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June 7, 2026Diabetes

The cluster-based approach identified 7 subgroups with clear separation in clinical traits (all P<.0001) and stronger associations with injectable T2D medications (e.g., S-obesity OR 1.60, P<.0001) and hypertension compared to the extreme-value approach.

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Why the study?

Does a cluster-based polygenic risk score approach better stratify clinical traits and comorbidity risks in patients with Type 2 Diabetes compared to an extreme-value approach?

Population

19,734 patients with Type 2 Diabetes in an East Asian cohort

Comparison

Cluster-based strategy using K-means clustering… vs Extreme-value strategy defining high-risk groups…

Design

Cohort

Authors

WSWAYNE H-H. SHEUJRJEROME I. ROTTER

Discussion

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Overview

A cluster-based approach using multiple pathway-specific polygenic risk scores better stratifies Type 2 Diabetes patients by clinical traits and comorbidity risks than a simple extreme-value cutoff.

Key Points

  • This research aims to evaluate the effectiveness of two polygenic risk score (pPRS) stratification methods for type 2 diabetes.
  • Analyzed a cohort of 19,734 East Asian patients with type 2 diabetes.
  • Compared cluster-based K-means strategy identifying 7 subgroups with an extreme-value approach targeting the top 20% of pPRS.
  • Evaluated genetic architectures and clinical phenotypes for treatment implications.
  • The cluster-based approach identified 7 distinct patient subgroups with diverse genetic profiles (P<.0001).
  • Cluster subgroups were significantly associated with medication use, showing odds ratios of S-obesity (OR 1.60), S-body fat (OR 1.54), and others (P<.0001).
  • Extreme-value approach showed limited clinical differentiation, reflecting a primary pathway without significant subgroup distinctions.

Structured PICO

Does a cluster-based polygenic risk score approach better stratify clinical traits and comorbidity risks in patients with Type 2 Diabetes compared to an extreme-value approach?

P
Population
19,734 patients with Type 2 Diabetes in an East Asian cohort
I
Intervention
Cluster-based strategy using K-means clustering across several pathway-specific polygenic risk scores (pPRS) to derive 7 patient subgroups
C
Comparator
Extreme-value strategy defining high-risk groups as the top 20% of each pPRS
O
Outcome
Separation in clinical traits (age, BMI, HbA1c, triglyceride, HDL-C, and GPT levels) and associations with medications use/comorbiditiessurrogate

A cluster-based approach using multiple pathway-specific polygenic risk scores better stratifies Type 2 Diabetes patients by clinical traits and comorbidity risks than a simple extreme-value cutoff.

Cite This Study

SHEU et al. (2026) studied this question.

synapsesocial.com/papers/6a250b8b7def13d035e1b934https://doi.org/10.2337/db26-2396-p
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 12403-P: Diabetes Clusters, Risk of Complications, and Differences in Partitioned Polygenic Risk Scores (PPRS) in Indigenous Americans2026
  2. 2Pathway insights and predictive modeling for type 2 diabetes using polygenic risk scores2025
  3. 3Enabling reproducible type 1 diabetes polygenic risk scoring for clinical and translational applications2025 · 1 citations
  4. 4B-225 Translating GWAS into Clinical Practice: Real-World Utility of a Polygenic Risk Score for Diabetes Risk Stratification2025
  5. 52397-P: Genetic Clustering of Fasting Insulin Reveals Distinct Pathophysiological Mechanisms Informing Type 2 Diabetes Treatment Intensity and Complication Risk2026