4-2.0). Each standard deviation increase in metabolomic risk score was associated with an increased risk of incident Type 2 Diabetes (fully adjusted HR 1.7; 95% CI 1.4-2.0).

synapsesocial.com/papers/6a27add2a963992e16267eaehttps://doi.org/10.2337/db26-1261-or
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Also Consider

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  1. 11476-P: Metabolomic and Genetic Diabetes Predictors in the Diabetes Prevention Program (DPP)2024
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  5. 5B-225 Translating GWAS into Clinical Practice: Real-World Utility of a Polygenic Risk Score for Diabetes Risk Stratification2025