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June 7, 2026Diabetes

Patients were stratified into five distinct body composition phenotypes with significant differences in metabolic profiles (e.g., Cluster 5 had highest insulin resistance and blood pressure), but no differences in cluster-specific polygenic scores.

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Population

256 patients ≤ 40 years old with newly diagnosed early-onset type 2 diabetes from the START cohort

Design

Cross-sectional

Authors

RZRui ZhangQRQIAN RENLJLINONG JI

Discussion

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Overview

Early-onset type 2 diabetes exhibits significant heterogeneity in body composition and metabolic profiles that are not driven by genetic susceptibility, suggesting a strong environmental influence.

Key Points

  • This study aims to identify body composition phenotypes in patients with early-onset newly diagnosed type 2 diabetes and assess their metabolic indicators and polygenic scores.
  • 256 patients ≤ 40 years with newly diagnosed type 2 diabetes were enrolled from the START cohort.
  • Multifrequency bioelectrical impedance analysis measured body composition.
  • Cluster analysis was conducted using K-means based on BMI, body fat percentage, and skeletal muscle index.
  • Five distinct body composition phenotypes were identified: overweight with high muscle (Cluster 1), normal weight (Cluster 2), overweight with high fat (Cluster 3), fat (Cluster 4), and severe fat (Cluster 5).
  • Cluster 5 showed the highest blood pressure, and elevated fasting insulin, ALT, AST, uric acid, HOMA-β, HOMA-IR, and leptin levels.
  • Cluster 2 had the best metabolic profile with the lowest triglycerides, uric acid, fasting insulin, leptin, and HOMA-IR.

Structured PICO

P
Population
256 patients (174 males and 82 females) ≤ 40 years old with newly diagnosed early-onset type 2 diabetes from the START cohort
O
Outcome
Identification of body composition phenotypes and differences in clinical metabolic indicators and polygenic scoressurrogate

Early-onset type 2 diabetes exhibits significant heterogeneity in body composition and metabolic profiles that are not driven by genetic susceptibility, suggesting a strong environmental influence.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a250b8b7def13d035e1b80bhttps://doi.org/10.2337/db26-2601-p
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Also Consider

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

  1. 1Data-driven cluster analysis on the association of aging, obesity and insulin resistance with new-onset diabetes in Chinese adults: a multicenter retrospective cohort study2025 · 1 citations
  2. 21655-P: Where Fat Is Stored Matters More than How Much: A Large-Scale Population-Based Study Integrating DXA Phenotyping and Causal Inference2026
  3. 32272-P: Unsupervised Machine Learning Reveals Three Distinct Clinical Phenotypes in Type 2 Diabetes with Differential Complication Burden2026
  4. 42351-P: Assessment of Phenotypic Clustering of Incident and Prevalent Type 2 Diabetes: A MESA Pilot Study in the Definition, Etiology, Function: Integration to Enhance Type 2 Diabetes Treatment (DEFINE-Type 2 Diabetes) Consortium2026
  5. 52396-P: Comparing Cluster-Based and Extreme-Value of Polygenic Risk Score Approaches to Stratify Patients with Type 2 Diabetes2026